diff --git a/.ipynb_checkpoints/Stock_Price_Prediction(Updated) MultiLayer LSTM-checkpoint.ipynb b/.ipynb_checkpoints/Stock_Price_Prediction(Updated) MultiLayer LSTM-checkpoint.ipynb
new file mode 100644
index 0000000..2c98d88
--- /dev/null
+++ b/.ipynb_checkpoints/Stock_Price_Prediction(Updated) MultiLayer LSTM-checkpoint.ipynb	
@@ -0,0 +1,4826 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "id": "8tzEK_mSvRoh"
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.impute import SimpleImputer\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.svm import SVR\n",
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, GradientBoostingRegressor\n",
+    "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error\n",
+    "from sklearn.neighbors import KNeighborsRegressor\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense,LSTM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 206
+    },
+    "id": "NbBSc2jLvZWx",
+    "outputId": "457e0a63-90a0-4e1c-b846-95e4e50c31dc"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Adj Close</th>\n",
+       "      <th>Volume</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>01-01-1996</td>\n",
+       "      <td>18.691147</td>\n",
+       "      <td>18.978922</td>\n",
+       "      <td>18.540184</td>\n",
+       "      <td>18.823240</td>\n",
+       "      <td>12.409931</td>\n",
+       "      <td>43733533.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>02-01-1996</td>\n",
+       "      <td>18.894005</td>\n",
+       "      <td>18.964767</td>\n",
+       "      <td>17.738192</td>\n",
+       "      <td>18.224106</td>\n",
+       "      <td>12.014931</td>\n",
+       "      <td>56167280.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>03-01-1996</td>\n",
+       "      <td>18.327892</td>\n",
+       "      <td>18.568489</td>\n",
+       "      <td>17.643839</td>\n",
+       "      <td>17.738192</td>\n",
+       "      <td>11.694577</td>\n",
+       "      <td>68296318.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>04-01-1996</td>\n",
+       "      <td>17.502312</td>\n",
+       "      <td>17.832542</td>\n",
+       "      <td>17.223972</td>\n",
+       "      <td>17.676863</td>\n",
+       "      <td>11.654142</td>\n",
+       "      <td>86073880.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>05-01-1996</td>\n",
+       "      <td>17.738192</td>\n",
+       "      <td>17.785366</td>\n",
+       "      <td>17.459852</td>\n",
+       "      <td>17.577793</td>\n",
+       "      <td>11.588827</td>\n",
+       "      <td>76613039.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Date       Open       High        Low      Close  Adj Close  \\\n",
+       "0  01-01-1996  18.691147  18.978922  18.540184  18.823240  12.409931   \n",
+       "1  02-01-1996  18.894005  18.964767  17.738192  18.224106  12.014931   \n",
+       "2  03-01-1996  18.327892  18.568489  17.643839  17.738192  11.694577   \n",
+       "3  04-01-1996  17.502312  17.832542  17.223972  17.676863  11.654142   \n",
+       "4  05-01-1996  17.738192  17.785366  17.459852  17.577793  11.588827   \n",
+       "\n",
+       "       Volume  \n",
+       "0  43733533.0  \n",
+       "1  56167280.0  \n",
+       "2  68296318.0  \n",
+       "3  86073880.0  \n",
+       "4  76613039.0  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load the dataset\n",
+    "df = pd.read_csv(r\"D:\\Jupyter Notebook\\Contribution\\Stock-Price-Prediction\\Data\\SBIN.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "id": "2QdTvORzwEJw"
+   },
+   "outputs": [],
+   "source": [
+    "# Drop the 'Date' and 'Adj Close' columns\n",
+    "df.drop(['Date', 'Adj Close'], axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 206
+    },
+    "id": "xMfr71b2w3eX",
+    "outputId": "a6990b82-ef82-454f-c0ba-fc0c21cc91ca"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Volume</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>18.691147</td>\n",
+       "      <td>18.978922</td>\n",
+       "      <td>18.540184</td>\n",
+       "      <td>18.823240</td>\n",
+       "      <td>43733533.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>18.894005</td>\n",
+       "      <td>18.964767</td>\n",
+       "      <td>17.738192</td>\n",
+       "      <td>18.224106</td>\n",
+       "      <td>56167280.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>18.327892</td>\n",
+       "      <td>18.568489</td>\n",
+       "      <td>17.643839</td>\n",
+       "      <td>17.738192</td>\n",
+       "      <td>68296318.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>17.502312</td>\n",
+       "      <td>17.832542</td>\n",
+       "      <td>17.223972</td>\n",
+       "      <td>17.676863</td>\n",
+       "      <td>86073880.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>17.738192</td>\n",
+       "      <td>17.785366</td>\n",
+       "      <td>17.459852</td>\n",
+       "      <td>17.577793</td>\n",
+       "      <td>76613039.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Open       High        Low      Close      Volume\n",
+       "0  18.691147  18.978922  18.540184  18.823240  43733533.0\n",
+       "1  18.894005  18.964767  17.738192  18.224106  56167280.0\n",
+       "2  18.327892  18.568489  17.643839  17.738192  68296318.0\n",
+       "3  17.502312  17.832542  17.223972  17.676863  86073880.0\n",
+       "4  17.738192  17.785366  17.459852  17.577793  76613039.0"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Handle missing values\n",
+    "imputer = SimpleImputer(strategy='mean')\n",
+    "df = pd.DataFrame(imputer.fit_transform(df), columns=df.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "id": "dUw_auE7w8JA"
+   },
+   "outputs": [],
+   "source": [
+    "# Select features and target variable\n",
+    "X = df[['Open', 'High', 'Low', 'Volume']]\n",
+    "y = df['Close']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "id": "FD2542-uxMhN"
+   },
+   "outputs": [],
+   "source": [
+    "# Split the data into training and testing sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "id": "IfBxpcjIw-h7"
+   },
+   "outputs": [],
+   "source": [
+    "# Scale the features using Min-Max scaling\n",
+    "scaler = MinMaxScaler()\n",
+    "X_train_scaled = scaler.fit_transform(X_train)\n",
+    "X_test_scaled = scaler.transform(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "BUsngQNexIjX",
+    "outputId": "6b739018-4a7d-48d9-a3ec-591a5cd0784e"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5659, 4)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "GAb1kDEZxQD6",
+    "outputId": "4babae88-adba-49ad-9d56-514bd7abde50"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1415, 4)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_test.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "id": "SrzPIdvxxSWL"
+   },
+   "outputs": [],
+   "source": [
+    "# Function to evaluate and print RMSE, MAE, and MAPE\n",
+    "def evaluate_model(model, X_test, y_test):\n",
+    "    predictions = model.predict(X_test)\n",
+    "    rmse = np.sqrt(mean_squared_error(y_test, predictions))\n",
+    "    mae = mean_absolute_error(y_test, predictions)\n",
+    "    mape = mean_absolute_percentage_error(y_test, predictions)\n",
+    "\n",
+    "    print(f\"RMSE: {rmse}\")\n",
+    "    print(f\"MAE: {mae}\")\n",
+    "    print(f\"MAPE: {mape}\\n\")\n",
+    "\n",
+    "    return rmse, mae, mape\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "id": "1V0eOGD_xVCq"
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "metrics = {\n",
+    "    \"Model\": [],\n",
+    "    \"RMSE\": [],\n",
+    "    \"MAE\": [],\n",
+    "    \"MAPE\": []\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "4gNvcwrH55rP"
+   },
+   "source": [
+    "# **1. Linear Regression**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "id": "uTbRFCB4xXHU"
+   },
+   "outputs": [],
+   "source": [
+    "# Create a linear regression model\n",
+    "model1 = LinearRegression()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 241
+    },
+    "id": "UKaUaJ6sxaYG",
+    "outputId": "a28ae8d2-9430-4a1b-e9ba-475edfb7c788"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5286    257.350006\n",
+       "3408    129.464996\n",
+       "5477    279.350006\n",
+       "6906    588.500000\n",
+       "530      21.644367\n",
+       "Name: Close, dtype: float64"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 80
+    },
+    "id": "6iJA5FrBxdEs",
+    "outputId": "6667305e-dd8d-445e-df54-c470c5b1e5ae"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Train the model\n",
+    "model1.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "k-T73PFExiZD",
+    "outputId": "c794cf2c-c031-42c5-83ec-9432a9082d0f"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 1.6881364643681729\n",
+      "MAE: 0.9433353485344458\n",
+      "MAPE: 0.006085435990852853\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "rmse, mae, mape = evaluate_model(model1, X_test, y_test)\n",
+    "metrics[\"Model\"].append(\"Linear Regressor\")\n",
+    "metrics[\"RMSE\"].append(rmse)\n",
+    "metrics[\"MAE\"].append(mae)\n",
+    "metrics[\"MAPE\"].append(mape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "qEVWWYIS592D"
+   },
+   "source": [
+    "# 2. Support Vector Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "id": "LeUTf8Vhxj_k"
+   },
+   "outputs": [],
+   "source": [
+    "# Create an SVR model\n",
+    "model2 = SVR()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 80
+    },
+    "id": "ud3Yhe5Vzvyh",
+    "outputId": "08f14bf4-3c1f-4973-c72a-52e2c264e759"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVR()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SVR<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.svm.SVR.html\">?<span>Documentation for SVR</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SVR()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "SVR()"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Train the model\n",
+    "model2.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "eiqL4fTuzxWH",
+    "outputId": "4a88e15a-0922-4c3c-906b-3d5d6efee119"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 147.71103599153602\n",
+      "MAE: 110.99419106508152\n",
+      "MAPE: 1.9715076513294716\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "rmse, mae, mape = evaluate_model(model2, X_test, y_test)\n",
+    "metrics[\"Model\"].append(\"SVR\")\n",
+    "metrics[\"RMSE\"].append(rmse)\n",
+    "metrics[\"MAE\"].append(mae)\n",
+    "metrics[\"MAPE\"].append(mape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "PlDcozy-6OGR"
+   },
+   "source": [
+    "# 3. Random Forest Regressor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "id": "iaN8nOOO6cBg"
+   },
+   "outputs": [],
+   "source": [
+    "model3 = RandomForestRegressor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 80
+    },
+    "id": "wZ7x_Yp06fI_",
+    "outputId": "8f64c153-17f2-4939-9344-58db634aee31"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-3 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-3 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-3 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-3 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "RandomForestRegressor()"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Train the model\n",
+    "model3.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "IwK7IZ3E6g_n",
+    "outputId": "1fa15097-6587-4b81-a0f0-e65ac6c6ba8f"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 224.9384544830217\n",
+      "MAE: 162.99895472804317\n",
+      "MAPE: 0.7510058260536917\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\DEEPANSHU\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "rmse, mae, mape = evaluate_model(model3, X_test_scaled, y_test)\n",
+    "metrics[\"Model\"].append(\"Random Forest\")\n",
+    "metrics[\"RMSE\"].append(rmse)\n",
+    "metrics[\"MAE\"].append(mae)\n",
+    "metrics[\"MAPE\"].append(mape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ijTIDEEa6izO"
+   },
+   "source": [
+    "# 4. Gradient Boosting Models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "id": "EO6OFflr6nJo"
+   },
+   "outputs": [],
+   "source": [
+    "model4 = GradientBoostingRegressor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 80
+    },
+    "id": "vrwnbrEi6o1X",
+    "outputId": "dbf1c6ff-ffcc-4b47-fbf6-8ba7f64a5f16"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-4 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-4 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-4 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-4 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GradientBoostingRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html\">?<span>Documentation for GradientBoostingRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GradientBoostingRegressor()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "GradientBoostingRegressor()"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Train the model\n",
+    "model4.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "-pTBa0fD6qqx",
+    "outputId": "16225599-0077-447e-a1f5-3e2aa2f7ba8f"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 224.41069433522418\n",
+      "MAE: 162.27122816197573\n",
+      "MAPE: 0.7378541693598378\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\DEEPANSHU\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but GradientBoostingRegressor was fitted with feature names\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "rmse, mae, mape = evaluate_model(model4, X_test_scaled, y_test)\n",
+    "metrics[\"Model\"].append(\"GBM\")\n",
+    "metrics[\"RMSE\"].append(rmse)\n",
+    "metrics[\"MAE\"].append(mae)\n",
+    "metrics[\"MAPE\"].append(mape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "eGcU-e6C6sJI"
+   },
+   "source": [
+    "# 5. Extreme Graident Boosting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "id": "0GQmPNFd6uxx"
+   },
+   "outputs": [],
+   "source": [
+    "import xgboost as xgb\n",
+    "# Create an XGBoost model\n",
+    "model5 = xgb.XGBRegressor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 253
+    },
+    "id": "kfo1ZNft6xTp",
+    "outputId": "edfefbbd-744d-4c08-80bb-7ca2ffb3f765"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-5 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-5 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-5 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-5 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-5 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+       "             colsample_bylevel=None, colsample_bynode=None,\n",
+       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+       "             gamma=None, grow_policy=None, importance_type=None,\n",
+       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
+       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+       "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBRegressor<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+       "             colsample_bylevel=None, colsample_bynode=None,\n",
+       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+       "             gamma=None, grow_policy=None, importance_type=None,\n",
+       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
+       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+       "             num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+       "             colsample_bylevel=None, colsample_bynode=None,\n",
+       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+       "             gamma=None, grow_policy=None, importance_type=None,\n",
+       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
+       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+       "             num_parallel_tree=None, random_state=None, ...)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Train the model\n",
+    "model5.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "7QwLt9iS6zSj",
+    "outputId": "bfc728a1-e169-44ea-8a4e-4b18e29f8c0e"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 224.66436370022384\n",
+      "MAE: 162.62070643817412\n",
+      "MAPE: 0.7441437311249671\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "rmse, mae, mape = evaluate_model(model5, X_test_scaled, y_test)\n",
+    "metrics[\"Model\"].append(\"XGBoost\")\n",
+    "metrics[\"RMSE\"].append(rmse)\n",
+    "metrics[\"MAE\"].append(mae)\n",
+    "metrics[\"MAPE\"].append(mape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "sUD1VQBF605K"
+   },
+   "source": [
+    "# 6. AdaBoost Regressor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "id": "0foTLiQp63Y9"
+   },
+   "outputs": [],
+   "source": [
+    "model6 = AdaBoostRegressor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 80
+    },
+    "id": "bkzSWYA365MO",
+    "outputId": "c1f2bc96-a89c-4c83-873b-0836fa97c154"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-6 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-6 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-6 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-6 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-6 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-6 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-6 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;AdaBoostRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.AdaBoostRegressor.html\">?<span>Documentation for AdaBoostRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>AdaBoostRegressor()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "AdaBoostRegressor()"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Train the model\n",
+    "model6.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ZKxqdmp166pF",
+    "outputId": "b027a6a7-45e7-49e3-f93c-ed787d86e83f"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 212.10179381390049\n",
+      "MAE: 150.43845140660002\n",
+      "MAPE: 0.7023135843804711\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\DEEPANSHU\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but AdaBoostRegressor was fitted with feature names\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "rmse, mae, mape = evaluate_model(model6, X_test_scaled, y_test)\n",
+    "metrics[\"Model\"].append(\"AdaBoost Regressor\")\n",
+    "metrics[\"RMSE\"].append(rmse)\n",
+    "metrics[\"MAE\"].append(mae)\n",
+    "metrics[\"MAPE\"].append(mape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "mtfkPIRi67xo"
+   },
+   "source": [
+    "# 7. Decision Tree"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "id": "E6EyzrH36_Fq"
+   },
+   "outputs": [],
+   "source": [
+    "model7 = DecisionTreeRegressor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 80
+    },
+    "id": "DTp5VIYx7AWt",
+    "outputId": "8c71c048-7309-4aa2-ede9-517e62deaee3"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-7 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-7 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-7 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-7 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-7 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-7 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-7 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-7 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.tree.DecisionTreeRegressor.html\">?<span>Documentation for DecisionTreeRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeRegressor()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "DecisionTreeRegressor()"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Train the model\n",
+    "model7.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "3YC-pSgv7Dh4",
+    "outputId": "cd1a1793-3a36-4028-919f-90d79f0c1b46"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 224.85857555038172\n",
+      "MAE: 162.88870413804315\n",
+      "MAPE: 0.7490024715971244\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\DEEPANSHU\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but DecisionTreeRegressor was fitted with feature names\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "rmse, mae, mape = evaluate_model(model7, X_test_scaled, y_test)\n",
+    "metrics[\"Model\"].append(\"Decision Tree\")\n",
+    "metrics[\"RMSE\"].append(rmse)\n",
+    "metrics[\"MAE\"].append(mae)\n",
+    "metrics[\"MAPE\"].append(mape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "WfJAZHnP7E_2"
+   },
+   "source": [
+    "# 8. KNeighbors Regressor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "id": "smujnWTRzzDL"
+   },
+   "outputs": [],
+   "source": [
+    "# Create a KNN model\n",
+    "model8 = KNeighborsRegressor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 80
+    },
+    "id": "zeokqhKd0Aj8",
+    "outputId": "018dff42-47da-4f2f-8bf9-16938de97723"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-8 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-8 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-8 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-8 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-8 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-8 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-8 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-8 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neighbors.KNeighborsRegressor.html\">?<span>Documentation for KNeighborsRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsRegressor()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "KNeighborsRegressor()"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Train the model\n",
+    "model8.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "X2uNfESC0CA8",
+    "outputId": "6c449192-697f-4bb9-9a8e-bc9aed955532"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 224.35603706259303\n",
+      "MAE: 162.1962430618594\n",
+      "MAPE: 0.7365233640314862\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\DEEPANSHU\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but KNeighborsRegressor was fitted with feature names\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "rmse, mae, mape = evaluate_model(model8, X_test_scaled, y_test)\n",
+    "metrics[\"Model\"].append(\"KNN\")\n",
+    "metrics[\"RMSE\"].append(rmse)\n",
+    "metrics[\"MAE\"].append(mae)\n",
+    "metrics[\"MAPE\"].append(mape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "X3yNCskZ7KMV"
+   },
+   "source": [
+    "# 9. Artificial Neural Networks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "syd9MRhf0Df1",
+    "outputId": "1c546101-530f-4e5c-af43-79fd68b68347"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\DEEPANSHU\\anaconda3\\Lib\\site-packages\\keras\\src\\layers\\core\\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create an ANN model\n",
+    "model9 = Sequential()\n",
+    "model9.add(Dense(32, activation='relu', input_shape=(X_train.shape[1],)))\n",
+    "model9.add(Dense(16, activation='relu'))\n",
+    "model9.add(Dense(1, activation='linear'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "id": "pdlxN-Dp0IZr"
+   },
+   "outputs": [],
+   "source": [
+    "# Compile the model\n",
+    "model9.compile(loss='mean_squared_error', optimizer='adam')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "qcryLURL0KIH",
+    "outputId": "54e02177-ee86-46bf-cfef-496cb59b8577"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<keras.src.callbacks.history.History at 0x20440ba3590>"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Train the model\n",
+    "model9.fit(X_train_scaled, y_train, epochs=100, batch_size=32, verbose=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Xu6Cwjey0MaP",
+    "outputId": "92beea16-08fa-45d1-c817-88b1b1e3c688"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step\n",
+      "RMSE: 2.7756459435872696\n",
+      "MAE: 1.7743607020880396\n",
+      "MAPE: 0.013310535016859144\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "rmse, mae, mape = evaluate_model(model9, X_test_scaled, y_test)\n",
+    "metrics[\"Model\"].append(\"ANN\")\n",
+    "metrics[\"RMSE\"].append(rmse)\n",
+    "metrics[\"MAE\"].append(mae)\n",
+    "metrics[\"MAPE\"].append(mape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Yet4TgKq7OZl"
+   },
+   "source": [
+    "# 10. Long Short Term Memory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "id": "keiZDN4w7UH0"
+   },
+   "outputs": [],
+   "source": [
+    "n_features = X_train_scaled.shape[1]\n",
+    "n_steps = 10\n",
+    "n_samples_train = X_train_scaled.shape[0] - n_steps + 1\n",
+    "n_samples_test = X_test_scaled.shape[0] - n_steps + 1\n",
+    "\n",
+    "# Reshape the input data\n",
+    "X_train_reshaped = np.array([X_train_scaled[i:i+n_steps, :] for i in range(n_samples_train)])\n",
+    "X_test_reshaped = np.array([X_test_scaled[i:i+n_steps, :] for i in range(n_samples_test)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "nRRTkQTD7Vjd",
+    "outputId": "8c0e1de9-8382-482f-dc46-d6148e3f535e"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\DEEPANSHU\\anaconda3\\Lib\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+      "  super().__init__(**kwargs)\n"
+     ]
+    }
+   ],
+   "source": [
+    "model10 = Sequential()\n",
+    "model10.add(LSTM(128, return_sequences = True, input_shape=(n_steps, n_features)))\n",
+    "model10.add(LSTM(64, return_sequences = True))\n",
+    "model10.add(LSTM(32, return_sequences = False))\n",
+    "model10.add(Dense(64))\n",
+    "model10.add(Dense(1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "id": "3UJtO3wC7WWe"
+   },
+   "outputs": [],
+   "source": [
+    "# Compile the model\n",
+    "model10.compile(loss='mean_squared_error', optimizer='adam')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ld9dofMD7YNO",
+    "outputId": "2b3021f3-d88f-431c-a059-a9b6e065a29b"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<keras.src.callbacks.history.History at 0x20425d47d50>"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model10.fit(X_train_reshaped, y_train[n_steps-1:], epochs=50, batch_size=32, verbose=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "lOTdB8Bj7aXM",
+    "outputId": "c844bad9-4c1e-447f-dad9-7b86f57dee9b"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step\n",
+      "RMSE: 3.8029934605035436\n",
+      "MAE: 2.8947993486353303\n",
+      "MAPE: 0.045649848386935823\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "rmse, mae, mape = evaluate_model(model10, X_test_reshaped, y_test[n_steps-1:])\n",
+    "\n",
+    "# Store metrics\n",
+    "metrics[\"Model\"].append(\"LSTM\")\n",
+    "metrics[\"RMSE\"].append(rmse)\n",
+    "metrics[\"MAE\"].append(mae)\n",
+    "metrics[\"MAPE\"].append(mape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m177/177\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step\n",
+      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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DoUOHmDlzJiNHjuSBBx7wbc/Ly2P//v2W84vEd999x44dO/j+++99XkMABw8eNC2zVq1aLFq0qMx2FUGqa9eujcfjYeHChSFjVPlva9u2LR999BGapvH7778zdepUHn/8cVJTUwPKWRAEQRDMIFPMBEEQBKGEUA/ncGxKiv+DdHJyckgviO7du7NmzRqWLVsWsP2dd97B4/H4vDcuvvhicnNzy6xiFY6HH36YcePG8eijj/Lggw/qOkY1qampdOnShY8//jiiF1CfPn1YvXo1LVq08Hlj+X9UGCTCkZaWxgUXXMDy5cs59dRTQ+Zf6nlzwQUX8Mcff7By5coAGR988EHUfEqny73++usR04WrJ8FUqVKFTp068dlnnwWkLy4u5r333qNhw4accMIJUeVEo1q1amXKI5RHmVU8Hg+appUxeLz11lsUFRUpzy84bygbEHzChAmmZV5wwQUcOXLEt0JdKXrqSjT69OmDpmn8888/Ietr27Ztyxzj8Xho164dY8eOpUaNGgH9jd46JwiCIAjBiAeRIAiCIJRw0UUX0bBhQy699FJOPPFEiouLWbFiBWPGjKFq1arcc889vrSlb/KnTZtG8+bNSUlJoW3bttx777288847XHLJJTz++OM0adKEWbNmMX78eG6//XbfQ/51113HlClTuO2221i3bh0XXHABxcXF/Pbbb7Rp04b+/fuX0e+ee+6hatWqDBkyhKNHj/Lyyy/r8vxRyYsvvsi5555Lp06deOCBB2jZsiW7du3iiy++YMKECVSrVo3HH3+cOXPm0LlzZ4YOHUrr1q3Jzc1l8+bNzJ49mzfeeMPwVDkjvPTSS5x77rmcd9553H777TRt2pQjR46wfv16vvzyS18cqGHDhjF58mQuueQSnnzySd8qZn/++WfUPM477zxuvPFGnnzySXbt2kWfPn1ITk5m+fLlpKWlcffddwPh60konnnmGXr27MkFF1zA8OHDqVy5MuPHj2f16tV8+OGHMb/WVqhevTrnn38+zz//PLVr16Zp06YsWLCASZMmUaNGDVvz7ty5MzVr1uS2225j5MiRJCUl8f7775cxBBrhX//6F2PHjuVf//oXTz31FK1atWL27Nl88803lvU955xzGDJkCP/+979ZsmQJ559/PlWqVCEzM5Mff/yRtm3bcvvttzNz5kzGjx/PFVdcQfPmzdE0jc8++4yDBw/Ss2dPn7y2bdvy/fff8+WXX1K/fn2qVatG69atLespCIIgJD5iIBIEQRCEEh5++GH+97//MXbsWDIzM8nLy6N+/fr06NGDBx98kDZt2vjSPvbYY2RmZnLLLbdw5MgRmjRpwubNm6lTpw4///wzDz74IA8++CCHDx+mefPmPPfcc/zf//2f7/hKlSoxe/ZsnnnmGT788EPGjRtHtWrVaNeuXcTl6wcPHkyVKlW48cYbycrK4q233tIVx0gV7dq1Y9GiRYwcOZIHH3yQI0eOkJGRQbdu3XyeKPXr12fJkiU88cQTPP/882zfvp1q1arRrFkzevXqFRBU2g5OOukkli1bxhNPPMHDDz/M7t27qVGjBq1ataJ3796+dBkZGSxYsIB77rmH22+/nbS0NK688kpeffVVLr/88qj5TJ06lQ4dOjBp0iSmTp1KamoqJ510Ev/97399acLVk1B06dKF7777jpEjRzJo0CCKi4tp164dX3zxBX369LFcLrHmgw8+4J577mHEiBEUFhZyzjnnMGfOHC655BJb861VqxazZs3ivvvuY8CAAVSpUoXLL7+cadOm0aFDB1My09LS+O6777jnnnt44IEH8Hg8XHjhhXz00Ud07tzZss4TJkzgrLPOYsKECYwfP57i4mIaNGjAOeec4wt436pVK2rUqMFzzz3Hjh07qFy5Mq1bt2bq1KkMHDjQJ+ull17izjvvpH///mRnZ9OlSxdfoGtBEARBiIRH0zTNaSUEQRAEQRAEQRAEQRAE55AYRIIgCIIgCIIgCIIgCOUcMRAJgiAIgiAIgiAIgiCUc8RAJAiCIAiCIAiCIAiCUM4RA5EgCIIgCIIgCIIgCEI5RwxEgiAIgiAIgiAIgiAI5RwxEAmCIAiCIAiCIAiCIJRzKjmtgBsoLi5mx44dVKtWDY/H47Q6giAIgiAIgiAIgiAIStA0jSNHjtCgQQMqVAjvJyQGImDHjh00atTIaTUEQRAEQRAEQRAEQRBsYdu2bTRs2DDsfjEQAdWqVQO8hVW9enWHtREEQRAEQRAEQRAEQVDD4cOHadSokc/2EQ4xEIFvWln16tXFQCQIgiAIgiAIgiAIQsIRLaSOBKkWBEEQBEEQBEEQBEEo54iBSBAEQRAEQRAEQRAEoZwjBiJBEARBEARBEARBEIRyjsQg0ommaRQWFlJUVOS0KkKCkZSURMWKFZ1WQxAEQRAEQRAEQSjHiIFIB/n5+WRmZpKdne20KkIC4vF4aNiwIVWrVnVaFUEQBEEQBEEQBKGcIgaiKBQXF7Np0yYqVqxIgwYNqFy5ctTI34KgF03T2LNnD9u3b6dVq1biSSQIgiAIgiAIgiA4ghiIopCfn09xcTGNGjUiLS3NaXWEBKROnTps3ryZgoICMRAJgiAIgiAIgiAIjiBBqnVSoYIUlWAP4pEmCIIgCIIgCIIgOI1YPQRBEARBEARBEARBEMo5YiASBEEQBEEQBEEQBEEo54iBSHAEj8fDjBkzYppn165dGTZsWEzzFARBEARBEARBEIR4QAxECc7PP/9MxYoV6dWrl+FjmzZtyrhx49QrFQWPxxPxM2jQIFNyP/vsM5544gm1ygqCIAiCIAiCIAhCAiCrmCU4kydP5u677+att95i69atNG7c2GmVopKZmen7PW3aNB599FHWrVvn25aamhqQvqCggKSkpKhyjzvuOHVKCoIgCIIgCIIgCEICIR5EBtE0yMpy5qNpxnTNyspi+vTp3H777fTp04epU6eWSfPFF1/QsWNHUlJSqF27Nn379gW807G2bNnCvffe6/PcARg1ahSnnXZagIxx48bRtGlT3//FixfTs2dPateuTXp6Ol26dGHZsmW69c7IyPB90tPT8Xg8vv+5ubnUqFGD6dOn07VrV1JSUnjvvffYt28f1113HQ0bNiQtLY22bdvy4YcfBsgNnmLWtGlTnn76aW666SaqVatG48aNefPNN3XrKQiCIAiCIAiCIAiJghiIDJKdDVWrOvPJzjam67Rp02jdujWtW7dmwIABTJkyBc3PyjRr1iz69u3LJZdcwvLly5k3bx4dO3YEvNOxGjZsyOOPP05mZmaAV080jhw5wsCBA1m4cCG//vorrVq1onfv3hw5csTYCUTg/vvvZ+jQoaxdu5aLLrqI3NxcTj/9dGbOnMnq1asZMmQIN954I7/99ltEOWPGjKFjx44sX76cO+64g9tvv50///xTmZ6CIAiCIAiCIAiCEA/IFLMEZtKkSQwYMACAXr16cfToUebNm0ePHj0AeOqpp+jfvz+PPfaY75h27doB3ulYFStWpFq1amRkZBjKt1u3bgH/J0yYQM2aNVmwYAF9+vSxcko+hg0b5vN2KmX48OG+33fffTdff/01H3/8MZ06dQorp3fv3txxxx2A1+g0duxYvv/+e0488UQlegqCIAiCIAiCIAhCPCAGIoOkpcHRo87lrZd169axaNEiPvvsMwAqVapEv379mDx5ss9AtGLFCm655Rbleu7evZtHH32U7777jl27dlFUVER2djZbt25Vlkepp1MpRUVFPPvss0ybNo1//vmHvLw88vLyqFKlSkQ5p556qu936VS23bt3K9NTEARBEARBEARBEOIBMRAZxOOBKDYHVzBp0iQKCws5/vjjfds0TSMpKYkDBw5Qs2bNMsGe9VChQoWAaWrgDRLtz6BBg9izZw/jxo2jSZMmJCcnc/bZZ5Ofn2/uZEIQbPgZM2YMY8eOZdy4cbRt25YqVaowbNiwqHkGB7f2eDwUFxcr01MQBEEQBEEQBEEQ4gGJQZSAFBYW8s477zBmzBhWrFjh+6xcuZImTZrw/vvvA17vmXnz5oWVU7lyZYqKigK21alTh507dwYYiVasWBGQZuHChQwdOpTevXtz8sknk5yczN69e9WdYAgWLlzI5ZdfzoABA2jXrh3Nmzfn77//tjVPQRAEQRAEQRAEQUgUxECUgMycOZMDBw4wePBgTjnllIDP1VdfzaRJkwAYOXIkH374ISNHjmTt2rWsWrWK5557zienadOm/PDDD/zzzz8+A0/Xrl3Zs2cPzz33HBs2bOC1117jq6++Csi/ZcuWvPvuu6xdu5bffvuNG264wZS3khFatmzJnDlz+Pnnn1m7di233norO3futDVPQRAEQRAEQRAEQUgUxECUgEyaNIkePXqQnp5eZt9VV13FihUrWLZsGV27duXjjz/miy++4LTTTqNbt24Bq349/vjjbN68mRYtWlCnTh0A2rRpw/jx43nttddo164dixYtCggODTB58mQOHDhA+/btufHGGxk6dCh169a19ZwfeeQROnTowEUXXUTXrl3JyMjgiiuusDVPQQjH3XfDgw86rYUgCIIgCIIgCIJ+PFpwQJlyyOHDh0lPT+fQoUNUr149YF9ubi6bNm2iWbNmpKSkOKShkMhIHUsstm6FJk28v/PyoHJlZ/URBEEQBEEQBKF8E8nm4Y94EAmCICgkL+/YbzG/C4IgCIIgCIIQL4iBSBAEQRAEQRAEQRAEoZwjBiJBEARBEARBEARBEIRyjhiIBEEQBEEQBEEQBEEQyjliIBIEQRAEQRAEQRAEQSjniIFIEARBEARBEARBEAShnCMGIkEQBIV4PMd+yypmgiAIgiAIgiDEC2IgEgRBEARBEARBEARBKOeIgUgQBEEh4jUkCIIgCIIgCEI8IgYiwTKjRo3itNNO8/0fNGgQV1xxRcz12Lx5Mx6PhxUrVsQ036ZNmzJu3LiY5inEB/7TzQRBEARBEARBENyMGIgSlEGDBuHxePB4PCQlJdG8eXOGDx9OVlaW7Xm/9NJLTJ06VVfaWBl1SvOJ9Bk1apQp2YsXL2bIkCFqFRYEQRAEQRAEQRCEGFLJaQUE++jVqxdTpkyhoKCAhQsXcvPNN5OVlcXrr79eJm1BQQFJSUlK8k1PT1ciRyWNGjUiMzPT9/+FF17g66+/Zu7cub5tVatW9f3WNI2ioiIqVYreROrUqaNWWUEQBEEQBEEQBEGIMeJBZBANyHLoYzS0SXJyMhkZGTRq1Ijrr7+eG264gRkzZgDHpoVNnjyZ5s2bk5ycjKZpHDp0iCFDhlC3bl2qV69Ot27dWLlyZYDcZ599lnr16lGtWjUGDx5Mbm5uwP7gKWbFxcWMHj2ali1bkpycTOPGjXnqqacAaNasGQDt27fH4/HQtWtX33FTpkyhTZs2pKSkcOKJJzJ+/PiAfBYtWkT79u1JSUmhY8eOLF++PGxZVKxYkYyMDN+natWqVKpUyff/zz//pFq1anzzzTd07NiR5ORkFi5cyIYNG7j88supV68eVatW5YwzzggwKkHZKWYej4e33nqLK6+8krS0NFq1asUXX3wR8VoJiYnEIxIEQRAEQRAEIV5w1EDUtGnTkFN97rzzTsDrxTFq1CgaNGhAamoqXbt25Y8//giQkZeXx913303t2rWpUqUKl112Gdu3b7dN52ygqkOfbIu6p6amUlBQ4Pu/fv16pk+fzqeffuqb4nXJJZewc+dOZs+ezdKlS+nQoQPdu3dn//79AEyfPp2RI0fy1FNPsWTJEurXr1/GcBPMgw8+yOjRo3nkkUdYs2YNH3zwAfXq1QO8Rh6AuXPnkpmZyWeffQbAxIkTeeihh3jqqadYu3YtTz/9NI888ghvv/02AFlZWfTp04fWrVuzdOlSRo0axfDhwy2WEIwYMYJnnnmGtWvXcuqpp3L06FF69+7N3LlzWb58ORdddBGXXnopW7dujSjnscce49prr+X333+nd+/e3HDDDb4yFBIbiTskCIIgCIIgCEJcojnI7t27tczMTN9nzpw5GqDNnz9f0zRNe/bZZ7Vq1appn376qbZq1SqtX79+Wv369bXDhw/7ZNx2223a8ccfr82ZM0dbtmyZdsEFF2jt2rXTCgsLdetx6NAhDdAOHTpUZl9OTo62Zs0aLScnR9M0TTuqaRoOfY7qPiNNGzhwoHb55Zf7/v/2229arVq1tGuvvVbTNE0bOXKklpSUpO3evduXZt68eVr16tW13NzcAFktWrTQJkyYoGmapp199tnabbfdFrC/U6dOWrt27ULmffjwYS05OVmbOHFiSD03bdqkAdry5csDtjdq1Ej74IMPArY98cQT2tlnn61pmqZNmDBBO+6447SsrCzf/tdffz2krFCMHDkyQOf58+drgDZjxoyox5500knaK6+84vvfpEkTbezYsb7/gPbwww/7/h89elTzeDzaV199FVJecB0T4pu//9Y0r++QpsklFQRBEARBEATBaSLZPPxxNAZRcOyWZ599lhYtWtClSxc0TWPcuHE89NBD9O3bF4C3336bevXq8cEHH3Drrbdy6NAhJk2axLvvvkuPHj0AeO+992jUqBFz587loosuUq5zGnBUuVT9eRth5syZVK1alcLCQgoKCrj88st55ZVXfPubNGkScA2WLl3K0aNHqVWrVoCcnJwcNmzYAMDatWu57bbbAvafffbZzJ8/P6QOa9euJS8vj+7du+vWe8+ePWzbto3Bgwdzyy23+LYXFhb64hutXbuWdu3akZZ2rFTOPvts3XmEo2PHjgH/s7KyeOyxx5g5cyY7duygsLCQnJycqB5Ep556qu93lSpVqFatGrt377asnyAIgmAPeXlQUAB+4egEQRAEQRDKFa4JUp2fn897773H//3f/+HxeNi4cSM7d+7kwgsv9KVJTk6mS5cu/Pzzz9x6660sXbqUgoKCgDQNGjTglFNO4eeffw5rIMrLyyMvL8/3//Dhw7r19ABVjJ+eI1xwwQW8/vrrJCUl0aBBgzJBqKtUCTyT4uJi6tevz/fff19GVo0aNUzpkJqaaviY4uJiwDvNrFOnTgH7KlasCHinH9pBcJn85z//4ZtvvuGFF16gZcuWpKamcvXVV5Ofnx9RTnBZezwe33kJgiAI7qNhQ9i7F44cESORIAiCIAjlE9cEqZ4xYwYHDx5k0KBBAOzcuRPAF6umlHr16vn27dy5k8qVK1OzZs2waULxzDPPkJ6e7vs0atRI4Zm4hypVqtCyZUuaNGmia4WyDh06sHPnTipVqkTLli0DPrVr1wagTZs2/PrrrwHHBf/3p1WrVqSmpjJv3ryQ+ytXrgxAUVGRb1u9evU4/vjj2bhxYxk9SoNan3TSSaxcuZKcnBxdephl4cKFDBo0iCuvvJK2bduSkZHB5s2blecjJA4SmFoQ4pO9e73fq1Y5q4cgCIIgCIJTuMZANGnSJC6++GIaNGgQsN0TFPFV07Qy24KJlubBBx/k0KFDvs+2bdvMK55A9OjRg7PPPpsrrriCb775hs2bN/Pzzz/z8MMPs2TJEgDuueceJk+ezOTJk/nrr78YOXJkmcDh/qSkpHD//fczYsQI3nnnHTZs2MCvv/7KpEmTAKhbty6pqal8/fXX7Nq1i0OHDgHeVdaeeeYZXnrpJf766y9WrVrFlClTePHFFwG4/vrrqVChAoMHD2bNmjXMnj2bF154QXmZtGzZks8++4wVK1awcuVKrr/+evEEEnQjAasFQRAEQRAEQYgXXGEg2rJlC3PnzuXmm2/2bcvIyAAo4wm0e/dun1dRRkYG+fn5HDhwIGyaUCQnJ1O9evWAj+A1xs2ePZvzzz+fm266iRNOOIH+/fuzefNmX3n269ePRx99lPvvv5/TTz+dLVu2cPvtt0eU+8gjj3Dffffx6KOP0qZNG/r16+eLx1OpUiVefvllJkyYQIMGDbj88ssBuPnmm3nrrbeYOnUqbdu2pUuXLkydOtXnQVS1alW+/PJL1qxZQ/v27XnooYcYPXq08jIZO3YsNWvWpHPnzlx66aVcdNFFdOjQQXk+QmIi3kSCIAiCIAiCIMQLHs2uYC4GGDVqFBMmTGDbtm1UquQNi6RpGg0aNODee+9lxIgRgDdOUd26dRk9erQvSHWdOnV47733uPbaawHIzMykYcOGzJ49W3eQ6sOHD5Oens6hQ4fKGItyc3PZtGkTzZo1IyUlReFZC4IXqWOJxfr10KqV93dODsglFYT4oNTj7+efQcGaB4IgCIIgCK4hks3DH8eDVBcXFzNlyhQGDhzoMw6B15tl2LBhPP3007Rq1YpWrVrx9NNPk5aWxvXXXw9Aeno6gwcP5r777qNWrVocd9xxDB8+nLZt2/pWNRMEQRAEQRAEQRAEQRAi47iBaO7cuWzdupWbbrqpzL4RI0aQk5PDHXfcwYEDB+jUqRPffvst1apV86UZO3YslSpV4tprryUnJ4fu3bszdepU32pXgiAIgiAIenHer1oQBEEQBMEZXDHFzGlkipngJFLHEguZYiYI8UnpFLOffoLOnZ3VRRAEQRAEQSV6p5i5Iki1IAhCQlEBkBXMBEEQBEEQBEGIIxyfYiYIgpBIFAH8CRyBcu+eKQiCIAiCIAhC3CAGIkEQBIVsSwKaeH/n50Kqo9oIgiAIgiAIgiDoQwxEgiAIgiAI9YEqTishCIIgCILgHBKDSBAEQRAEYQfwNxxKcloRQRAEQRAEZxADkSAIgiAIQgnbkp3WQBAEQRAEwRnEQCQ4gsfjYcaMGU6rIQiCIAiCIAiCIAgCYiBKeH7++WcqVqxIr169DB/btGlTxo0bp16pKHg8noifQYMGmZbt1DkJgiAIgiAIgiAIgpsRA1GCM3nyZO6++25+/PFHtm7d6rQ6usjMzPR9xo0bR/Xq1QO2vfTSS06rKAi6kGXuBUEQBEEQhFiQU5DD2yveZtfRXU6rIsQxYiAyiKZpZOVnOfLRNGOPm1lZWUyfPp3bb7+dPn36MHXq1DJpvvjiCzp27EhKSgq1a9emb9++AHTt2pUtW7Zw7733+jx3AEaNGsVpp50WIGPcuHE0bdrU93/x4sX07NmT2rVrk56eTpcuXVi2bJluvTMyMnyf9PR0PB5PwLYffviB008/nZSUFJo3b85jjz1GYWGh7/hRo0bRuHFjkpOTadCgAUOHDo14ToKgEv9aZbDJCoIgCIIgCIIp7p97P4P+N4hzJp/jtCpCHCPL3BskuyCbqs9UdSTvow8epUpl/WvwTps2jdatW9O6dWsGDBjA3XffzSOPPOIzjMyaNYu+ffvy0EMP8e6775Kfn8+sWbMA+Oyzz2jXrh1DhgzhlltuMaTnkSNHGDhwIC+//DIAY8aMoXfv3vz9999Uq1bNkKxgvvnmGwYMGMDLL7/Meeedx4YNGxgyZAgAI0eO5JNPPmHs2LF89NFHnHzyyezcuZOVK1daPidB0I3YHQUhrtGkDQuCIAhxyOd/fg7AhgMbHNZEiGfEQJTATJo0iQEDBgDQq1cvjh49yrx58+jRowcATz31FP379+exxx7zHdOuXTsAjjvuOCpWrEi1atXIyMgwlG+3bt0C/k+YMIGaNWuyYMEC+vTpY+WUeOqpp3jggQcYOHAgAM2bN+eJJ55gxIgRjBw5kq1bt5KRkUGPHj1ISkqicePGnHnmmZbPSRD04u81JM+ZghB/iOefIAiCIAjlFTEQGSQtKY2jDx51LG+9rFu3jkWLFvHZZ58BUKlSJfr168fkyZN9BqIVK1bY4kmze/duHn30Ub777jt27dpFUVER2dnZSmIgLV26lMWLF/PUU0/5thUVFZGbm0t2djbXXHMN48aNo3nz5vTq1YvevXtz6aWXUqmSVHVBEARBEARBEARBCIc8NRvE4/EYmublFJMmTaKwsJDjjz/et03TNJKSkjhw4AA1a9YkNTXVsNwKFSqUiYVUUFAQ8H/QoEHs2bOHcePG0aRJE5KTkzn77LPJz883dzJ+FBcX89hjj/liJfmTkpJCo0aNWLduHXPmzGHu3LnccccdPP/88yxYsICkpCTL+QuCIAiCIAiCIAhCIiJBqhOQwsJC3nnnHcaMGcOKFSt8n5UrV9KkSRPef/99AE499VTmzZsXVk7lypUpKioK2FanTh127twZYCRasWJFQJqFCxcydOhQevfuzcknn0xycjJ79+5Vcm4dOnRg3bp1tGzZssynQgVvdU5NTeWyyy7j5Zdf5vvvv+eXX35h1apVYc9JEOxCZqoIgiAIgiAIghAviAdRAjJz5kwOHDjA4MGDSU9PD9h39dVXM2nSJO666y5GjhxJ9+7dadGiBf3796ewsJCvvvqKESNGANC0aVN++OEH+vfvT3JyMrVr16Zr167s2bOH5557jquvvpqvv/6ar776iurVq/vyaNmyJe+++y4dO3bk8OHD/Oc//zHlrRSKRx99lD59+tCoUSOuueYaKlSowO+//86qVat48sknmTp1KkVFRXTq1Im0tDTeffddUlNTadKkSdhzEgSVSNwhQYhvZIFLQRAEIR4xuuK1IIRCPIgSkEmTJtGjR48yxiGAq666ihUrVrBs2TK6du3Kxx9/zBdffMFpp51Gt27d+O2333xpH3/8cTZv3kyLFi2oU6cOAG3atGH8+PG89tprtGvXjkWLFjF8+PCAPCZPnsyBAwdo3749N954I0OHDqVu3bpKzu2iiy5i5syZzJkzhzPOOIOzzjqLF1980WcAqlGjBhMnTuScc87xeUh9+eWX1KpVK+w5CYJdyH1aEARBEARBEIR4waOJqZHDhw+Tnp7OoUOHAjxhAHJzc9m0aRPNmjUjJSXFIQ2FREbqWGIxbwv08Nor2ZcNx+mPLS8IgoOUOg69/jvcdqqjqgiCIAiCYRq+2JB/jvwDgDay3D/iC0FEsnn4Ix5EgiAIKpHpKYIgCIIgCIIgxCFiIBIEQbAJsRUJgiAIgiAIghAviIFIEARBITJpVxAEQRAEQYg1mqyfKyhADESCIAg2IbdpQRAEQRAEQRDiBTEQCQlBZiZs2CDeG4LzyLQyQYhv5DYiCIIgCEJ5pZLTCgiCCv7xBuzn8GFIT3dWF0EoRQyWghB/SLsVBEEQ4hGPvKYUFCAeREJCETyw1zQoLnZGF0EQBEEQBEEQBEGIF8RAJCQ0f/8NK1ZAUZHTmgjxwK5dsHGj01oIgiAIgiAIglE0TkuGJKfVEOIaMRAJCc3hw14PokOHnNZEiAcyMqBFC9i712lNBEEQyidZWTLNTxCE2FNYXEhRcXy/Ub457SjLG8PH9Z3WJAKaxoqPGrJsWlOnNRHCIAYiwTKjRo3itNNO8/0fNGgQV1xxRcz12LFjMzVrelixYkXM81bN559D27awapXTmpRP1q1zWgNBEJyigoRwcIwtW6BqVbj4Yqc1EQQhHOv2rmPi0okUFhc6rYoyCosLaTy2MSePPxktji3UQ6ocBeDyqg4rEoGduxdzWvE/dCjawtGsTKfVEUIgBqIEZdCgQXg8HjweD0lJSTRv3pzhw4eTlZVle94vvfQSU6dO1ZV28+bNeDz2G3W2bt3sK49wn1GjRpmW7/F4mDFjhjJ9+/aF1auhXz9lIgXBVawE/g/Y77QighBE/D4axD+lQ4dvvnFUDUEQInDiaycyZOYQ3ljyhtOqKGPD/g1kHs1k3b51CWX4ciOFxQW+3/FsjEtkZBWzBKZXr15MmTKFgoICFi5cyM0330xWVhavv/56mbQFBQUkJamZsZruxDJiHiLW5uOPb0Rm5jEr9QsvvMDXX3/N3LlzfduqVnWfuf3IEac1EKwg973wnFbyvRt4z0E9BCEYabeCIAjR+WX7L9x15l1OqyEIgmLEg8gomgaFWc58DI5ak5OTycjIoFGjRlx//fXccMMNPi+X0mlhkydPpnnz5iQnJ6NpGocOHWLIkCHUrVuX6tWr061bN1auXBkg99lnn6VevXpUq1aNwYMHk52dG7BSWPAUs+LiYkaPHk3Lli1JTk6mcePGPPXUUwA0a9YMgPbt2+PxeOjatavvuClTptCmTRtSUlI48cQTGT9+fIAeixYton379qSkpPCvIR1Zl7c8bFlUrFiRjIwM36dq1apUqlQpYNvHH38cNr/8/Hzuuusu6tevT0pKCk2bNuWZZ54BoGnTpgBceeWVeDwe338hfpEHxNixMnoSQRAiMGECXHop5OQYO05i8wmCfjZsgAcegJ07ndZEEATBXsSDyChF2TDdIU+Ta49CpSqmD09NTaWg4Jhb3/r165k+fTqffvopFStWBOCSSy7huOOOY/bs2aSnpzNhwgS6d+/OX3/9xXHHHcf06dMZOXIkr732Gueddx7vvvsu48a9TIMGzTl8GKpXL5vvgw8+yMSJExk7diznnnsumZmZ/Pnnn4DXyHPmmWcyd+5cTj75ZCpXrgzAxIkTGTlyJK+++irt27dn+fLl3HLLLVSpUoWBAweSlZVFnz596NatG++99x7fbtrEmHvuKZu5p+QThWj5vfzyy3zxxRdMnz6dxo0bs23bNrZt2wbA4sWLqVu3LlOmTKFXr16+sgymqMi7SlbNmpCaGl0nQRAEQYjGbbd5v994A+691/t7zx6oVQsqhHkN+NVX0Ls3DBsGY8fGRE1BiGvOOcc7hvv1V/j+e6e1EVSjyeTisMz4cwbLMpfxWNfH8HjUBunTKI6eSIg5YiAqJyxatIgPPviA7t27+7bl5+fz7rvvUqdOHQC+++47Vq1axe7du0lOTga8U7FmzJjBJ598wpAhQxg3bhw33XQTN998MwBPPvkk//vfXPLycjlwoKyB6MiRI7z00ku8+uqrDBw4EIAWLVpw7rnnAvjyrlWrFhkZGb7jnnjiCcaMGUPfvn0Br6fRmjVrmDBhAgMHDuT999+nqKiIyZMnk5aWRs7JJ7N7+3aevf32QAVOBlKg6GDk8omW39atW2nVqhXnnnsuHo+HJk2a+I4tPYcaNWoEnEMw27d7B+07dkDHjpH1EdzB7t3w2mvw73+DOIYJQvlB02DRImjTJvSLDzdy+LD3e9486NEDrr4aPv44dNrhw73f48aJgUgQ9LBrl/f7p5+c1UMQYs2V064E4KyGZ9G7VW/L8jwemcDkdsRAZJSKaV5PHqfyNsDMmTOpWrUqhYWFFBQUcPnll/PKK6/49jdp0sRn3ABYunQpR48epVatWgFycnJy2LBhAwBr167lttLXlSW0bXs2S5bMD6nD2rVrycvLCzBMRWPPnj1s27aNwYMHc8stt/i2FxYW+uIbrV27lnbt2pGWdqxM2p59dllhKd6v3Ag1XU9+gwYNomfPnrRu3ZpevXrRp08fLrzwQt3nBN6le4X44vrrvQ9bkyZ5DXyCIJQPPvkErr0WWrSA9eud1sYYzz3n/f7kE2f1MINM7xXikT/+gMGD4bHH4KKLnNZGMIMHD2keiO9F7u0l84isOFZeEAORUTweS9O8YskFF1zA66+/TlJSEg0aNCgThLpKlcDzKC4upn79+nwfwne2Ro0apnRINTGXqrgkoNHEiRPp1KlTwL7S6Vsqo97rya9Dhw5s2rSJr776irlz53LttdfSo0cPPonHEbgQns+BFlBQEptjfond859/9Ivwd76VZx1BiEM88NFHQCVv3BFB0IOmeYeIQvmjb1/46y/o1av8GDmrJlhd9xTnk9USsoux/SL+tv03bvnyFsZeNJbuzfW/QA9HdkE2KZVSqCCeOYIipCYlMFWqVKFly5Y0adJE1wplHTp0YOfOnVSqVImWLVsGfGrXrg1AmzZt+PXXXwOOW73mV0gK/TDcqlUrUlNTmTdvXsg8S2MOFRUds9nXq1eP448/no0bN5bRozSo9UknncTKlSvJ8YvKuTpIL73oyQ+gevXq9OvXj4kTJzJt2jQ+/fRT9u/3LtKdlJQUcA5CnHIF0BbWVDMvopyMDZVRkO+0BoIQiKbB4YZALjDaaW2MI0aK2PPmm1CnDixb5rQmgm3UA+4GLcRCvbtqAvOBC2Ksk0O8nwFHWkKT4v1Oq6KMSjleN/G0CoBm7zL33d/pzqrdq+jxbg/LsjKPZFLl6Sp0f8draIq37l/TJAaRGxEPIsFHjx49OPvss7niiisYPXo0rVu3ZseOHcyePZsrrriCjh07cs899zBw4EA6duzIueeey/vvv8/GjX/QoHlzckI8VKekpHD//fczYsQIKleuzDnnnMOePXv4448/GDx4MHXr1iU1NZWvv/6ahg0bkpKSQnp6OqNGjWLo0KFUr16diy++mLy8PJYsWcKBAwf4v//7P66//noeeughBg8ezMMPP8xPmzfz3gsvmD73aPmNHTuW+vXrc9ppp1GhQgU+/vhjMjIyfJ5VTZs2Zd68eZxzzjkkJydTs2bNMnmUl7dKiYCVG6z/w5lc8+j88w/QLGoyQYgpa64DKgIjnNbEOHoMRPktgE+Ax+3Wpnxw663e7xtv9E43EhKQucApUBzCCHR0CtAG6BpblZzi+pLx/iWFa51VxCZUB2IOJqtAXcyJ6X9MB+D7zd8rk2k3HvFPcT1yhQQfHo+H2bNnc/7553PTTTdxwgkn0L9/fzZv3ky9evUA6NevH48++ij3338/p59+Olu2bOGqksDQRWGclB555BHuu+8+Hn30Udq0aUO/fv3YvXs3AJUqVeLll19mwoQJNGjQgMsvvxyAm2++mbfeeoupU6fStm1bunTpwtSpU30ePVWrVuXLL79kzZo1tG/fnvEPPcRdo82/6tWT3+jRo+nYsSNnnHEGmzdvZvbs2VQoWSJmzJgxzJkzh0aNGtG+fXvTeghCeaPA3hd1gmCYeHsDG4zHA1SOnGb703gfaD+MgULlhShlLsQ5p3i/tD5ldxXXi60qgr2oDGPhRjRN49//+zf3fBVi9ecQ9EyD+2ogbz3LER4t0VuBDg4fPkx6ejqHDh2ietByJbm5uWzatIlmzZqRkpLikIbuZknJd8oROMXC1BwVOmQchYZVy26vdxQaVQ0+Kras2gh5TYAd0LHRse2h6pinB/BfqDcSdi50RN1ySemD4asr4c52UKkSlM4c1NtTztsCPUoWudt1BOo61CbcTmlZJ/8Nua0cVUUQgGN18rWV8FQO7DjL+9/tg6TSl92jRsHIkXDuUPjpZeB50P4T+piU9ZDX0vvbTec3ahQ89iuwDrRNTmujH8+jwOPQ5N+weYrT2gh24DMcF4AW9EK0wn7QjvP+dlN7so0PvKXxU4WmnNM/jhpqBDZt+YpmP3lX6Cq4JoukJGMLAxnB89ix1xDaSGs15qVfxpK/7P/4JQc+G66R+W5F6lcsmbZ1fWjZGw9spMXLLQDIfzifpIpRwpCUXO+vGt3Lxee9aElfgB07f6PBd94b7KFLt5BerbFlmYI+Itk8/JEpZoIQI/Lr452y0ChaSryuzMD+2jYqJAiCICQGfv7g6waW/AhjHHIzG5sBo5zWwgQlU/V2PuSsGoIgmEOLoWnv/FR4tx7csce6rFZZK+ldEyiJbKHnLAqKCkzlVbXwgKnjhPhDppgJgospFgORo2gNgJeBE0wer1IZQRBiQ7zNMXsT2A9HSxcmjTf9/dgqL5KFBKeoCLZudVoLVSTSKCd2Hef846FxEsxsYF1WlaLD1oUIQhBiIBIEQQhD8XTgbmCR/mPc+my27C84YSx8/KPTmgiCu4m7ife3AOmwpKP3ryfe9BeEOGPKFDjzTJg/3/ixV18NTZrAZ5+p10uIDypYHCgezD2oRA9BCIcYiAQhVrjVciCUobj0AeuMku8Qy9rGG93/gL/vhWsznNZEsEpuLixfHoeGDME9+NWdg44poY+1a2HECNijYDqGIKjgpptgcRJ0+xq+W2Ds2BkzvN/PP69cLcEC/kP0WE43M8prP4/mpldrMuHX0LGA5FFDUIEYiHQisbyFUkxXhTDHSd1KMFy6zP2hLiU/WjqqhqCA3r2hQwd4802nNRHciFEPov6bbVFDGaee6n2YHjzYaU0EoYTRwE/e7xd2O62MoB73mlkarH6AzxpA+sr7Yp+5HYNaNw2UBR9iIIpCUpI3snt2drbDmghuYONG+OMPKC5WJzM/Px+AihUrqhMqWMKq+69gABkbGKZ0WsMbbzirh5AYLNrvtAaRKSz0fi9e7KweQmzYtg3GjIFDh5zWJAweYMSxv/vqOKaJoJBAryH3DkyuLFmRuX8cr5Dr8Yj5we3IKmZRqFixIjVq1GD3bu8rgrS0NDweeXoMRXG+d+qDkxTmQW6IWh1uu1H25wLJXlf3dKPTjvLwvZQoLabi4mL27NlDWloalSqpbY47dsBDD8Gdd0LHjkpFC4I6pDsVBKEU6Q/KPeec4zUSLVkCH37otDZC+aF8dT6nJ0OeUTuYPP+WG8RApIOMDG/QjlIjkRDI3pLvpFxISnFWh/w8yEkOvT03OfgoE/l4gEIo2gHVDb513VsAmtchjU1+2ytUqEDjxo2VGx4HDYI5c2DqVPHgNIovBpGUmxAH/PUXbN8Oq1dD9ereti+Yx23Nfto02LwZ7r9fX3qjdxLNRWP+kPeqCkBL0GSxnnLBtm3e72+/dVYPITpaQhlVjnU+iR76oULBYZaUrBhZUFwEFZOcVUhwHWIg0oHH46F+/frUrVuXgoICp9VRTl4erFjhjWmRZKKPuLjku8VimNVGpWbGdbhpOYw4sez2fy+H+08MPsp8PjevgOEGz/WKjZDXzPv7T7/tlStXpkIF9e6Wa9cqFynoQQv5UxDUcSpwAxR+6f3bunXJ9mSgSAxEVnHbs0H//t7vHj3g9NOd1cURpgI3QvZ/nVZEEJAbe4LiSShjV2QqFezz/dYoclAT0DSFMTsEZYiByAAVK1ZMyDgxAwbAp5/C0KHw0kvGj99S8p2mQYpDHkSlOhwhUIfS7YdRo1u4fPSwtQLklhzjUDEp46+/oG5dqFHDaU3cjdseNIUEYaX3a0drv22VgQMlnxI0DUaNgtNOgyuvjJl2cY9bHxP27o2eBki8B9gbvV9Z9zirhi4SreydoANwLxQ957QiNnEhcDsUvOW0ItZxa19phoAYRHE8eLPzmkxZMZW3j1TkrcusVt6yWmqaRm5hLqlJqRZlCyqQKFECn37q/X75ZePHxnEfGntifCeNdm1eeQWuvx6KDL48WLvW67FQr5553coLd93ltAZCPLBgAfzrXwYMACXk+BuIWgKpQINjm77+Gh5/HPr2VaBkOSLewywYXcVMEFzFUmAAZCWAASUk3wBXwCYHFqESIhHnHb8BzE6h8wCTlk+ioEj9bJpe7/ci7ek0Mo9kKpdthuWZy5n992yn1XAMMRAJAezcaSz9Ndcc+330qHfZZacDVQv6GDrUGwDy88+NHTd3rve7ZPG1hEQr80M//g+Xn18TPp0gaMB2oOvt8G4juPsBtfJ37FArT4gTEvQ5p7ied60HVxOi7P/+G0aP9o6RBP0UtXRaA3vJl9XPXEZ8Wtb9tX58weOGj3diap0Woqy/3eANOvbR6o9irU5IOrzZgUs+uIQ/9/4ZPXECIgYiIYDzzzeWvtT7CGDbVrj1Vu8ba6dwdfceY+X0vgU/csRePco1lzmtQBwQg3aRlwc33RTYX5ll0yYYNw6ysqzLugloBLAGeAp+6GFdZnnkwAEoTpAwBtk58N1K8c4NxyNOK2CC1q3hgQe8H6H8ommwfLnTWgh6CGXAcCv+Q/2R34+0VfP4KRV1rN+/3mkVHEEMROWAOXO8Uwz0eAf9/bf1/GTlCSHesfI+xe4b6AcfwNlne1etMkSCehXoYfx4mDIFrr7auqyTT4Z774UHH7Qua2rQ/6MOBfmPZ1avhuOOg4svjp5WL04Ogpt9B93bwfA55mXE8ypm0Zjmds/VEJWn1Nj300+xVUWwCZPt5f33vYvB+CiPT9uCQHwFBK/gKZ+mkvJ51uWMCy/0TiO6884wCa4AdgJd1OQXizefu3ZB3C0o50R/GClO0H3AZ1BkUK94j88R79xwA/z6q9dI4WZ274b583X0BzGoT5kKp7TnZAD3wrxf1clUTXnyPpkwwfudKC8mdl/i/Z6YYV5GIscg2lXyoutz4L/IM7YQP0ya5LQGdpCoLTB+zyteh+iVgONcaJUQA5GQuFQDusK2f8Ls/xyvIeE7E7JDzBG329V/9WrIyIBOneyRP2eO1+PADIWFanWxQvYVeA1/r4ZJ8AJwJSxtGiuN4gcrQ4NY3ZwPHzZ4gILxjpG23ewk6HYrzJhhPV+rKO2T/gBehJ3DFMpUTOmlLqgA/Ag87aAyNlNUAXgcOE//MZs2wR9/hN/vOgPbaXhXPhJ8bbkv8Aww3fVBiYDbgD2wQoO333ZaGcEO9PQZmzYFbTAwWHjvPXjxRUMqCYJteGx4U7yiMexrAdUL9imXbQUxEAmJy4/AfNgVbXqFmdrgPyWt5AZp9+D6/fe938uXwznnwIoVfiooyPvCC73eVr/8Yuy43buhZk248UbrOqjgUOkUmHCeYyXkVdQv8/PP4e67TaskGODbb+GZZ9zzsLpvHzRqBPfoXGo6ewHwF7y1wVa1dKG0DEtWYM06Q6FMFYQ4xyXNgXMABdPh3Mrqc/EGpvlB/zHNm8MppxhfNc4xlgPfwD9p+pIb9iBySR9jhi+WOK2BDl4HagNTYNAgh3URHGPLlqANBtrdjTfCfffBny6Ll6vFrb+K4MPAAKl/Vbi8iuLs8Vr9T072/m+RvVptBhYRA5GQuJzq/drXy+Z8LgfmQ75B1/iff/YaV/SSl4z3YeAe77G9bDqvrVuNpZ80ybtKyXvvGTtu3ToT3iAO0bcvUBF4B7jZYWUSnIsugv/+F2bOdFoTL+PHe1fFevllnQec7P3acKZtKgn+pJTdVOTCO/yaNfCvf6mJdwdwsK75Y9fHWezJzFSbBMfxM16Bi7x2BcE2+gFDvQH5Bfsxuwx8vODxM3oYCch9YRWY0QC0Imsdr6eCCwcnYYineEkqcfwK/fPPPwwYMIBatWqRlpbGaaedxtKlS337NU1j1KhRNGjQgNTUVLp27cofQb7heXl53H333dSuXZsqVapw2WWXsd1wBFfBMg3wTmUzsMzI/PleL6AGDfQfs6Qz3ukE47z/d+3Sf6wyFPUXv/8OJ54IDRuG3l9UpCYfpfQHbgQmOq2IfRSX3i9jMEYoLPQGUA73sGrUUCmUpVgDBuEzWiUc84FlTiuhj7P/De9eAN2GOK2Jd3W7hCSex7MGdY+nANuCMbTj7JVvOo5lqHGB3nqYbjLPj4CXYEuIFwFOIs2vvGJnLBF3GOcqe6BGBfEgcoQDBw5wzjnnkJSUxFdffcWaNWsYM2YMNWrU8KV57rnnePHFF3n11VdZvHgxGRkZ9OzZkyN+a3MPGzaMzz//nI8++ogff/yRo0eP0qdPH4pc+XTtcnS6sEeisKb+tF9/7f02cqkKKhvTBwwGVv438GJsuqhZXwPj4EjPGGSmCgPXt7zw4YfwarhYT1F4803vEuytWuk8wEPk4OM24dflxh3rTgWmACY9l/8GLgYW+m90xxjGS9fAv7F8+fnII8amnR6eBfwbtrvAwJyoBiI9U8zcGsjaaN1N8Bf9gk0sWg+VF0DPdxUJ1GPM6gIctJbN4UrWjhf0Yk/HomkaBUX2rbBTM8pT/fBvhzPkyyFYPT87YhC5ja1N4UALqFwYx4NfCzhqIBo9ejSNGjViypQpnHnmmTRt2pTu3bvTokULwNuQxo0bx0MPPUTfvn055ZRTePvtt8nOzuaDDz4A4NChQ0yaNIkxY8bQo0cP2rdvz3vvvceqVauYO3euk6cXn8ywT/Q338Cnn9onXxmTgXvh90gP4eH6VoN95vKTgHsAJ8vFiM6V8AZMFQK4/nrvQ3KZIJQ6WLgwzI47gekhpgl9AuyEvR1CHGMjzz8f2/xUsquRteMvK4CvgfOVaKMOp70niovhySe9xtENemNN1S75DrHAQcy4BLgBcnMd1EFQgtH32Pn58MorsHZt2e1OUVQECxbAVVd5p80L9jNoA9AD5iqIGanb2DrSel6CWjYf3MzEpRPJK4zN24LLPrqM9GfNupFF5/P64Yf0hcWFjPllDBOXTeSfw36rFjls7NE0m1c2MoGmadQrMcbWOLo2cuIExVED0RdffEHHjh255pprqFu3Lu3bt2fixGOvFTdt2sTOnTu58MJjy3ckJyfTpUsXfi65iy5dupSCgoKANA0aNOCUU07xpQkmLy+Pw4cPB3yEElR4soTpa3r1gquvDlxyOjcFbwBOs0FU3wGamTw2ClkGPZWyU4FdwJthEoQolyNVowi1+GCrnLeBwU4rEUhurgtWjzsFOAsOHlQo81XgGlgS7FnU1/u15SqFeenlHKC+Ypkx8ACwmsXfocaOLn6BFiunCn/vDScfsA0zE3gPthsI0O8Gfv8dnn02/Kp8pVXSrd5BdmD0VF96CYYOhZNOOrbtr78gORluu80BhYDLL4euXeGzz7xT7gX7ybMSaDeo7w821Osdj7j4FlJuaPVKK4bMHMLon0bHJL+Zf80kpzBHqUz/LqdLhFkg/nGVCoudHTR7IpofnG0Zh/MOk1/kN6CpUD7d9hw1EG3cuJHXX3+dVq1a8c0333DbbbcxdOhQ3nnnHQB27twJQL16ga4c9erV8+3buXMnlStXpmbNmmHTBPPMM8+Qnp7u+zRq5Lan8Pgm2vho//5jvxefh9cjxewyzDcCX+jQKQYD5sWnAXWAW8LoYL8KpjBUNtfbpoYpsrOhalVo08amDPSWzSrgF9hvw30kN0m9TFP0w7si4g7FcqOMBTQN/u//jAd/D5Bh/lDApbHAKF+GADs4mOy0BuEJ5R02fRo8+CB88kmYY0p/KBpfFxbCxIleA4pbKTbYBn79tey20SXPhhMmWNfHDLNmOZOvYA//+5+NwhX3+UuXQsuWXuOkm9E0jel/TGfNnjWmjt9ycAtXTruShVsCXbZLDSXzN88PzM+1I3Y1WD+/xDRv7s3eS/qz6Zz42onHNnri7E2SIhw1EBUXF9OhQweefvpp2rdvz6233sott9zC66+/HpAueK6jpmlR5z9GSvPggw9y6NAh32fbtm3WTkQIxEC/UaTigfqUYz+dnnJhFDvVjbeyMMPSpd6Hd7esRrTbLcYcO/jImWxnfANjB8KNB9XK3bbNuzJbVpZauVYoD23WTbjU7hee44Fzo0/n02U49EsTrt698QYMGQKtW0cXN2QI9O4d3rtJGcHeGzZnZ5hIbbg93qnDQnwTZdyanR0bNVRwxRXe/uQqJzySDfDNhm/o90k/Th5vbqWJAZ8PYMafMzh/auBE8WE1YE9zaErwQEB/zzL82+GM+n6UKb1UYHXY4MQqXf4GquD8PQ726vM2zgO8Uw99iIEo9tSvX5+T/P18gTZt2rC1ZNmejAzveunBnkC7d+/2eRVlZGSQn5/PgaC1H/3TBJOcnEz16tUDPoI6nHzIUekppHparluf/dyqlyswUTim6k2E+4+yehinF/rrKkA74C7zMkJ1Cx07wp13wv33mwzAbcMYJk4vUdxSFG8FPhlYCFuOVyBLx7n/9JN+cRMnwldfwYoVpjUyhesMRNEItZhBO+AJwMq0J8EQbmj6Zuqu6vF1vBizlu5YGj1RBAIe+P0YWwdqV4R7Kppzk9x6aCtjfhnDYwseUxZ8evvh7ew6as/yzFPqwWfBYQJMPDipjBvkNm+tjIqQ5tfOPJ5EfvMbHkcNROeccw7r1q0L2PbXX3/RpEkTAJo1a0ZGRgZz5szx7c/Pz2fBggV07twZgNNPP52kpKSANJmZmaxevdqXRrCHeArw6YbBQKwxO/1k927o1Mk74Bfs53AGcBR41mlN9JOVBbNnx2YVqGIFjTdUU9i92/v92mtQvbp3NblEwc4ptRs2xFm8oQgUhxsB2XTDyMqCM86AUaOip43Uf29qrEwlAA4fUifL9umYQeVi6jGlGeA35s+tAvwA3GxaK2usAB4GnnIo/xhwKBd+3Oq0FrHh4YfD9MEXxFyVqGhJeIP2V3NaE2fR8ATE6NGLoeDWOuQfzT9Ko7GNyBiTYUqfiBQXMKg6XFkV0vKOOV44YaAx4rU0488ZXPTeRWQeyYye2CIpBfvJbO71KivFIzGIYs+9997Lr7/+ytNPP8369ev54IMPePPNN7nzTq8PrsfjYdiwYTz99NN8/vnnrF69mkGDBpGWlsb113uDoaSnpzN48GDuu+8+5s2bx/LlyxkwYABt27alR48eTp5ewnMo3KAyRLuXpWhDP6Qa8Q55YRz0GQAFilfIDNbrP8/CoudhiIG3x4mGVuaHgWMNHrP6GiAFuN94Xk5x7bVwySXe2EDxgJ5LcuutBoXaYUSIomh+vndanBkmT9ZnmIjE119741V07er9H4/9uv974lh7EL31FixZAo89Zm8+hl8OhCmHgmTgDaC7DhlNALtiwUXAaB3c2Q7YCPiFHFlxKXAeoOOliK1T6E4L/Ltli9fD8Z9/QqaOK+puhfMaw+TVTmtiP1ure1cnnTcv8rVzw4vLnIfwBu2fae54M7cAO5d597HnF9j7m+nDVRtnfvvfWex6PznqMvTbDm7ht0bwVQOl2QOB4Vo8ls/PvtobrNmV067k2w3fcu8394ZM/9PWn7jvm/vIyj82TVDTNL5c9yVbDxmzStfJ+hOANL/r5JEpZrHnjDPO4PPPP+fDDz/klFNO4YknnmDcuHHccMMNvjQjRoxg2LBh3HHHHXTs2JF//vmHb7/9lmrVjpm7x44dyxVXXMG1117LOeecQ1paGl9++SUVK5bPi+o0mqO1KjRmujJN877x/fVXdzwI/acnzHoP3jR5I9fLLxfjXc97qr35KOMCygysnULTTNS1mC05pU7U7Nne7zfeUCfTTlzQfJUwZox3Wlwp4c4r1PbBg72GieXLzef/5iSgL/yypew+h1fK1c0Uv9/hDER29ffKvG5V6KdDxporgFuBuTrkbQbWwIEYD7uMFsXm0pVa/VYLK0jRd+zrr0ONGvCb+edOQ/TsCc89B5deGpv87CT/BO/3q/uc1cMOynQjp8EHH0CPHtCwoQMKGSCv9HHr/IjJlHHX7LtIfSqV9fttDBxZcATmdIZvz4Ii/e6udt7COmX9Rr0KBdwWZYX7ylmbODMFelUBXLj8u5Psyd4Tcvv7n53LzZkv8vL8//i2fbr2Uy776DKajGtiPWPxIHKGPn36sGrVKnJzc1m7di233BK4BJTH42HUqFFkZmaSm5vLggULOOWUUwLSpKSk8Morr7Bv3z6ys7P58ssvZWUyhfz4I1x8Mfz9t9OahMG/D1UZgwjo0gXOPtv75leJwGCM6FsSm++3Wgp0iUB+mGUy3TitZFdl4DvAwkOv60kU64ZJ/JtNrOObuI0FvwEP6E8fymhz8KD5/Dd3Bz4FogRJjhdUTF80QqxeNKgyUmfVNZ73jhivDBfL7vGOO7zxyv71L5syCDKulY65rBh1hdDYugLkEzDvOx3pTOhwxx3OrbYXCqN9zWuLX6NIK2L0j8aWla+kFTCrAdwZxcACkJtzbPpUYWH4VSh2Hg290jVgW2fdNhmu1B1rTO0NSvmUNUVYjWc0vi60qQzn7j+2pPX8TfMjHGEMmWImCGE47zzv1IIrr3RaE/OYDey3tCQu3tSpQfIsaROfPPpo+H0jR8K55xp/Q75+vbdeLVpkTqdMly1TrVFO6sZJwETQFMdCiUb79uaOC3lNagFXA5XN66OaaP3Uhn7AM8f+x9ppZ9dpJT/8PS5mAD+YjAXjAP5ThAodzNsKUe9nRitGnHh/hSKh+ttzgSFB/5cAnZxRR9BJcCVsCrkd7cmquAhuu80e2a6juBDyDwJw2tHF9K4Cr+owWu/P3u/7nVOQEzZd/TGB0Zqt9iV6YvlcVw0+s2H6mFGcWLksIH+PevNDkp+hKdpK5+EIVS4ejxiIBCEi27YF/t8bzj0/1v2OA7XYVN8TJyPZcKf29tvhj3n8ce+KN++/byyvvn1hxgxvUGxXEifXLOYsAm4G7VOnFdFHyMu4EPgYGBlbXaxwqKW+dAsXhtiYBFSNfuy+ffpfnhYBXA6cBxvjZKGPVb8f+x02SLVNGHmBG+0+umVLCMO6FvTtAMUxDm2h+lQ3b46wcyDwD+TaGWvJ3ztkIXA63gDagmGWLvVO8Yo2LtlncdpbqLZaWA/4ALjOmuzyTMHsdvBJTcjeTmXN+ooYxyXsE28CP5AAN1aDZY2gLuGNfcF48FCvYsQFgvUjMYgEIZD33/cG2QuHYUNQPcBmjwOVxqmI3aeZvjWO39JG5U5gGuQYfCW/IUGmqvijx3ioaX6r/aj2BogFpS7S7dSKLSqCoUNh2rRj2wLKU+Udq/QhbwTwAuCCWBFRL7XOfqdHd5gf7GG9BTgCWRHGOtOnQ+3aMGKEvnziJe6QPwcOHvvtb7CJhfe9ygDHTZt6Dev+C8GWXo84vCw+jF4HywYpYIdfIOGIdX8q0AB2vmA9T0O4yMsxnrjmGm+Q6AEDIqerXRt2KV5V/OAAvMahD9TKdQMqV5Na/M9iLv/octbtXVdmX9LhNQBsX/Oa5XwuSc5mXwt4oXaERH6dj5mVvUx75RzdFFodk0ac8Foc27PzqN81NHEj12z0GQ7W5p0MaJ8Cd1fQH+ekcdEedjaHBQrGdXZ4O8UD5fOshaj8+af3pmpqIbgQtUrTgJ3AFjgaJ7XO12fqXP4zngfllnkVuBaWtDB2WPEJwLcEBAyNZ/TeZ3v2hBNPdGdcJ+UYaBgffwyvvAL9+/sd7j9GMhl7IeIwqxJwH6ZXcVGKKiOFB74LjoFR4k3/V4T+7N6SRUJeiPUDsM0UFHgDqv/1V+D2cC8U7DJ8aRpe46pC+f5xuUqrj6pVzMwQa6Oh0VMNdc2zWh/77TPcR5IRgxfKc+aYO24fsZ866VZ0r/j6BCxQ7KVVdJyORGbaio4YPHaSeSSTBi+qmyN15ltn8sW6L+j9Qe+waf7eV9Z4ZJRHq3mXXb6vZvg0ZqclWWbmMZfEQB309W5lDUmhj/OX/eYyHUs2xgi9MYhS0NE5l3BWvte4eE6qQV3i8a2XTcTJo7oQawKW5mwGjITiCB2rP9G8eHY6MBXBdEDCV4HDsKQ6nHSSRSVC6GCmL1L+olunQE3HNNw8g28686YBPYEfjR0XM2zwKtA0r2fe+vXeJa8THgNlGPUt7s02qqDYG8oWjPYXNnvF+Ht7uHlc9fLLcPvt0Lp19LRgnzfRwWrAUQKWWLcFRdfCzH1ThUePofxUCDEZ2ywkis7/BxMGi3VAbaCzGhVswdbg0GZ5GNaYCMhuBGUrGH4FfBE1lW38vO1nW+RuPLAx7L7iIr2WvrL0nd6XouLQhoXRQQu+aBH+2Uqx3/Q5W2+kWohfOLJMsyeC+SGaNmv2rNGxCp66cnRrcG+7EQOREJ3fgFGQM95pRRygZEnp/Idh7Vob5Pv1Oy+h75bkVFd19D/R0xhFO96iAJvupaYfcnQsc5/nNxaoUsXe67l+vd9bVBc/wNtN+by9JyaaBhdd5I1fppcfwxigY10vfi9ZiZIu9uaj6yHcvz841y5N7Ef1NbT8LOBgP1s6m2mxcypEJZYxKovqAZPxxnGKwhEbp/EVFsJxoTyKzNa1S61o4yz/VwNWN4Zqxdm6jykqLjDdrH7d/hvfbPgm5L4RftfEk1CjhOilleqXJJ4MIIfzDnPy+JNp9UoriiN6HqnraFKP2PHw537EQCREp473q8jmaUDFxZCjPwZZSFR2cwGD7FPwGspsZBgw3d4sLJHTz2kN4oNo99qsLOA4oKrXQBSNsA97Ue5/n3wCrVrBJZcY19Ep8ivi9SZ7xGlNnCHqw5OC6+ZmTx89bNkC334Ln39u7n7hpAeDq5qdi5R5+WV4/nlzxyo3EMV5+xCOsf9F4N94V4ILJug629kv7tplfWzrGrQivrQww2xMHTg5GS4tWK77mMJi8x5EAAdyDuhLaHFgZDZmkFXMVN2P60dPYyf+ZWUkxo9//Ktgw1ZAOShs0I3WPKRMVjwhBiIhPN2B0yLsD9cXmqxV3bpBWhrs3WvueMC+QW86cKZ6scF92C/7Q6czg+6BrgyIY8qBXLyBIg5ARSurZwbV9eJi75vKUl55xftdGsvC/15qqY35o7juLD4Jbzyqx9XKddGzsDX0lvcayEmJnswqTpSrSuNmrPU3Ynxw21Scq68uu5KpCnJz4Z57vMGh9QYK9g/Po7qYtunx7nLg2nQYCneMiX2+TjNmjHfhAjPtvjDCqo/B7cvKrWzRrxYOjjMyDq+gj44XW9GoZGByaHGxjmCNO76G5SOguGwErqP5R6MeruEJiNHjBq8a1Tq44ZxCoTcGUVQ5Ab/lwcYqYiBKYF4Dbjd5bGYKMBfQb+T3EWoQrGfKzoIF3u///c94nonCyt+jp4l3DwCn+f57OOGEEEF8FaDn9ru7NMBpJdhfQd2D4OmnQ+PGMQ58rfAOsn8/fG1TbBZbhkU2tENlIk+AxWeH3hVpjKgBnISptWFLdS8qglmzrC8dHQ6rY1wtwLU+TBprWYTPW6Wwl4HFUBCiDdpxi/j0U/jXv9TL9Q8KrTdOy4V+v1U/82RafRFkU+VZ/jK8fp89st3M8OHelx3LTYxFjRCP46q9QAHe/nbECH0B1o2iaRqDpvXhrhnepeAqaEHePDEoN13Tv76/GNY+T/GGKTz/83MBu3IL9XUsgQYUjfGLx/Plui8N6KmgMGw14rinkispqyg5CNaw8v5acDl3WTg2M1Tkd70uudIuQxOqXEzcC/QaFMwaHpx+c/3dd1537FBTo1RwwQXe7+7dvYFrx6uOrRWl/lf2K98chQaW0tWM1q6FdqUBlz0cq2PufHnkY/BgONLYHtkuP/VjKFS02ETdyvo38CQwzXy+48Z5H+qaN4cNG8zL0YPHQ1zdb4zENovkbaRpwN3e30vzyu63q0g2b7ZJsAWcWrTBbmL5sv/oUUhKguTksvv694etW2HhQqgYg9XbylANSC2Zmm0jltqMA33Q+iJoVREaHIYdfbzbTj4ZBg5Um8/GvauZWjQLsqGgcJIyuUbqt8dA4t83zeTTtV8wtpn3/0lGYkv5PdSs3r2aO2d7g5BqIyO9VdH47ng4bMeq76Y7Afs7D63YvmXu1RBHAwOXIh5EgrtwYgBiApeMIZXgpnMpLvYabvr0gd27jR3rv9yzXl5/Hf7+2/hxdqMiXkFWM7xT2dz+1rnkPj53bpjdOu7z762By+ZDXol3uabBww/D9NKgXgk6VojWdo2+FT9aEpQfnfHG/MevpT9Ly3xj+EVpTLN0KbRsiXfFuZOjpT5GXnW885L66asKdj2c2yG22PiqyIB9LwJifT/J3Gm/8aAMESpRvHU1WVlQrRo0bBh6/7Rp8MsvsGyZ9//778Oll8LhwzFS8DCwC45U8o4P1q+3p31WiLML99hK7/eO6se2DRpkXe5LL8F//BYk8WRvP/ZHC+GiZPFanJ4Mn9WHVhFWN/ag6Z4edTD3YIBKvzWGaoXGYxDtPLpT1yFJ2Vu5IA0urwoUKXDfjkdXNgUM+3pY1DSlV6dbKnSONoW+fBajUsRAJJgmbHdttmF+AuyB7BKLf79+0KmTMbdZNxk7VBF8X1Rxji++eOy36geF+fPhwQf9Vs8ygP+5Gp2i0l7vUsUtgUl41wJuoH5KVrTqv2VL4P9gT4GjR72xuKyyfhhQE3jBuiwrrFvnnZ4SDa0ScJG5PG48Cb68AAaXrMA7Zw489ZS3DwH39AtbtsBDD0FmZuj9sQiQqzKLWI/BrrgCtCrACmA1FIKui/vHAKAH8FHgdi3Mb7tQlkeYgvc9W7ilwseAjZvgTIXxActbkOqVvwPjYe8QfekHDICZM+GZZ8zlZ3a8sTENbr0HWg2CF182JyMSti8uPlOtzCNhDHRWDfPDhsELL8DKldbk6GVJY7iyKsyOEPja6oNq3fzt0ROF4OqqcE6pISJvP6wdAznBN+9jFdq5vkNvxu68MWgUk/nHS77/kc6mQv4B5jWEnxoBEWIXmZ3CVs66/4iIgUhQj9kWdhVQE5Y29f6dPh0WLYKfflKkl0JUGfmDxYTqvtu2DTS26O7iI+h4nxmvEp3n3K0bPPssvPGGiTwMYupN4jzgJuAn4B/ICtELamV+6FUoepKrr4q8/5dfDOZZSjKgIHikHVx9dfQ0+Y8Bva3l83tJ+QcHu7XFI8SEzK5d4emn4aoodUAvrh3MHI8tymVnA7WP/S/UmUd+tWO//S/bgh9Cp7erXN3klK/rYSYGFUzTgMpA6YOY0Tw9sGaNWp3KE+vT8AarfMrYcftNLqixfKX5+GRvdQN+hAdDTKsMiRGPOjtXMasA2DRlHoDmx+Tn6S2bcNwIjPC+qIolLSNOBbPWc1YM5fkUhWq5//Bxffixkfd/zg9XwfLh5H17fkA6jws9ftynUQiCXPam+a2qFqnZVizw8waLOLCLi1JwNWIgEvSjd9UHVe0yFUiHxx5TJM8guQZvtGZOW8/45Y8/vN4Q8YYdU0yi8csv0KxZlEDnQXFuMsO4NheWXQzD3ewGjsKuJBgF5AYvY1rr2M+z+8Bff8VIr2iVvGR/wb9t1MElY4XSGC6//GLOaKXEzqWwLELps78LsB2YHmKnRYprAaPVyVvzx7HfsYj5YncWpfJtmT52PRTriOdhNOti8E6FPVLW4HeQEDbj4PqruG07HYOvlFg9d+aafAowq59WDNdcY+5YrvR+FehdfcXIqoGGldGP7VVqA14Ppe4KZL0DjIZNoeKQOkQFQyVYNm0FTedgzq9Sp+XvCdiVuud7AJKz1hvQxRpazDx+XNLpRSFaGw2cbe2SQV8cIwYiQT8Wlk03EpzTx37gIHy3WP8hKqe9vfG6mYyMoXeQZcpIplPHhHGpfwR6XQyb0+CKvtbF7doF+E312r5dh1HFYFkqfSgtiUVwQzN4DMirFz7pho1w3XUK846Exfpl+vBGeAOc2oUZxVoAY4HjvQHSrYp0U9stVSWzdKUrHV5jRsl6FbjWmowAA0CMF1pQ1dxV9BuGDSHvw8H/RE9mVLccgKpAJdhVMfD4JzT4KpoAxdfKLW0qx8QDeuYO9XrYwXybVqu0gqXLfr8qLSzSSZ2oIy5awkjXKmZ+PFAz8L9uDyK/zkfv2Fz10vGeIDOHGYw7vgcesXDLQjYf3Gwq71gS7L2lBe40K9WsOgmHGIiEkOhpW+HSaKpqVanL+UlqxO3bB2++CQcPqpE3e7Y31o4dS4vGkjKX0cH+UdOAc/DGCwna/s47UaYSPA5ZdwF/ABOs65IdVA6NGkHr1rB3r3XZkSjTrgxej706HyzsPg8n2VsF2Ip3HWDc89DHD8Aw4H8wIVQdVTjWdMspq6SwY+B/M4FlTU/hVEB8vKf1I0jhbB0xwlTWu2WbdSRyoqLH4EIuOsv4MevMeIVGO5czCPRMaYrX+Kug3H/bCCd9BDNXRU8b6Tk8O9u6Lo70lxYyDTn+PtW8PLOYbgoGHuCNrGIGcGeNwP9mpphF4rmfngu9Q4GxyIwEvZ5GetIty1zG+VPPp9lLzUxoYhJTHgSREQ8i64iBSAhJLJdYDcauZn3llXDrrd4gi7roFXn3JZd4Y+18+KFOeSFOzDF39ruO/QxWIaxOenXtC7wDhRFWpQhHMcCPwBw44PcG6+OPvcu3nhxl5aKiR0p+3Kw/z1B1vViDQ2Eqot1Ld2sAU3UklPtfWNbVLflRMiXGNUVVGojzdDXiovUfhs/b5oJyypge9rTuDr3ZtlXMVMmNcp2cnCalMuvtIWLLlpF/Kt7p6DbzVVRXphIUFUCxWzqtRcBc2FN6P98EfAx/n2FddLdMWNsfLk03L+PVV6FKFXjvPev6hGPjRujcWb3caO30p5+gcWOYMUOnwH5wWNFTnd6+yryfhv6GUsHi434FHQaiYH0inf/9c+9nzZ7EDHz22/bfdKXTLMaF8ugxPxRmw5rwc8oje2+5pQONX8RAJOinevQkgOV2WabJn2BNXqnQhQu9P2fN0nnMTVH2nwRcC1u3WdBLDz1Q/2boFcXy/PkUuBGWnx81ZRn8r/1+v95pcbhphrG8B3SkjGeTKaLovKYGMFBBPuWZoDJ2jQdRFFTqGZNVufwy0fNC+BXF/Y7l4vLz0jCz6qJRYvXQ72h9t7viBZ9bb+BXm/MEeusNoK+g7LfvgL9MBnI2it66sico/tTOViYzvBfvVFsgu7T9NQ6X2I8wet59N9AAbrzRpD6l4iOUw+DBJjwPFUSkv+gi2LbN+3JTL3tsnhq2Zs8f0RMpxugUszLH29AfHsg5UGabWzxE42S4E53fH4EVD5g71oXBw+MNMRAJpglrvFXdLo2shhXLPuEPYBr8bcUTU4++c4CVFvJwiGwT8V+M9ulKYnGE8uwKlXAxMAd26wjUGpEohrO8ijp0scpPUNDcDsEhuAW4NMJ+O06wjFucDXm4nLXhXnBaaTN6jo1Q1u++ayFvC+h5CH7++RjooUhOwOmYFeqSp5lIahw8pFNIrKfW2NyfNPoD9tgQwysUjnSNcxXKegL4B+/U3WAiVC4jM7nNrNhW0CJ6mmhN0PKKZFYIUyCz13+lK51KYjFdKDgPvWNRO1cx0xvfKN6GN3mFeYHT3cLNFd/zc2wUEkIiBiLBNEY6JUtTTFOiJynFialxOzJ0Jgyhm1tWTNFFvN2FdBLqEkS65+82MXUugA8i560bKwefAHvt9CIL5gsdaWysX670IDrXXvH79plfilov8dR9+WgXevPHBhZDMIvtQardcEGsxFUJ+r93tyVNQuKGIopKT6cVKIvSsVVThbIeLvkea02MmXhmjuJQRbY6/LGbo3mHy2wLLqr8ovwyafKL8rnu02MrdzgVZsPWaqjzpCpGT+LjcN5hNuzXH3dh4tKJpDyVwhfrjg0KwxnC8kJcJ/1rJcVbg3YfYiASQuKkd15cGU2MEOMydeVDsUmKwRtXqG2M8nPyrXyQDLsGKsU17JEr6GSh/VkcOaJYoNUV6RT3SabEXRJ685YHrQp2CJct1pKot2/dT8bxVgAm9Y2nJuI04fo9S2Xo0AWYGGF1VCMYGdes3/+37rTNc9ZGTTP2l7LWxOyCbI7kH/X935+jb45ngHFD+WAtNquY+VO5KJvdzWGazhffGS9k0PKVlqzbu05X+iEzhwDwwLxjN9xwz3ybDm6MLCyoYUX6JxhHDESCaewyIlkxbMRqbKYsHxf0YU4GJA8mnGHmz3bAROD3KAJMlKcLLkEAMnU6ECXlESdlalTNiE1XCzN9MlImFvoCPf2IO4bPoSk0MSXWKHYb7UuvbcK8ZOkeYptT56bv+Sdhicv7kgGdI3kQaZWAK8yr4aYxllWqOfDUeCjvMHlF+ubbpYXo/IK9SZb9Ez1o2ZSVb+tTTvHgwlxVsarDsVxbHVnCcRXhWp33wwer5/BzQ1iw4WuLOpSloLiwzLaAqWlBDUvNMvdCKWIgEkJiabDvwqcAN/QVIU/DxoGD6YcEF5QVBA7Ydje0L59wdd3MoE7zWK9rZWIj6JCXEANQN0+biRUqz9WFd/d9fR3K2GDsJNtWMVPVt0aRY5shyoZyiVjWTfTLOaQzXpHpoonhqs+xxGxdibkRUnF+wfpHKobd/wY+V5s/EPacdu6E226DQg04D0i2IW9VxKAeDK8JBSGmG5mljxbdI6l7hJURBwYYTzS/X/E/WDF6Do/UgrNT4YQjMZijbQSzA3E3PCy6BBcOIYXyjpXmGe7YWHTbUfsVl/Y7ZfRWVFhWB5AW4+HGLeXu/mRH44zBKmZ2qK1Uz4ehOMQdPqLeFoLE66m3ewysxqMHpZfV/1xUyvXH3wgFfIJ31XBVqK6TZsrBqHHNP7mVvq9GDZg50/zxytBxDgoWuEoM7L7XGaiLkTyIDsU4JtSgQTBhAvA08APwXmzzjwkGG3s1Lcd0VsHVoGVx2RXIgrmjRvh9UzMgJSfE8sUuGbyFfQ4K0znrDYYdeExgL1ZBKzIswycr6ArtzdoTIbX+KESCNcRAJMQES0GqDRCxn7sU2I2SAJDK7gPBD7KKxMYrSqfYWEAD09PV5LbkQkxclALgY7xdRqw4chgef1x/+mintbe+JXXKF7FouH55TAeuAexeTPCdd+GGm6CgIGiHW282Fl60/Oc/SjUJj8Wy63A0ehq3E/P7XCLeWMOc06pVJT/uLfkOsaKdW4rDvykcyDnAhe9eyLsrHVqukvDlEq28alSAjwzcLysXHPTKtXMVM9d20qoJPM/tO39RINMtLSR+EQOREBI9TcuVzS+SUl8AdYBvFeSjhfmt95gYoNcbQfeK4LHW33+qsY40pvNRnN6ITgkxNcxmnFpd5hngWuCMcAls0mvkSO8Ug1Co8DBSqXao6hstLpIjuORm5X/6P1iRY6AcB6bCB2/BuI8sZFiKnumu6kUqxfbFG3QUwMqqNusQC1zSplThptPJPxXYhqNLhpkZmzzxwxPM2TiHf834l3qFdBM9BpEnRJr2KdDO6lQ+BQO6AINTAg4QKwKnJ0deKW1YTRU5ualFxydiIBJC42DbSrwuMTymXPhtlG1ZoL9Rx4QCTtwPQwbytSCvoGxcvSgKBP2PVRm45f5phx4KyrA05MRW66IMk6cvHic5ufbqYQgH5oTa1l/Y1Tb85ObrvMam8C+Xq4EKMLeRjfkpJC6eidzSdypA71TwMsni4Tq5BKPV5cCbgMm4i069UAE4kBt96pYTaNgRGtUr0cz0rMjEZ+ei19tpQl1Y0hieqmVzni6Z7hfPiIFIMI1d7a/MgEVFkGrrIqwLj/mrUsXpYoCqeBQqCOuqHGUVqOHDLeZroj7Fagqnrcj9XDluW83Kbfr4EwvV/I3my5ZbEBSlrUhTioCL66AT2O5RFTFzB/MOwlbDisHgmFol81nFfCxgcqAWyovHLuyt4vHU20Yvc7vPZnB64DeEN7KF1sVFnUaCIwYiISRWjOLx1F0qJUb9lt7ydWTg55en/8OgmaLRNc0xOFFlNflYuZQrjD74GVhNxY2sX++0BmVx2rhoGtXzHS2waxd8912ceHQoJhbnbIsHUYneTj70O1aFT5ZHBzsJrlJuNvYK1nHb5XXvLV3tKmbqPZLiAZ3n7PGUmS4YIbFpbQQvYiASTGMo1oqVjAy0c1d3rSGUS+gurOTkbsyHxgXg1richu/HEdJrHiDDijbErlIoaiwdLlIjJ5gvvoApU8wdG5N+wI5MzgNmqRNtxVDQuDF07w5ffukvUMeBcdapLVxY8iPGQaqViYnlTc/mvHQVT7hEq+HgtQqVEQIIvk/GWTMvQ7D+ET2ILNZ7O5753VL+sfIE0qycsY2FpT5ItZ/ByWTFsaKR/hzVXfdwZRjq9HUb4Txi3rCKlKAQEuVdnhFjklvufIIS3qsM25PgZYPLQempMqpuUaOBExTJYmH0JKVY0t8l1tAjM+yRe/nlcNNNsFvBMmJx9ba7N/wTKYKjP+0V5+0fIyff+/3NN+GTB/TrBmOUmSLo+Ndfh1W/WxO5bJm1442gzPAXRpCRZ5WcbDW6lMHmthZpfHBAQWzceOoqVGDaeKFinOaisV5EVVykZzRU2yvsOnXjBh/zLbNsHXdvK7dzVbTwGC+PYEONNa31rqhjRM84arQuRQxEQkj09FExiOPpxb19eSAWlud1FPtGAAH8tc6mfCyiAQ8AfwdtMz1w1vtwHwYz9d/RGERtFckJcw5HjiiSrxq3tmdVVACCA0kGnXPMq11Q/vePUCjaxMnk5FjIz/yhlgWtXKkq8xDY3C7iytibwCTadXAyuLMZwhV/pNN48kkYPdoObUrzjrNCtIAnlIeKclcxtfJUTIELL9vCsWHfeFiQWY7qol2IgUhIKGI1fVdVNrauYhaHAzi3BFsul9PAXYqZF2plFoaTscIxItVt/33fAHthr97VdJxoMwqvqxbmdzg++ADS0uC119TpEExhYRgDabQg1VFO4KiL5vvGvK+1oS84dEi9zFiht38tky7B+lRXxa2zoEu4MdSePfDII/DAA5Ctw3tQC/rWQ3rxUTY3hQeClikvKi7icN5hA5IiaWQdtZdadcWxLs941ASnB7z68i9r9JFVzOxEDERCSHQ1V4MrMxjKP2i0vm+fzgMd7BOiZh1qPq2N/bLZh+KwDxcxLttYZRf2PhJme3DxqL6GMStmt9w/w5XficAZEfYbwMypFhfrSzd5MkyaZCKDCKga24QUo1d2D+/Xms5qdAlFzAwVEerQpvolPwyW+Q03eL/vuktf+pycwOlweqp1hw5Qvbp+nSLJjFloM4PtNWCWogv6JDMqvPWW3x+nn7UEUzixiFlYdNQhozJzc4/9Liw0eHAE/B/aryxYQZMkeKZ2YJrzp55P+rPpbD20VV3GBvB4PNSuUKRWaElH5+/94ryhpTxgb5DqcF5w/5p2Gd9vmm9KZrwiBiLBFHv2wKpV+tNbDVLdtasVATaj26XHpuw1+OoryMy0R74h/MrCgK1fSX4qccFzyjFcpYx5+vQJU0c9Qd+lrAUWQXY1BZmbqCd62tPRozB4Ltz8nVovgvLiwfaA0ekONpTL1nplt9nR5N5/3/gxRu6x/uh6OWBTHYv3qmtGf73G5EQiLm5LBi6mI8vcxxCjxtdQyYu0yEaWcAGrf972MwAf//GxMSWiaqSPlKKjVPY7XPXELbfhgupmC7EKiB6Kd4q+ZOXsbo7l7wRiIBJMUbcuXHuN/vSGHnhCpF29Wv+xq1fDYaverFGIdrP9ExiYC5sNyjHT/U2bBr17Q/PmQbL1ClOdLtzhxbBkidq3V0oIuVKCpcON4Yn4N2GYNQuGDjV+3JGa0dPYwREd3i3784APgPdhjx3LllvEkeGUgUz/d5Z9ariNggIiGtDDklR2kxkDop11wV8fFQ+iEfOKdIAbOk836GCA8mKMjkacXbawRDR09QOu0nfNQ6U56bWTIiZ0QxmG0qFunn2eS3YGlVbvkRTLpV/0o39lMg+R9LM77tA9Do1FnUIMREJInO7ozXZRf/0FbdtCixZK1YlI6YDV/4Z6Wi68kwLn6p0aZ4GvvvJ++7sQG0LlxfZ/SxMk99PP4IwzYMgQe/KLRzSNMpXdzHjDLbGborFzp9Ma6EePgTXP71rlxnldNINRw0BwmeaE8N6JLCDo/2XA3QZlqORE7O2DvgVseNlh1iigabB0aeTjLRscYtyOyvhElMN2rAenF4IyVa9cci3dYIQ7VAH4CPgEckzqk5yzXaVKjmGnSccqgVOcXFBxwqG0UoeWZfQ6BXoYmbvKEtz6GGIgEszRFxhrn/jgGER6WbLU+713r1J1yhLlTXBeivf7n+BVgMox2Vne7ylTwqcJZ+gIVwVU3KNcEnLJOFYVLDnx4mJYvBjyXOgBA3FwHRKJeCtsvTMW7DivR/BOg3xJXyyl/IqAUQ+6nkCKgfQlbVrX6bY2qAtQVAQdO6qPuRVTgjr88hVVoixmb6Hx1lUkGkfSQm8Pdz2z/Z72CvRkEOICr20aKp1DNSH/oOlD1Zpd3BekOhb5l115LDbGrICYT5EeANwQ0C7OEQORYI5PgQFh9gW12fXrYbuRFw/B7TqEi71RYtFXmMnCzmNct3JTFeA6QGfQ1XDX7MknVSkUH9h9GZ9/Hs48E66+2uaMKtssPww5OTg/5jKIKnXtXsnQ+vs69+DfX+oqtsdLvu8uO703FItPCNpgpcD8jn35JeOHH6gE6IntFaYgJk7UlUwXSj1TTBxb5mHZaptxyUt/1QHgnW7fdherE8vcF1o4qTlnhN6ua/qY+WyjyA1diCkeOL6Swoy2fgqf1ITlIxQKNYjih4pPVk5WKi/muMFNTlCKGIiEkFgNKl3KgQPQqhWcHGLqsqH8y4tRwMGbuw+7VlB6CW+8ls+syX3kEfjnH2syouLkaDjG99lx47zfM2fanFEeHDnZ+GFWL8VTL1sUoBOV4yNVohwxEtuZp0vHoHv2RE9TFDzasuFc9D6zbEs1mUE60A20CCNHK+3AaSME4BIlrNP6RH3p9NaZ4Osa62JatszEQQbqohMxqrNPM3iAIh1i3Y3+1QS2N4PjCo51lManD/mxbJj3e+3zpvQxmveBnAO60kX0aIlC06WDvTKUTFMLTVj9FAxeVMdimvXXrDJqaQbysXItBC9iIBJsZfPmkh8G+44ybfshBcokIDk5kF0VWATcGrjPrPeA7cvcd9eXLFL/XhpvyS7PME1T98C/axd8/rn+4Nwx84x14EFo0+0Rdto0dvncgFFq+nQDgrXAa6VyOGJrHbDJQyNeh2Off+r9jie7QDRdQ10LJd6qtYB5sCvSAhXxVJBhyMqBkybBiBn60u9L9/vjkvPfMUFnwjhpuO90Uisv+DIlyoyUcJ5QAZttuub+MWBm/DnD97tRySyAZtl/mJbtr3JBUWxXOvn3//4dYa+awuzom0qscwqVTZhdKcxK7B4txDKQfT7sY1qeoAYxEJUnDLRfN94rX38dfvghcho7Y9VEJVqh6SlUAwW/cSOkpcEnJwNnAG94DUal5+qE90AsBiFuJuTKH92h7/vw0msh0oeQUWbgGjazYz/jJUh1RGyqrzk19Kft1w/2GQgs78Z+0k6s9illjM8xKsA97XQk8tPN7S8fY6VfuOu970J9xxfoCnZiEKurmOmwrt36HawdDM9foU+lVcHTB93AJU4roBh/Q78N9V8r+4yqDDcYn2x7mRFG2pXTrmTJjiUKczrGvhxjQUYP5B60lN83G77RmVJ1kOqy/LT1J8t5BONGb5s700Ns1Knm4n8Ws27/30r1KY+Igag84UAfEKrf+fe/4b//jXJciG13NIUuX6nQyj1YGTi8/nrJjyrHtqWlQc+ellRyFKP3KRVVOuwlUDSo278A+ATe0BmDJ7hO+J/jpk1qdIonjLYRDdhV1Xx+h/WuGuWCQb8ZCgshKyvMzigNSvkpG23AJhUoihTouXOJKjG+nnbGhzIre/p0uO46yM62rsOYMVC5Mnz3XeR0hp9NFJdbiJfXbKtSdlsAcdr2Q6G3OIP7YUeLwIbMK9r4NGS4ylqs48XAw8Bse8Tr5qrpV4XdF/5lrsbHf3zMur3rwh5bbLDTyC+yw1IdCwLPc2/2Xs6dcm4Mc4zNkaF4tS6h23mkFYxKOqlPPj2TWyuIgcgqYiASbOWL44BnCIxLtB+eeSbycSH7/4tLZEUg3MOk6vGEFuJP1DxCnZMNd+p589TLBH2qWn0RoSvorZ9x0a4XH0bkRk1bspLdvjAu8kZO4c8/DSTWSWFT4Bugq3rZKjD6sPt/wOfBHiMm6onrgrwr4j8PQdXax6ZpWsHph3sl9I9NNkqrk7+waSYyC7O/Xz/4aBqMHVuSTOf18q8HpT+HD/d+D/aG1iA/P8yx+rKwjd9/d1gBkxQWQbMP4cKPnMk/LrpHA0omO7SIgmo0vIs7PkWgE5ld18t/alHwNKP8ojCNPgKz/57NtZ9cy4mvBQfRcrqnCEfsVjHLPJJpUbbKMnTgeugs6tG17VWjvCAGIkE9fv3GY02BB4DP/fZ/C4yyKWu33kPAtSMqvWVm94pIunkKdirsuQw/48Y4Dovd5X7oDeBCEma953FOK6AAVf1YyC7nWyAL1mzUe0B4Qhp0rU4B0puhaqzoZmJAaocB0nLxPA1kwkYFxkN/3noLkpPhk0/UylVBmWlwLr1PB/PqEth8HcxRaOAcNQrOOadk5ccER/dUbgXEely6NcQ2/5enTk1J1xNg+NsN33JcBahoqybGCiC3MFdXmakIMK0Cs1p8uOpDpv2hLxBjmXO1VMkNHBuhDqloZ26YDuoWxEAkxIaL/X4fD4yMnNx0O6+Md7Wsi6MlVEfMPQ00v6+bgLahkzli0FFZFhHmzOd61GcXkHWMrmmujh7Yv36F08vKgK8ow/yxTpGXpz+toaLpZUCInVOEFMkJq2IF2GbTG3N3DJFNoFPxo0dDDER3Gs8u5uNQPef3IFAPlvYo+R9OyQjK+8qmItDGm+0tt3g3XRMiuLXhcrC74MxUYAeWiDpow+j9scfg55/hvffK7itzWirO0+4nkLjtjMwTqnnceivcdeex/3Z5g0cP7xU5RVZ+Fl8vf5l9LeDnRkalW9EsOmWDfztjSQgZmycIs5pd/9n1Jo+MDR4ix0syG2BbCI0YiARXYtYSnPMvYCjhJ1/bSKwDh65rB0wCOijOQNV9z8b7p5us/AFeFAb1eqBObN9kBuDQvTRTgy5+cT+iqhFUID/8ACkpMDKKkdn/eN1l+tWxB68AA2uM69sSe2J7xgTXeBrawOrVUK0a9A/22jDzuttCOZm51/gbmlXfq0KK+xBYA0cGqM0rEuGmsRlCT1uPVH4uujeZRU85JsBpBhIHBn+9+HuA5efDm9Pgp7XHtqls/+HOrV6IPjHaA/z6/euZWs/7+8xIceMM6mP1dNMjPinbV3GCvXSScjO9sXlsogKQYeBe9udeG2Ie+JFwfUycIQYiIaEoOj7MDsU9jb8hQK9oXW9t9ORd8r2zoc6Mo8kLPoEwSiRqTJZQRLwOCsthlclBEGBdD4euZ/tv4QcDd55gNe++2/v9+OPKVArgiAvuivcMDb3dbW1wzx5j6SOtYqYBHVdBv5WRBBjLzxA6ZJfG5pmuzws/9kTouGJedUq8hQ4NObYpKalsMqXGKl3TQGzAgtDC2K7YrQyXdUWWiXg+FiuN4RdaFgv3t9+O/S7SgIPAamMy9J5yuJDB75vwTK6Uv4+zU40fFw2rdfWtkEaZECWkoDMLmIYXJK9C4RFLssNPgfNu/18DeLSWfnn9P4lRAL8SgrW3Z0pfovVs5nHBUFgQEgMz3YpyT5geQHNjh7jphX+4qVLhisl1IUkUPKC5arUYG9h1TuD/qPF0g8q0sCYwAThTf5621JNEuzAmaGPQO6RMbBO/C/Pu37C0LUwPDjCeqDg7Y8I2Ij0jhTQQBf93ulyCFFq/XoHMCPTta+64WMeRsaXK2bi0vOAl1Ppdsag6HZONH1Mpz9jy9aFRf3aXRFzVMHZBqu3WoU+01RuDaFC4S0m+mhamI3B1UNnERwxEgnoUtGkngujl5cGnn+pMrGp+turznANsUCyzBD23oIA0Vs/Nf2UcT9hdtqH83qTAM8uWqXU2FGaZwK/+GFyCPli9fx4EhgC/hUjsIErri4Hr/IjCbCOxebM3/k4AC4/9LK2b4erz/v1QEGHqyi49L0fjeLxYZiqpDeeivH9QEfTT73coA5EZb1y78T/tVq0MjAuCD9bBl18aS+8Uwf2bW65VTIizk/VXN1Sf4NRzt38MIuNFqoX4VRZ941T33kgixdhxG69W9zPoxSjuQ8RA526KPZEAiIFIKMOWLbBvnwUBTrZRC3n/979w9dXO5O3DiXuDzoGfUdWc7quzshTFpAjC7tMyI9+pVUnCMcCAZ4lRj4G8puH3rYrxktUB3uCxzdrHkyqFhbkWRUXQ7Ck43CP0fj1s2mT+2JgTphxUjt3dNl3Qn5UrYMiQqMlCE+q86gMPQKX6Oo53ui8Lof/48ZHTWFI5trM0AG9bLPWMKlOndQav/22RBQWseg7ZYVxVL9KHYXVtjhFk13ghVl2aW7tO54w7gfkaLR9zU7U0Kvu/5bDh3IODmntsykcIjRiIhDI0bQrPPO20ForR0ad88IHNKujotZ02qgDKgqfa2o/rkF21KjQqsxKGPhYuhGXLw2QdIW+7b12uqB86UBmjxdAp3x1im2bjand2yXXZnfloPjDRaS0EHzEYI0+caCC+XqQ+0QPMBZ6Bo68qUCySUjWsHR6SkVAUJVacpX75QwvHmqCwEJoPgVZDITs7aOcXwFE4oiM2nlZkQQk3POMF6WDldNyM1TGD2cPV3xvDS3zyh2OvSaKtlKYK+/Lx85TSOYjW9F5khYPyWBjDonl6/Xfef23XoTzjsmGoYCtx8nAJ7vOIiIgFXU3dvEvzs+l6uubNtgI9du82d9w3SXDbm9bz14Vf/fnySxjzQvj9e5NDbxciYLIeBbQDHWVdWAi71EzJp2JHNXJUoefhSdd4Uadx9dtv7fH+s0qk/vrAgdjpYTslFyPcvcBw13OS9yvv3BCyDLumRtinIzButOy04CxGwcaB0eWW4pbbZzgO5uCdij4bNu4P2nkpkATLWuoQZOX+U3qs2ScQBYUc3JZTbYyHFGunB//syi7PHhuqm1jFzAybDmzikfmRJ1rbG8o4XJhuk3ID3JKLYP9yKI3Rk6DeM1oYl8JTKmYFpQvkrcUvcyj3kE1aCWIgKk8kZt/iGkyFlgu6JrqesWy+4VsSb1E31xgG7wL+F/tsL7sM/v4r/P5N1WKniyUahN4ct0ug65hydOWVkJEBy8N4nhmh+GHrMkxh9/VJD/wbrru46CL4z39s1iUC4cbhkcbnT79lLA+n2sLrr8Ndd8UwQ7/zrBAcwwoCKoGeFya2F1uIDI5GM5jEUb/mb3fNqgBTp+g7Ll68V91IrIsu2rUyUl2tpL0mNXhVAnNyI3E0P1SnEkyIqUpxwKGf/gVfd2DXj/8GwBNGc0+CGo5C4e+5dLAFjPt1nN8+47IGfDaAW764RZF2iYUYiISynAOc7rQS7ibgnYFTd5sonaFrPIFMEuDAEXyupQFxdcgpLISXXoJVq9ToVWbVHdvnlYXefOggjBrlDa7uGsNaKT+G3hyyTkbR3c3V2F/17du831OnWpdb3MS6DKPs3w/FNr5Fz6kAtAvaGOHal4n7YjO6+8uahKyUi85QqU0Uonj2hNpduu2OO/TL12vE0hs01pOnT14k7DashQzqa2+WjrJ4sckDVXgQxQsW9Y25cc1P35C33BjFIHqoWuDKA9amZekLUm0HkfOzbxWzBnvnAFBv2zslesRbw7Gf3MJc08duP7yd91e9z1vL3yK7IHi+rVDJaQUEFxLm4U43CvowJx547bppDh4M110HtAix08K9xe23CquDIlXX4/XXYdgwtTJNocIt3u93YQ147HuonAx33mtdtlKaqRPltrevYdO2B1pCsZstWhHo2xdD8VB69sQ7VaWEaOW2JURcE80Dr70GKSl4y88lhDuVvY2B/cBXNmaigJDdnMq+L4LuhvtYl93IzIVrTXzKnGN6qFRmhcUeI4bGOO3SgdDtUZenuguuUTisXg+7Ts3tBhzVcYMCr0OMzl2htbWwuND3u7RswnlplUfEg0hwHU5OcSLV/KHh+t7Jk0sepkIeZD6/aP2kE1MYbAvaa2Fptd9WAqOBEPEvVKC7mMMkzPH3wr4GeFenvI7A9/C1wWXjnSRindRZeazW6+7Ag4ryCVB5NvAyrD3TlFqOs2AB3tWmdDI3OfC/mbafnw937YCb10BBUMyhmD+g6FiR7s/uJT8utlsZk0QLFg1wCnC7TnkmLmoZD0v7XrA7R9BJOjH9SkXz0DSgscmDrUxTjFXbNpCPmw0ibsYNzbG2X7wjPXGOauTvZtayl23UyFmOqxh7K70Vw4qmmXddrll08JgObqiMCYR4EAmuw1JXZaGDyOuJ/odzNVmawpefXW7CCTZQ+qMXcDUwwmlNQrNvn9+fKKt/hapr213keWELil8XflfysYu9x9so3E6eM5h+pvUs82sCT3l/5/3mt2MVaDdbl2+IOOr3LD3MKppqq4x4KHcjfZCLzudHyr4X8QQbQmO8ilpMUTQ4s2rkdLJKmM7bdQ/bYeLvFGaxp3nkI4Ontp1QGU748x7dOev3pHBR47cbzR3+UmfmrHBahYTFUQ+iUaNG4fF4Aj4ZGceWodA0jVGjRtGgQQNSU1Pp2rUrf/zxR4CMvLw87r77bmrXrk2VKlW47LLL2L59e6xPRVCM6SlmYY7Tc687HLx6lEFi3Zjc0DnHCivneljHyjZGMPuWwv5FUZ0SYI1o5WLZPmRQQLl9k+xAUOhiv2lnAdfpFCieHXN1fAS38YICZ/QwirI3qKmoe32oOFae3uZZZgl3lXkE66xF2Ocg54XY5iL13EWCFIye07ArhEM0sXYUceXcf6Kmsdovprh+rk3sK2+weSgWwyYPhA9IagINjftrwrAa/tuEUhyv9ieffDKZmZm+zyq/SLLPPfccL774Iq+++iqLFy8mIyODnj17cuTIscBnw4YN4/PPP+ejjz7ixx9/5OjRo/Tp04eiIj0L8woJh1/r9l9NSHWjVzUQD5aja264Yh1UYvXh2n/g4sbzAwvnGO64a81qUoLHhUGqDeB7QAx3Dm44t1A6uLR+JgwOrtgX3MZfe826TKP9WUTDSbTpxcayKssQYEOE/UHl419eRofv/uWiqkk9/zxUqQKffqpIYBTc0EXZjsqTtCorzgo8lrcKI2PIWOUXCuNlYiFIdTy99Ylwo7DusxP6eNWxieKFinl7eLY2jK0DnmIFKygkGI4biCpVqkRGRobvU6dOHcBbYceNG8dDDz1E3759OeWUU3j77bfJzs7mgw8+AODQoUNMmjSJMWPG0KNHD9q3b897773HqlWrmDt3rpOnJVhAlWFA12otZgkV+M/BlSpcScxeKVjD6L3RSbuA2CSM48FYuYVMG0aAW42YTmCmuauISaWMCPkFOS6ryc7t/Xdjwl9UOwNsK5A9omQ68aBB1mW5FWUxiJzAar7VYZ0SRfTh+rYaRLQmZN91j5xznBWjI7hj4laMMVAhPWUGXebLy1PktwKaJk4lwThuIPr7779p0KABzZo1o3///mzcuBGATZs2sXPnTi688EJf2uTkZLp06cLPP/8MwNKlSykoKAhI06BBA0455RRfmlDk5eVx+PDhgI/gLkx7RPj1Hb+uUaJKdBx6HaMqW91y9AzcXTuPKn4JeMMecs3amKniCG5+hhH70DFC1c0HHtB//O4Qq5y5jjhuazGrq8HeRZ7Qv03j9kYXR3XEMVUVZHyidRHlFvsMRPoFG1XBE+a3voNj02mo8MQxZYx0RZ9of2+iUX69nZzAUQNRp06deOedd/jmm2+YOHEiO3fupHPnzuzbt4+dO3cCUK9evYBj6tWr59u3c+dOKleuTM2aNcOmCcUzzzxDenq679OoUSPFZyY4hn9HaWAJ5jLHOoTpJbYTkFjdB2K/3oN5QtqHTNaDomLYuzeMUBehQj1D18wT9K1EaDkgRHmMHq3/8DdPDdrgoEdm2NmOVnQyOPcq4oOC03Uv2Ajk/yfCeYXsv8ose2ZOpQCOAwaAZnJVUqOX2Yl7sRJbm8v7/vKK4nUZDO+3kjoSwee1L3tfyHSGcMhgUGfX166ZuqZCi40HNho+JlQ9PZBzgKz8LOsKWdIiOuMXj2fyssmKdUksHDUQXXzxxVx11VW0bduWHj16MGvWLADefvttX5pgdzJN00K4mGEozYMPPsihQ4d8n23btlk4izgiXgYDqvrcGC03XfrQELZ464bfaemSKCon3TroyC9WVUzlfVnlINmO8YIemWaztXUaplH0thF3jMmA0NcmXrpZV+Cia6mHcndtY2CdDYjvrKqAvwbehbznQ+Rnw0V0yXOiYQxNvVVZbi4or0Rtyx4MXquo8czUlVTgamIeft32kzLZsSZj5+ewI2glBZs7AuuGw/D6DfhsgEXpcDT/KMc9dxzVnnEwgKAO8grzuHP2nbzz+ztOq+JqHJ9i5k+VKlVo27Ytf//9t281s2BPoN27d/u8ijIyMsjPz+fAgQNh04QiOTmZ6tWrB3zKBS64KetFtaqq+21D4nYBM3TKdeAaBQ+YjdyEVOvrP7WwgiWfYmA58L/oySzfdPWWgY2rn5m6Dpo9sVUMY8XXXKd4Wx4GPKFVj6NuNqbEs7dCfoknimuurYvLMmJfFKsCPMP7VdhXkTzXXPhjxGJhAtvGIy4sTzdhpXj0HGuk7th3qTRqH/7dQGqP32+jOdnEQf36u509R4yu/l22VNfuWVuyx6CPmlZsMG9rFBYV8O3x8HPA5CEX31QdwlUGory8PNauXUv9+vVp1qwZGRkZzJlzbJ5Qfn4+CxYsoHPnzgCcfvrpJCUlBaTJzMxk9erVvjSCAyjojeP1jVxYLiNk/1Nmk44+ylc20p9F5jS85e4WFNRpueQOEeLa5TePb6OHEITftXzzTdiz59j/5b1jr45yTBqRQxKh3hucSWe8T7PYj8ZbwGE7MTLOUlpscg0iYrmO+k+XDbWgStg/kY8Ljfk3eRW0QgOpjylUxWPUoGDPjfpI/lH1QiOtYhbmonisPjBpGvdVOWjwkEAzUFJRFr9s/8WaHrGiKJueaZBRyWlF3I2jxTN8+HAuvfRSGjduzO7du3nyySc5fPgwAwcOxOPxMGzYMJ5++mlatWpFq1atePrpp0lLS+P6668HID09ncGDB3PfffdRq1YtjjvuOIYPH+6bsibEJ5a6OjumcuklxoMeVUY0uwbMVh+etTC/VcgOyEfl6yg7pi+oFxlXKFy0QhdG6oNt/YqJB/OEw+9cC4nBYMUvv/V/AT2P/c9PsztzNUSquzFfZdPhfK1Qphjj8BzC4d+fOnZv8cB6p/KOA1S2GavXOFo8GY+BHILTml2xq0aFY8c5OT5aumMJzWp389MlfkdrfVKyLR3f+cBXnLvoK+YdD9uN2P0so7ZzjhbCpjzhqIFo+/btXHfddezdu5c6depw1lln8euvv9KkSRMARowYQU5ODnfccQcHDhygU6dOfPvtt1Srdmx+49ixY6lUqRLXXnstOTk5dO/enalTp1KxYkWnTkuwiCfM1A0rvPUWcJZioSVYiuFj4kRL+y9V3Zgld2ZNvCjsHhLYWb6ahvMWKJcFg/bFqNZCbAxi2TLggtDHK1EiCKcvlRPcCrwH/GVzPgFlGwvf6mixeSJ56cSoIoRToVDhisCGz6Wc32+s4pbiu8ppBYJIFK911ePCm7+8md8yFAkNm4s6GW6p37mFuTz/0/P0OaEP7eu3ty6wMCvCYFDvWdtbydsnQzcbX6a45dqWFxw1EH300UcR93s8HkaNGsWoUaPCpklJSeGVV17hlVdeUayd4CSqb9YK1kqIipnOy8qbogQZz5giFvEXwmJjLCGzMhwtDwM4tjKTTSOLvlcCB20QHM4T0qYyyssDksPv12OkrBAqzfVmNTrGmyXfL1sXpZ+56kXGwpAeK2N9fm7gfytxh2LddcWjR1Mo4qTLD8tmpxUoxxgZLxzMPWRAcmTBgXGEEqQhBnDs/J9b+DTrlj/BWz8+ypaHFLTW6VWpdNrr1uWEQJXnU/B7ldU7FtG0VhuqJkcOWq1hJQZRvPeE7sVVMYgEwTbesj+LqANPPTGIgM2bFShjByGUtTNIdUDWFubJR0sSs9uLihhEKsdUKQpl6cTMw5mSUzYybcxj+BCzWRmXbdOY+rTTomVsUvD7x35aVT0n24IeLiTWnpfRsmvYMCZqRER5kSTiM6hCDN2/E6jthSJhTs9IHMsEQM8qa04YozJ2/o/3M2BLM2PHRTqf6nvmW9RKLZFK9adVEznl+058+34DZfl5Hd6Dpymq4Vi5y02jFDEQCa7E7fevgMG9QmU1Db75RqcOqvJUJEe14EhFrLILd/3yvZGCMVnJsi5kdzJ7cOwIvjz+/4cPh2HDYqiMDuIxCO6ff0ber+eUfGlUtaeg0cnixQplxzs2lMM//5jPP6CLitBZu9F7Z2kdHYlc2KbtnMpaiq3G7tguXMQdd8Q2PytYubaa5uSsbf2aG4ldZBX78gov91RtT9h95iTCpgObTMmMFf66F699HoC+KTYE8hZighiIBPVYXjfcmTccbhm8/nOcvnTKLOcWpi/H25uoBx5wWgMbsFBvdz7kIoOGifMYMwZeekmtGoYMHaHahEv6EaewbShu9wOlyeudMKwIsc3M+UYoRzc2jS+aBv7XPOZWFxWMkW0tJq5hXrdndo4rieo1baBdPxBlPGrEO8cT4Z+xY/3yj7Wl0UFyCnNC79BZlOFWQTNHWVm1YxD6N9jDKvBFsnTUKhEDkSCYwa9vLH0gNPOgHco4s6FelKwVP6TEhZEnqJzMTgMaPTqq6LjEjdOhQmGmjZQ5xH/DR8An5vUxr0R44qU+BbT7CAF+9Bq8zPQjRuqD7eWqZ7qqBSVcY4gNRztnsg2uN2amapdHVFQnJ4uySGGg87CoanMxD5Rl/lA919RIDKJ/V4+Wwu0dW+xQY4AJL8NtBpBgTb8+3qQcZUY+qYsqEQOR4EpMN3Mn+09TUaqDROiRoXgVs/fPh9zoyWJOpDqgMiiz25e5DxAfSr5Vjzt3jTnKEFG9fihfDidkfuGUSFKbt48YjHMCsrhbnVzbqpML6qlSFaJcY8cCugcooUCGgSmyyuMyKSoneeyIU1zQZ7gS1eWSt5+iL1tzXmHktSaD21HsXk7GviLYkaObqnP0wNaJUeblGTEQlSfiqPXEhVeLBVS8PFNZRF/4/Tbyptvp6xTrQK+WUKCrIeNFvBDtQTk2WpjjuxDb4qTv0tt29PYHIVcxi5KfkaLyGD1AsI8IXq6WL5Hi5w5Ng99+0z+lyaPD4J4o00iN3L9dH6svGicH/k2QSxiVUNc4YFuYgjBSN/Yvf5iKRyIbh0KhJDZQXA0Co6Mn6HYw1ksxnm6sNumaYPVIBWIgEtyHcxH2dGNVhSl54ffpfnBT2J/tyDz228nBr79nkP8DZ7jyVmGgMiriswz42oQOBytDp8kGMwsiIW9hRgOluqD9+zg1xvkpPHe76pKh/sNAWo9mLL0dqKx6UW9zkRI4PQ2rkYG0Dl+ziRPhrLPgggv0pdc8RNU5nvvhgHFDtL7Xfyq9LdrEkNUWjo2jC67nOqn0wAYPv2cuNXFcoBLRdAo3vUrVEu2mCTMQd2V7cfqNrg0k3hm5BzEQlSdi1ZLKa4s18Nbx88Xe7+AGqGnR5ejNzwibTC6OEOmmrnSlMRfVqW/qwsUmjis8FRbdZPy4qOVoNV6Bi8o2IVBR8V2/RGHioVWKsNPE9cjLg+XLE3JMXgbXnqMHJk3y/ly0yJoct2G2yF1xKh6kb7KRwMC9ZdHC/gkjRBGRAgpH80ANq6iOzkezzTvE6vz+EETQ1W0xiCJpY0hXnWXolRkcpFo6ErsQA1F5Io7aUcS3CVberCriH7/FBMxkWVgYZocOYaVJ4uhymiLSPcNNt0m7r0M0+ZrH/BtBV9WhMMo4taKQm4ySPhyo+JrHoLdiHHqA7wUKmkZIYOKcLr4YOnTwerAoEmmJ70JNh7QBo89LMTEuNSV2o1033ZzcTCzKyY19uA4sBcPXI19pDEfz8xRVeP/ompIVMwu2MxXO6lhF7epm9uK4x1g5QgxE5YisbNi712ktdGCh/Zs5tBgIZ68Jxwch3NXdNIX1l1+MrRKiY0q6owQPmJRO9XDjCScoBw9E2BnmOihxyDFRYRwdhqiqky7yRAqliluMcJNskDl/vvfbt7y2wn4m6ng+xP78CNOarWLlMtpdBfZ3AzYBnykUGkHpnBOhQGFWdhJHz4XKCW6OEcsixuVk95BE5RSz/CIrHYuRgJf6kslwLjY4ce+O5i1kxcPKTOynREcMROWJTjBpqtNKuI9zgIYAySYFaAFftqNnUNe5s/16gOq57PYQVcU4OIdSlAepjvU90aF7sOW4Wm6pI07oobnj9F0xHVJh/S03w1EjDgY2ZJ85oOTH5RaEGKx3dhgcgwlZVka9t4ykdbrtmcAtbcx1t1mnnG78hHonC6kvmZBGBLveAgZn5fJGotr7xt1nK1hFDETljOVtnNZAH7E0PPwK7AK06rHL0xI23OtsGQuYOMb/uttyT79DrTjNhinokYg+T98ELr/LW64HZo83EMxVOTGMCxE32FhPd/xjk+A0m/oHM0GqL7FBjxCUOV0n25EqDOptV3XyJ2S1Uly+bvcsjkbEFwNaxL9q9bD9AIPiFcs367lhSI2wWbh8AGOQSEYzN8XbUTktTdOKdaZU0wt5Ig4q47GnswcxEJUzjlRxWoPEw5VL3n6EsZVm/DByOhGDVMfgXqbrHuV/Qq+FkGEy76IiOOMM6GXlzbQOohpIrHh4xHq8YSY/qzq6Z0wVlxju3myKV+TRsbqUWf76W2dCI3XpWSALjpzp/ev4beJ8pxWIHxy/VjHC0enV0i87hq4XsEZWmNSb1kKFc62jtOJGtGSHiRXhoqmw5nn4bQiJ3+h0Brt2uaeXW4i0boeQgOg21FqgvAyuglFy3qr6rX4u0CHBK8Kff8LSpcB6pzWx+EYwltfJRF7Bh2S3jkm2CY/eOuNKA7hTGCmL+71fO/5PgSyncIuR3x+D5WbHi4pEaROOnobTZRjD/N0W51Bpk7B0cuobZ6xX+vrqr9ncFmqHx2P49FbuXMl9397Hlc3CJDBb1itGAFAxo1eYBG5bHy08dvn+SPDrsogHUTnjq6+c1kAfsfKIKHA4mmSo/t7pwYQbB7+unurdwl7xbgnkq4QQwd2jUeb0K6pQRIiHarXP77edMYgSpo25/TxC6BcwjcnjjvtPGTWjlGvw7kIDC0Q4iWP30Rjl65Z27arxCvr00dsMkyh29AT15Gyn+eOFX8ccy8diOSz6Z5FVdSKSnbNbqTw3zLxXV/NccONxGWIgEhILg238/8K93XWKOOujgu+HKtX3l+2WgV407BgnxXscCFcQJ/XHjdja9qLIru2vh51qmInpo5dwx9qx9JfiQtIrLpYrQMVDU16xwv48jMRpnDsXhg+H/PzA7UbK0ukXV4I5QrVNXUYVnZUjzfAawAYViSusnZBHK+L9DEWqhGD9/g1h9zl5KTTMT21xU1ymREOmmJU3Et0kaHAQ8+qrwCu2aGIap40hTjoM6z3eN1iVQWv8r9AVJX9XXGKny6gElWq44oHPgA4b1oN2ssNquKQeRFVYkZ5KvHlCyCgogNxcqFZNgfxoqAhGHKUcgncfOWImE/vo2dP73bAhDBhiToZbqr4q3ND9hWIdUNRAnbyoXhcKCsKMh45WVpMowYND4+x1VNsqmh1ZztmpkXJzU611pkeINBXMXeUT/yS6uaBcMXAg9OgBxZGMsU/ETB1LmF7FzM39g4t1M1vcqleb839r5b9il53TDlx8WXSTaIN35dgUONlODh1SICRSEHmdIlavho8+spaXCrZvIz4bqxb0XYK9gVPtFF4W/1PTc0844QSoXh0OHIgizKW4oRqaMfBu2mQsvW0zhzzExXX2YfGCGzn8RGtZ6ULPdY2Fd5m/QahYizxFS+/DfyiPEletUBeBpOKciPutL+gaWlt74u/EUwMXQiEGogTinXdg3jxYuTJCosoxUMRiv7B9uxo1dNHYughfpx3tvEPsL9PhO9Sn2jEQ1HUz8/NhDNYhhrMWzOEBJgduskMvPatEuy3GQVxTWshOzu0Ll58TT6YeuPN2HclClVu8YKLvNowbrAouYfNm7/fPP8eg77Kh3DUXjJxVnFZ5u28ssjfES9yghf0TO4Lrb4WoFdq8oplHMk0fG40WSbaJVkP+MSu8SkOQ5lhYZ7tupOWsM9SBC25zgmoiehDFAXO+jWFms62LKO1WVEzZcEOAzrAqhNhheYCZfOxnXp7+wwzlazDAqF62Vgb+bfJgk7hiWpBqok3fsHjO8Xrbr+imYNwK6p1lERrOX0yXtL9YPdjHYrqz05c0mJD6RFCyWHPmHFR476rU27AsB9pSp05QaCZkjsWCcl0dV6iQlWk9KsdxkWha0b7VaL49/thvu00mYaVHyvaT444lc1tFLMFNy86bmeaYqIiBSHAfd1q4Fxht2wpiWpQOot3TxZnADX2iAR3coG6BEyvQqa5k8eDqb1U/s9fEyUpWHSqouDsrcTNQIEMFbtBDaRAohbLcRPB5xeghMCQ2v2w+6WdI3gFH3X4tM4BugMf8Q2Kk4zYDDQrhGSPGF4fac1HJCnOx9N6O9TOnihBlKp7Zo4uw3jnoMS50TDGQjSXccJOKR2JbbmIE0ocYiBKJ24FHnVZCAR0t3JwcaPdu8Pqxin95qzLmGxWTH2mnhZWAEuH6lBLuvmb6ksXBeKa83qRCPmu5+HrZrpqdGdjZR9ixilk0VMlWHaS69Pe9wMTYVOcyXlAGA05HOmZtZyg8Hn5tbkKxWLIdmAebTgqaOavopcygTNhZCf7r9mVvOobfZec4IdKY6r/vwUEV8eZK8KDjXGI0JkqgoZe70VnQqlf8cub6Wg8+4SKHJVdTXsfeicl44DHYnBw1pSAoIdjNPeBZwEQn7H9IwJz0cLJcNgJxQh0NueFFwuOGqUkmOBpqo8IKZmsAWr1JjehwD1DHqDLuweilK4w0K8KMkcNNvAjcDKuOi5rSGmbvQdGOs+Il5QQl01W3tQ7crGoxvPWbDeoTAzQtxHSyxVDgsmv1zAA4f6Y6ecq8gwx1InoTB6YLnpIlnh3O4NHU3zPcfSUjBUMXSnG7vV8wQa6bYleYJGZTzBSibKGkKILsNgaEvUcbzddq7JhI+bn07mP7tYkboQmGA6MG18S+vBPQEdw16ktrq/XMSeOQAzGY8iK6VIahdLqzqjats97r7veC0uVVioFx24z8oPILFuEKr1QVMQ+dehqKQb4//wyEWC6+yP6sDbOqr1p50Qzv6ovfZAcQvDdChXRDk3Mad5VBlGsZKy0CMrIQD0tMQ2UQDyLBlSSkR0SomDWx10Ip/wRviNF1U1k/TF+DRKyjTuLENJ9QuOS6uiqc1dt2aRFfWDEMuKRa2Y7RvjkeghtHJYpBKRRnAnMtZBmbGDHxy+HDCoVZrHOxrrL+19VsPdGvs/mzc2NTVoWV4N3hsa/EnO0LzOeuetqccAwxEAmJRZz0FZG6+Xi6aZ5Zxdn8y8Ug2a9C2NJhu7wArLYHJafnRGwzl18XFbjCE8MhEskbMGJVNVCPY1LlFXgUlRFpotwXAz1NqOIU8dId/d//Qc+eJcGoQyntxj4nxjrpubfYcf85kndEucwNB9YH/I9tUQYWUqAHVLy0GHNoWmSfGzc2M8EYYiASXEk8TjGLZwICVxqI95MT/Oa0HJV/qCkztgwJlMcLcBla0Ldi9u6FDX8bOCCey9IAEu7hGLojaCiso1FFWY/FaRkzVaSMagaFKL2HhJKVIPU+5itjqc7Ppjo8dizMnQvz5+NdvS04Wyee22N4rTxB2YU6XadMF3M2zfP7p6ZQHp7/SMD/ePWEiQXhTDp2LDPv7pIQ9CAxiARXEk+rmFnBzKArUR/sNC0w6HWk8/ToSFNusLB0cXkgOzvGGcbD6lS4o6tUaXCxFSuxfUqOMXyuZ0WXGRZF5WrmfI30RY7UQaMBqBOYaNdK77U0XGQeoKbRg3RS1fs5nAykRUhnpI3Y3U/ZbRQ1k96AHHPTqQILtVgzF6T64kjX2Gb2ZO2JsFd9R2K5GsZwoGhk6pcdBirDuEEHlyEeRIKgCjP9iwv7JBeqVAabnU7iCykEe0gEI7WL6oabisURYuxFkLA4tVSklf02UC6mV5vhCJAJW04KvdsT9O3P7t2wSEdAfjeja/qYyvzwKGuSkQwF4fY8XVtR5ib4ffeqmOYX3pbnTI+vLNcYG2dktTx9iIFIUE95HbgY6HMO7Ie8PPtUMYquKWYGMdoFR5xRIf05kKAeU25dstuNZe1GnUpwsWrRsVF5AzN21eXp5MUw5Hlgf5DqmBRFXFf+xGR349DbI9W3evWgU6eSFdAUEvX518n6Y3G8p2ombIWoZRCXTwUxoXyvwGVDUDkBEAOR4FIS3dtvcR6ccUGIHQ4FDwy0EJkXo7LbTfQ6YIZw5ZvQRaWiUlmdTuCEh0Dss7REQhovXUqs+kblUwCjBXt2Q6WPl8DGJjFbxpGOc8NlU8mcOU5rYB4rVTX4Gj+z8BnbdNHw2DK1yFlbm9XzsbslhZdvvNzit9VHqndOeWO5ETEQJSDleqDu5nP31+06WPVeWXUTyVNG6e0jjDBXPFDYjY4VdBKiHMKcp5KH1Fi2Kzddixiet6nTdktZRdEjgbplWzC7eE90r4FQmRlMG5w+Wp4W4kzFkpDFYHB6kZGxYry1gWj3jTJjL7/0xcWKdVErLnJewVGqQ1Cssw29tvg1y/pEJrEMRP6VyA497LtdOh3a23z+KuJfCaERA5FgG3pvQqEwfWg8jWKax1k3pUdZhe7Kei5lnMykEJzkOOBMizIsXuy334YTT7SoAyjtMGwzKiZgw7DDcG9JZIyCVCs3lgTplRCGbadQ4enk0HTymAx8jK6e5/dbtYEolqgqWjsuUZINMp3kiVrh95krP+dunkb1VTutzYCsoKRGgmFHFCs3ozLIKmaCK4nHtqpqCrXTz1dh8zca+8HENdR0WogM1Q+nC9QinjC/ywuWPSIvinF+QcfP+AMGdQVUBD9VWAFi3cWWW8/W0lXMgs7flvLXArKMGQEORAZPzNZ7vbxcDqCM10yU9HFdFGa8jisA1aGoyA6FYoMW5DVnpH3Zfb1fqHPsd6imKcGD4ws39A/qa4zUwVLEgygBiUfjSiJgplsx5WLvYjS7rBlhZOmq6tIeBAOori6Ds4AmgEFv/Xjrx+NN31gTy/JR5vEUA50Toto4cR9PsLGDakzZGn4EDsDemmp1iVrHY3wt9Xj3O7JYYMTYMPp6ioToT8JgPT5OfJeOdHmxRQxECYhbGpEjXZFbTj4UOnRTHhQ0UXBhuTjxsitcnqaLx03lGk4XN+mogzJv6cuJn66p5uCSa2trUw5zjnbm+edaG4VHwYpzp65j3fCMFOwN5uZxhyJc0lR1E9bWEOlane39+qOdam1ih6k2FYJ4NfjHqdr6sKmfMTtdTN0y9+YzTejr7TBiIEpA3OKlaeUGE48xiGLWUdlwjm6Ycm+kvriljruFeB3MAfFxh48HHQ3iijbkBh3QcXkVXH9Zbj1OiJcyVFAnHbtvxKCMw674qeOcg2eYxUuVAG+/rqpvv7PKETWCYoijq5hpbhhJhye2cXaMriagL5WK6ytTGfUhBiJBcBG63kS6+GE15rEvVAySrYs4JsvmaxOqfC1lGQ/3yRjrGA9FogLV51k65grbh7m534rBRVd6+okSPMaNU7NClZ3xCK7xgZFVzMKkLSxUo4rTTM079rtYsZdC1HFBjGPL6U0zKO2o7boI+nHVi8BYVuqY4KbCdQdiIEpAynU1d+DkS7M0Mz0s3rpQO9EInBsfcTEcFwVejCV21Bf/ejtuHHz//+3dd5wV1fk/8M+lLbDA0mSXFRRUFBRsoAgqYEBsiMT8IsYSjSaKIohiVNR8JUVQo1ijicSIHTWKsaCAKEVBQRQpKoqsgMBK36UsW5/fH3f37u13Zu6ZmTMzn/frhe69d8ozZ860Z86cmWfDTNIGEPd/j7PrLTKrVgG7d7sUjM8Z3Z9kU5xKk1CZpvUHh+ajapwoGXNfWdZpJf3BxA/jkUfOTL/kLENhFa0Hmt0I/JRnOaSI01cC93+f/XTqpFrWVN9/dEL93zUevjIyvX2krRRGa4z5Ci9R/wWye+tx0GXfN5EddF6hOpaXfjy8G6RoWmWWa2Wzw7c8Lt97bomq6nOgSf3fKosjZXcCCmai+2p77lngjTfCfyvvgyjOTTcBZ5yhaGKKaLF+NAiiVy+3IzBIw2NRJplWb1arX4O6Y9X33xkbLub8w+T6t/XCMKTgbZopphs7gvl5ZMtKEkpg/Vwx2XhXLwCq/gHgaGvTjPZxL+C2btlPJxMj9S3hETMP7dNCQMb6qPJ6Id2kzFZRFY/+eHh3C7t3JKnLRiwkj9XFaqYPJBVzzSb2vv/ui7LKMgVR6I8JIh9y/c1Ybh5M3eg42PlZauuNU9RNS1VTaSdXkC1Vfwzwq2l2TBieuJj32vZla7xeKwyfYfFnKUNixdB5e5YrIePoyTq60GjF33RT+P+WEz3qQsG2QxROTCPxj5h5Tob4JcUHa3VK4qdoSYNQhgt3He+Cp+VcJbLayXT2803HzPJn80iAlWW3Xl5LNi3BiytftDy+lzBBRP7isQN7QrjiUrcMNhxfMk1yZ3mGAaJpuF4dC+k4AG8BO/enHkTZuVNzaFnWQVVZCXz7rdtR2Mcrd+brWmtkFa7Ku/ZOlZsDj96Z1tbEsHYFFj/dUNqPSj38MLA/zbEgnWTnG1lJs6DV1WnqqQMVJpt1kNAHkc2PTXr9mLuzbKfpcexaZHeLMruKnTH2lC2ssl9q9ZukRw7utSJJNgOt2CqrK22ORg9MEJFtXHnETGNJTzJ0WU4Xjqo79iV+V2EwDl1fQmBnXAeqkby+ZDPP+HH3AViQxfRsoOmqtp0IMHw40KMH8MILAMYCGGRyImkKz+kbsrpus06JX35bkmO6HE+iZfH4mStC2e9zbF3MYQoTL2Y6qTYx+oEDwBFHhPdfrklRFpE+I9OMGv8uKr/tulLVH6v1ds0Og8+iBtS2fdvUTCjLg7bqlkZe2S5UPg53XztAJP4hVH9igsgn/JhQCSK3OreMrj6qQjB7AbS3BugVdXc47Qlc3bRdrPduXPAmnafqMjhN8fT8QEEnrFa8XwHgNeDBFwE8onbaqqtNpG565azRy5wq46sdmo/HJBynnazzbwOVSHEsyBCH8qRkiunNmwf8+CPwzjuK52fCsmXJvzdyrejlR8ziF8/+GwEhT+R9nRZdJqc90sHgOJk24FS/G10DyYezlkBR2AeRhx4fvLUt0HXfCrfDcEQjtwMg9Tx8bPM0SwkDix1N2sqlffWL3wE4IeNgyildXJvLLtvTA3Uj2iBFLF55DMkWc8P/W2/msRqXiAZJ22jLAUwHcIeCaWVVB+seT8vwWJJvpVnOUMj5m1uZZmcpHIfXZQWsXfibvQFltq/uyHgC4GwAP5ubn1JZ1Ktqp2+dK64/36wGMDD76di9acYnBlR0Uq2L6LTZmi7qppoVJ/e1pnZQdg1rj+ZVpW6H4AgmiMg22WzGlsf1z/ElJc88mWByXVRV2RGEvyQ7f5KQB/tvTMa+x+s9r8LYDUiKUpdrLlEwLT9sXoHk0L6jJv6ZJI8wc9ww83Tz5uYA3kszMRefXTD08gvFb6lz+vi8Zk3UvJP8bqgMDMYcAnBza2PDOkGffbX6nQ/bavkriag7y7vpiooKrFmzBlW8qqMUsjkoWh7XhX1HXawqTgKM3KnmoxxRi66gDAJcjHosfIY6H+gWRDaq24+8pnzCqieYnfkqMkRZMNLniTKalb1ZOmzqSorQ7pakFoNUnPdIaVOzDAPk2jTjaCnKyIk35ZmmOmF2Y/qf1fYdLmiaIn6z0/PSo0ZB59ab0ywxVK88tDwOMb1b2r9/P66++mo0b94cxxxzDDZs2AAAGDt2LO69917lAZIx7IOolht3phw+mYhcMNu1zl1qyeGHKuzJZXA76Eyv5PX4Ra9VMavFhjIIASgGcJHC6WXzu102bsh+Gkpi93E9Tnv+7fL+JTrBXG1X36Jxy+jHfZaZpJTbhxQAmgThksOsjXZ3dEs47eqwsRXqZtgi2TYlTL+MGfsoyiDd2NqtbsMy14v4ZJanklsuMn05PWHCBHz11VeYN28emjZtGvl+yJAheOWVV5QGR/YoK3M7gsy8uPla2cHq0ku87uUdfwFiKl7dF84gW1rW+qRsdKZzC6hvtrodgR6Mbloar0pPcvqiZOnSzMMILLyW3OkFEXvrotFjTdo+iNzmSh8H7svm8bHJijsd8W7SQVfJV1zIYEmnToxYbTumiIkdhh6tzHSIwX6mr0/ffPNNPP744zjttNNingU8+uij8cMPPygNjuzR4bfOzCebVk2W9wFu9gWg4GgoIROJAIVHXy32uelksay2LZrTnaumuSjQffVlw+snmSpaE9jVCXmpwr4WI/stb/U36RqdE4dmpV2UDPXfydbPVvsKyrgNa7CTEgGWLDE2nNFhoofNtr8ix12b/Gsj9U15CzAH64eh9Rv9IZtH8RTKtF402MTMsRRw+pGybUGkml7RmKHFHkp7phNE27ZtQ4cOiT1m7tu3j51HecRe5Z1OBJvyWm9036VwHxdK8Xe2vlQ4LcsyPcLkTBS20j7B5yFaHsVsCkrLZVXNyEJm2WLCyEiBKOsspUuCW5ugyokZZ/e6Tnaq/dZbwAEDrcOdKBItDket0v/M7TEzp9ejn64hrSRzMnVCnW3ppHu0yvy6dmsr908d0Z3pBNFJJ52Ed999N/K5boOeOnUq+vXrpy4yMkWLA7JKVvcBujyzlUySlWTleGj7G8cUNVP5vC9wotV4KCnliSAPHGudPmdUXsQWJ2h30k9E7erPFK5b5/7aHhuzKI8veqf4QduF1YOZNzOZkh83H7PjWxAf49ffArg7w0iKA0v5iJna2TjOT637Mkq1rI7sr+tn3iAAN/LsYrRsdD3dM9onkNUWVEb6hjL16FpA7siafuJ08uTJOPvss/H111+jqqoKjzzyCFavXo3Fixdj/vz5dsRIBsTUV03qriuPmOlM0d45UjSaP2JWmelNJgq4WU18dLNLayxmWK7omR6VYB2uZbB8La0GG3ZSB1qqn6Zt0iy/7fUvlH7+Xha/WCt6ZB4nm/JOOmqKstX5/M2N9xm5WR7J5q3r6lHRv4ybr4KPSURYWJaMCRCb9peud9pssKxCxnqIy4rR/pyCwHR7i/79++OTTz7B/v37cfjhh2P27NnIz8/H4sWL0bt3qtta5CSe9LvEwn4rYb/I/jvq+X35FLKrjxodOH1y7af9Z7pFkQy/W56Xj8qPfCAg9XG3wqRh1p1UK4tEvbrjSdpWQh6uM6rK3u4ikKj/+pEd5Zdt8kJlB896dBYNP1ch11nqs75Xr1549tlnVcdCFOHFbd7TF5ZGOiFyevlUzc9HnR8mWxQ/vlI5ms8XzxibCiFTs34zdDlftBUro3l+fHTE5Xqg6lwj1TYb6aTa4nSd7HjcsoBsy0nPGUx0VE4W2dB60fWWPia42ZIrk+veuR7Pux2EB5huQTRz5kzMmjUr4ftZs2bhvffeUxIUmafjpphNTLYenBS/6SybWONP9Ny62NfhZMBKCIaKyycngikv5iWL9eeTslFJh20B0HOf7kXaVHEfr1BdtplkoluKGIpTxbI40H9Ywnc292eo4yqeaWEcHZdDJdNvMbOR2UZaQe+k2vqaMfiIluLyVVWPbE98xW0UM76dkWYYI2Xk971ImOkE0e23347q6uqE70UEt99+u5KgyDwdT9B0jMkWtfsTp5ZX5364bWNjXwdeqaaB2Z7iOF3ftTxFtWHdO30u7la5Gik6w7EpzGLb0hGulpU3DUn70R2hJHHEf5GpVZTN6yEE44+nf/ihzYEk4dR6PM/COJFy06Ky6Uu34jHaIkW3V8HrpGFNecrfrJRa+kfYjU/RjiGjxSegTnWgf1Q/MH3u/f333+Poo49O+L579+5Yu3atkqAoO35Iwtt6cNI4w2LkokG3A7crzNRxmwrM6c0s5WJkE4j9ff4Fj0cu/EP2TNaTDK+ybArMx4Wdtvw02r9sa+J2BGqYOce791774vhuTfLvfXczI8vl0a04jMaTbdxpq2mSH9V0Uu0m3dZ0rFM2P5P8BwvlnulaxalHzKys71kHKw/Dl0xfKufl5WHdunUJ369duxa5ubmWA5k8eTJCoRDGjRsX+U5EMHHiRBQWFqJZs2YYNGgQVq9eHTNeeXk5xowZg/bt2yM3NxfDhw/HTz/9ZDkO0oPvTjBSSN7MNv04dhSN9sUd/yieiT4StF82FYJ68akjD60LW24meGj567hyU8WD5eRF0cVcbDVBZHJdOdPBr7GZxp9LWT23SraN7C1NMU9rs3AWtz8lskkE2HOe7+ZbzOrnrbpFTrohgvLmraT9aTkeRXCYThANHz4c48aNww8//BD5bu3atRg/fjyGDx9uKYilS5fiqaeewrHHHhvz/f33348pU6bg8ccfx9KlS1FQUIAzzzwTe/bsiQwzbtw4zJgxA9OnT8fHH3+MvXv3YtiwYUkfgyNneaKjQoUyXmRk+WYQO8XcDfDAejNVRj46diZdNdn0QeQB2rTUcpio2CY176DdkfkbmIkr248uFc1NIfvPE+I3I9906h+/HAbvlljelZgYUXE3j0pZqm8eqjNe3a347XrBa4ujOl4zj5ipeiNa6nl6bW3owXSC6O9//ztyc3PRvXt3dO3aFV27dkWPHj3Qrl07PPDAA6YD2Lt3Ly699FJMnToVbdq0iXwvInj44Ydx55134sILL0TPnj3x7LPPYv/+/XjppZcAACUlJXj66afx4IMPYsiQITjhhBPwwgsvYOXKlfjggw9Mx+Jl3LmaoPjsReW5g2snrxr2yxZ9zNCxervR6iDpcdQvj71o8vY8Pzyia5TKt5hF6NbzrYF52NoHkdl5ZEPLHaXxQe1I1MXc/FC1Elx+M1soyUxSnTtEl6nZfVvG9THU3PQ8Q/EK1O0GTo0d24RZovebrtzgpf6TMnUsbW7dZlMPolttSdJkk5EElJfeEOcUS4+YLVq0CO+++y6uv/56jB8/HnPnzsWHH36I1q1bmw5g9OjROO+88zBkyJCY74uKilBcXIyhQ+uPQDk5ORg4cCAWLVoEAFi2bBkqKytjhiksLETPnj0jwyRTXl6O0tLSmH9+4vYuRkXnm7odUO2iy8VoTHFrcqFuqHNZIzFlGEaTVWBZ1puKyledq5tUhNfWj1c6HxZRu//JtNi67OuyonIZ/FAeWdK1CHSNq46ZbcnWc6ljk3/thfO3tB3s6l4BnGBzGSS91+WLg4R9Um1W2W5uqlrwaMGRZfFReaXRyMpIoVAIQ4cOjUnMWDF9+nR88cUXWLp0acJvxcXFAID8/PyY7/Pz87F+/frIME2aNIlpeVQ3TN34yUyePBl//vOfs4qdMvNbqyZbsIwiTO3Xzbw5zsEytnNW6Vp7WJ6vwoAfBfAXAB9pEIsSusWjmET+Q7ZeloQcmIfONKpjfroOSpBi2aotneVnR+tiDtiGmLHOu7iyolttZG7NqnWtckTAqq4tRLzUTstdhg4djz76KK655ho0bdoUjz76aNphx44da2jGGzduxI033ojZs2ejadOmKYeLzyiLSMYsc6ZhJkyYgJtvvjnyubS0FJ07dzYUtxdwN+oSDxe810JXeaKvctmD3NntjbX/v1pgLiZN4q+TbTg63X3OFAs7qTaobpkU7ixsaWnmEqv7Y0cSNlH10cj8jGy/bq+6ZMuRKu71vzE3nVRMvTg0aroVFcDcuQDOMTEBG/k6SWiQyhtr6V91bo7X32ImUf1X+PEw6DZHHknkioswlCB66KGHcOmll6Jp06Z46KGHUg4XCoUMJ4iWLVuGrVu3onfv3pHvqqursWDBAjz++ONYsyb8/szi4mJ07NgxMszWrVsjrYoKCgpQUVGBXbt2xbQi2rp1K/r3759y3jk5OcjJyTEUp1f4rbWOrYtj0/PlSi62fLYe00mos1bONAK0M69J1cFoNp1U21B+m38GUKBueo53Ul1XlopmbDURENO5rsVYnCw7bS+8nNovpxhG0v8MACgrA5o1MxmTxsrKANQtT4byd/LcRWBse8y6Ltu8TCEACw9N/C6ZkmNST8euXkKiu3m87Tbg4YdNTsBtVjoAT8PJRY+fl93zzmb6fj59s1Yu7m0k5teFuljFcMewIYt9BXlp56MPQ30QFRUVoV27dpG/U/1bt26d4RkPHjwYK1euxPLlyyP/+vTpg0svvRTLly/HYYcdhoKCAsyZMycyTkVFBebPnx9J/vTu3RuNGzeOGWbLli1YtWpV2gSR39nS8agFfktapZRFeWuyqrRgpr4Etdxs78RVgZ2qu3Tz+H5ERYsiu+q7k31UJ52X17oLSFdgmVprpelU+MYboYRbXXjEF/HED42Nl+xcRWmVCCVOMOP2aKQFpMsHIAGw7ODE75IOGxWr2X29inXxz38qmIhCivP/2ok/Rxhr+71wa7UkvK/y61qwh32vs/fGSZbVKGOSSilPoo1P3RullT1TTydXVlbiqKOOwjvvvIOjjz46qxm3bNkSPXv2jPkuNzcX7dq1i3w/btw4TJo0Cd26dUO3bt0wadIkNG/eHJdccgmAcIfZV199NcaPH4927dqhbdu2uOWWW9CrV6+ETq+J7Jbx4t3AybBbj6e4scMzlcjM4u2VGR/Bt7jwrl3wxsuyzqgOWcy++sBnR1s7Hh2yo4hCIWdvJih/A58bbLpd/swzwFNPZTHtWrpsShtaWhzRhvrgRhXT6TFTNwpAl3qYjLYtHW3yepPE74y011BRTJlzsbFzSdc1iNH15u3Vmz761L/au9Q699sTHZeaKHVdUueZShA1btwY5eXljvU0f+utt6KsrAzXX389du3ahb59+2L27Nlo2bL+7OOhhx5Co0aNcNFFF6GsrAyDBw/GtGnT0LBhQ0diJHvY2vqI238iSfG3g+IvVtOdEHj7JECxVI+eGRxXNbG6602xXTreKkLx/HTtayYEe8qWu1djoutFzWD34rCdRvtxQ/MzUoEzTEinbcBKsirb/ULM6URDAKdnNz3HmXn83YO2bwd27ABqHwzJilOpA0f6n1HKfLnY10JIPbfefBZfRl6rFV5i+jX3Y8aMwX333YeqqirlwcybNw8PP/xw5HMoFMLEiROxZcsWHDhwAPPnz09oddS0aVM89thj2LFjB/bv34+3337bVx1OGxWYx7l8Jj4pEoLG5yIOB+ZUzkrb8o7TIE3l0OmuaI3i3LyuCRa7ObFObXnEzCsblAWWli3VOFHf17xtJRrKSKDk4OGFOm24hYXF/rTMJI2iz0er7gawwPi4fqTT8RkA/nQX0L6921GY44FN0LUVrSKx5GoVNVVu8c8MO9GpXKyQbhu0TUy/APOzzz7D3LlzMXv2bPTq1Qu5ubkxv7/xxhvKgiOLNKm7RpJWrlz82TTPjCdQmqyXZGJi1/BInE09yXhyb7UDYLv7zzAoq4uXkPptUPXFlNNl+loV4Jse7DzU0sGvKiuA+fMBONCoWZf1GUr5IXE4T97cylDQrixSqpsH8d9bfEub1Wui6husjWcXL1Y3Mwwtn4F9kdH1bb1lTyhhJl5/i1m29H2QyxvCXVgnlqJbrZ28znSCqHXr1vjVr35lRywUQKkuJr20ORvepRtsuu7Gvkzla0/tiCGbpIOfDrleOM5ZfsQs1fTUTi6jqRXAQw7P01dSrDC3OlBWyVIy9ShgUB/gD+8pmFYGXtg/uM7KI1cKpqGS0n7zbKBzNeQ2AmAhgA8yD2YoYWGkU3cDPJkoTsOOxbErgZTxzWCWNhpnVmjieUWmfpySlyKTSIlMJ4ieeeYZO+IgisFtldKp28GzmoRl1QeR6nMOswmiDCvT6cRCTXXtHz6vXKr3sZ4uLoOtDC0nqqclqcc+SJipJqr3R3GP5dYYfOQs4Q60wpAcF7UwZuuv1X2Ep8vL4wyvs7r3+Bh4BNYOOnd8rIKTfSa5s71pspVrEoYfGe6DqKamBn//+99x6qmn4uSTT8Ydd9yBAwcO2BkbmRC9jaz4Cjj/fNdCiQTjtzsCdkg4EbXY/NsPzJyMZvHEsmd5ZXtS3YIoqH0QxewcbDiTFjj7FrOUQQRBskbXPlp2pclGDTuc8/I+yEroWXdSrXF51YWWbp16/TFpJ6WrK2aX26kXIDnDyrLoWVMytjDKdvpi5L16tcNmPzcFU9BzPalmOEF033334fbbb0dubi46duyIKVOmYOzYsXbGRiZEH5D/8hfgnXfci0WFtJtfttum6jvntdPLeBJp9HjhwjHS+O7ZPmZOyiJlbmCcTOtF55PZeKlCtbwMNiUeSDGrd/IzrF9fnY+nouCtVEo6OvbxhmH1eJ20qb/qOmmh3ONHMR2T3Y9jJ/su1TyjYo8/Fpo5npgpAp2remTZgrDvMyJVy12bZxsKxbaxyXSzQuc6FWHzTt7OVkl+2BxStUkTLa5wvMdwgmjatGl47LHHMHv2bPzvf//Dm2++ieeee47P7ZEtPFWtQnH/J0vSFl8WZWvXG2h8s7q9tK05yQcr2Kb+2S1N1AfFmZXALn+GFgZ2736iZ++p8wpFVB3/Alh0gebMtqJ+Jm7uZ+1vWWLX0pmPW5tWNKGkf1ogtdPIPJWgHMsNJ4jWr1+PYcOGRT6fddZZEBFs3rzZlsDI+2x7JEbXrdPCXWgrTdddu+vv9Gvu0915DmUexm8aJL70w1903a59TOUjZpG66cE66krVs2GmgWgR5gVurAed1r1OsVjhwX1YHZ1CN93wLu0JjrEl83bV02nt6cpgM0jKmuEEUUVFBZo1axb5HAqF0KRJE5SXl9sSGJnju23E23v57DjwuIMXOLWYfrioyqqTalJL1R17m9dNKKRB3Xd7/g5yYlF12ZytLqst8etSKC6JaUEUt2Iy7WOsFp1drXZVqLtxaSrELOuQF2/seDBkAN6NGzDWesUumd79lYyyaLPYQLRpyeRDpt5i9qc//QnNmzePfK6oqMA999yDvLy8yHdTpkxRFx0ZFrN9+WB78eIBNSON+yDSXhb1wY9VyVf8toLsWB47WpsAnok1K5rUr4SLZk3iUs3M6ne6qigrct3quAlGzq2UJo79UM89vL6zYWTVZfPmQTNVw+gsdKludryjzd/vfcte6nVf/0umbnF0qT86MJwgGjBgANasWRPzXf/+/bFu3brIZ3/1QO9hZwN43u0gyBBuMpaYSiD6vIwlm0fPQlB/RFRd3j5ff24KxCHbqWU0/coe9SHosDoTisFkKxU7bw4p68zf5Q51rR7/QhIbW6rt35c36Eg78Z0uB/0a0moCyO5SS55UcaftTnwrK0PvoFC+QwvGDtJwgmjevHk2hkFKPeB2AGHZ9EHkyc3PoWObHWVjaJq2n/XaPP1Us/VkZYvi9fiDfU4YGJ4+9zcSe6Y3xnl9O7UqUzLFwXLx8ypIVY46P+7lBlfqANdBCuoLxt2i9uYexpaXTcVNc0/5Hrzz3Tv4TRaTVJIMSvE7H1VLZLgPIiIneemi3WtNX3XlhxdR2nr3GynqUAjZdfqhS4ufFGW3bavlSLLj8garZPZpJiJi08k0d3TG2H9O7hil83X7Yjr5zXKtaBZOArdXYTpG6qqXk2q6141Y9dHWiE2JCgdFx2/HK+m9fJPlijevwCVvXJL0N6OvoReEMidyPF6HdMIEkU/Y9sYwojo2H5xMHfwUvsXM8lMHHj5Ye8mHHzo8w2zrlIfqhdI6bGVaQTpueahemKVqNUrKLLhCmdaDHY/dOiHV42INUg9j17WUj6u69pStU4MrMd1gmd+3wpoSK0P/OKlaCSqYs9o3ziVOb8a3M0zOIelcTc3T6HiUHBNEZJtsNklbN2fFE6/bT1o51CXbAN1IgEvKD3qIv6NnqoxsWh6n11PaEzFVfWu4KVUshzgaRfY03H6S0SJMB+qfktYANhSWnxPMRpfN6TIwnH8ykkTSjKFjgKT9GFF3XqLkGKdZWVlaJC12ljbJcv1k01LGjlY2urAj+eXaMUOrVjnmC0H142Neb+lmlKm3mBEZoqJVh5e2P8U7bS8tup0snO8ml2H9NLC4/pKN5slumnTvpPp2xdNzmIq+Z6xOwsuPSjjJkRPvIO3YJcXfGQYFYGtCwbbHKnVgYcGqq5N/n/Xj3hoXcuTc0sm+r7y47YvCczCDVHRSrXHVM8CdimL1HMWdsvbixuRNplsQVVRUpPxt+/btWQVDZIhX9w8G9qYCDe4suz1/A+pCdL2syH9Ccf/XgU6xZJIiVquJWN8KSqsks51Ue6E/o0wx6rQeQin+BjDiApvm6YFzNB23lfJyF2Zq47rK9PhZdEuMzMcHY4G6u149UPEtsLsD52xa5Ch7otLEhDTcddjCdILooosuQk1N4v2Fn3/+GYMGDVIRE1mgUx9EIsCaNdndNdFocWyV7GAWlJ2PKUkqhAC49zhr48b8rLCy2X1ykizWrLrtsGNDC3oFVrT8Ttx1VnncsPjyEPtpcjCJ3zdUVLoTh92yrf5s9ZaZmW2prFuaH0dnHUowOFAnDxwA2rTJfjpeyK9amb4XWmFFJ1KslF38K9ydZLp4RWXayLmVmylqI63YPFAVlTCdINqyZQuuvvrqmO+Ki4sxaNAgdO/eXVlg5F3V1UD37sA779o0Ax+dQHrhoKcjAbASwP7GBoa1qb5ocwdSlzgs4ibgDm3qr810XcylS9yOwH3x60b5viAuey5QdDzQtVIl07D+z4Swb0g/qpIXODhYVobeUGZ/GJasXAmUlbkdRT03yslPfbvYsyRe2vFE2b0SJ+cAn3bObjLJlj66H6uUZe6jeuUk0wmimTNnYsmSJbjpppsAAJs2bcLAgQPRq1cvvPrqq8oDJO967jm3I9CMwX2U0X2Z33Z5ZloziABa3oC3caVImj4BrB7/xIY39ni+BUCW5aGizyFV0q0Kr68mo1QmBJSuWo3qiUq6Ldb2bfV/W44t/iUJZh+b04UYiy3rt9dEz0PXHY2TfRAZGEZVwl7buhfH7OIa7dDaK8ufjG/f6jbzWCzsDPRtmt1kkpePgRY/MZXCwxXEYaaPA+3atcOsWbMwY8YM3HTTTTjjjDNwwgkn4OWXX0aDBnwpGgE4HMBcoKKt9UlwE3aWF8t771410/HKsqe+O2J9mqHIf2J1ORV4+hnr0yWK54tTX5UL0VPhtFyW9qIs/s1Z8Z+j/n700fCjNso0A3Be6nkn44UEd7LFUHljKdtOqmOSHZolYuqYScg4kfQP4uWTqfVlwzS9xq4EUuaHxRJ/zzRO/K9NFITeDukPDmpKJ/NU/Pz2vWiWdkmdOnXCnDlz8NJLL+Hkk0/Gyy+/jIYNG2YekWyjXXX9BbA+Q/PldLx8FyClZP0NZdHG3gPnsaYkFEWGsti40eKE/Sakfvtf/wnw+2aKJ5qCtqsnm1ZZ7s3efamWX9sV7ZJfuh2AQnHr1swjRtH7+ekvh/sv1I4XNkYj25cD2+ATAMqj34/MR8wM0e2RXxXn4Gb7clfxFjN3ZdcHkb61027Glvuw0B40qI5/DlPiPlkrQ6kx/kxCUNaSodfct2nTJumGu3//frz99tto165d5LudO3eqi44M0zGhUtXK7QicYfhAYKidsfGdj4arPCvxyxPTKNTG8wavn5JkJV0lutixKLSiZQsCq8mqdL/5bQeSDbd3uoqmq8s61ekxS7O8HLsORgNofnzUF5rtT3XZRuKFQgCuczuKWJoWVVqaVTfF7Fsjad84l2KjSf8Iuz2xNiyPfVt6srZNiTLH0mLxSOCcZVbD8iVDCaKHH37Y5jCIHKR4v7Vhg8EBNT5yRe//o0+QrwLwOIDmTgeUgcDEiV6G4azetEo2misnVAb7lUg6qsZ10jWqy0TTTnFV11VJ+MNDMpVv3TLZtb1wO1TmT38C8NcUPwa8nI0mv7JtyLE/J7vx3aZ832hggptyEG5+lS1VddyBbSW6tYeqN2rq8pZ7bx0Gk0f74ooX0bZZWwzpMsjZcNL4ee8WdDE5jpFWRY12fWEpHj8zlCC64oor7I6DfCibi0+Vr1+2W12TeGuvtTTP7muV6PX2DIAuAP7PpnnpQNc7iskkDdVARahSHQillHAR5mL9YmsIB7Gs66XZJ9VImt8HIOatW1b87W9ImSCSdPOuGybJ77ol0u0+ZnnpmBjNSNi6LtoeQ1djmaldd/ZVfIn6b+Q7r1Y8ZdIvv9O7octmXAYAqLhjf9JYVK2tbNZ7dJlsLP0JPSr2orVN8woaS28xmzVrVsL3s2fPxnvvvackKDKPVd4DjN69Uzu5rG1yaD7pJLvQ9fzj6g5638mZeXy9hOzOwDoo49uWnAkDgC+KMz2jrZBctGiR/fMIAdZ7n30B4WeUSAteu5Yy1AeRpsvk2v4xxYw1bfjqCZtKN+HH3etNjxfKWGJ6lai6TSmb7spjx71p1k1ZR0NhphNEt99+O6qrqxO+r6mpwe23364kKDJP14NekGTcdRvct+u2KneXODOftP2lJLurq+itLVbLO2mCyoU7u2JgtuUpvtehhYkGISgVX1etntLFrGu9zgvTSxGr1scon/RBlClpPnmymvkok8XjsRZnp4aG13GGytGN/YiX9l0uaqTZW8zc2F+n66Ra58NHvN/OuAwnZflKd6elL199Sz/+zW5vf/d2xjHIGNO7pO+//x5HH310wvfdu3fH2rVrlQRFlHYT1nT7thJWVo+lOXTitWKFs/MzynB5axa3VSlP2Aws37ffKg1FLZ+sn6DTOgGkgbTJGx9tA6buBdux3Jp19GsHq48a+1027RDspGvuDkDq4AwGpKoz4gahTDM1Nh9XW5bXHgTP2L8ERzaxMLqHNmL3HtWKnW90/buoJXByk8QGLMovGgNysmM6QZSXl4d169YlfL927Vrk5uYqCYrIVqq37Sz6TMjiLfdatP5QycyhUWDiRMAnz9Fk80aq3btSjBfyXz1ymy3lacM07TqRNhOqLn27ZCyLUNz/LfDzZpaw/0lTTraXg4qOfuOZXO/aXj8YbLHl58e3DfVv6cLyN1Q0T12rnjGpo/dClazrDHlUi/hXsXub1VfHG59+NmJrxgvt9yVO38QOOfNjfsFhOkE0fPhwjBs3Dj/88EPku7Vr12L8+PEYPny40uDIOK8eFCxdTGW7/are/v9ifdRs1pvKi6uadPNRNxtT0p3IiZlHEzKUk5cOB57Yzs0WqGYLpUs4dsfhdL330nZmSVY7c2VRpFXZyp7pmsgPuZo80WXb1l3dOlJSXg5u+Nom5gzQcf9ob3HquMR6E5syt1ZbA6l7YZ7x+WcasoEj1crDOxoTTCeI/v73vyM3Nxfdu3dH165d0bVrV/To0QPt2rXDAw88YEeMZIBTB8bSUrXzSpXkCMbm5zF2969j7+SV0+ouq06xkHoW168brcNShprkB8+1XksXrwcS0d/81u0IEnmiCngiSGN8tCjZSdsk17EoIlRd2Ko6P1cxnfS7S0kYgG+YSr/8qapItq1ePtu0BDU1yR7NyoYz6zJxLkGvQ+qYfrFiXl4eFi1ahDlz5uCrr75Cs2bNcOyxx2LAgAF2xEeaySsErr0MwD8zD5tNCxcvHiccf829TWWk02u603E9QaNpuZhhSx0yuV7cXo0p+WD9pn3Ux6bl0+WxMaV80pyrvI0z8zEqFHL2WG/o8SIjDKyvtQCeATBO0SwpMyOrN3L+lGYdrl8PPLQEuEnRC5GM1HFVj5gFnRevHYyya9GueecaLOmc+P2vWgC7UuaN3CnohM0kfoUnC0sE+HwssHsFcPpbaafv4+pjmukEERDuaX7o0KEYOnSo6nhId3uBfz1ubFBtj3c67wESb6qkG9SeELRdcbEMnwhkGE7n6mBIFgsgIY23U7f4qEAyVQ1lF8w602QZN250OwI9aLI6bHd8JbCvMbCgBECe2mknfZtlwPsWMqqunJYsATAoxUANgJtvjkoQOVBpNXuJmYlOqq0Jd8gc1+Gwggrqbh2XqP9aH9/8WCp78an3347h/1ek2LlYa++UZDpZZPWMrG+BAN89Fv7w8weW5xU0lvZJ8+fPx/nnn48jjjgC3bp1w/Dhw7Fw4ULVsZGubjA2WFYtiKyPqi8D5eHWwS1teTsUk6nObW2LQl8rfkzxg8N34TPiRQgAtXXUjqSt6iojCX8YGMemunL77VEfVM7D4Et2apJ06vZOurfvavRYSLYyFXd0jDrEm0BRTPsah///iZrJZaawnitNVmjaB9GBdP0IXwzg1WyjMcernVSrml8gblZkKZTNq2wdp+kZvWT/KJ2OpW0H08eBF154AUOGDEHz5s0xduxY3HDDDWjWrBkGDx6Ml156yY4YyQC/7Vy1PHG0QXxCSGB85+PUTiqyKlzcKyY89SYmkmm6vc/WorGb/ZkY03aZrNabEHAAQNf9wK9Krc/eb/tAJzfD++6zZ7pGE+krV9oz/6z5rE6ZZWjxjVRUM5W5ysSwBtm9GtO9tMLz6tbd8RmG+7W6WRpZX3686HRnmdzbydW1hLF+6mC9PZadkrXwsfvNZqlkfMTMAW4tu9NMP2J2zz334P7778dNUQ/n3njjjZgyZQr++te/4pJLLlEaIBElp3IX5caBPNv9utsX0MkSVJ8sAnCsPfM7kOIxBUEWdSEYxzlnCfCvTcD6g4H1AA5SUcYWp+G5TqDdoricdjY0OQ9FO+CMSXMHdvRmirJByKO7oAzlGNMyzsPZFrePsWYZ6oOobqALTEzYge1G2b7aTAvODB1125x2sHXqbrJtyWx8xEB9zDa9cS3hc+x8Wic79vq4rtnJdAuidevW4fzzz0/4fvjw4SgqKlISFJEXN2enHg+zYz46lHf8Yik7X8p0Mq9w4Z97Vt20klF+wq7TivfRLdT1GxRP0GLZ6NSfWLK6q0sCy+g+1Wi8W5sm+fJRw+HYZvtx9kw3ft0arXaSZFw7OTav6Pk4lCAytGhubG+aPmJGtbJcP84Vuf9XbmWNDc0NbZS+6hhfX2Za5MQPafZ6qKKq3NwIAWY6QdS5c2fMnTs34fu5c+eic+ck3aBTcOm6P7cpLisnJ5aOzZLFuBpJeLwuTflltaxO1kMbV0q6i33LJ8aJ/URmz+sV00fl4ejTlXXl5sH1n3GV1y6T0YRbo2RnVu2zCUCN6hx7pms0/OpC4Cnd64eCdRE9iZBGLYjYSbU7TLVsclCGBkSGWK9SsWM2UNWKUs1kLMpuJc75ZnqGIexZut+0BA5tbMukDcnqRpGBDSf6EblVW3V9/ls/ph8xGz9+PMaOHYvly5ejf//+CIVC+PjjjzFt2jQ88sgjdsRIRuh44NcxJh+QuP+rpstd/WjxF2ZKQ1RZTw9WOK0klK8aRcteVQWL78T0JxXFKik/WJxGHNsuFjXopBqHODCPeFHL3ShpM/c0mqiPQVdjzZaNSqr6FzJTzholiESMJSK0e6OWQpY2kSy3q/H5BgZy63zZxX1GYsuRYF80TE53EwH2raopB2UaItmc00cT7DXpD6ZP6a+77joUFBTgwQcfxKuvhrv579GjB1555RVccIGZh3pJJS2b1ur6FjON91zLBgHdPnV+vtHlHX9B9d13QM2pDgRh03qx6wIx6Tb3mj3zAoDi3sD4FHtstzf/Nm0A7Kn9YLa83Q4+iZISoLLS4sga7V/SJXudDtPR1bze3OAZy8JkL7OmE0SqaFT3jNA1XKMtylKOHzUBO1oQZXO+Z2RUpSGbzDZdvwmWb7To2lLnq2SPnMZRFZfTjxWH0pR6pkVKDFXDkwET6hJeYkvT7NS7HeudW9vHthvYWY6fsU4auHOmX2nbw9I931/+8pf45S9/qToW8hlD59QpBtIy4ZUtg3uVBUepnV7WGgALFkBp58ubNwOIO2lK+ya+LOrDnq7Wx03H6YNEjR1NgAVKFmTv3uynoc25YQho3RrAcovji/o78OUViidYy5a3X2p29uRGi8ikj5gFxIFGwEc9jA/v5OoRRfu7TIp+jPrgVB9EBgrSbKtBN3bJT2bRCveoZQD6KQvFUcr6XFTY2XVTh5u/bdmzBUc6OkdvSdZXz9fbvnZlvuHvU0uXODQ6/WSaVu4wPGz0HFRK9lY3PzJ9GnPYYYdhx47EFbR7924cdthhSoKi4HClE1Wbtm0rj2wkG8VwmahcjnTT+h2wpwJKT6yPP17dtDI5kLH5bIBpdjGvFZ3OAXpaG82WfquCzGCZNQxwgujJHsCGDI9KuMWpKl8ZldB1rA8im/flXthfbDaQHNJ1MXQs344Nqx2d3/hZN2Y9jfi3WvndMU8cgwPVB9wOIwkT68FE5T/mp2mm5xKbgNJwQ9OU6dOYH3/8EdXViTuN8vJybNq0SUlQZJ7ZKv/ee8Do0UC5ph26B2UTdmo5KzK0QMiUlFrRVl0sALBNo+OZjidmZkhIs2Xwy/mZoseE3OzTyy+rIisKC8Fo8l5Vh6teVGrgcRo3mXxaMMUXJuZhxyNmNo/o586stTpWRnEjrHRlsd+Fc7TPD0n9m/EWKW6uYIn6r3rJHiXLCQF7KlQ04TYnUysau8qgkVh99j8sc+sfH+/8TDL8iNlbb70V+XvWrFnIy8uLfK6ursbcuXPRpUsXpcGRcWYPeueeG/5/164AblEeTtZ0PYhnxcXjW6u3gH2/BBq62UFotI8TFzP+osqmJ87IDs0VTEOn4/K7AHpZHFdRh+oq9oFObkO6bpPZPOqcOKCxGelUlXXmeDmpqqQmAtfpLWZGJdse/Jw00oFu57wPPgA88JvMw6WrFm5UGberafWBHchv6NxGv/MwYLbY3dIreeVMv+6dqdADKpx/K5nbdcwphhNEI0aMABDuwOmKK66I+a1x48bo0qULHnzwQaXBkf02bnQ7Ah+xkJhOuqPJtPexsN8t/39A0U7giBQtgTJdICk/OTwWwM70g6Q7YVJ5MrVlC4Du5sfT6XzO9Vj2Rf1ttq7oeLQ9N7vRA30x5XpltEDFY72hpH8GgtW+rNyoKkpa9GXqpDqqFZUXE0QeDNkwXXdPCrsOUjOsooBmvNITQeqxduVHv8HxDs6veQOgTXWpg3Os175BusSU8aOgmT6IrIje5++v2Jd6QIPsjlcXhhNENTXhQ0bXrl2xdOlStG+v6UPmpI0yEx1VJgjIGXayC8lMux6r/TalO4nPNE07Vkf8NNMmfWysDzffDOBL+6ZPARNXjwOyK9OGlY6IVZ/uBbgLokSZkinORAHAWOJDRb+I5edETzD76alSUmJ9XI0WIyu6tdSpo11YBndi6eLOb1iDX1avTvpbCD7s7FeAkr0/OT7bgeXLHZ8nIGiSro9DE1Oy+xwpup69+tVUDGa/pIaYPo8pKipickhDVnezbt/ptvQWs2yPKRp1Up10Opla81icruGnKDxyzFZ2bjFU0XTckk0fRE5k/gJIRRHYvRnOnWvzDFxi5zWH4Ul7ZB+qAyevEQUOvRgjujWZQ8tnpBxXrlA/TdKLsnX2hKLppODfquW/EyAriTwzj5jtLMvwOIFCU9pVZT2NZH1B+ZHhBNFnn32G9957L+a75557Dl27dkWHDh1wzTXXoFzXHo8DwPT2eyKAm4Aal+u5K28xs0mmdWC4CyKDA35pstVLpke2Fi0CpkxJHue3efafLJrJCQqg7jh8n6LpuCWL9eLfk7Qs+KhQ0m0i/3jcn6+5t3U/pXsfRDrW3TQxOV1O27YBZfvTDxMSBXXI7mNlsukbKcxrjU0/2YWBj07VtGTLvtiIVCu2k7HRmUR0kj+3wrvnTXRsXrkKmvcG5REzw0U1ceJErFhRf/th5cqVuPrqqzFkyBDcfvvtePvttzF58mRbgiQbLAMwBXhsj9uBJJc2WaBoHzlbzWQMS3YgTbYoRpuBmz0wpx08BJx6KjB+PPD92sSfnzsCgOI3mVXGvYwg7RNmGu6PA3di9JXbATijKtv+HjU6hwtaFQWsbZfK+6UJYsF7wNSngAMZ3s6k5OZDKMXfbutgbPvw0427BFaWzc/lkaXsWn/4bEcpYutjGWt3/WDbtIniGU4QLV++HIMHD458nj59Ovr27YupU6fi5ptvxqOPPopXX33VliApM8udClp9U4/NnLj4Psv+WcRKskylSZJB1UZX5jFZRZOoF4DfAvsz3GFVZcAA6+OKwHfnFlmxeE6iReJN4v7vsqo2bkfgEIfL26nZWTp2KD6nd+16kheyeoh+xMyBx3jN9C1kZPPw880PS8vmQHkoa0Fkor6pOI9yrJsKz1RK+wpky54ttk07HUlyhamyFY39dcgrdUcvhhNEu3btQn5+fuTz/PnzcfbZZ0c+n3TSSdjIV2K55m4Vr5kmxy1ZamGkun2dwgyXCIAVAJ4FcL666aazY3vs5/iDRPQuPekdTV4MAcjubq+EkPbY+e23wJgxwKZNAI4zOFGvr5fCLMeXuCLwenkY9Kcv3I4gLP5CK6uXJcQx3I+bull6n0adVBtaMRZeHOG23//e7QiIrPDXnjKcNLFvmdo0tG3Syum0ZoPySJhqhhNE+fn5KCoqAgBUVFTgiy++QL9+/SK/79mzB40bN1YfIRkyvWnmYZK6UWkYztB0W8+YBU/2e7JlydSXkcU9r+G3hHW1Nn3TCmI/fhx3kyImXk3XuS6y6qQ6TX3q0wd4/HFg5EiL0zcbi0/E7AssrpsSu+v8b9VObtn/A77fnnm4aGVHqY0BcLEvjyhBeotZtjf2HW8YkGk/YyQezfZV/83PPIwVMYdgDbYrFcwuxq5dtoSRQFXxmllPqp+stdNPpc6/HUw3N7R2O4J6mVqf+2R3kYK/l66O4fOYs88+G7fffjsWLlyICRMmoHnz5jj99NMjv69YsQKHH364LUFS8KTtUFmzk7NsOP0Gl7Iy4F//Svwtvj8gR8S9/XRRv+SDJZWh5QupsW9f+P/LlpkYyez26aPtGYCyutnP7lex3qZ+kjsrYG592pBJcS1BFMCE9ouLgKavAk98nGagdJ1UO73tXwxgcIZhQkmqcPwXZm4GOVEXHlV7LuHnBKeZcpo6FWjbFti31754gs34yvDMLtXtV0PbINlbzFpueEHZ9Fv7eYfjYYZXy9/+9jc0bNgQAwcOxNSpUzF16lQ0adIk8vt//vMfDB3q9fdFE9koyV2+fQc7N3sB8Kc/AaNGJf727tvOxWGJho+YaXPCEsoiFoMjZurYlUgXdiaI0t6cCCX909cuawlUjAQeHu52JAZd4tB8nK4ADdTOM1k99+F1b0bXXFP7hwPLrkPLRyeFi9T4Qnuj+tn7iJlbkj2ilbcuyZ3mKGZKYXpB5mGyY7yeHVr8hsrJeVojowMedNBBWLhwIUpKStCiRQs0bBj7MORrr72GFi1aKA+QginrNwm5wEqHvzuTdDRdcUT2saTyzpcAZiT5wWPHNL80d3edP89n3BX0uuny8utwoRWYTcrISy40LowkL+xUz4blz+b4t3o1gIH2TV93phatIYCTADTJNKA+nF51QzTr/9Tt3Y24HoEezNTDZja3IErWAiqpHUvRvuRze4PxENOrJS8vLyE5BABt27aNaVFElI3iChsn7uOTn0w2TQQwwuUgrLDQV1NQlOwGpllt7Rty/01mlRUuPeJoI6/cbXdy1Tt10Wn4LZAmuL2N+JmbRdstyXch8XeC5NcXWRvPL0Viat1OBrAYwAk2BRNFh8S2k/ybSPHhcvl5hxjtwFa3I9AKn/wjLaU9WAZkX5VKNoefSps6s3SFD4/DlgwEXrrLxumHAPzT5PAmjR9vfhyv2FXudgTBcc891i60MlXZb74FFi60FJKjDpQB5R6qbzruwiVZx/3xdUrHwI1aYmywrw4AY3fYG4r2/uh2AOaZ6qQ64OfSqhluqeI5FpbLg2Vxywe3uh2CVpggIldZuTPrp06qLbG43007msfKVIdDjwePf+Z1BvAWgGttnMdRwGPzbZy+C6I3p8rTUw7muj8/acNEXdyX3HWXfXfiBwzIMIAG+4NFnwBdnXoLpVEalEvWNDs+ZlWkh2Ye5LWtwPFNgdeihtWsCCy77ilgjobJXmVvMVM0HfuZi9Qz9c8rzYdN8H7iy1j8K7d+rXR6XudqgujJJ5/Esccei1atWqFVq1bo168f3nvvvcjvIoKJEyeisLAQzZo1w6BBg7B6deyrj8rLyzFmzBi0b98eubm5GD58OH76ia9D9IpUyZ5gbH7WZPWaew8duwTA3r1A797A2rjOIjx/vPKKhQCGmRzHSh1bZGEcytoH96idXoM0696pTdZSgihTnb0WwE3GO6l2bf/UBtgS9FYfWVLyOKEbx1mVde7XCqelmTX3AH5+nc4PJvrvFMAz54S/bmlsOHcXR1AtNjzj7LJknVRn4pFqFeMRu98c6zGuJog6deqEe++9F59//jk+//xz/OIXv8AFF1wQSQLdf//9mDJlCh5//HEsXboUBQUFOPPMM7Fnz57INMaNG4cZM2Zg+vTp+Pjjj7F3714MGzYM1dUe7OU4YIYC2Nws+W/+28UmZyXZU1kJDBliYV7mR3Hd1P8AX/wG3uw3yQsyvZLdwN1mJXIdmo9DfHgT0RvOBqrtag0/xcT0zIegxngA692aeQoB3BbcWP+Bb1lthoatOlW1fCzdq2Y6TrAjke7mZrBu1zo0qPHQM74BYTTB1Z3dKMdwNUF0/vnn49xzz8WRRx6JI488Evfccw9atGiBTz/9FCKChx9+GHfeeScuvPBC9OzZE88++yz279+Pl156CQBQUlKCp59+Gg8++CCGDBmCE044AS+88AJWrlyJDz74wM1FIwPmAPipb/Lf2EIktU+/AOZeb368tGX6gOVwbLXkMAC3AGjjdiREpL337OmDKMILxyXbXxmslheK1HSQBls7eMnnLQF24Wof1x4x88QG6A3VS0ejxZ5v3A7DBqwkQaRNH0TV1dWYPn069u3bh379+qGoqAjFxcUYOrS+MWhOTg4GDhyIRYvCzyMsW7YMlZWVMcMUFhaiZ8+ekWGSKS8vR2lpacw/0osnd0dWbl1YGGfXBQAutDCvdBJfTKiFPRqfaHuyjibDO8/KsUjd87qddwHTrVjf7BCc5YmbQfHrPdMG3siuQMLcKLMnDgEOc3625FshhGzYabq5OzkrFzipqYsB2ERqgvJMB0VzPUG0cuVKtGjRAjk5ORg1ahRmzJiBo48+GsXFxQCA/PzY1y7l5+dHfisuLkaTJk3Qpk2blMMkM3nyZOTl5UX+de7cWfFSUbbS3QX2wvmkYVYeh8giaeK114mnOg/3VR3wG9ePKu5athz44gu3o3CRy9mxm3z2uKLneXBnzQRvcvvcDsDHPLiZaInlSAk8cRdCP66fyh911FFYvnw5Pv30U1x33XW44oor8PXX9T2Jh+I6cxCRhO/iZRpmwoQJKCkpifzbuHFjdgtBzvLR2ZuT/QYIAM/dCEi1X+f+nnQ1DpjJJIUvGe2kmojIDDeuYd2+brbS+TE5z+vryftvYXOH6wmiJk2a4IgjjkCfPn0wefJkHHfccXjkkUdQUBB+kD6+JdDWrVsjrYoKCgpQUVGBXbt2pRwmmZycnMib0+r+kV5s3Zxtmril6wMrI1l9zb3H3mIGsLNf22XqpJqsudztAFyUoj754hzN4DL4YllV8eBjeUbWn5s9EyQLT9OiJIOCuP54eucVQayd6QSjPFxPEMUTEZSXl6Nr164oKCjAnDlzIr9VVFRg/vz56N+/PwCgd+/eaNy4ccwwW7ZswapVqyLDkDelbejip23TwWURADjEufllS6DolcM28c1FoC5nabrEQYHDRLQ7bjoB+GU3t6NIIr4+xO/rGwN5m50KhoJA1fmEmcn45RQmGnflevBj3Qoam7vSS++OO+7AOeecg86dO2PPnj2YPn065s2bh/fffx+hUAjjxo3DpEmT0K1bN3Tr1g2TJk1C8+bNcckllwAA8vLycPXVV2P8+PFo164d2rZti1tuuQW9evXCECvvASdt+ObiOxMnHzETeOrtKl5s8eRF2hSxph2lk/8ZffI27SNmkvRP8oNk672741EQZfRsH7cjcJc25zM+YuURLTs6ILdOp1i8w9UE0c8//4zLL78cW7ZsQV5eHo499li8//77OPPMMwEAt956K8rKynD99ddj165d6Nu3L2bPno2WLeuvch966CE0atQIF110EcrKyjB48GBMmzYNDRvyasPL0m3Oum7qjsWlawHYIFULIuGjUUpUdQWq3A6ijqtHIwqy1wcYGy7uafZYvDKhZByqFzwcepuq9bc/R9GEHMA6S6QvV0/Jn3766bS/h0IhTJw4ERMnTkw5TNOmTfHYY4/hscceUxwd+RaPSt6R4uRahxZmnzR3OwKi9JzOWYSQupWNBpts1rz2Fkgt+GHFE9ksiJuJHcenFtp1nOJ9IR1OuLPg9U623cJNibSU7jX3utL9BrIHizR9/6YuL9DdHdydvxlOvi2P9OH0JuLFfQzZzAf7Ht3e/pns2OeDYg40185nWHEog9cW/cntEMgFTBCRlmw9Vmp0QOSFe3osHiJ/cDuhq8RpxgbzxbJSxIGeWYzsUF1glfM2N9bf1q3u7qv4+nFv+MOuF9wOQTPBqLdMEJGWgrH5wfm3mPkI3zxknM5vgyP7OL3e13wLVCV5DOuyy4ARFzgbiy1ap/mN25hvKD202HCcYlUjFfqdBqCt21EQ2Y17TCuYICItpXvEzE+buqVzR4sFMG+7tfHcku4lZgLepSfKxOkWipdXAmiR+P2LLzobhytCKf4mz1F5bOFhioxwo54U/9WFmcbg1kH2U99SLRj1lgki0pKtm59G23bIwb4Nbuvl3LxUSZkg0mgdesH7h7sdAQXCUAAFbgdBWvHivlrzBN8//pH4nReLmeq5ck5zmQvzjBJiM3AibTFBRN6j6THFqbBqNGsSfO8DLs1Y03qgo1Ue6lCb1Fkx0O0IovBsI5gCvp+2Y/Ef8eDNHkoviAk+9kFEnhSQastTNtJSuuNG0I8pkut2BLEmjLFv2jq/xYyIDHoFwCq3g7BZwPZH29wOwCMOHGnDREfYME1yVcB2H0SOYR+c1jBBRFpK13fGl184FwcZkOPCPEOBvzFN5B0XuR2As4KQvDbaKLG62tYwlPPqxUQQ6hz5B8/fyCmiOP0alLrLBBFpydaTHZumzfMz9dI+oh6UvTQR6Y/7o+Ty3A7AHKc7dldl6ZduR0DZSPdiFiLSh+qEk66YICItefFgKQ52OB0U6Tqp5h1TItKRlV3TsmLlYejBYwkiz7rZ7QCIjOPpG5HemCAiLXnx4PF8lYWRPHq30gmC9M39WXREpIuKCmD3bmDSJODDD82P36cA2F+pPCwyyauPmJG3BfOGVyAXOhA2lW5yO4SIjzcsdDsET2rkdgBEydh62LArszDYpulSUjy1ICJdfLoIaHNW7YcuAK4wP43t+8EWN0QBtJC9vhPZ4vqZo3HhYW5H4T1sQURaSvu0lo+ajmw9ye0I9CWSug8iJoeISCt1+6qDAWj2pkki0tsLvMFIZIuRLd2OwJuYICItpe2DqFuWE2d2wRd8lCckIj/oAuAnAKusjX6AOzXXlXTnKQKR3ULcynytXUO3I6j3yEFqpxeUwzQfMSMtpT10DHMqCnLTu5uAUOPkv4kgOHtpItLf3QBuzW4SY5opiYSyUNUC+L6721EQ+duAilWo2vKN22GQTc5o7nYE9uFbzIhcxBeC0YuD2WEokdctcTsAJ2WZ4JmdIiFOztpW4HYERP7XCNVuh0BEKTBBRFoK5hsdKEGKVkKvvQb8+KOjkRCRBUvK3Y6AiIiIiIxigoi0ZGt+iMknz0jVSfX77wN/+bOzsRCReUsC1YSIiIiIfEuC8YwLE0SkJVs3vyZ2TpxUYjdDRB7HhDwRERH5ARNERO6x9RGzpuon+U/1kySk6YPoZbCzciIiIiIicoRIMPrOYoKItOS1/Ox1bgcQNIcB+D+3gyCiTCo1et0tERERkVUhtiAicg+fSiAgdR9EROQN0091OwIiIiKi7LEFEZGLapghIiIiIiIiIi2wBRGRa5gfIoCdVBMRERERkfvymrR0OwRHMEFEWgpGfpYyYYKIiIiIiIjcVhMKRseKTBCRltiCiAAmiIiIiIiIyH37GrZyOwRHMEFEWmKCiIiIiIiIiPQQjCtUJohIS3zEjIiIiIiIiMg5TBCRliQYCVrKoAGfMSMiIiIiItcF4wKVCSLS0vZgPOJJGTA/RERE5FHVbgdARERmMUFEWpp/otsRkA6EGSIiIiJvKnc7ACIihQLyiAsTRESkrRomiIiIiLzpgNsBEBGpE4z0EBNERKQxtk4nIiLyplCV2xEQEakTCkiKiAkiCqSlzDx4QlUw9sNERERERKSxoFyWMEFEgXRyQ7cjICMqa9yOgIiIiIiIgo4tiIiIXMYEERERERERuU3YSTURkbs6NXI7AiIiIiIiCrqgvDuHCSIi0lbnZm5HQERERJYE5WqKiMhHmCAiIiIiIiK1gvE0BhEFRjB2akwQEZG2AvKoLxERke9IB7cjICIis5ggIiIiIiIiIiJKJSB3rpkgIiJtBWM3TERERERE5D4miIiIiIiIiIiIUpCA3LpmgoiIiIiIiIiIKOCYICIibQXkUV8iIiIiItJZQC5MmCAiIiIiIiIiIgo4JoiISFtlB9yOgIiIiIiIiC2IiIhc9dddbkdAREREREQUDEwQEZG+znc7ACIiIiIiIrYgIiIiIiIiIiKiAGCCiIiIiIiIiIgoJbYgIiIiIiIiIiIKOCaIiIiIiIiIiIgoAJggIiIiIiIiIiJKRdiCiIiIiIiIiIiIAoAJIiIiIiIiIiKilNiCiIiIiIiIiIgo0EJuB+AQJoiIiIiIiIiIiFIQ9kFkv8mTJ+Okk05Cy5Yt0aFDB4wYMQJr1qyJGUZEMHHiRBQWFqJZs2YYNGgQVq9eHTNMeXk5xowZg/bt2yM3NxfDhw/HTz/95OSiEBEREREREZEPsQWRA+bPn4/Ro0fj008/xZw5c1BVVYWhQ4di3759kWHuv/9+TJkyBY8//jiWLl2KgoICnHnmmdizZ09kmHHjxmHGjBmYPn06Pv74Y+zduxfDhg1DdXW1G4tFRERERERERD4hAemDKCQatZXatm0bOnTogPnz52PAgAEQERQWFmLcuHG47bbbAIRbC+Xn5+O+++7Dtddei5KSEhx00EF4/vnnMXLkSADA5s2b0blzZ8ycORNnnXVWxvmWlpYiLy8PJSUlaNWqla3LaJegZDSJiIiIiMg+8hKvLIjizWsxEIOGz3M7DMuM5jy06oOopKQEANC2bVsAQFFREYqLizF06NDIMDk5ORg4cCAWLVoEAFi2bBkqKytjhiksLETPnj0jw8QrLy9HaWlpzD8iIiIiIiIioqDSJkEkIrj55ptx2mmnoWfPngCA4uJiAEB+fn7MsPn5+ZHfiouL0aRJE7Rp0yblMPEmT56MvLy8yL/OnTurXhwiIiIiIiIi8gVtHryylTYJohtuuAErVqzAyy+/nPBbKBTbzFFEEr6Ll26YCRMmoKSkJPJv48aN1gMnIiIiIiIiIvI4LRJEY8aMwVtvvYWPPvoInTp1inxfUFAAAAktgbZu3RppVVRQUICKigrs2rUr5TDxcnJy0KpVq5h/RERERERERESJ2ILIdiKCG264AW+88QY+/PBDdO3aNeb3rl27oqCgAHPmzIl8V1FRgfnz56N///4AgN69e6Nx48Yxw2zZsgWrVq2KDENEREREREREZEVQum5v5ObMR48ejZdeegn/+9//0LJly0hLoby8PDRr1gyhUAjjxo3DpEmT0K1bN3Tr1g2TJk1C8+bNcckll0SGvfrqqzF+/Hi0a9cObdu2xS233IJevXphyJAhbi4eEREREREREXlcUF5z72qC6MknnwQADBo0KOb7Z555BldeeSUA4NZbb0VZWRmuv/567Nq1C3379sXs2bPRsmXLyPAPPfQQGjVqhIsuughlZWUYPHgwpk2bhoYNGzq1KERERERERETkRxKMBFFIJCBLmkZpaSny8vJQUlLi2f6IgtLkjYiIiIiI7CMv8cqCKN683FMx6IKP3Q7DMqM5Dy06qSYiIiIiIiIi0lMw2tUwQUREREREREREFHBMEBERERERERERpRKMBkRMEBERERERERERBR0TREREREREREREKQWjCRETREREREREREREKTFBREREREREREREAcAEERERERERERFRSmxBREREREREREREAcAEERERERERERFRKsIWREREREREREREFABMEBERERERERERpRBiH0RERERERERERBQETBAREREREREREaXEFkRERERERERERMHGTqqJiIiIiIiIiCgImCAiIiIiIiIiIkqJLYiIiIiIiIiIiCgAmCAiIiIiIiIiIkqJLYiIiIiIiIiIiCgAmCAiIiIiIiIiIkqFbzEjIiIiIiIiIqIgYIKIiIiIiIiIiCjgmCAiIiIiIiIiIkqJj5gREREREREREVEAMEFERERERERERJQSWxAREREREREREVEAMEFERERERERERJRCiK+5JyIiIiIiIiIKtmCkh5ggIiIiIiIiIiJKKRSQFBETREREREREREREKQQjPcQEERERERERERFRSmxBREREREREREQUeEwQERERERERERFRADBBRERERERERESUSjAaEDFBRERERERERESUWjAyREwQEREREREREREFHBNEREREREREREQpsQUREREREREREVGghdwOwCFMEBERERERERERpRCM9kNMEBERERERERERpRQKSIqICSIiIiIiIiIiolSECSIiIiIiIiIiIgoAJoiIiIiIiIiIiAKOCSIiIiIiIiIiopT4iBkRERERERERUaDxNfdERERERERERAEXjPZDTBAREREREREREaXE19wTEREREREREQVcMNJDTBAREREREREREaXEFkRERERERERERAFXI8HoppoJIiIiIiIiIiKiFGa1v8jtEBzBBBERERERERERUQr3DL7H7RAcwQQREREREREREVHAMUFERERERERERBRwTBAREREREREREQUcE0RERERERERERAHHBBERERERERERUcC5miBasGABzj//fBQWFiIUCuHNN9+M+V1EMHHiRBQWFqJZs2YYNGgQVq9eHTNMeXk5xowZg/bt2yM3NxfDhw/HTz/95OBSEBERERERERF5m6sJon379uG4447D448/nvT3+++/H1OmTMHjjz+OpUuXoqCgAGeeeSb27NkTGWbcuHGYMWMGpk+fjo8//hh79+7FsGHDUF1d7dRiEBERERERERF5WkhExO0gACAUCmHGjBkYMWIEgHDrocLCQowbNw633XYbgHBrofz8fNx333249tprUVJSgoMOOgjPP/88Ro4cCQDYvHkzOnfujJkzZ+Kss84yNO/S0lLk5eWhpKQErVq1smX57BZyOwAiIiIiIvI8eYlXFkQJLtEibWKZ0ZyHtn0QFRUVobi4GEOHDo18l5OTg4EDB2LRokUAgGXLlqGysjJmmMLCQvTs2TMyTDLl5eUoLS2N+UdEREREREREFFTaJoiKi4sBAPn5+THf5+fnR34rLi5GkyZN0KZNm5TDJDN58mTk5eVF/nXu3Flx9ERERERERERE3qFtgqhOKBTbxFFEEr6Ll2mYCRMmoKSkJPJv48aNSmIlIiIiIiIiIvIibRNEBQUFAJDQEmjr1q2RVkUFBQWoqKjArl27Ug6TTE5ODlq1ahXzj4iIiIiIiIgoqLRNEHXt2hUFBQWYM2dO5LuKigrMnz8f/fv3BwD07t0bjRs3jhlmy5YtWLVqVWQYIiIiIiIiIiJKr5GbM9+7dy/Wrl0b+VxUVITly5ejbdu2OOSQQzBu3DhMmjQJ3bp1Q7du3TBp0iQ0b94cl1xyCQAgLy8PV199NcaPH4927dqhbdu2uOWWW9CrVy8MGTLErcUiIiIiIiIiIvIUVxNEn3/+Oc4444zI55tvvhkAcMUVV2DatGm49dZbUVZWhuuvvx67du1C3759MXv2bLRs2TIyzkMPPYRGjRrhoosuQllZGQYPHoxp06ahYcOGji8PEREREREREZEXhURE3A7CbaWlpcjLy0NJSYln+yNK3203ERERERFRZvISryyIElzi7bSJ0ZyHtn0QERERERERERGRM5ggIiIiIiIiIiIKOCaIiIiIiIiIiIgCjgkiIiIiIiIiIqKAY4KIiIiIiIiIiCjgmCAiIiIiIiIiIgo4JoiIiIiIiIiIiAKOCSIiIiIiIiIiooBjgoiIiIiIiIiIKOCYICIiIiIiIiIiCjgmiIiINHPuAbcjICIiIiKioGGCiIhIM+82dTsCIiIiIiIKGiaIiIiIiIiIiIgCjgkiIiIiIiIiIqKAY4KIiIiIiIiIiCjgmCAiIiIiIiIiIgo4JoiIiIiIiIiIiAKOCSIiIiIiIiIiooBjgoiIiIiIiIiIKOCYICIiIiIiIiIiCjgmiIiIiIiIiIiIAo4JIiIiIiIiIiKigGOCiIiIiIiIiIgo4JggIiIiIiIiIiIKOCaIiIiIiIiIiIgCjgkiIiIiIiIiIqKAY4KIiIiIiIiIiCjgmCAiIiIiIiIiIgo4JoiIiIiIiIiIiAKOCSIiIiKyLFTidgREREREpAITRERERGSduB0AEREREanABBERERERERERUcAxQUREREREREREFHBMEBERERERERERBRwTRERERERE5Eu5+9yOgIjIO5ggIiIiIiIiXxr4udsREBF5BxNERERERERElJUdoZZuh0BEWWKCiIiIiIjIgoWbgBYr3Y6C0hK3AwiOlY27uh0CEWWJCSIiIiIiIgsauh0AZRZyO4DgCDEbR+R5TBAREREREVkgANq1dzsKIiIiNZggIiIiIiKyqKDA7QgoLTZqISIyjAkiIiIiE/LXuh0BEekiBGAAH2EiAgBUMxtnWnnTTm6HQBSDCSIiIiIT2mx2OwIi0oUI8Ge3gyDSRE1Ndcznz4XPX2byU/f/czsEohhMEBEREZmwe6fbERCRTpq5HQCRLqQ68zAUo6pxntshEMVggoiIiMiE4p/djkC9g/a7HQGRN/GBGvKj8we8ZWm8BlIV85nbh7+sb9Au8rc0yHExErITE0TkmEbFzs9zNIAxAJquc37eRJTe+nK3I7DoV24HoN71q6yPXm4kjQAAKS1JREFU26iRujiIguCKfW5HkF6Z2wGkMHaF2xEExzudzrc0XnyCiPxlQ4O2kb9Dp77kYiRkJyaIyJR2B6yP22CvujgMza8SeBzAowBaM8lNpJ3Wbgdglc+6VOi9BWjR0vr4zZuri0W1Xy93OwJ9tdrodgT+d9zK5N+Pz3U2DrOauh1ACq0tnoO2a5d5GFKjYVyCKC+Hj09lEgrp0cv97sOuNTdCh0G2xEHuY4KITMnJ4qyhRQt1cRhx8Ef1f3fs6Oy8ybsaZ5EEVal9ANpl5zBxq4VB64Hu3a2Pn6/xmUSNwzcmvKQpb/Qr0aFD6t8apDjvOcyeUHyvqcXOnnr2VBsHpdYtL/aNXDVSY2r8vak2GrLdns6XGBvwl8XA8HVATtvMw5InaXxaRzp6PItx2x+kLIyUnov6u12r+r+7ZlnTT8tu9AQztyueoAayKeIWGiVDfquwf8VGcY9QPWCi75rFetxQslUDALo/ndTVhmm2s3Bh3iBDvTwJ1p98CwmQzQ3MN6yPaomZBlx90lyJNzd33eK4Y9baO/12W4HfpPjN7FPZv3D4Me67AFzs7CxTatIk/P97ktxcOLIo+ThONyA6aBtwvclxDrYlkuwUmmwJdCiAfwA4RsHx9Oks9xcTtwCLAKwEMD37cFJq7nLit+D4u2M+t21RaHzkAW9hSYu+aQepOfiC+g+N/JJM8tgJX7N8oIUdZ0fG7Kgxdua494gxNkfiX0wQ+cRfTB64ugEoiPtuBICTU0zn6+1ACYBfGpx+su6G/u5AEuAiAH/9ESjcCLzUq/77JwCMTDFOujdWtwZwHYB3AHRP01/KsZtMhYn8SqBhivJonKacDgJwL4C9CJ9kpHKXuXAAAJ2yuBj52wZgV5rfp2QY/3EXj42tAZwZ9fmmXOCZPamHLwGwNe67nBTr7Kq4E7UTDLROuqz2/0cgfBJ5NoBUp0t5BrepYwwMd2bmQZSaDqAxwvuKN0vUTLNBmvUW7SQBem4F8gycSK8DsAXALwxMt/1XQDMDCcaHt5ov79+kuNAEgO8AfArgZQA3mJwuABx5FDAk7ru/1v7/bgD/BTAxzfjHAIhvpPm/FMcSFadrywA0NZjIvaAQOLf27/FR379YDhQ3AJ43ue/OlpmL9O4/A/EvzMv/1tp8L0vSqUzPw4FkPUg02Wo+MfpM1/B6ccpfEa7v2wG8YnCc+wDcDKD9tvrvTs4wzu0G9p2H1P5/dFOgf9T3g2qAA1me9zQ6ADxaBdxWCTwA4GqL0zmnTThREq1/0iHr/Qhg9Or0wyS772c0h5Op7AHg/0XtR24C0M9ga/DDvwd+RngZrkd4H3BB7bq4zOI6OTbNFVP8Oc65WxKHOb4j0A9AT4TPR6eUWovjr9XArDS/z40/QTHo7syDGHPwuTEfDzo4/uiSwv/bBXQ6H6d0PD7y1ZpWp8QMUt3uFDToFfVK+Iv2AL/OfAKxoMVpkINOj3xef1jskajm6DuSj9jveeC8rxO+XtP+XOBw41vjvsNHGR42K78sBo5JXBbJOyb1OC27hf9/RrpaFTM183GZcUiqq7V626SxoUm16Hk7xMgb4tqcAAz9FMjtYmi6QcAEkU/c0iCcHJmc5DcBEN/VwBqEL3xeQPji858AZgB4vrZGxF+v7ysH6hrkfJchlv8cAPIBIOrAvhfAL+JO6AdY7KTxdwCaRT02EH3XPAfAXV2ATZ2BHlG36PIRviAtBXBBRf337+8NX9hsAnArwhfjfwRweg2wAOGTzycA5AF4OyphvVOAhlEn3AOiDvTNojt7XQ/8HeGLt2i7yoB3UyRFfhv1/S+jynA0gC8A3Ibw3cf4FtOtvwkv32bUX9zVKRDg0mJgTYoL4oYA2hu4UOqE8En2rwFcHvV957319SOZCzNMt1eS7wRAqhcrxc+r1/wMM4jzPoCi2nnsAjAbQDXCF2PHAPhF3MVtXWL00Np5H1Q7/HiEW0/MTLEuJ+UCJVFl3rp18uFafxz+f2gzMC3q+5EA3kNi/QGADwBsDAEnApgEYB+AsfPqfz8F4TrdCMC/Q+F4k3kOQAXCZdBvaf33TTNciH6S/mcAwHE7gRcRrpfx6k4B2gE4Jqq8H0+XaUwmasEaRrXMm4zwxUEyb4WAlR2A+VHbdMEm4K5i4Mqo7aDu4rwA4fKpk/NN/d+3Rn9fAWxvCLydZJ5n/FD/d5uy8PQOS3NFPWJ5eJ3WOefw2N//A+BPAJ5COOHfAOGEW/y2DwDdkyRrzov6u0Pb8L7zBwCDEd733YnwBdZEhPexdwP4V5rzwnlRfz9dBZyV4uziUYT3N/HiJz1zfeIwfwXwKsIX5K/HJcx+uQI47VtgfE14//QDgNUAeiC8jS5GeN/1QA0wpgr4TQ7QEkDHqO3z5JrY/Vq8vwD4d9TnPyB8mPsQwNQ040W7KMX31yY5sJ5zEtAGwD211z9nVQOHGzxri958/wvgP82AYXHDdEzSFKvjj8DdcYnWdE8f3v4dUAngkNp9kdUWga33pt+npOoutx3CZXpnkt/6fxdOIp2L8LHzVgAPAhjdpn6YdIeOswFMDoXr9mlfJR+mH+rPl/IQXoZTaz+PbgD0PCpxnIO3JX4XLboldPfGwJhGwL2Nw8ebfwMoS7EdLhDgKyQvi7a1K+aGDfXfzUc4+Z2qK6pGADpkeLvhQoT3E9Gi1+Mf0uwzpgFoAWAQwsfYZE+PTWsQPof6HuEkTKc0N5MOi0qMvHMoEP3kXwjAm7XHwecN3pCK742lT5phb4r73KkjsAPh9fFi7e/xdfimVkBO3L65TVxG+JcARu0I/92uKjytOxoCQ5H6Mj03qk+43ibOjSYaHzTRCX8HLtoLXFwZ+33HcxDqfiOQPzj1uAPfBn5TAzRpDQBo3qJL5KdDzngdGLEJOO4e4KR/ouHQT4C2JwL9XwbOXBQeqHGKs8/T/hv5s89Rv0EoktAJoXWn2L1hg6NvAU6Pawt7iQBdLwPyeiRM+siDegF9/w2c8EDSWX8m7YHBH0Y+5x55LZYd/PvI5/LGUY9mDf4QOQ0M7Ni7XReOqc6Zi/BJqHPkY1njduGWPd3HA82i2v8NX4fQeauAiysxtyK2s8GFjY4Azv8uPN2OQ9G6WeuY338MtcbPTQ/Djsb5KcOShqk7IawMJU/kVAuAsz8HDvl1ONF20OlA+/7Ar/cAp6VoX3f8fcBprwEXbECTRmmeNW17Uvj/v5gLNC9E6Ffb8HmLUxKHO2c5MLIsvOznfAG07wsMmhk7zP8ze0LqI0JSUlIiAKSkpMTtUJR4WkROEpF3RGRz1PcficgfRCTTUm4WkQMi0uN5EYgIykS2bo8dZraEf2ta+//of3X6/hT+XFg7bllN/TCjikWK4sY7XETOqYn97rFv6/9uvkPk+tXhaf1+X/335SKyVESqMyxXne1R099hcBwRkfVR4+0Xkb98Wf/5u10i2C6CJSJHFdd//+KM+vH/F7Vs324Lf/fEKpGxn9Z//+9NIntF5LnaOLeLyLUi8mmKmKLL6tRdsb/9VUQaiMgdtdOp06EkPPyGKpF++0VuOCCyT0R+Ny9xXUJExorIyyLykoj8HDWd/XXD7BDZtjP83YcSrn9DZ9ePf39N+LeCosRpfyMi7ydZluh69FaxyOnfxP52QEQ6flL/+dJd9X+HykUKF4rMEZELk0z3pOdSFGaU3ZX1w59VJVIlIutqlzmT9SLyexFZFV9OIrJWRNqUJ8a08ieRfq+LfPRd6ul+IyIvikhN7b9kijaI4CWRRrfXf1cZ9XunuPn+X1ns+L/7rv63FpsT4xwm4f1HedS0v5fE4f6wXOT0BSLVcYGOq1veuLi3RW0bm2pExkdNq6eIlInI80nmgx9Enlpd//noqJjr1H3+tYjcKSLz4+b9jYjsjvr8Q+3wh1dIgmvmijT4XuTFFeFl/1zC+526eRy3InG+df/+/r0IPhDBCpEttdv/j7tFDlsh8uC3Itd+JdJqrcjRu0XalIpsqZ3/exLehqtFZKOIXC/hfXk65SIyQ8L7gGNE5CcJl/mze+rj2SMieSLSQ0SSLGpSLyRZruiyXivh/USNhLeZut9LReQJEfmkdrgvRWSkiFy1M3Ya0dMsKhXJrar/PCBuB7+1VASl9b/P3WVwIeLMLoraDg+Ev/uxTOTf68NlfmzUvqCidrmeF5GZknjM+VJElkli+dy3UeRHCf8TEekvIv0kXBfrhvlP1DbwgoSP1dHbbt2mdF917LT/WJ16nXwqIlFVUkREjp5QP8yztd9NE5FWkrht3CoivSV2Hxb/7/O4cVaKyDUi8nrt7zdWhb/fJ+F98kci0ua5JNP5NjzcZVWJvz2yW2SXiBxcFbt8yWwVkZOniTR/TWRnipOdahG5RET+Vfs51bJF777u+Ln++9+LyGcicouIbEoy/f0i8lXt+PtE5JS9IigLj3v5e+Hjl4jImiqRRjtFjnk1vHyhGpGjS8O/9aqd170plrN37XnGGAkf5/8Z93v0cpwu9ec6r0rqMky2Tf93X2K5zJXwce7nJOO+XFtoN4jIoRLet8aM/0X4/yf+NzxcVdQ0qiV83Kwb9ogfJanNInLtz7HTzVsisrMsvF1uTT5ajN73hcdrfF/42P4HCW8HddN7RcLrL75MvhGRIRI+10m2vdV9/reBGEREhsdN55WoOv5/IrJTwvuB9SnGT1ZvN0f93XdheN948rrE4QqKRfrELcPBW8N/t6vdN8uLEHkRUvPB4Mjf8f9KXmqWGNi2z0QWXymyP+oqZNcqkW8fkbUb5sZOoybuRKHqgMhno0Q2/s9gKUry2EREtn4ssuoekeoqkZpqkQ2vi+zdILJrZdT8o/bkB7aLLLpCpPij2Omve17kjcL6cTbPrv9t9zciCy6Urf/tJEUv5smnz7WSn4qXhn/79A8iy24WEZHNGz+IjW3/lnBcIrJ769KU5TvzKYhsmllfTp9cJjL7VJHqKvngs7+JvAgpfwEiZdFbpISHr66K+erbTZ/JE9MOiUx789LbYsfZsy5m3tV7N4bLp7oq8t2i986PHefzceHf5g4RObBd/v1Uc5EXIS/PHS01bx5aP73FV0p10YsyZ8E4+eGn+KNN6vX52bpZIvti97SV7xxbP8y+jVL1ck7956ryhOF/+vJvkd/ffqaVvPby0SlnvW/OkNj1tPsbkTkDYr/zMKM5D+8vqQJ+SxCpUlUlMvUZkRlvJv/9gIQv3OIPOnV2i8jDFSLFtZ9rROTMapH+1fUn1qUSPhnuLeGT5woR+SJqWutqwge7jXHzrjuxPtjisl0l4ZM8M2pE5CIR+V3t5zXVscv83QaRTZvDJxBHHRB5qSpxGq98LfKPLxK/r7tw32cyphelPoZfJfk9SQiyU5KfQFVUibSqveAaWC1yhYhcl2H+27aJrN+Q/LdXJHyhUHeRM6iiPtb/SWLSa6OIvC0ioyV8khcvclJTe4zcsDuqnkT9HpfzkLUSvtBZtEHk5v+I7NqdYaFq/WWPyJ/2GBs2negEwn4JX6z/S0SmSuI2o8LGjSKlpcl/OyCxCdL4VfePqN+m1iYSc6rD34+W2AuCaHXjjKoJJzTMir4ArSvytRK++Im+RigTkc61CbbcbSLPvxou35NE5EwJ16Ez94rMiar4X0k44WQmGbxNUidN4s9lRUQO3RuO6e3y+u/i94vvisiKlSKLUmV766YvqROA2aoSkSEVImMqMw6aVIWInCvhi9Zza7fnc9Nkl74TkdVppve1xG4DZ9X+/URR+HORhJN690pskrvOt1tEztsuMqDUepmti7oAPpDkLkPdzZArTUwzJLHr/rMUw9WIyAMSvplTIeFj0ksZpl0uIneLyGIJHy8qRORICZfdTxLeDj5JNbKI/PyzyB+mivx1Z2yCK9MNlrplOblK5HYJJwmS7aejxe+Lo2OILp8ffoj9PfpmzMjK+m1xi4i0rBG5LMN8RcLnL0a9Ujuvl0Tk3iUieFrkk7jM10cSW1fN2rlT5O056ePaL/XrYZeE96WpNtW6hLGkGObU2u3zyrjtc5uEv++aZJy+tfuxi/fWf7dHRJrWiHSpTWD2SbGhJTum1A36zx3h354Wkfc/Ehl2m8j2ZBt03LQeTbOfik5Aj0s9WErbtomM+7PImjWJ0432sYgcU5M8KX9yZWKd+FrCx3aj1W+HiEyQ+unUSHhfZ3R/tlpE2laJXLs/fJy7ofb7uundELURfiAig6N+G1Ut8q2EE4xjaofZICJj94dv/OSKyNy5Q6TmlRYiFSUilXvDyY6PfxNzAV81Z5DBaKN8/WA48fTOMebHTWL34utiYir/9JrMI31+o8jq+8zNaN9GkS1zLMUoIrLl+xek5OfkJwEfzLlMXv1wjEh1pcj+YindvlyGPTdInlr6r6TDi4jU1NTI4rVvy859aTaoZONtXyYVK+8RqY7bQVTury/HDwbH/PTDt9Pk83eHSGVF3IlxVXk4gVX7/ZbSzfLGV9OksrpSZOdX4WRW8TxT8cn+LbL/h+dk7fpZyX//7olwjO+fEv782bXhz7NPSz58daWUfPxbObDuRalJdhIXbd3z4Wm92jpmfFk1WWT7EnPLoSGjOY+QiGjUPaw7SktLkZeXh5KSErRqle5BGUpmB8JNeWsQ7ldjdpph6ypbpha+K2unOyjNMKsRfuTHzS7qFiK87ElakZtSWfvPytui/4nwYxvvo74vBKuqEV6mk6C+E83Pa6f7RwD3Wxh/KcKP1DyA+sfrBOHHo3IQfhyoBnq+gXw3wmUb3TdDDYBnEX4ULLEBs73WI/xo3fFx35cj3GntUACjEH78oACZt9dVAD4DcJWBYVP5BOEyOT3DcBsQ7ix/DIC6xtVG9yt22YvwI7vHRn33FYC3EN4fNgcwE8kfrfKqPQDeRfhRtZYZhk3nG4QfAW4L99bjIyVA8xDwhxSH/50IP+plNK49CD8eeBeAnxBe93Y+z1+DcGx2ltsyAM8A+DOM9zGTztsI97f2HIALkvxeDqAJEpepovZ7N7yC8L762EwDaqAE4ceQz0Xio1slCO+T4h/+KEF4fzUsbpzy2mEPAGiK5HW5BOHj3KFZxg2EHw9diPDjnun2md8i/PjojXC+028gvO86B+HzEqt9Q9VZh3C97pRpQIMuR7gLiSIAXeJ+WwPgdQBjET5/TrtNiQBSBTSIqy1f3QmsngQ0aAIMLwKam+iIGgBqqoHiD4D2JwNN2mQePhMRYO8PQNMCYNtCIP8XQEO+JtWSsi1ATQXQ/JDs3l5hJ6kBtn8GtDkWaJQLVJUBm98BCs6MPKpofdoSrpttjgOapnlFpUcZzXkwQQQmiFRZg/CrW411HUZBVHeCSUREwVYDdoRJZIfom2e2qdilJrlDRI4xmvPQ/Q3D5CHZtqIh/2NyiIiIACaHiOwSgs3JIYDJISIf4/GZiIiIiIiIiCjgmCAiIiIiIiIiIgo43ySInnjiCXTt2hVNmzZF7969sXDhQrdDIiIiIiIiIiLyBF8kiF555RWMGzcOd955J7788kucfvrpOOecc7Bhwwa3QyMiIiIiIiIi0p4v3mLWt29fnHjiiXjyyScj3/Xo0QMjRozA5MmTE4YvLy9HeXl55HNpaSk6d+7Mt5gRERERERERka8YfYuZ51sQVVRUYNmyZRg6dGjM90OHDsWiRYuSjjN58mTk5eVF/nXu3NmJUImIiIiIiIiItOT5BNH27dtRXV2N/Pz8mO/z8/NRXFycdJwJEyagpKQk8m/jxo1OhEpEREREREREpKVGbgegSigUivksIgnf1cnJyUFOTo4TYRERERERERERac/zLYjat2+Phg0bJrQW2rp1a0KrIiIiIiIiIiIiSuT5BFGTJk3Qu3dvzJkzJ+b7OXPmoH///i5FRURERERERETkHb54xOzmm2/G5Zdfjj59+qBfv3546qmnsGHDBowaNcrt0IiIiIiIiIiItOeLBNHIkSOxY8cO/OUvf8GWLVvQs2dPzJw5E4ceeqjboRERERERERERaS8kIuJ2EG4rLS1FXl4eSkpK0KpVK7fDISIiIiIiIiJSwmjOw/N9EBERERERERERUXaYICIiIiIiIiIiCjgmiIiIiIiIiIiIAo4JIiIiIiIiIiKigGOCiIiIiIiIiIgo4JggIiIiIiIiIiIKOCaIiIiIiIiIiIgCjgkiIiIiIiIiIqKAa+R2ADoQEQBAaWmpy5EQEREREREREalTl+uoy32kwgQRgD179gAAOnfu7HIkRERERERERETq7dmzB3l5eSl/D0mmFFIA1NTUYPPmzWjZsiVCoZDb4VhSWlqKzp07Y+PGjWjVqpXb4ZDmWF/IDNYXMoP1hYxiXSEzWF/IDNYXMiMI9UVEsGfPHhQWFqJBg9Q9DbEFEYAGDRqgU6dOboehRKtWrXxbqUk91hcyg/WFzGB9IaNYV8gM1hcyg/WFzPB7fUnXcqgOO6kmIiIiIiIiIgo4JoiIiIiIiIiIiAKOCSKfyMnJwd13342cnBy3QyEPYH0hM1hfyAzWFzKKdYXMYH0hM1hfyAzWl3rspJqIiIiIiIiIKODYgoiIiIiIiIiIKOCYICIiIiIiIiIiCjgmiIiIiIiIiIiIAo4JIiIiIiIiIiKigGOCyCeeeOIJdO3aFU2bNkXv3r2xcOFCt0Mimy1YsADnn38+CgsLEQqF8Oabb8b8LiKYOHEiCgsL0axZMwwaNAirV6+OGaa8vBxjxoxB+/btkZubi+HDh+Onn36KGWbXrl24/PLLkZeXh7y8PFx++eXYvXu3zUtHKk2ePBknnXQSWrZsiQ4dOmDEiBFYs2ZNzDCsL1TnySefxLHHHotWrVqhVatW6NevH957773I76wrlMrkyZMRCoUwbty4yHesLxRt4sSJCIVCMf8KCgoiv7O+ULRNmzbhsssuQ7t27dC8eXMcf/zxWLZsWeR31heq06VLl4R9SygUwujRowGwrpgi5HnTp0+Xxo0by9SpU+Xrr7+WG2+8UXJzc2X9+vVuh0Y2mjlzptx5553y+uuvCwCZMWNGzO/33nuvtGzZUl5//XVZuXKljBw5Ujp27CilpaWRYUaNGiUHH3ywzJkzR7744gs544wz5LjjjpOqqqrIMGeffbb07NlTFi1aJIsWLZKePXvKsGHDnFpMUuCss86SZ555RlatWiXLly+X8847Tw455BDZu3dvZBjWF6rz1ltvybvvvitr1qyRNWvWyB133CGNGzeWVatWiQjrCiW3ZMkS6dKlixx77LFy4403Rr5nfaFod999txxzzDGyZcuWyL+tW7dGfmd9oTo7d+6UQw89VK688kr57LPPpKioSD744ANZu3ZtZBjWF6qzdevWmP3KnDlzBIB89NFHIsK6YgYTRD5w8skny6hRo2K+6969u9x+++0uRUROi08Q1dTUSEFBgdx7772R7w4cOCB5eXnyz3/+U0REdu/eLY0bN5bp06dHhtm0aZM0aNBA3n//fRER+frrrwWAfPrpp5FhFi9eLADk22+/tXmpyC5bt24VADJ//nwRYX2hzNq0aSP//ve/WVcoqT179ki3bt1kzpw5MnDgwEiCiPWF4t19991y3HHHJf2N9YWi3XbbbXLaaael/J31hdK58cYb5fDDD5eamhrWFZP4iJnHVVRUYNmyZRg6dGjM90OHDsWiRYtciorcVlRUhOLi4ph6kZOTg4EDB0bqxbJly1BZWRkzTGFhIXr27BkZZvHixcjLy0Pfvn0jw5xyyinIy8tj/fKwkpISAEDbtm0BsL5QatXV1Zg+fTr27duHfv36sa5QUqNHj8Z5552HIUOGxHzP+kLJfP/99ygsLETXrl1x8cUXY926dQBYXyjWW2+9hT59+uDXv/41OnTogBNOOAFTp06N/M76QqlUVFTghRdewFVXXYVQKMS6YhITRB63fft2VFdXIz8/P+b7/Px8FBcXuxQVua1u3aerF8XFxWjSpAnatGmTdpgOHTokTL9Dhw6sXx4lIrj55ptx2mmnoWfPngBYXyjRypUr0aJFC+Tk5GDUqFGYMWMGjj76aNYVSjB9+nR88cUXmDx5csJvrC8Ur2/fvnjuuecwa9YsTJ06FcXFxejfvz927NjB+kIx1q1bhyeffBLdunXDrFmzMGrUKIwdOxbPPfccAO5fKLU333wTu3fvxpVXXgmAdcWsRm4HQGqEQqGYzyKS8B0Fj5V6ET9MsuFZv7zrhhtuwIoVK/Dxxx8n/Mb6QnWOOuooLF++HLt378brr7+OK664AvPnz4/8zrpCALBx40bceOONmD17Npo2bZpyONYXqnPOOedE/u7Vqxf69euHww8/HM8++yxOOeUUAKwvFFZTU4M+ffpg0qRJAIATTjgBq1evxpNPPonf/va3keFYXyje008/jXPOOQeFhYUx37OuGMMWRB7Xvn17NGzYMCFruXXr1oQsKQVH3RtB0tWLgoICVFRUYNeuXWmH+fnnnxOmv23bNtYvDxozZgzeeustfPTRR+jUqVPke9YXitekSRMcccQR6NOnDyZPnozjjjsOjzzyCOsKxVi2bBm2bt2K3r17o1GjRmjUqBHmz5+PRx99FI0aNYqsS9YXSiU3Nxe9evXC999/z/0LxejYsSOOPvromO969OiBDRs2AOC5CyW3fv16fPDBB/j9738f+Y51xRwmiDyuSZMm6N27N+bMmRPz/Zw5c9C/f3+XoiK3de3aFQUFBTH1oqKiAvPnz4/Ui969e6Nx48Yxw2zZsgWrVq2KDNOvXz+UlJRgyZIlkWE+++wzlJSUsH55iIjghhtuwBtvvIEPP/wQXbt2jfmd9YUyERGUl5ezrlCMwYMHY+XKlVi+fHnkX58+fXDppZdi+fLlOOyww1hfKK3y8nJ888036NixI/cvFOPUU0/FmjVrYr777rvvcOihhwLguQsl98wzz6BDhw4477zzIt+xrpjkWHfYZJu619w//fTT8vXXX8u4ceMkNzdXfvzxR7dDIxvt2bNHvvzyS/nyyy8FgEyZMkW+/PJLWb9+vYiEX+eYl5cnb7zxhqxcuVJ+85vfJH2dY6dOneSDDz6QL774Qn7xi18kfZ3jscceK4sXL5bFixdLr169fPc6R7+77rrrJC8vT+bNmxfzCtD9+/dHhmF9oToTJkyQBQsWSFFRkaxYsULuuOMOadCggcyePVtEWFcovei3mImwvlCs8ePHy7x582TdunXy6aefyrBhw6Rly5aRc1bWF6qzZMkSadSokdxzzz3y/fffy4svvijNmzeXF154ITIM6wtFq66ulkMOOURuu+22hN9YV4xjgsgn/vGPf8ihhx4qTZo0kRNPPDHy+mryr48++kgAJPy74oorRCT8+s+7775bCgoKJCcnRwYMGCArV66MmUZZWZnccMMN0rZtW2nWrJkMGzZMNmzYEDPMjh075NJLL5WWLVtKy5Yt5dJLL5Vdu3Y5tJSkQrJ6AkCeeeaZyDCsL1TnqquuihxPDjroIBk8eHAkOSTCukLpxSeIWF8o2siRI6Vjx47SuHFjKSwslAsvvFBWr14d+Z31haK9/fbb0rNnT8nJyZHu3bvLU089FfM76wtFmzVrlgCQNWvWJPzGumJcSETElaZLRERERERERESkBfZBREREREREREQUcEwQEREREREREREFHBNEREREREREREQBxwQREREREREREVHAMUFERERERERERBRwTBAREREREREREQUcE0RERERERERERAHHBBERERERERERUcAxQURERESBNnHiRBx//PFuh0FERETkKiaIiIiIyLdCoVDaf1deeSVuueUWzJ0715X4Xn/9dfTt2xd5eXlo2bIljjnmGIwfPz7yO5NXRERE5JRGbgdAREREZJctW7ZE/n7llVfwf//3f1izZk3ku2bNmqFFixZo0aKF47F98MEHuPjiizFp0iQMHz4coVAIX3/9tWvJKiIiIgo2tiAiIiIi3yooKIj8y8vLQygUSvguvpXOlVdeiREjRmDSpEnIz89H69at8ec//xlVVVX44x//iLZt26JTp074z3/+EzOvTZs2YeTIkWjTpg3atWuHCy64AD/++GPK2N555x2cdtpp+OMf/4ijjjoKRx55JEaMGIHHHnsMADBt2jT8+c9/xldffRVp8TRt2jQAQElJCa655hp06NABrVq1wi9+8Qt89dVXkWnXLdO//vUvdO7cGc2bN8evf/1r7N69OzLMvHnzcPLJJyM3NxetW7fGqaeeivXr12dd5kRERORNTBARERERxfnwww+xefNmLFiwAFOmTMHEiRMxbNgwtGnTBp999hlGjRqFUaNGYePGjQCA/fv344wzzkCLFi2wYMECfPzxx2jRogXOPvtsVFRUJJ1HQUEBVq9ejVWrViX9feTIkRg/fjyOOeYYbNmyBVu2bMHIkSMhIjjvvPNQXFyMmTNnYtmyZTjxxBMxePBg7Ny5MzL+2rVr8eqrr+Ltt9/G+++/j+XLl2P06NEAgKqqKowYMQIDBw7EihUrsHjxYlxzzTUIhUKKS5KIiIi8ggkiIiIiojht27bFo48+iqOOOgpXXXUVjjrqKOzfvx933HEHunXrhgkTJqBJkyb45JNPAADTp09HgwYN8O9//xu9evVCjx498Mwzz2DDhg2YN29e0nmMGTMGJ510Enr16oUuXbrg4osvxn/+8x+Ul5cDqH/8rVGjRpEWT82aNcNHH32ElStX4rXXXkOfPn3QrVs3PPDAA2jdujX++9//RqZ/4MABPPvsszj++OMxYMAAPPbYY5g+fTqKi4tRWlqKkpISDBs2DIcffjh69OiBK664AocccojtZUtERER6YoKIiIiIKM4xxxyDBg3qT5Py8/PRq1evyOeGDRuiXbt22Lp1KwBg2bJlWLt2LVq2bBnp06ht27Y4cOAAfvjhh6TzyM3Nxbvvvou1a9firrvuQosWLTB+/HicfPLJ2L9/f8rYli1bhr1796Jdu3aRebVo0QJFRUUx8zrkkEPQqVOnyOd+/fqhpqYGa9asQdu2bXHllVfirLPOwvnnn49HHnkkpr8mIiIiCh52Uk1EREQUp3HjxjGfQ6FQ0u9qamoAADU1NejduzdefPHFhGkddNBBaed1+OGH4/DDD8fvf/973HnnnTjyyCPxyiuv4He/+13S4WtqatCxY8ekLZNat26dcj51j4/V/f+ZZ57B2LFj8f777+OVV17BXXfdhTlz5uCUU05JGy8RERH5ExNERERERFk68cQT8corr0Q6jbaqS5cuaN68Ofbt2wcAaNKkCaqrqxPmVVxcjEaNGqFLly4pp7VhwwZs3rwZhYWFAIDFixejQYMGOPLIIyPDnHDCCTjhhBMwYcIE9OvXDy+99BITRERERAHFR8yIiIiIsnTppZeiffv2uOCCC7Bw4UIUFRVh/vz5uPHGG/HTTz8lHWfixIm49dZbMW/ePBQVFeHLL7/EVVddhcrKSpx55pkAwgmjoqIiLF++HNu3b0d5eTmGDBmCfv36YcSIEZg1axZ+/PFHLFq0CHfddRc+//zzyPSbNm2KK664Al999RUWLlyIsWPH4qKLLkJBQQGKioowYcIELF68GOvXr8fs2bPx3XffoUePHo6UFxEREemHCSIiIiKiLDVv3hwLFizAIYccggsvvBA9evTAVVddhbKyspQtigYOHIh169bht7/9Lbp3745zzjkHxcXFmD17No466igAwK9+9SucffbZOOOMM3DQQQfh5ZdfRigUwsyZMzFgwABcddVVOPLII3HxxRfjxx9/RH5+fmT6RxxxBC688EKce+65GDp0KHr27IknnngiEu+3336LX/3qVzjyyCNxzTXX4IYbbsC1115rf2ERERGRlkIiIm4HQURERETqTJw4EW+++SaWL1/udihERETkEWxBREREREREREQUcEwQEREREREREREFHB8xIyIiIiIiIiIKOLYgIiIiIiIiIiIKOCaIiIiIiIiIiIgCjgkiIiIiIiIiIqKAY4KIiIiIiIiIiCjgmCAiIiIiIiIiIgo4JoiIiIiIiIiIiAKOCSIiIiIiIiIiooBjgoiIiIiIiIiIKOD+P7ysMWdiihNCAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1400x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "y_train_actual = np.array(y_train[n_steps-1:]).reshape(-1, 1)\n",
+    "y_test_actual = np.array(y_test[n_steps-1:]).reshape(-1, 1)\n",
+    "\n",
+    "# Generate predictions\n",
+    "train_predictions = model10.predict(X_train_reshaped)\n",
+    "test_predictions = model10.predict(X_test_reshaped)\n",
+    "\n",
+    "# Plot the results\n",
+    "plt.figure(figsize=(14, 7))\n",
+    "\n",
+    "# Training set\n",
+    "plt.plot(range(len(y_train_actual)), y_train_actual, label=\"Actual Train\", color=\"blue\")\n",
+    "plt.plot(range(len(train_predictions)), train_predictions, label=\"Predicted Train\", color=\"cyan\")\n",
+    "\n",
+    "# Testing set\n",
+    "test_index_start = len(y_train_actual)\n",
+    "plt.plot(range(test_index_start, test_index_start + len(y_test_actual)), y_test_actual, label=\"Actual Test\", color=\"green\")\n",
+    "plt.plot(range(test_index_start, test_index_start + len(test_predictions)), test_predictions, label=\"Predicted Test\", color=\"orange\")\n",
+    "\n",
+    "plt.title(\"Stock Price Prediction - Train and Test\")\n",
+    "plt.xlabel(\"Time Steps\")\n",
+    "plt.ylabel(\"Stock Price\")\n",
+    "plt.legend()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 506
+    },
+    "id": "O8DHEHgI0wNg",
+    "outputId": "5cd999c9-0cca-4d23-da28-8655ce099354"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a DataFrame for metrics\n",
+    "metrics_df = pd.DataFrame(metrics)\n",
+    "\n",
+    "plt.figure(figsize=(20, 5))\n",
+    "\n",
+    "# RMSE Plot\n",
+    "plt.subplot(1, 3, 1)\n",
+    "plt.bar(metrics_df['Model'], metrics_df['RMSE'], color='lightblue')\n",
+    "plt.ylabel('RMSE')\n",
+    "plt.xlabel('Model')\n",
+    "plt.xticks(rotation=45,ha='right')\n",
+    "plt.title('RMSE for Different Models')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 506
+    },
+    "id": "mwniKbys0xJ0",
+    "outputId": "ff2fe79a-78c2-4e13-efef-75e89882656b"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# MAE Plot\n",
+    "plt.figure(figsize=(20, 5))\n",
+    "plt.subplot(1, 3, 2)\n",
+    "plt.bar(metrics_df['Model'], metrics_df['MAE'], color='lightgreen')  # Changed to plt.bar()\n",
+    "plt.ylabel('MAE')  # Set y-label to MAE\n",
+    "plt.xlabel('Model')  # Set x-label to Model\n",
+    "plt.xticks(rotation=45,ha='right')  # Rotate x-axis labels for better readability\n",
+    "plt.title('MAE for Different Models')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 506
+    },
+    "id": "vxK0_IkL1zaU",
+    "outputId": "69c4c24b-f6d5-4a4c-f85a-baa8455475e5"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# MAPE Plot\n",
+    "plt.figure(figsize=(20, 5))\n",
+    "plt.subplot(1, 3, 3)\n",
+    "plt.bar(metrics_df['Model'], metrics_df['MAPE'], color='salmon')\n",
+    "plt.ylabel('MAPE')\n",
+    "plt.xlabel('Model')\n",
+    "plt.xticks(rotation=45,ha='right')\n",
+    "plt.title('MAPE for Different Models')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Stock_Price_Prediction(Updated) MultiLayer LSTM.ipynb b/Stock_Price_Prediction(Updated) MultiLayer LSTM.ipynb
index 19a6861..2c98d88 100644
--- a/Stock_Price_Prediction(Updated) MultiLayer LSTM.ipynb	
+++ b/Stock_Price_Prediction(Updated) MultiLayer LSTM.ipynb	
@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -137,7 +137,7 @@
        "4  76613039.0  "
       ]
      },
-     "execution_count": 5,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -150,7 +150,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "metadata": {
     "id": "2QdTvORzwEJw"
    },
@@ -162,7 +162,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -254,7 +254,7 @@
        "4  17.738192  17.785366  17.459852  17.577793  76613039.0"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -265,7 +265,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -276,7 +276,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "metadata": {
     "id": "dUw_auE7w8JA"
    },
@@ -289,7 +289,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "metadata": {
     "id": "FD2542-uxMhN"
    },
@@ -301,7 +301,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 8,
    "metadata": {
     "id": "IfBxpcjIw-h7"
    },
@@ -315,7 +315,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -330,7 +330,7 @@
        "(5659, 4)"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -341,7 +341,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 10,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -356,7 +356,7 @@
        "(1415, 4)"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -367,7 +367,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 11,
    "metadata": {
     "id": "SrzPIdvxxSWL"
    },
@@ -389,7 +389,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 12,
    "metadata": {
     "id": "1V0eOGD_xVCq"
    },
@@ -415,7 +415,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 13,
    "metadata": {
     "id": "uTbRFCB4xXHU"
    },
@@ -427,7 +427,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 14,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -448,7 +448,7 @@
        "Name: Close, dtype: float64"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -459,7 +459,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 15,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -882,7 +882,7 @@
        "LinearRegression()"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -894,7 +894,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 16,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -933,7 +933,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 17,
    "metadata": {
     "id": "LeUTf8Vhxj_k"
    },
@@ -945,7 +945,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 18,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -1368,7 +1368,7 @@
        "SVR()"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1380,7 +1380,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 19,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -1419,7 +1419,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 20,
    "metadata": {
     "id": "iaN8nOOO6cBg"
    },
@@ -1430,7 +1430,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 21,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -1853,7 +1853,7 @@
        "RandomForestRegressor()"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1865,7 +1865,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 22,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -1878,9 +1878,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "RMSE: 224.93042098590243\n",
-      "MAE: 162.9878683280432\n",
-      "MAPE: 0.7508043759857392\n",
+      "RMSE: 224.9384544830217\n",
+      "MAE: 162.99895472804317\n",
+      "MAPE: 0.7510058260536917\n",
       "\n"
      ]
     },
@@ -1912,7 +1912,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 23,
    "metadata": {
     "id": "EO6OFflr6nJo"
    },
@@ -1923,7 +1923,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 24,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -2346,7 +2346,7 @@
        "GradientBoostingRegressor()"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2358,7 +2358,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 25,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -2405,7 +2405,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 26,
    "metadata": {
     "id": "0GQmPNFd6uxx"
    },
@@ -2418,7 +2418,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 27,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -2871,7 +2871,7 @@
        "             num_parallel_tree=None, random_state=None, ...)"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2883,7 +2883,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 28,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -2922,7 +2922,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 29,
    "metadata": {
     "id": "0foTLiQp63Y9"
    },
@@ -2933,7 +2933,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 30,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -3356,7 +3356,7 @@
        "AdaBoostRegressor()"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3368,7 +3368,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 31,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -3381,9 +3381,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "RMSE: 211.58009272421995\n",
-      "MAE: 150.12127349523968\n",
-      "MAPE: 0.709215367666854\n",
+      "RMSE: 212.10179381390049\n",
+      "MAE: 150.43845140660002\n",
+      "MAPE: 0.7023135843804711\n",
       "\n"
      ]
     },
@@ -3415,7 +3415,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 32,
    "metadata": {
     "id": "E6EyzrH36_Fq"
    },
@@ -3426,7 +3426,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 33,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -3849,7 +3849,7 @@
        "DecisionTreeRegressor()"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3861,7 +3861,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 34,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -3874,9 +3874,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "RMSE: 225.19713405326468\n",
-      "MAE: 163.35574713804317\n",
-      "MAPE: 0.7574890717636951\n",
+      "RMSE: 224.85857555038172\n",
+      "MAE: 162.88870413804315\n",
+      "MAPE: 0.7490024715971244\n",
       "\n"
      ]
     },
@@ -3908,7 +3908,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 35,
    "metadata": {
     "id": "smujnWTRzzDL"
    },
@@ -3920,7 +3920,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 36,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -4343,7 +4343,7 @@
        "KNeighborsRegressor()"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4355,7 +4355,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 37,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -4402,7 +4402,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 38,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -4430,7 +4430,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 39,
    "metadata": {
     "id": "pdlxN-Dp0IZr"
    },
@@ -4442,7 +4442,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 40,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -4454,10 +4454,10 @@
     {
      "data": {
       "text/plain": [
-       "<keras.src.callbacks.history.History at 0x14e125cf390>"
+       "<keras.src.callbacks.history.History at 0x20440ba3590>"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4469,7 +4469,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 41,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -4483,9 +4483,9 @@
      "output_type": "stream",
      "text": [
       "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step\n",
-      "RMSE: 2.7823218514274872\n",
-      "MAE: 1.7367742329144598\n",
-      "MAPE: 0.011622923464328404\n",
+      "RMSE: 2.7756459435872696\n",
+      "MAE: 1.7743607020880396\n",
+      "MAPE: 0.013310535016859144\n",
       "\n"
      ]
     }
@@ -4509,7 +4509,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 42,
    "metadata": {
     "id": "keiZDN4w7UH0"
    },
@@ -4527,7 +4527,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 43,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -4535,7 +4535,16 @@
     "id": "nRRTkQTD7Vjd",
     "outputId": "8c0e1de9-8382-482f-dc46-d6148e3f535e"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\DEEPANSHU\\anaconda3\\Lib\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+      "  super().__init__(**kwargs)\n"
+     ]
+    }
+   ],
    "source": [
     "model10 = Sequential()\n",
     "model10.add(LSTM(128, return_sequences = True, input_shape=(n_steps, n_features)))\n",
@@ -4547,7 +4556,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 44,
    "metadata": {
     "id": "3UJtO3wC7WWe"
    },
@@ -4559,7 +4568,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 45,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -4571,10 +4580,10 @@
     {
      "data": {
       "text/plain": [
-       "<keras.src.callbacks.history.History at 0x14e227ab710>"
+       "<keras.src.callbacks.history.History at 0x20425d47d50>"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4585,7 +4594,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 46,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -4598,10 +4607,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step\n",
-      "RMSE: 3.3970840606919817\n",
-      "MAE: 2.3823417967059495\n",
-      "MAPE: 0.023917546379577784\n",
+      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step\n",
+      "RMSE: 3.8029934605035436\n",
+      "MAE: 2.8947993486353303\n",
+      "MAPE: 0.045649848386935823\n",
       "\n"
      ]
     }
@@ -4618,7 +4627,58 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m177/177\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step\n",
+      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "y_train_actual = np.array(y_train[n_steps-1:]).reshape(-1, 1)\n",
+    "y_test_actual = np.array(y_test[n_steps-1:]).reshape(-1, 1)\n",
+    "\n",
+    "# Generate predictions\n",
+    "train_predictions = model10.predict(X_train_reshaped)\n",
+    "test_predictions = model10.predict(X_test_reshaped)\n",
+    "\n",
+    "# Plot the results\n",
+    "plt.figure(figsize=(14, 7))\n",
+    "\n",
+    "# Training set\n",
+    "plt.plot(range(len(y_train_actual)), y_train_actual, label=\"Actual Train\", color=\"blue\")\n",
+    "plt.plot(range(len(train_predictions)), train_predictions, label=\"Predicted Train\", color=\"cyan\")\n",
+    "\n",
+    "# Testing set\n",
+    "test_index_start = len(y_train_actual)\n",
+    "plt.plot(range(test_index_start, test_index_start + len(y_test_actual)), y_test_actual, label=\"Actual Test\", color=\"green\")\n",
+    "plt.plot(range(test_index_start, test_index_start + len(test_predictions)), test_predictions, label=\"Predicted Test\", color=\"orange\")\n",
+    "\n",
+    "plt.title(\"Stock Price Prediction - Train and Test\")\n",
+    "plt.xlabel(\"Time Steps\")\n",
+    "plt.ylabel(\"Stock Price\")\n",
+    "plt.legend()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -4630,7 +4690,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 2000x500 with 1 Axes>"
       ]
@@ -4659,7 +4719,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 49,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -4671,7 +4731,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 2000x500 with 1 Axes>"
       ]
@@ -4696,7 +4756,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 50,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -4708,7 +4768,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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P+39Tzpw5cfLkSYwdOxZTpkzBvXv3YGVlhfz586Nu3bof1Atbo0YN/Pnnnxg/fjz69u2L58+fw9bWFsWLF//oXsfRo0cjPDwcXbp0QXR0NBwdHf/1Fr0fo1WrVsiYMSMCAgLg6+sLY2NjuLm5Yf/+/ahUqZJSr06dOtiwYQOGDx8OX19f2Nvbo0ePHnj9+nWatXkXLFgANzc3LFiwAIGBgUhOToaDgwMqV66MChUqvDOWihUr4uTJkxg8eDAeP36MzJkzo3z58ti3bx+cnZ0/eduJvmYa+RzTWYmIiIiIPjEuh0VEREREqsDElYiIiIhUgYkrEREREakCE1ciIiIiUgUmrkRERESkClwO6y2Sk5Px4MEDWFlZfdK74xARERFRWiKC6OhoODg4wMjo3f2qTFzf4sGDB8iTJ4++wyAiIiL6pty9e/e9N0dh4voWVlZWAFL+89J79x0iIiIiSp+oqCjkyZNHycHehYnrW2iHB1hbWzNxJSIiIvpC/m2IJidnEREREZEqMHElIiIiIlVg4kpEREREqsDElYiIiIhUQa+Ja0BAAFxdXWFlZYUcOXKgcePGuHbt2r9ud/DgQZQrVw4WFhYoUKAA5s+fn6ZOaGgoihcvDnNzcxQvXhzr16//HE0gIiIioi9Er4nrwYMH0bNnTxw7dgy7d+9GYmIiPD09ERMT885tbt26hXr16qFq1ao4c+YMhg4dij59+iA0NFSpExYWBl9fX/j5+eHcuXPw8/ND8+bNcfz48S/RLCIiIiL6DDQiIvoOQuvx48fIkSMHDh48CA8Pj7fWGTx4MDZt2oQrV64oZd27d8e5c+cQFhYGAPD19UVUVBS2b9+u1Klbty6yZMmCkJCQf40jKioKNjY2iIyM5HJYRERERJ/Zh+ZeBjXGNTIyEgCQNWvWd9YJCwuDp6enTpmXlxdOnjyJhISE99Y5evToW/cZFxeHqKgonQcRERERGRaDSVxFBP3790eVKlVQokSJd9aLiIiAnZ2dTpmdnR0SExPx5MmT99aJiIh46z4DAgJgY2OjPHi7VyIiIiLDYzCJa69evXD+/PkPupT/5l0VtKMdUpe/rc677sbg7++PyMhI5XH37t30hk9EREREn5lB3PK1d+/e2LRpEw4dOoTcuXO/t669vX2antNHjx7BxMQEtra2763zZi+slrm5OczNzf9DC4iIiIjoc9Nrj6uIoFevXli3bh327duH/Pnz/+s27u7u2L17t07Zrl27UL58eZiamr63TqVKlT5d8ERERET0Rem1x7Vnz55YsWIFNm7cCCsrK6WX1MbGBpaWlgBSLuPfv38fS5cuBZCygsDcuXPRv39/dOnSBWFhYVi8eLHOEIMff/wRHh4emDRpEho1aoSNGzdiz549OHLkyJdvJAEAEkb/pO8Q0s105DR9h0BERESp6LXHdd68eYiMjET16tWRM2dO5bFq1SqlTnh4OO7cuaM8z58/P7Zt24YDBw6gTJkyGDt2LGbPno0mTZoodSpVqoSVK1diyZIlKFWqFIKDg7Fq1SpUrFjxi7aPiIiIiD4dg1rH1VBwHddPjz2uRERE9C6qXMeViIiIiOhdmLgSERERkSowcSUiIiIiVWDiSkRERESqwMSViIiIiFSBiSsRERERqQITVyIiIiJSBSauRERERKQKTFyJiIiISBWYuBIRERGRKjBxJSIiIiJVYOJKRERERKrAxJWIiIiIVIGJKxERERGpAhNXIiIiIlIFJq5EREREpApMXImIiIhIFZi4EhEREZEqMHElIiIiIlVg4kpEREREqsDElYiIiIhUgYkrEREREakCE1ciIiIiUgUmrkRERESkCkxciYiIiEgVmLgSERERkSowcSUiIiIiVdBr4nro0CE0aNAADg4O0Gg02LBhw3vrt2/fHhqNJs3D2dlZqRMcHPzWOrGxsZ+5NURERET0Oek1cY2JiUHp0qUxd+7cD6o/a9YshIeHK4+7d+8ia9asaNasmU49a2trnXrh4eGwsLD4HE0gIiIioi/ERJ9/3NvbG97e3h9c38bGBjY2NsrzDRs24Pnz5+jQoYNOPY1GA3t7+08WJxERERHpn6rHuC5evBi1a9eGo6OjTvnLly/h6OiI3Llzw8fHB2fOnHnvfuLi4hAVFaXzICIiIiLDotrENTw8HNu3b0fnzp11yosWLYrg4GBs2rQJISEhsLCwQOXKlXH9+vV37isgIEDpzbWxsUGePHk+d/hERERElE6qTVyDg4OROXNmNG7cWKfczc0Nbdq0QenSpVG1alWsXr0aRYoUwZw5c965L39/f0RGRiqPu3fvfuboiYiIiCi99DrG9WOJCIKCguDn5wczM7P31jUyMoKrq+t7e1zNzc1hbm7+qcMkIiIiok9IlT2uBw8exI0bN9CpU6d/rSsiOHv2LHLmzPkFIiMiIiKiz0WvPa4vX77EjRs3lOe3bt3C2bNnkTVrVuTNmxf+/v64f/8+li5dqrPd4sWLUbFiRZQoUSLNPkePHg03NzcULlwYUVFRmD17Ns6ePYtffvnls7eHiIiIiD4fvSauJ0+eRI0aNZTn/fv3BwC0a9cOwcHBCA8Px507d3S2iYyMRGhoKGbNmvXWfb548QJdu3ZFREQEbGxs4OLigkOHDqFChQqfryFERERE9NlpRET0HYShiYqKgo2NDSIjI2Ftba3vcL4KCaN/0ncI6WY6cpq+QyAiIvomfGjupcoxrkRERET07WHiSkRERESqwMSViIiIiFSBiSsRERERqQITVyIiIiJSBSauRERERKQKTFyJiIiISBWYuBIRERGRKjBxJSIiIiJVYOJKRERERKrAxJWIiIiIVIGJKxERERGpAhNXIiIiIlIFJq5EREREpApMXImIiIhIFZi4EhEREZEqMHElIiIiIlVg4kpEREREqsDElYiIiIhUgYkrEREREakCE1ciIiIiUgUmrkRERESkCkxciYiIiEgVmLgSERERkSowcSUiIiIiVWDiSkRERESqwMSViIiIiFRBr4nroUOH0KBBAzg4OECj0WDDhg3vrX/gwAFoNJo0j6tXr+rUCw0NRfHixWFubo7ixYtj/fr1n7EVRERERPQl6DVxjYmJQenSpTF37tx0bXft2jWEh4crj8KFCyuvhYWFwdfXF35+fjh37hz8/PzQvHlzHD9+/FOHT0RERERfkIk+/7i3tze8vb3TvV2OHDmQOXPmt742c+ZM1KlTB/7+/gAAf39/HDx4EDNnzkRISMhbt4mLi0NcXJzyPCoqKt0xEREREdHnpcoxri4uLsiZMydq1aqF/fv367wWFhYGT09PnTIvLy8cPXr0nfsLCAiAjY2N8siTJ89niZuIiIiIPp6qEtecOXNi4cKFCA0Nxbp16+Dk5IRatWrh0KFDSp2IiAjY2dnpbGdnZ4eIiIh37tff3x+RkZHK4+7du5+tDURERET0cfQ6VCC9nJyc4OTkpDx3d3fH3bt3MXXqVHh4eCjlGo1GZzsRSVOWmrm5OczNzT99wERERET0yaiqx/Vt3NzccP36deW5vb19mt7VR48epemFJSIiIiJ1UX3ieubMGeTMmVN57u7ujt27d+vU2bVrFypVqvSlQyMiIiKiT0ivQwVevnyJGzduKM9v3bqFs2fPImvWrMibNy/8/f1x//59LF26FEDKigH58uWDs7Mz4uPjsXz5coSGhiI0NFTZx48//ggPDw9MmjQJjRo1wsaNG7Fnzx4cOXLki7ePiIiIiD4dvSauJ0+eRI0aNZTn/fv3BwC0a9cOwcHBCA8Px507d5TX4+PjMWDAANy/fx+WlpZwdnbG1q1bUa9ePaVOpUqVsHLlSgwfPhw///wzChYsiFWrVqFixYpfrmFERERE9MlpRET0HYShiYqKgo2NDSIjI2Ftba3vcL4KCaN/0ncI6WY6cpq+QyAiIvomfGjupfoxrkRERET0bWDiSkRERESqwMSViIiIiFSBiSsRERERqQITVyIiIiJSBSauRERERKQKTFyJiIiISBWYuBIRERGRKjBxJSIiIiJVYOJKRERERKrAxJWIiIiIVIGJKxERERGpAhNXIiIiIlIFJq5EREREpApMXImIiIhIFZi4EhEREZEqMHElIiIiIlVg4kpEREREqsDElYiIiIhUgYkrEREREakCE1ciIiIiUgUmrkRERESkCkxciYiIiEgVmLgSERERkSowcSUiIiIiVWDiSkRERESqoNfE9dChQ2jQoAEcHByg0WiwYcOG99Zft24d6tSpg+zZs8Pa2hru7u7YuXOnTp3g4GBoNJo0j9jY2M/YEiIiIiL63PSauMbExKB06dKYO3fuB9U/dOgQ6tSpg23btuHUqVOoUaMGGjRogDNnzujUs7a2Rnh4uM7DwsLiczSBiIiIiL4QE33+cW9vb3h7e39w/ZkzZ+o8nzBhAjZu3IjNmzfDxcVFKddoNLC3t/9UYRIRERGRAVD1GNfk5GRER0cja9asOuUvX76Eo6MjcufODR8fnzQ9sm+Ki4tDVFSUzoOIiIiIDIuqE9dp06YhJiYGzZs3V8qKFi2K4OBgbNq0CSEhIbCwsEDlypVx/fr1d+4nICAANjY2yiNPnjxfInwiIiIiSgfVJq4hISEYNWoUVq1ahRw5cijlbm5uaNOmDUqXLo2qVati9erVKFKkCObMmfPOffn7+yMyMlJ53L1790s0gYiIiIjSQa9jXD/WqlWr0KlTJ6xZswa1a9d+b10jIyO4urq+t8fV3Nwc5ubmnzpMIiIiIvqEVNfjGhISgvbt22PFihWoX7/+v9YXEZw9exY5c+b8AtERERER0eei1x7Xly9f4saNG8rzW7du4ezZs8iaNSvy5s0Lf39/3L9/H0uXLgWQkrS2bdsWs2bNgpubGyIiIgAAlpaWsLGxAQCMHj0abm5uKFy4MKKiojB79mycPXsWv/zyy5dvIBERERF9MnrtcT158iRcXFyUpaz69+8PFxcXjBgxAgAQHh6OO3fuKPUXLFiAxMRE9OzZEzlz5lQeP/74o1LnxYsX6Nq1K4oVKwZPT0/cv38fhw4dQoUKFb5s44iIiIjok9KIiOg7CEMTFRUFGxsbREZGwtraWt/hfBUSRv+k7xDSzXTkNH2HQERE9E340NxLdWNciYiIiOjbxMSViIiIiFSBiSsRERERqQITVyIiIiJSBSauRERERKQKTFyJiIiISBWYuBIRERGRKjBxJSIiIiJVYOJKRERERKrAxJWIiIiIVIGJKxERERGpQroS13r16iEyMlJ5Pn78eLx48UJ5/vTpUxQvXvyTBUdEREREpJWuxHXnzp2Ii4tTnk+aNAnPnj1TnicmJuLatWufLjoiIiIiov+XrsRVRN77nIiIiIjoc+EYVyIiIiJShXQlrhqNBhqNJk0ZEREREdHnZpKeyiKC9u3bw9zcHAAQGxuL7t27I2PGjACgM/6ViIiIiOhTSlfi2q5dO53nbdq0SVOnbdu2/y0iIiIiIqK3SFfiumTJks8VBxERERHRe6UrcQWAf/75B7t27UJCQgKqV6/OdVuJiIiI6ItIV+J66NAh1KtXD69evUrZ2MQEv/32G1q2bPlZgiMiIiIi0krXqgI///wzatSogXv37uHp06fo2LEjBg0a9LliIyIiIiJSpCtxvXDhAgICAuDg4IAsWbJg2rRpePDgAZ4/f/654iMiIiIiApDOxPXFixfIkSOH8jxjxozIkCEDXrx48anjIiIiIiLSke7JWZcvX0ZERITyXERw5coVREdHK2WlSpX6NNEREREREf2/dCeutWrVgojolPn4+ECj0UBEoNFokJSU9MkCJCIiIiIC0pm43rp163PFQURERET0Xuka4+ro6Pivj/RM1Dp06BAaNGgABwcHaDQabNiw4V+3OXjwIMqVKwcLCwsUKFAA8+fPT1MnNDQUxYsXh7m5OYoXL47169enp5lEREREZIDSlbi+S2RkJAIDA1G2bFmUK1fug7eLiYlB6dKlMXfu3A+qf+vWLdSrVw9Vq1bFmTNnMHToUPTp0wehoaFKnbCwMPj6+sLPzw/nzp2Dn58fmjdvjuPHj6e7XURERERkODTy5oDVdNi3bx+CgoKwbt06ODo6okmTJmjSpAlcXFzSH4hGg/Xr16Nx48bvrDN48GBs2rQJV65cUcq6d++Oc+fOISwsDADg6+uLqKgobN++XalTt25dZMmSBSEhIR8US1RUFGxsbBAZGQlra+t0t4XSShj9k75DSDfTkdP0HQIREdE34UNzr3T3uN67dw/jxo1DgQIF0LJlS2TJkgUJCQkIDQ3FuHHjPipp/VBhYWHw9PTUKfPy8sLJkyeRkJDw3jpHjx59537j4uIQFRWl8yAiIiIiw5KuxLVevXooXrw4Ll++jDlz5uDBgweYM2fO54otjYiICNjZ2emU2dnZITExEU+ePHlvndRLeL0pICAANjY2yiNPnjyfPngiIiIi+k/Slbju2rULnTt3xujRo1G/fn0YGxt/rrjeSaPR6DzXjnRIXf62Om+Wpebv74/IyEjlcffu3U8YMRERERF9CulKXA8fPozo6GiUL18eFStWxNy5c/H48ePPFVsa9vb2aXpOHz16BBMTE9ja2r63zpu9sKmZm5vD2tpa50FEREREhiVdiau7uzsWLVqE8PBwdOvWDStXrkSuXLmQnJyM3bt369w963Nwd3fH7t27dcp27dqF8uXLw9TU9L11KlWq9FljIyIiIqLP66OWw8qQIQM6duyII0eO4MKFC/jpp58wceJE5MiRAw0bNvzg/bx8+RJnz57F2bNnAaQsd3X27FncuXMHQMol/LZt2yr1u3fvjn/++Qf9+/fHlStXEBQUhMWLF2PAgAFKnR9//BG7du3CpEmTcPXqVUyaNAl79uxB3759P6apRERERGQg/vM6rk5OTpg8eTLu3buHlStXvncs6ZtOnjwJFxcXZSWC/v37w8XFBSNGjAAAhIeHK0ksAOTPnx/btm3DgQMHUKZMGYwdOxazZ89GkyZNlDqVKlXCypUrsWTJEpQqVQrBwcFYtWoVKlas+F+bSkRERER6lK51XDt27PhB9YKCgj46IEPAdVw/Pa7jSkRERO/yobmXSXp2GhwcDEdHR7i4uOBd+W56elyJiIiIiD5UuhLX7t27Y+XKlfj777/RsWNHtGnTBlmzZv1csRERERERKdI1xjUwMBDh4eEYPHgwNm/ejDx58qB58+bYuXPnO3tgiYiIiIg+hXRPzjI3N0fLli2xe/duXL58Gc7OzujRowccHR3x8uXLzxEjEREREdF/W1VAo9FAo9FARJCcnPypYiIiIiIiSiPdiWtcXBxCQkJQp04dODk54cKFC5g7dy7u3LmDTJkyfY4YiYiIiIjSNzmrR48eWLlyJfLmzYsOHTpg5cqVyq1WiYiIiIg+p3QlrvPnz0fevHmRP39+HDx4EAcPHnxrvXXr1n2S4IiIiIiItNKVuLZt25brtBIRERGRXqT7BgRERERERPrwn1YVICIiIiL6Upi4EhEREZEqMHElIiIiIlVg4kpEREREqsDElYiIiIhUgYkrEREREakCE1ciIiIiUgUmrkRERESkCkxciYiIiEgVmLgSERERkSowcSUiIiIiVWDiSkRERESqwMSViIiIiFSBiSsRERERqQITVyIiIiJSBSauRERERKQKTFyJiIiISBX0nrgGBgYif/78sLCwQLly5XD48OF31m3fvj00Gk2ah7Ozs1InODj4rXViY2O/RHOIiIiI6DPRa+K6atUq9O3bF8OGDcOZM2dQtWpVeHt7486dO2+tP2vWLISHhyuPu3fvImvWrGjWrJlOPWtra5164eHhsLCw+BJNIiIiIqLPxESff3z69Ono1KkTOnfuDACYOXMmdu7ciXnz5iEgICBNfRsbG9jY2CjPN2zYgOfPn6NDhw469TQaDezt7T84jri4OMTFxSnPo6Ki0tsUIiLSo4TRP+k7hHQzHTlN3yEQqY7eEtf4+HicOnUKQ4YM0Sn39PTE0aNHP2gfixcvRu3ateHo6KhT/vLlSzg6OiIpKQllypTB2LFj4eLi8s79BAQEYPTo0elvBNH/40mTiIjo89Nb4vrkyRMkJSXBzs5Op9zOzg4RERH/un14eDi2b9+OFStW6JQXLVoUwcHBKFmyJKKiojBr1ixUrlwZ586dQ+HChd+6L39/f/Tv3195HhUVhTx58nxEq4hIrfjjg4jI8Ol1qACQclk/NRFJU/Y2wcHByJw5Mxo3bqxT7ubmBjc3N+V55cqVUbZsWcyZMwezZ89+677Mzc1hbm6e/uCJviFM7Ij0i99BIj1OzsqWLRuMjY3T9K4+evQoTS/sm0QEQUFB8PPzg5mZ2XvrGhkZwdXVFdevX//PMRMRERGR/ugtcTUzM0O5cuWwe/dunfLdu3ejUqVK79324MGDuHHjBjp16vSvf0dEcPbsWeTMmfM/xUtERERE+qXXoQL9+/eHn58fypcvD3d3dyxcuBB37txB9+7dAaSMPb1//z6WLl2qs93ixYtRsWJFlChRIs0+R48eDTc3NxQuXBhRUVGYPXs2zp49i19++eWLtImIiIiIPg+9Jq6+vr54+vQpxowZg/DwcJQoUQLbtm1TVgkIDw9Ps6ZrZGQkQkNDMWvWrLfu88WLF+jatSsiIiJgY2MDFxcXHDp0CBUqVPjs7SEiMlQcH0lEXwO9T87q0aMHevTo8dbXgoOD05TZ2Njg1atX79zfjBkzMGPGjE8VHhEREREZCL3f8pWIiIiI6EMwcSUiIiIiVWDiSkRERESqwMSViIiIiFSBiSsRERERqQITVyIiIiJSBSauRERERKQKTFyJiIiISBWYuBIRERGRKjBxJSIiIiJVYOJKRERERKrAxJWIiIiIVIGJKxERERGpAhNXIiIiIlIFJq5EREREpApMXImIiIhIFZi4EhEREZEqMHElIiIiIlVg4kpEREREqsDElYiIiIhUgYkrEREREakCE1ciIiIiUgUmrkRERESkCkxciYiIiEgVmLgSERERkSowcSUiIiIiVdB74hoYGIj8+fPDwsIC5cqVw+HDh99Z98CBA9BoNGkeV69e1akXGhqK4sWLw9zcHMWLF8f69es/dzOIiIiI6DPTa+K6atUq9O3bF8OGDcOZM2dQtWpVeHt7486dO+/d7tq1awgPD1cehQsXVl4LCwuDr68v/Pz8cO7cOfj5+aF58+Y4fvz4524OEREREX1Gek1cp0+fjk6dOqFz584oVqwYZs6ciTx58mDevHnv3S5Hjhywt7dXHsbGxsprM2fORJ06deDv74+iRYvC398ftWrVwsyZMz9za4iIiIjoc9Jb4hofH49Tp07B09NTp9zT0xNHjx5977YuLi7ImTMnatWqhf379+u8FhYWlmafXl5e791nXFwcoqKidB5EREREZFj0lrg+efIESUlJsLOz0ym3s7NDRETEW7fJmTMnFi5ciNDQUKxbtw5OTk6oVasWDh06pNSJiIhI1z4BICAgADY2NsojT548/6FlRERERPQ5mOg7AI1Go/NcRNKUaTk5OcHJyUl57u7ujrt372Lq1Knw8PD4qH0CgL+/P/r37688j4qKYvJKREREZGD01uOaLVs2GBsbp+kJffToUZoe0/dxc3PD9evXlef29vbp3qe5uTmsra11HkRERERkWPSWuJqZmaFcuXLYvXu3Tvnu3btRqVKlD97PmTNnkDNnTuW5u7t7mn3u2rUrXfskIiIiIsOj16EC/fv3h5+fH8qXLw93d3csXLgQd+7cQffu3QGkXMK/f/8+li5dCiBlxYB8+fLB2dkZ8fHxWL58OUJDQxEaGqrs88cff4SHhwcmTZqERo0aYePGjdizZw+OHDmilzYSERER0aeh18TV19cXT58+xZgxYxAeHo4SJUpg27ZtcHR0BACEh4frrOkaHx+PAQMG4P79+7C0tISzszO2bt2KevXqKXUqVaqElStXYvjw4fj5559RsGBBrFq1ChUrVvzi7SMiIiKiT0fvk7N69OiBHj16vPW14OBgneeDBg3CoEGD/nWfTZs2RdOmTT9FeERERERkIPR+y1ciIiIiog/BxJWIiIiIVIGJKxERERGpAhNXIiIiIlIFJq5EREREpApMXImIiIhIFZi4EhEREZEqMHElIiIiIlVg4kpEREREqsDElYiIiIhUgYkrEREREakCE1ciIiIiUgUmrkRERESkCkxciYiIiEgVmLgSERERkSowcSUiIiIiVWDiSkRERESqwMSViIiIiFSBiSsRERERqQITVyIiIiJSBSauRERERKQKTFyJiIiISBWYuBIRERGRKjBxJSIiIiJVYOJKRERERKrAxJWIiIiIVIGJKxERERGpgt4T18DAQOTPnx8WFhYoV64cDh8+/M6669atQ506dZA9e3ZYW1vD3d0dO3fu1KkTHBwMjUaT5hEbG/u5m0JEREREn5FeE9dVq1ahb9++GDZsGM6cOYOqVavC29sbd+7ceWv9Q4cOoU6dOti2bRtOnTqFGjVqoEGDBjhz5oxOPWtra4SHh+s8LCwsvkSTiIiIiOgzMdHnH58+fTo6deqEzp07AwBmzpyJnTt3Yt68eQgICEhTf+bMmTrPJ0yYgI0bN2Lz5s1wcXFRyjUaDezt7T9r7ERERET0ZemtxzU+Ph6nTp2Cp6enTrmnpyeOHj36QftITk5GdHQ0smbNqlP+8uVLODo6Infu3PDx8UnTI/umuLg4REVF6TyIiIiIyLDoLXF98uQJkpKSYGdnp1NuZ2eHiIiID9rHtGnTEBMTg+bNmytlRYsWRXBwMDZt2oSQkBBYWFigcuXKuH79+jv3ExAQABsbG+WRJ0+ej2sUEREREX02ep+cpdFodJ6LSJqytwkJCcGoUaOwatUq5MiRQyl3c3NDmzZtULp0aVStWhWrV69GkSJFMGfOnHfuy9/fH5GRkcrj7t27H98gIiIiIvos9DbGNVu2bDA2Nk7Tu/ro0aM0vbBvWrVqFTp16oQ1a9agdu3a761rZGQEV1fX9/a4mpubw9zc/MODJyIiIqIvTm89rmZmZihXrhx2796tU757925UqlTpnduFhISgffv2WLFiBerXr/+vf0dEcPbsWeTMmfM/x0xERERE+qPXVQX69+8PPz8/lC9fHu7u7li4cCHu3LmD7t27A0i5hH///n0sXboUQErS2rZtW8yaNQtubm5Kb62lpSVsbGwAAKNHj4abmxsKFy6MqKgozJ49G2fPnsUvv/yin0YSERER0Seh18TV19cXT58+xZgxYxAeHo4SJUpg27ZtcHR0BACEh4frrOm6YMECJCYmomfPnujZs6dS3q5dOwQHBwMAXrx4ga5duyIiIgI2NjZwcXHBoUOHUKFChS/aNiIiIiL6tPSauAJAjx490KNHj7e+pk1GtQ4cOPCv+5sxYwZmzJjxCSIjIiIiIkOi91UFiIiIiIg+BBNXIiIiIlIFJq5EREREpApMXImIiIhIFZi4EhEREZEqMHElIiIiIlVg4kpEREREqsDElYiIiIhUgYkrEREREakCE1ciIiIiUgUmrkRERESkCkxciYiIiEgVmLgSERERkSowcSUiIiIiVWDiSkRERESqwMSViIiIiFSBiSsRERERqQITVyIiIiJSBSauRERERKQKTFyJiIiISBWYuBIRERGRKjBxJSIiIiJVYOJKRERERKrAxJWIiIiIVIGJKxERERGpAhNXIiIiIlIFJq5EREREpAom+g4gMDAQU6ZMQXh4OJydnTFz5kxUrVr1nfUPHjyI/v3749KlS3BwcMCgQYPQvXt3nTqhoaH4+eefcfPmTRQsWBDjx4/Hd99997mbQkRERPRWCaN/0ncI6WY6cpq+Q0hDrz2uq1atQt++fTFs2DCcOXMGVatWhbe3N+7cufPW+rdu3UK9evVQtWpVnDlzBkOHDkWfPn0QGhqq1AkLC4Ovry/8/Pxw7tw5+Pn5oXnz5jh+/PiXahYRERERfQZ67XGdPn06OnXqhM6dOwMAZs6ciZ07d2LevHkICAhIU3/+/PnImzcvZs6cCQAoVqwYTp48ialTp6JJkybKPurUqQN/f38AgL+/Pw4ePIiZM2ciJCTkrXHExcUhLi5OeR4ZGQkAiIqK+mRt/TcJAUO/2N/6VEz9J3xw3YTYuH+vZGBM0/H+f+3tA77+NrJ9hoft0/W1t5HnQcOT3s/of6HNuUTk/RVFT+Li4sTY2FjWrVunU96nTx/x8PB46zZVq1aVPn366JStW7dOTExMJD4+XkRE8uTJI9OnT9epM336dMmbN+87Yxk5cqQA4IMPPvjggw8++OBDj4+7d+++N3/UW4/rkydPkJSUBDs7O51yOzs7REREvHWbiIiIt9ZPTEzEkydPkDNnznfWedc+gZRe2f79+yvPk5OT8ezZM9ja2kKj0aS3aQYjKioKefLkwd27d2Ftba3vcD6Lr72NbJ+6fe3tA77+NrJ96va1tw/4etooIoiOjoaDg8N76+l9ctabiaGIvDdZfFv9N8vTu09zc3OYm5vrlGXOnPm9cauJtbW1qj/MH+JrbyPbp25fe/uAr7+NbJ+6fe3tA76ONtrY2PxrHb1NzsqWLRuMjY3T9IQ+evQoTY+plr29/Vvrm5iYwNbW9r113rVPIiIiIlIHvSWuZmZmKFeuHHbv3q1Tvnv3blSqVOmt27i7u6epv2vXLpQvXx6mpqbvrfOufRIRERGROuh1qED//v3h5+eH8uXLw93dHQsXLsSdO3eUdVn9/f1x//59LF26FADQvXt3zJ07F/3790eXLl0QFhaGxYsX66wW8OOPP8LDwwOTJk1Co0aNsHHjRuzZswdHjhzRSxv1ydzcHCNHjkwzDOJr8rW3ke1Tt6+9fcDX30a2T92+9vYB30YbU9OI/Nu6A59XYGAgJk+ejPDwcJQoUQIzZsyAh4cHAKB9+/a4ffs2Dhw4oNQ/ePAg+vXrp9yAYPDgwWluQLB27VoMHz4cf//9t3IDgu+///5LNouIiIiIPjG9J65ERERERB9Cr3fOIiIiIiL6UExciYiIiEgVmLgSERERkSowcSUiIiIiVWDiqkLJycnKvxMSEvQYCX0szokkQ5b6GPP8+XM9RkL0bqk/p/TtYOKqQkZGKW/bqFGjMHHiRCQlJek5IvoQqQ+y2lsQP3z4EImJifoK6bPRJuZf82fza26b9hgzdOhQzJ07F1FRUXqOiD7W1/gj+fbt27h27RqMjIy+ieT1a3wP/wsmriqS+gu6fv16LF26FHXr1oWxsbEeo/q8vqaDkpGREW7fvo2BAwcCAEJDQ+Hr64tHjx7pObJP6/z586hZsyZevHgBY2Pjry7Bi46OBgAYGxvj5MmTiIuL03NEn07qE+T+/fsRHBwMb29v1d//PDVtG1+9evXVJuT379/HwYMHAaT8SP6aEp/Y2FgMHToU1atXx5UrV77a5PXmzZtYsWIFgK/vPfyvmLiqiLYXZMeOHTh69Ci6dOkCV1fXryoxuHv3LlavXo0ZM2bg4cOHSpu/BsnJydi2bRvWrVsHHx8fNGvWDJ06dYKDg4O+Q/tk4uPj0a1bNxw8eBBVq1bFs2fPvqrk9d69e2jfvj127dqF0NBQVKhQAadPn9Z3WJ+M9krAvHnzcPz4cXTu3Bnly5f/ak6aIgKNRoPNmzejZcuWKFOmDLp27YoFCxboO7RPJj4+Hu3bt8fPP/+MvXv3Avi6Eh8LCwt06dIFFStWRNOmTXH58uWvLnl9+PAhKlSogO7du2PevHkAvq738D8TUpWHDx9Kjhw5RKPRSNeuXZXy5ORkPUb1aZw7d04KFSokZcqUERsbG8mTJ4/8+eefIiKSlJSk5+g+jaSkJOnUqZNoNBqpXbu2Up6YmKjHqD6tCRMmSM2aNcXNzU1y584tT548EZGvo43Xrl2T6tWrS5kyZcTc3FyWLl0qIl/P51NEJCEhQTw8PESj0UjTpk2/qraJiGzevFksLS0lICBANm7cKK1btxYrKys5fPiwvkP7ZE6ePCnu7u7i4+Mju3fvVsq/hvOE1sGDB6V+/fpSvHhxuXTpkoh8Pd/DU6dOSe7cuaVq1ari4eEh06dPV177mt7Dj/X1dGd9peSNX1g5cuTA/v37UapUKRw7dgz79u0DoP5fY+fOnYObmxuaNWuGrVu3Yv/+/YiNjcWgQYMApPQ2q7l9qWN3cHBA69at8eTJE/To0QNAymXnr2Wsq7u7O06dOoUuXbqgTJkycHFx+Sp6XkUERYoUQadOnXDhwgUUKFAAtra2AKDqHp83v1cmJibYunUrWrRogT179uDIkSN6iuzTEhFER0dj0aJFGD16NIYMGYJq1aph79696NixI6pUqaLvEP+z5ORkiAjKlSuHwMBAPHz4ELNmzcKePXsAqPc88eDBA/z55594+fKlUubh4QF/f3/kzZsXzZo1w6VLl1T9PUytbNmy8PLyQnh4OMqXL4+QkBDMnj0bgHrfw09KXxkz/bvUvx6joqIkMTFRXr9+LSIiZ8+elaJFi0rDhg3ljz/+UOqp8dfYP//8IyYmJjJs2DCd8goVKoizs7Oeovp0tO9JWFiYHD9+XGJiYiQ2NlamTZsmJUuWlB9++EGn/o0bNyQhIUEfoX6Ut/Vy9O/fX9q3by/Hjh2TihUriqOjozx9+lRE1Nnzqn0PExMT5fDhw7Jo0SKpX7++1K5dW1avXq3UU1uPz5vHGG3vuEhKz6uXl5c4ODjIyZMn9RHeJxcfHy/ly5eXgwcPyp07dyRXrlzSpUsX5fXNmzfL6dOn9Rjhx/n777/l+PHj8ujRI53ykydPiqurq9SrV0927dqllKvpPHHnzh3JkCGDaDQaKVCggPj7+0tQUJC8evVKRFKu1H333XdStGhRpedVjccYrbi4OBFJaVezZs0kJCREevXqJaVLl5bZs2cr9dT0Hn5q7HE1UMnJycr4zsmTJ8PX1xeVK1dGr169cPnyZZQuXRq///47rl27hsmTJyMsLAzA/8aoqcn169eRI0cOnDt3TimbNGkSTpw4gUePHqFz586oUaMGQkND8ddff+kx0vST/x9Tt27dOtSvXx/r16/H8+fPYW5ujo4dO6JDhw44cuQIunfvjuTkZIwcORLdunXD69ev9R36B7l48SKqV6+OzZs348KFC0q5m5sbbt26haJFi+L333+HnZ0dypYti+fPn8PY2FhVvSLa93DXrl3o06cPnJ2d0blzZ0ydOhXGxsZYsGABQkNDAaT0vG7dulUVE7ZERDnGjB07Fg0bNoSTkxM6duyIZcuWwcTEBNu2bUPJkiXRqFEjnDp1Ss8Rfxz5/94pEcGLFy9gamqKP/74AzVq1IC3tzfmz58PAAgPD8fatWtx/fp1VfVohYeHo2DBgnBzc8N3332Hli1bYvXq1bh16xbKlSuHVatW4fHjxwgMDMSOHTsAqKvXLjIyEoUKFULx4sWRP39+3L17F4MGDYKrqys8PT3x119/oUqVKnB2doafnx/++usv1U1YvnXrFoKDgwEAZmZmAAB7e3tERETgn3/+QUBAAKpUqYKgoCDMmTMHgLrew09OfzkzfYihQ4eKra2tzJw5U/r06SN16tSRzJkzy6lTp0RE5PTp0+Ls7CxVqlSRCxcu6Dna9Ll27ZqMHTtWRES2b98uRYoUkQYNGsjEiRMle/bs8vvvv8vFixdl37590qlTJylbtqwytjc2NlbP0X+4Xbt2ScaMGSUoKEiio6N1Xnv58qUEBgaKo6Oj5MuXT3LkyCHHjx/XU6Tp8+rVK6levboyXrdWrVoyYMAAiYiIEBERT09P6dmzp4iIXLx4UapWrSpWVlby7NkzfYb9UdauXSuZM2eWn376Sef9uXTpknh5eUnt2rVl6tSpMnLkSNFoNHLnzh09Rps+I0aMkKxZs8r8+fNl5syZ4uPjI2XLllXG1cXHx4uPj48YGRnJlStX9Bzth9P2SEVHR0tCQoLyfM6cOWnGmIukHGudnJzk1q1bXzrU/yQyMlLq1asnGo1G/P39pU6dOlK2bFnJkCGDNG3aVIKCgmTFihXi4uIirVq1km3btuk75A8SGRmpXGE8efKkVK9eXVq1aiWbNm2SqKgoCQ0NlaZNm0rFihUlQ4YMkidPHtFoNOLh4SHx8fGq6ZG8f/++ZM+eXTQajTRu3FimTZsm169fF5GUcbxOTk5y9epVuX37tvTq1UtcXV1l0qRJeo5av5i4GrBbt25JiRIlZOPGjTplzZs3FwcHB/n7779FJOVL3bJlS1VdpkxKSpIpU6aInZ2d3LlzR+Lj42Xz5s1SunRp0Wg0OhMKtO7evSubNm2Sy5cv6yHij5OcnCx9+/aVzp07i0hKonrixAnp1auXjB07Vk6cOCEiKQnQsmXLlPdUDRITE2Xnzp1SqlQpKVGihOzbt0/Kly8vnp6e0qZNG5k2bZp4eXkply9Pnz4tnp6eykFZLU6fPi3ZsmWTBQsW6JRrhz78/fff0qpVKylXrpwUK1ZM+VGpBnfu3JFy5cpJaGioUnbz5k0ZOHCglC9fXvbv3y8iIjExMTJgwADVXILVJi1bt24VT09PqVSpkri7u8sff/whz549E39/f9FoNDJw4EAZNGiQdO7cWaytreXMmTP6DTwdoqKilH+/ePFCPD09xdnZWa5evSpRUVGyYsUKGTx4sOTIkUNq1qwpGo1GNBqNfP/99xITE6PHyP/dgwcPpHbt2jJnzhxlSEBYWJhUr15dPD09Ze/evTp1Dx8+LMOHDxcfHx85d+6cvsJOt1evXsmFCxekYcOG4uzsLNWqVZN+/fqJra2tBAQEyMqVK6Vr167y+++/i4jI9evXpX379uLh4aHKDoBPhYmrATt//ryYm5vLoUOHlLLk5GS5cOGClCtXToKCgtL8qlRT8nry5EmxsbGRxYsXi0jKl3jTpk1SokQJ8fLyUuppf3WrTXJysiQlJcn3338vVapUkdOnT4ufn5/Url1bypQpIy4uLtK0aVN5+fKlvkP9aLGxsbJv3z7JkSOHdOnSRV6+fCmHDx+WVq1aiZWVlWg0Gjl//rxSXzt+S02WL18uVapUERGRZ8+eyYoVK6RevXqSK1cuCQgIEBGR58+fS3h4uDx+/Fifof6rN48Xjx49EgcHB+U7qPX333+Lk5OTzJgxI80+1JK8alcPGDNmjBw4cEDq1q0rWbJkkQsXLkhiYqLMnz9fateuLdWqVZOuXbvKxYsX9R3yB3v8+LHY2dnJkiVLlLKoqCipUqWK5M+fX+c79+zZMzl16pSMGTNGGjVqpIof/rGxseLl5SXu7u6yaNEiJXk9fvy4VK9eXerVq6fToaOltvNf/vz5JSIiQg4fPiwdO3aUMmXKyK5du2Tv3r3SsmVLqVChgmg0GnF2dlaOnX///beEh4frOXr9YuJqIFJ/4bSJWmxsrLi7u8uwYcOUL65IyomjVKlSaSYzqVHv3r2laNGicv/+fRFJSWy2bNkiTk5OUqdOHaWeWiYrve3y1MWLFyV37txia2srzZs3l3Xr1omISFBQkLi4uKQZPmDI7t+/Lzt27JBNmzbJixcvRCTlUvL+/fvF1tZWmjVrptQ9cOCAHD16VETUN5Egdbx79+4VjUYjw4YNk8qVK0uDBg2ke/fuMn78eNFoNKqZzJP6GKM9CT569EgqVqwoAwcOlNjYWJ12N2rUSDp16vTF4/yvkpKSJCYmRry9vWXMmDEiktIrV7BgQZ2JWCIpl6NFUj7DapKQkCC9evUSS0tLCQkJUcqjoqKkevXq4ujoqJO8aqlhiJX2vYiNjZVmzZqJq6vrO5NXtQx7eNPZs2fFyspKevXqpZQdOXJEfH19pUSJEkrP/5UrV8TPz0/pcaUUTFwNQOoTyowZM2TatGly9+5dERHp27evuLq6ym+//abUi4mJEXd3d5k1a5Ze4v2vUrd3+/btUqBAAdmyZYtSFh8fL1u2bJESJUqIq6urPkL8KNqT/v79+2XIkCHi6+srQUFBEhsbK1FRUcqJRFtvwIAB4unpqXPJz5CdO3dOihQpIkWLFpW8efNKnTp1lOQ1OTlZ9u/fL9mzZ5f69evrOdKPp31vtCd47Wd12rRpUrp0aendu7ecOnVKkpOTJTk5WVxdXZXk3JCl/s5NnDhRunbtKs+fPxcRkSVLlohGo5G5c+cqP6Jevnwprq6uyhh0Q6d9P0T+l/gUK1ZMLl68KE+fPhUHBwedda+XLFmic7lcTT+sUrdzyJAhYmJi8tbkNV++fKqa9/C24+Dr16+lSZMmUr58+TTJa+3ataVKlSqyc+fOLx3qf3L58mXJlCmTDB06VER0r2AcPXpUmjdvLiVLllSGy6mpF/lLYeJqQAYOHCh2dnaycOFC5VJAQkKCtGjRQkqWLCkNGjSQUaNGiYeHhzg7O6umF1IkpcfjXUvq1KhRQzw8PHTK4uPjJTQ0VFxdXeWff/75EiF+EuvWrZPMmTNLmzZtZODAgWJiYiKtW7fWuYR89OhRGTx4sFhbW8vZs2f1GO2HO3v2rFhaWsqQIUPk1q1bsmrVKilcuLBygwgR3eT1+++/12O0H0ebEGzfvl3atGkjtWrVkn79+ik/ON48sfr7+0vBggVVddlu4MCBkitXLpk9e7bOJKSpU6eKkZGRNGzYUFq3bi3Vq1c3+GNM6hO69r1bs2aNtG3bVhISEqRu3brSq1cvcXR0lB9++EHpZX7+/Ll4eXlJUFCQXuL+WC9evEjzGYyLi1OOMytWrFDKo6KipHbt2mJtba0sEWXILl++LDY2NuLr6yv+/v5y8+ZN5SpcbGystGnTRlxcXGThwoXKD44jR46Ij4+PqiZCnjt3TmxtbcXW1lbn2Jn6e3b06FHx9fWVkiVL6ozlpf9h4mogFi9eLDly5NC5vBMdHS1JSUmSnJwsQUFB0qRJE6lTp4506dJF6VVQw3izyMhIKViwoOTPn19atWol58+fVy7RiYjs3LlT8uXLp/S6ak9I8fHxqhr/eevWLSlatKjMnz9fKcuYMaMMHjxYp46fn5+4uLioZhLBpUuXxNraWoYMGaJTXrZsWZkyZYoMHjxYjhw5ogxxOXDggBgbG0vr1q31Ee5/snHjRjE3N5f+/fuLn5+feHl5iZWVlTJJSSTl89qhQwfJli2baoYJiIisXr1a7OzsdH5AxsTEKJPn9uzZI3369JFmzZrJgAEDlJOpISav2mPEyZMnZeXKlSKSskpJ0aJFZd68eRIfHy8TJkyQ7Nmzp/lRPHToUClWrJjcvn37i8f9sW7cuKHcVXD+/PnKcCOtIUOGiLGxsc4l5cjISGnQoIEqJkPOnj1bNBqN2NvbS6VKlcTe3l5KlCghAwYMkD179khkZKR89913UrduXfn111+Vnlc1zX84c+aMZMiQQbp27Squrq7i6ekp+/btU15/s+e1TZs2kjt3bjl48KA+wjVoTFwNxKhRo6RNmzYiIvLXX3/JvHnzpGjRolKtWjWZNm2aUi/15BZDPKG86datW7J+/XpZsGCBLFy4UJycnKRAgQLi6ekphw8flqioKImNjZUyZcroLMSvlkt3qeO8fv26MrTh+vXraRY31162u3Hjhmp66ZKTk6VJkyZiYWEhe/fuVdo7btw4MTU1lZo1a0rJkiXFzMxMFi5cKCL/W6T/2rVr+gw93SIjI8XDw0MZFymSMuu+S5cuYmNjI+fOnZNXr17JwoULxdfX1+An87z5HZo6dao0aNBARFJ6fqZOnSpFihSR3LlzK21+cxtDPMZok9Zz586JRqORiRMnyuXLl2XYsGHSqVMnJeYnT55Iy5YtxcXFRdq0aSOTJk2SNm3aSObMmVW1esCzZ89kypQpkjFjRtFoNOLt7S12dnZSvnx58fX1lQMHDsiVK1ckICBATE1NdSYtqeU4KpIyhMXIyEjWrl0rBw4ckMDAQOW20eXLl5f69euLpaWl5M+fX5YvXy4i6mnfzZs3xdTUVAYOHCgiKeeAUqVKiaenp86P4tTJ66FDh6RTp05y8+bNLx2uwWPiqgepL3Fp/z148GCxt7cXf39/KVOmjHz//fcyePBg6d69u5QtW1bu3bunsw81fGHPnz8vhQoVkoYNGypfzsTERJk7d640aNBAjI2NxcvLS1asWCG//fabZMqUSVU9WFrr1q2TnTt3ysWLF8Xe3l4OHDigTATRHohOnjwp3333nSpm9L7p2bNnUr16dalcubKEhYXJ+PHjxdbWVrZt26ZctmvVqpXkyJFDWSJKjR49eiS5cuXSuYScnJwst27dklq1asmoUaNEJOWSraFfCXjbRKyQkBDRaDTSo0cPKViwoLRo0ULmzp0rP//8s9jY2KTpgTTEY4y2XefPnxdLS0v5+eefRUTEy8tLMmXKJFWrVtWp/+jRI5k+fbrUqlVLqlatKu3bt1fFpXOtK1euiI+Pj5w4cULGjx8vVapUkT59+khERITMnTtXvLy8pECBApIjRw5p0aKFspLH9u3b9R36B0udrA0YMEAsLS2VYQ+vX7+Wp0+fyuTJk2XQoEFiY2MjdnZ2cuPGDX2Fm27Jycmyb98+CQ4OFpH/tfdDklc19Sh/SUxcv7DUJ5SZM2fKrFmzlB6CTp06iaenp8yZM0dJcPbv3y/lypVTxvuoxZUrVyRLliwyZMiQd8a+du1a6dq1q2TIkEHy5csnGo1GJk2apKrB6KdOnRJTU1OZO3euMgvWxMREmjZtqlNv6NCh4u7urizOb+ju3bsnv//+uwQGBsrr16/lyZMn4u7uLrly5RJra2vlxKhNbmbPni1OTk4GvxzU26RO0OrXry+dOnVKs9JDgwYNVDNuN/X3Z+rUqdK9e3elPYGBgeLl5SULFy5U1gy+c+eOuLq6GnxCp23XlStXxNbWVnx9fZXXrl+/Lt9//73Y2dnJokWL3rkPNQytSm3JkiVSoUIFEUn5To4ZM0YKFy6sLMMmkpLEb968WVq2bKncpMXQbxRx5coVGTJkiNy8eTNNr/6AAQPE1NRUli1blma7mzdvprmtrSG7efOmTJw4Mc1wDe3n8ObNm/+avFJaTFz1ZODAgZIzZ06ZNWuWTm9q6pmucXFxUq9ePfHx8THI3o93efXqlTRt2lS5a5JWfHy83LlzR+egGhMTI7du3ZIePXpI5cqV5erVq1863I92+fJlmTBhgtITJ5IyOcTd3V08PDzkyJEjsnPnTvnpp5/E2tpaNWNaL168KGXKlJHWrVvLoEGDlIThxYsXUrduXSlSpIjs2rVL5+Dau3dvqVWrlmqW9tJ+n5KSknTaMXnyZClRooQsWrRI57vYunVr6dWrlyQmJqrmu5h6IlbqG1toe3ESExMlNjZW6tatKzVr1jToH4za2M6cOSOWlpaSKVMmKVKkiBw4cEBZAeLWrVtSv359qVGjhs5EJUMc7vChJkyYIGXLllXaHxERIWPGjJGiRYvqjJ0X+V87Hz58+MXjTI+4uDhxdXUVjUYjhQoVkr59+yrjlLX69+8vpqamql4G6vz581KwYEHx8vLSWfVB683ktV69eqpbIUFfmLjqwcKFCyV79uw6iUxsbKxyQklKSpJp06aJt7e3lCpVSpmIZcgnltTi4+OlSpUqMmfOHKVsx44d0rdvX7G2tpb8+fNLjRo1dBKA+Ph4g7+bS2q3b9+W6tWrS/bs2WXkyJE6r61evVq+++47MTMzkxIlSkiVKlVUs3rAxYsXJUuWLDJw4ECdno1169bJkSNHJCYmRqpVqyZubm7KZLrRo0dLpkyZVLP0jvZzt2PHDmUGfb9+/ZSrHD169JASJUpIixYtZNq0adKlSxexsrIy+B7J1FasWCH29vY6M5fj4uKUu+0kJibKokWLpFq1auLi4qKKY8y5c+fE2NhYxo0bJyIilStXlnz58smBAweU4RA3btyQevXqSY0aNdIkQ2qR+vLwmDFjpGbNmiIiaZLXYsWKKUsqiajr5h6TJ0+W6dOny+7du2XkyJFiY2MjLVu2lNmzZyvtHDlypFhYWKS5OYYaXL16VbJnzy6DBg1Slgx8G+2PjZs3b0qePHlUcVczQ8DEVQ+GDBki3bt3F5GUmbALFy6UkiVLSt26deWXX36R+Ph4GTp0qLRv396gZ/a+S2RkpBQtWlS6dOkiV65ckQkTJoiTk5M0adJEZs2aJYsXL5ZChQpJ//79RcSwT5bvo53c4uLi8tZejitXrsizZ8/ee+AyJE+fPhUPDw/p3bu3zo+KiRMnKvcADwsLk5cvX0r16tWlWrVq0rRpU7GwsHjnUmeGauPGjWJmZiadOnWSfv36Sb58+aRKlSqydetWERGZNWuWNG3aVJydnVV3G0mRlMme2qEN58+fl5kzZ0rx4sUlZ86cMnfuXHn06JEEBQVJnz59VHGMiYmJkcaNGytjWrXelbw2bNhQypYtK2vXrtVHuB/t3r170qxZM9m1a5eIpCRvzZs3F5GUHxvaY+X9+/dlzJgx4uzsLD/++KO+wv1o+/fvFxsbG+WW1w8ePJBRo0aJubm5VKhQQQIDA+Xq1asybtw4yZYtm84qNIYuPj5e2rRpIx06dNApf/Xqlfzzzz9y9epVnSFj2u/drVu3OBHrAzFx/cxSJwDaf/fs2VMyZcokkyZNkrJly0rDhg1lyJAh8v3330uFChWUpT609dU43mXv3r1iYmIijo6OYmVlJfPnz1fG+cTHx4unp6e0a9dOv0Gmw7suDwcGBoqLi4u0a9dOWSlArYn45cuXpWDBgrJv3z6lDfPmzRNTU1P55ZdfpE6dOuLp6SlHjx6Vly9fStmyZcXS0lJVM7STk5Pl6dOn4ubmJhMnTlTKIyIixMfHR6pUqaJz8tCuemHItJ/N1J/R4OBgZSJWsWLFpHnz5jJz5kzx9/cXS0tLCQ8P16mvhmNM6vWcU9/p6m3J67Vr16R58+aqWvJKJKXnzd3dXby9veXUqVMydOhQ8fPze2f9fv36iYeHh6rGfWoNGDBAWrdurfQw+/r6StGiRaVDhw5SvXp1MTIykpUrV8qTJ0/0HGn6xMbGprniuHXrVunSpYtkypRJsmTJIp6enjpXQ9R6ztAXjYgI6LNITk6GkZERACAmJgaxsbGwtbUFAHTu3BkXLlyAr68vPD09UaJECRw4cAADBw7Exo0b4eDgAAAQEWg0Gr214b+4e/cuHj16BEdHR2TLlk0pT05ORosWLeDk5IQxY8YAgEG3UfseHD58GLt27UJiYiKKFi2Kdu3aAQDmzp2LFStWwMnJCRMnToSdnZ3Oe68Wy5cvR/v27ZGQkKC8H/fu3cOtW7dQtWpVXLx4EX379sWzZ8+wfft2mJubIyoqCnnz5tVz5Onz6tUrVKxYEb1790bXrl2RkJAAU1NTPHz4EGXLlkXHjh0xduxYfYf5QVJ/zu7fvw8zMzOYmpoic+bMmD59OtavX4/WrVujdu3aKFSoEP766y+0a9cOISEhyJcvn36D/0DvOgYmJibCxMQEAFClShXcv38fS5cuRcWKFWFmZqa8r2pz48YN9OrVCxkzZsQ///yD5ORklChRAhqNBsbGxoiLi4NGo4GJiQliYmIwd+5c2NnZ6TvsdFu7di2mT5+Ow4cPo1u3btiyZQv27t0LZ2dn3LhxA9u3b0fNmjXh7Oys71DTrW7dunj69ClWrFiBpUuXYsWKFahQoQIaNWoEIyMjzJgxA9WrV8e4ceNgZGRk0Oc/g6TXtPkrlro3IyAgQGrUqCEFCxaU7777TvmllXosS3x8vNStW1caNWqkmskfHyMuLk6GDx8uDg4O8tdff+k7nH+lfS9CQ0MlQ4YMUrduXalevboYGxtLixYtlDGDM2fOFA8PD2nSpInBT454l8OHD4u5ubmEhoaKiO5nWNsjsHDhQnF1dVVuSWzooqKi5M6dOzrjBiMjI6V48eIyYMAAEUlpm7YHr127dtKsWTO9xJpeqd+fsWPHipubmzg7O0uJEiXkwIEDIvK/cY/JyckSFxcn3t7eUqdOna/mGJN6eEP16tXF2tpa/vjjDxExzOW8PtTVq1fF29tbMmXKJLa2ttK9e3fx8vISb29vadq0qTRu3Fjq169v8GsJ/xsPDw8xMjISBwcH1cwDeB/tZ+7o0aNSsmRJcXBwkOzZs0tQUJDOBMlGjRqJl5eXvsJUPSaun9nPP/8s9vb28uuvv8r58+fF1tZWqlatqqwk8PLlS1mwYIHUrVtXlROx0mPZsmXSp08fsbOzM9j1WrX/76lPev/884/kz59f5s6dq5QdO3ZMsmbNqnN3qICAAPHy8pIHDx58uYA/obt370qOHDmkYcOG77zE+tNPP0mzZs3eel9xQ3Px4kWpWrWqFC1aVIoXL66MGxQRWb58uRgZGaWZ+NGwYUPp1avXlw71PxkxYoRky5ZNNm3aJJcuXZLKlSuLra2tcivMV69eye+//66qiVjpkTp5rVu3riruFPUhrl+/LvXr15c6dero3FHxa6A9vm7dulWKFCki69ev1ylXk9Q/ilPHHx0dLWfOnNEZ6pCcnCwJCQnSpk0bGTRokCqG6BgiJq6fSXJysty4cUPKlCmjrHl5+PBhyZAhg846g48fP5aff/5Zub+2iGFPkvhYV69elerVqxv0IvypFzdftGiRzni5AgUKKGM5tQebP/74Q0xMTGTVqlXKPrQ9sGq1du1aMTMzEz8/P51Z9JGRkTJw4EDJkiWLKnp5zp49K1ZWVtKzZ0/Zvn271KpVS4oWLaqcWGJiYmT48OGi0WikZ8+eMnHiROnVq5dkypRJNasHJCcny5MnT6RatWqyadMmERHZsGGDZMmSRQIDA5U6z58/l9mzZ0vv3r1VfYx5X1KjxvZ8iGvXromXl5d4eXnJoUOHdF5TY5L3poiICClUqJAMHz5c36F8FO1kutS3bn3fD8KEhATliqOaln40NExcP6E3P7C3bt0SJycnEUmZxZwpUyaZN2+eiKT8Glu1apXExcVJXFycqidifaiHDx8a7Ax77Xt39uxZ0Wg0Omuz3rlzR8zMzJQENTk5WZKSkuT169dStmxZmTJlil5i/hwSExNl/vz5YmJiIkWLFpWOHTtKt27dxMfHR+zt7Q22pzy18+fPS4YMGXSWKbty5Yp4eHjIn3/+KefPn1c+hyEhIeLq6iru7u7i5eVl8KsHpD7GvHjxQp48eSJZsmSR8PBw2bVrl84xJiYmRgICAuTZs2c6iZ2hH2O0x8K//vpLrly5ojNZ7mvpJU6Pv/76S3x8fMTNzU2OHTum73A+uWXLlknGjBnl+PHj+g4l3bST6erXry9Hjhx5b91ff/1VunXrJtmzZ1fFcdSQMXH9DPr06SNjx46VZ8+eSaFChaR3795ibW0t8+fPV+qcO3dOPDw85PDhw0rZ1/ALWo3eXNw89dqIWp07dxZXV1edX9YiKTOap02b9kXi/JKOHTsm33//vZQuXVqqVKkiQ4YMUcUl2MjISHF1dZU8efLolA8cOFAsLCwkX758kiNHDqlUqZJy28iYmBhJTk5W1fqJQ4cOlZ49e8qzZ8+kcePG0qlTJ8mUKZPO1ZybN29KnTp1ZPPmzXqM9OOsWbNGcufOLfb29uLm5iazZs1SXvsWk9crV65I06ZNdVZW+Frcu3dPqlevrppx82/666+/pG7duuLl5aWTvKY+n1+4cEGaNGkinTp1Mvi7mqkBE9dPIPUHdN++fVKoUCHZs2ePxMfHy8CBA8Xa2lo6duyo1Hn9+rX4+PhI/fr1v8mDsCG6du2amJiYKLdS1L6ny5cvl4cPH8rx48elSZMm4uLiIkuWLJF9+/Ypl87VkNB9DEPvmXubyMhICQwMlFy5ckm3bt1EJGW9XRsbGwkJCZE7d+7IggULJF++fNKnTx+JjY1V2mnIPxxTHyd27NghRYsWlT///FOSk5OlX79+YmpqqrNuZHR0tHh7e0vt2rVV8z5q///Dw8PFyclJFi9eLJs3b5aBAweKo6OjjB07Vqn7LR431XSDgfRKPU5Ujd6VvIqkfFb79Okjbdq0UeWyZYaIy2F9QuvXr8eWLVuQM2dOjBs3DgBw9uxZTJgwAadPn0bdunVhbW2NY8eO4fHjxzh9+jRMTU1VuXTS1yQhIQHDhg3D7NmzsWzZMjRr1gwAEBAQgEmTJmHfvn0oW7Ysjh49ilWrVuHXX3+Fo6MjTE1NERwcDBcXFz234POQVMsQiYqWZYuMjMS6deswePBgODg44MGDB1izZg2qVaum1PHw8EDmzJmxadMmPUaafmvWrMGxY8dgamqKiRMnAgCSkpLQokULXL16FXny5EH+/Plx7tw5REdH4+TJk6o6xoSFhWHdunWIiYnB7NmzYWJigvDwcAQFBWH+/Pno1q0bhg8fDgCqaRN9G65fv44+ffpARPDzzz+jcuXKiI+PR//+/REYGIgzZ86gdOnS+g7z66DXtFnlUi/8fePGDalevbrY2NhIjx49dOpdunRJZs+eLS4uLtKkSRPp16+fqidJfI3Onz8vvXr1EicnJ9myZYvMnTtXsmbNqkysSy0iIkLCw8NVPxHra3H37l1Zvny5DBs2TFnt4OXLl7JkyRIpUKCA1KlTR6mrvZlAixYtlMlKhtzTqo0tKSlJYmNjpVSpUqLRaMTHx0enXmJiovzyyy/Stm1badOmjYwePVp1x5iYmBjp1auXZMmSRTw8PHRee/DggYwbN07y588v/v7+eoqQ6P1S97zu379fBg0aJJaWlhzT+okxcf1IbzvZ7d69W7y8vMTBwUG5dWRqb16yU8slvG/FpUuX5IcffpBcuXKJsbGxst5u6suS3+IlSkN24cIFKVeunHTq1EmGDRum89qzZ89kyZIlYmdnJ126dFHKhw8fLra2tgY/1iz1Mebx48cikjIUon79+pI3b15ZuXLlvyalajjGpG7n+fPnpU+fPmJubi4LFizQqRceHi5Dhw4VZ2dnefz4sUH/4KBvl3YyXZYsWcTMzExOnTql75C+OkxcP0Lq5GXp0qXSpk0b5fm+ffukfv36UqtWLZ11Iw29Z4dSaJPXfPnySUhIiFLOhNXwXLp0SbJkySKDBw9W1iwVEfn999/l2rVrIpIy816bvPbp00cmTpwoFhYWBn8ySf15W7ZsmbRr105JtCMjI6VatWpSsWJF2bhxo5Kcqu0zmnppstS3cP3777+lR48e4uTkJL/++qvONhEREaq7BSh9e65evSoNGzZUxdKBasTENZ1SnxwOHjwoHTp0EHNzcxk8eLBSvnPnTmnQoIHUrl1bdu/erY8w6T/QJq9OTk6ydOlSpZw/PAzHs2fPpGrVqjo9qSIpN4HQaDQ6PaovXryQ3377TTJmzCgajUZOnjypj5A/WOpjzMmTJ6Vx48Zib28vvXr1Uu42p01e3dzcZNOmTaoZDqCl/S5t2bJF6tSpI25ublKrVi1lYsutW7ekZ8+e4uTkJEFBQfoMleijpP4xRp8WR7ank3YywIABAzB48GAkJSWhYMGCCA4ORq9evQAAnp6eyr2mBwwYgBMnTugzZEqn4sWLo1evXqhZsyYmT56MRYsWAYBqJid9C+7cuYNnz56hZcuWSlloaCgmTpyIpUuXonLlyqhWrRquXLkCGxsbNGjQAIsWLcL169dRrlw5PUb+77THmH79+qFTp07IkiULnJ2dERwcjJkzZ+Lq1auwtrbGpk2bkCFDBvTt2xfHjh3Tc9Tpo9FosHXrVnz33XcoV64cGjduDBMTEzRp0gRBQUHIly8f+vTpg7p162Lw4MFYvny5vkMmShdTU1N9h/D10nfmrEabNm2SLFmySFhYmIik9OiMGjVKihUrJr1799apN2jQINVdwqMUly9fFj8/P6lQoYK8ePGCPa4GQNuLERISIlZWVjrrWh4+fFi5NWZERIT4+PiIpaWlhIeHi4i6esx37Nghtra2cuLECaVsypQpUrx4cenRo4fS8/r8+XPp2bOnwY9lfXMZoFevXomnp6cMGDBAp/yHH36Q7NmzK+0+d+6cDBo0SFlzl4jIRN+Jsxrdu3cP2bJlQ5kyZQAANjY26NGjB549e4agoCBYWlpi0qRJaNCgAerVqwcjIyMkJSXB2NhYv4F/4+T/l3S6fPky7t27h5IlSyJbtmwwNTV963JPxYoVw7Bhw2BjYwMbGxs9RU1aN27cwLJlyzB69GhkypQJL1++xJ07d5A3b14AQJUqVZS6dnZ2aNmyJe7du4ekpCQA6uoxT0xMhIWFBbJmzaqUDRgwAHFxcRg1ahQ0Gg169uyJYsWKYe7cuQBgsMeYkSNH4tWrVxg/fjzMzMwApPQqP3/+HHZ2dgCAuLg4mJubIzAwEFeuXMHYsWOxceNGlCpVCsWKFWPvFREpOFQgHeT/l7zNly8fNBoNzp49q7yWPXt2dO7cGebm5ggNDcWAAQMAAMbGxhARgzyhfGs0Gg3WrVuHqlWrol27dqhUqRLmzp2Lx48fQ6PRKO9vak5OTrC3t9dDtPSm3377TblkXLlyZZQtWxZ9+vTBnTt3AADx8fEAUtb3BIATJ06gQIECBv+jQ/u5ExEldiBlfeEXL14A+F/b+vXrh5w5c+Lw4cNYunSp8joAgz3GODs7o127djAzM8OrV68AAObm5rC1tcXWrVuV53FxcQAAV1dXpb0AL7kSkS4mru+R+iQCQOm5KVGiBIyMjBAYGIi///5bed3Y2Bg1atRAy5YtcejQISWxVVNPz9cqOTkZz58/x5w5czBp0iScOnUKDRs2xLJlyzBr1qz3Jq+kX9r3pHLlyjA3N0dsbCyyZMkCPz8/PHr0CJ07d8a9e/eU3rznz5/D398fv/32G8aMGYNMmTLpM/z3Sk5OVo4PiYmJSvJWv359lCxZEq1bt8ajR4+Utj1+/BjVq1dH3bp1sXjxYty6dUtvsX+o5s2bo0SJEti3bx8GDRqES5cuAQCGDBmCe/fuoVu3bgBSklcAePToEaytrZGQkMDvIxGlwaEC75D6riyzZ8/GhQsXcPbsWfTo0QPfffcdfv/9d9SuXRvx8fGoU6cOSpYsiZ9//hm5c+dG586dMWnSJJw9e1YZTkD6oR0CEB8fDysrKxQsWBA+Pj6wt7fHrFmz8PPPPyu9Pj/++COyZ8+uqrtEfQu070X+/Plx+/ZtHD58GHXq1MGPP/6IFy9eYPHixShRogQ6duyIR48eISoqCqdOncLevXvh7Oys5+jfLfUxZsaMGdi7dy+ePn0KZ2dnTJo0CUFBQWjWrBnc3NwwbNgwWFlZYfHixciQIQOWLl2K5cuXY8OGDaq5c9u9e/ewdOlSmJqaok+fPqhSpQoGDhyIyZMno3LlyvDw8MC9e/ewfv165e5gRERvYuL6DtoTypAhQxAcHIzevXsjT5486Nu3Lw4cOIDffvsNW7ZswdixYzFq1CiYmZkhR44c+OWXX2BqaorSpUsjW7Zsem4FaTQabNq0CVOnTsWrV6+QmJioc0l17NixAIBdu3YhJiYGw4YN4/tmIG7fvo39+/ejevXqsLS0RL58+VC4cGG8fv1aqTNy5EhUqFABGzZswKFDh2BpaYmaNWti+vTpKFSokB6j/3faY8zQoUOxZMkSDBw4EM7OzvD29sbz58+xePFi7Nq1Cz169MCMGTMQGxuLfPnyYcWKFQBSxvEWLlxYn014L+0PwLt37yJ37txo27YtTE1NMXDgQCQkJGDIkCHo3LkzSpUqhSlTpuDMmTPInDkzjh07hhIlSug7fCIyVHqZEqYSf/zxhxQuXFiZ4XrixAnRaDSybNkypc7Lly/l/v37cvXqVaVs8ODBkidPHp1F0enL0s4gP3PmjJiZmcmgQYOkcePGkjNnTmnRooUy01yrX79+Uq1atTSzn0k/4uLixMfHR3LlyiV58uSRbNmySatWrUSj0Ujjxo3l+vXrcvPmTZ1ttCsOqGn1gEuXLknx4sVl7969IiJy4MAByZAhQ5q7Rt27d0+5e5aIyM8//yx58uSRv//++4vG+6G078GmTZukatWqsnDhQuW133//XXLlyiU9e/ZM8x6qbT1aIvrymLi+x759+8Td3V1EUpbfyZQpkwQGBoqISFRUlBw+fFi597mIyJ9//imNGjUSOzs73pvYAJw+fVrmz58vEyZMUMpmzpwpVapUkQ4dOsjDhw916jNpNSxRUVEikvI+rlixQiZPnizFixcXjUYjefPmlZw5c0rt2rXFz89P5syZo9xYQA2JqzbGo0ePirOzs4iIbNiwQTJlyiTz588XkZRl9lavXq2z3eXLl6Vdu3aSPXt2gzzGpP6/X7dunVhYWMjMmTPT3F536dKl4uDgID/++KNcuHDhS4dJRCrGxPX/PXr0SO7evSvR0dFK2Zo1a8TR0VHWrFkjNjY28ssvvyivbdmyRVq1aqWzjmRsbKyMHz/e4O+B/i148OCBVK9eXTJmzCjDhw/XeW3GjBlSqVIl6dKlS5qeVzIcb0tAJ0+eLK1atZIzZ87Inj17ZPjw4eLt7S1ubm7K2qaG6t69e3L16lWdY8xff/0lJUuWlBEjRoi1tbWStIqIHD9+XKpWrSpnzpxRyiIiImTlypUG19YLFy7orCV79+5dKV26tPJDPyEhQV69eiVbtmxRbtn6+++/i4WFhQwePJh3GSKiD8bEVURWrFghVatWlRw5coi3t7esXbtWRFIuPdasWVM0Go1MnTpVqf/69Wvx8fERX19f5eYCaujl+ZYkJSXJkiVLpHz58lK8eHF5/vy5zuuzZ8+W4sWLS69evXiDCBVZs2aNZM6cWe7evatT/vLlSz1F9GF+//13KVOmjDg4OEiePHlk7dq18urVK3n8+LH4+vpKhgwZpG/fvkr92NhYadCggXz33XcG//mcM2eOVK9eXSIjI5WymzdvSr58+eTgwYOSlJQk48ePl0qVKom1tbU4ODjI9evXRURk9erVBpeEE5Fh04h82+uNLFiwAD/99BNGjhyJrFmzYtSoUbC3t8f69euRK1curFu3DlOnTkVcXBzGjRuH8PBwrFmzBvfu3cPZs2dhYmKiMzuY9EPeshJAcnIy1q1bh0mTJiF79uxYtmwZbG1tldfnz5+PunXrIl++fF84WvoYIoJr166hTp062L9/PwoVKqQsuv+2999QLFiwAP369cOUKVNQoEABTJ06FTdv3sThw4eRJ08eHDhwAP7+/jA2NkadOnVgY2ODzZs349GjRzh9+jRMTU0N+hjz8uVLREREoFChQnj06BGyZs2KhIQEtGjRAlevXkV0dDRcXV3h7u6OLl26wN3dHT4+Ppg+fbq+QyciNdJr2qxnv/76q5ibm8umTZuUsilTpohGo5EDBw6ISEqv68GDB6VBgwaSI0cOqVSpkrRp00a5tGXot1r8Fmh7u/fv3y8DBgyQTp06yYIFC5Txx6tXrxZ3d3fx9vaWp0+f6jNU+gScnJxk0aJF+g7jgyxZskSMjY1lx44dStnChQtFo9HI0qVLlbKDBw/KgAEDxNHRUerWrStdunRRJioZ8oSl1Me/Y8eOSfny5SU0NFRERC5evCi//PKLzJ49Wx4/fqx8Txs2bCizZs3SS7xEpH7fZI+riODJkyews7NDlSpVsG3bNmWRch8fH2zbtg0LFixAxowZUbFiRRQsWBAAEB0dDVNTU5ibm0Oj0SAxMREmJlxRzBCsW7cObdq0Qe3atSEi2LlzJxo1aoRx48bByckJK1euxPz585GQkIDNmzfr3EqT1EH+v1fVxcUF9erVw/jx4/Ud0nu9ePECdevWxe3btxEREaGU16tXDzt27MCwYcNgZmaGJk2aIHfu3LC2tlZufaqlpmNMZGQkatWqBTMzMwwbNgx169bVWXouMjIS06ZNw/z583HkyBEUKVJEj9ESkVoZ5rWnz0yj0SB79uzYtGkT/vzzTwwfPhwxMTFo2rQprly5gu7du+PFixcYMGAAmjRpAjc3NwwdOhTPnz+HhYWFcocltZxQvjbaO5ppf3Pdv38f/v7+mDJlCjZt2oTNmzcjLCwMf/75J0aMGAERQbNmzdCuXTtYW1sjJiZGn+HTR9IOBejatStatmyp52j+nbW1NebOnQtbW1tUrlwZANCyZUtcv34ds2bNQubMmXHgwAE0atQIrq6u6NSpEy5fvqxsb+jHGO3379SpUzhx4gRsbGywf/9+mJubY8yYMdiyZYtyt8EtW7agT58+WLJkCXbu3MmklYg+2jfZ4wr87641W7ZsQaNGjZAjRw5kz54d27dvR65cuQAADx8+RHh4OMaNGwczMzMsW7bMYO8H/q1YvHgxzMzM4Ovrq9wG8+7du6hevTqCgoJQrVo1pZfq5MmTcHd3x5IlS9CmTRskJyfj5cuXsLa21nMr6L8QAx7P+jZnzpyBr68v/vnnHxQpUgT79+/XucnFH3/8gWPHjmH//v3YuHGjKo4x2vdg3bp16N27N7y8vDBu3Dg4ODggOjoaDRs2xOvXrzF06FA0bNgQJ0+exOHDh9GgQQODvzEEERm2bzZxBf6XvO7ZswdeXl5o2bIlZs+e/dbLyNoDtSFPkvjaiQg8PDzw4sULjBw5Eg0bNoSZmRnu3LkDZ2dnzJs3D23atEFiYiI0Gg2MjY3h4eGBypUrIyAgQN/h0zfs9OnT6NOnD6Kjo3H27FloNJo0wwK01HKM2b9/P3x8fPDLL7+gQYMGsLW1VWLXJq/x8fEYMGAAGjdujOTkZFUk5URk2Az/6PgZGRkZITk5GbVr18bmzZsREhKCn3/+GQ8fPlTqaC91aYcHqOGE8jXS/nDYt28fChQogAkTJmD9+vV4/fo18ubNi65du8Lf3x/79++HiYmJcoLUaDTsYSW9c3FxwZw5cxAbGwt3d3fExsbC3NxcOb4A/7v0rpZjzM6dO+Hr64v27dsjc+bMAFLaICKwsrLCpk2bEBMTg19++QUxMTFMWonok/gmelxT92BER0fDysrqra9v3boVjRo1Qo8ePeDv74+cOXPqI1x6h/j4eJiZmeHp06do3LgxRAR9+vRBkyZNcPv2bYwcORL79u3DqFGjkCNHDoSFhWHhwoU4fvw4x9SRQThz5gxatWqFrFmzYu/evbCwsNB3SB/N29sbJiYm2Lx5MwDdIRz//PMPHB0dER0djWfPnsHR0VGfoRLRV0QdP+3/I23S2q9fP0ydOhUvX75M83pycjLq16+PTZs2Ye7cuQgJCdFHqPQOIgIzMzOsXLkSPXr0gJGREU6fPo2BAwdi48aNKFiwIMaOHYt27dph6NChGD58OPbt24f9+/czaaXP6o8//lD+PX78eAQGBr6zrouLC0JCQnD58mX06dPnS4T3WSQnJ8PV1RVRUVG4fv06AChDqR48eAB/f3+cOXMGVlZWTFqJ6JP6qntcU/cAnDt3Dj4+Pli9ejXc3d3fWl/b83r06FFUqFDBoGf0fouOHz+OWrVqYe7cuXB3d0fGjBnRsmVLPHr0CAEBAWjUqBGMjY0REREBc3NzGBkZwcbGRt9h01csPDwchQsXRt26dZE3b14sWrQIf/75J4oVK/be7a5fv44CBQqo4vK59jgaHh6O+Ph4WFpaIkeOHDh79iyqVq0KPz8/9O7dG8WKFUNCQgImTJiA5cuXY+/evcibN6++wyeir8xXnbhqTZ06FdHR0YiLi8PEiRPfWzd1squmNRS/BcHBwZg0aRKOHTumJKTJycmoWrUq7t27h6lTp6J+/frIkCGDniOlb8np06dRqVIlmJqa4vjx4yhevLhyR69/86H19EV7PNywYQOGDRsGjUaD58+fw8/PD/7+/jh58iT8/PxQsGBBiAiyZs2Kw4cPY9++fXBxcdF3+ET0Ffrqhwq8fv0aJ06cwNixY3H16tV/rZ96mR0mrYZB+9sqPj5emdQCAK9evYKRkRGCgoLw5MkTjBo1Cjt27NBnqPSN0K4lDKT8wDUyMoJGo8HYsWMBAMbGxjp1gP99jlMz5KQVgDIh0s/PD926dcPJkyfxww8/YPLkydixYwdq1aqFzZs3o1WrVihQoADc3Nxw7NgxJq1E9Nl8dT2ub1vj8cGDB5g4cSIWLlyITZs2wdPTU3VrQX5r3vU+Fi9eHO3atcOsWbOU8tOnT2PYsGEwNTXFrFmzkD9//i8dLn1DUk/2vHz5MnLlygUjIyNcvHgRDRs2RLVq1bB27Vo9R/nfab+DvXv3RlJSEgIDA3Hv3j3UqFEDtWrVwvz58/UdIhF9g76qHtfk5GQl2UlOTkZcXBwAwMHBAcOHD4evry++++47HDlyRFneigyP9oR5/PhxzJo1C5s3b8bNmzfh4OCAuXPn4tdff0Xv3r3x9OlTPHr0CBs2bED27NmxatUqJq30WaVOWocPH44ePXrg6NGjsLCwQIUKFbBy5UocOHAALVq0ULbp0aMHgoOD9RTxh9P2EL/ZU3z37l1UrlwZr1+/RsWKFVGzZk3MmzcPALBmzRocOHDgS4dKRN+wr6bHNfUJZe7cuThw4ACio6NRp04dDBgwAADw9OlT9OvXD+vWrcPOnTtRuXJl9rwaqA0bNqBNmzbInz8/nj17hvLly2P48OFwdXXFihUr0Lt3b1haWsLMzAxRUVHYtWsXypYtq++w6RsxbNgwLF68GIsWLUKVKlWQJUsW5bW9e/fC19cXDg4OyJQpEx49eoSrV68a7NAj7bFTeyyMjIzUmdTYvXt3HDp0CC9fvkTjxo0xbdo0mJqaIiEhAW3btkXhwoUxYsQIg20fEX1dvprEVcvf3x9Lly5F69atkSNHDgwaNAgDBw7EiBEjkDFjRjx9+hQDBgzAb7/9hrNnz6JUqVL6Dpne8ODBA4wcORJubm7o1KkT1q9fjyVLluD58+eYOnUqKlasiEePHmH//v0wNTVF2bJlkS9fPn2HTd+IU6dOoVmzZvjtt99QtWpVvHz5EhERETh16hQKFSqEcuXK4ebNm5gxYwYyZ86MUaNGwcTExCAnYmmT1tu3b2P58uXYuXOn0sPq7e2NNm3a4Pr162jVqhUePnyIq1evIkOGDEhKSsKIESOwbNky7N27F4ULF9Z3U4joG/FVJa5r167FkCFDsHz5cri5uWHXrl2oX78+RARt27bF3LlzkSFDBjx+/BgLFizAkCFD2EtgYE6fPo3Ro0fj5cuXWLhwIQoWLAgA2L17N+bMmYPnz59j/Pjx8PDw0HOk9K06e/Ys2rdvjzlz5sDCwgLLli3Dzp07kZiYCBHBokWLUKtWLZ1tDHGFEm3SeuHCBTRp0gTly5eHlZUV8ubNi8WLFyMuLg6dO3fG6NGjsWrVKowfPx7R0dFwdXVFTEwMTpw4gZ07d3IiFhF9UYZ1JP0PkpKSEBsbi759+8LNzQ3btm1D69atMX/+fOTIkQONGzeGra0tRo4ciezZs2P48OEADPOE8i27ePEi7ty5g9u3byM6Olopr1OnDgBg/vz56NmzJxYtWgQ3Nzd9hUnfiNRDkLSsra2RkJCAgQMH4syZM+jYsSMmTpyIIkWKoHXr1njw4EGa/RjaMUbbrnPnzqFKlSrK3QK1t25t1qwZxo0bh8DAQNja2qJPnz5wcXFBUFAQnj59ijJlymDWrFkoVKiQfhtCRN8c1fa4asdjpR6j+vTpU0RFRcHKygre3t5o1qwZBg0ahJs3b6Jy5cp49OgRxowZoyStZJjWrl2LgIAA5MiRA1OmTEGJEiWU17Zu3YoVK1Zg/PjxHB5An1XqpPXixYuIjY2FnZ0d8uTJg7t37+Lo0aOwtbVFtWrVYGpqChGBq6sr+vTpg7Zt2+o5+n9348YNlCxZEgMGDMDYsWOVoQzaH/M3b95Er169cPfuXaxfv57DAYjIIBhWN8AHSn1CuXfvHszNzaHRaJA9e3bY2tri8uXLePnyJWrXrg0AsLCwQJMmTdCmTRtUqFBBn6FTKtofHc+fPweQ0itlZWWFpk2b4vXr11iyZAlGjBiBcePGoXjx4gCA+vXro0aNGrzJAH1WIqIcY/z9/RESEoKEhAQ8f/4cPXr0QNeuXeHr6wsgZa3op0+fokOHDgCA1q1b6y3uD5WcnIygoCBYWVkhe/bsAFLWlE1KSoKJiQlEBAULFsTQoUNRvXp1XLx4USdx5aRWItIX1SWuqU8oY8eOxZYtWxATE4OkpCTMmDEDdevWhaWlJW7cuIGNGzfi9evXGD9+POLj4+Hm5gaNRsPhAQZAe+LbvHkzZs2ahevXr6Nq1aqoVasWOnToAD8/P4gIgoODMWrUKAwfPlyZSMeklT43bVI2e/Zs/PrrrwgJCUG+fPlw8OBBTJ8+HZGRkRg4cCCKFCmCmTNnYseOHUhKSkJYWJiSABraRKzUjIyM0KtXL7x69QorVqzAq1evMGTIEOXGCdr2lytXDra2tggPD9fZnkkrEemNqNTIkSPF1tZWtmzZIteuXZOaNWtK5syZ5e+//xYRkcWLF4upqakUKVJEKlasKPHx8SIikpycrM+wKZXNmzeLpaWlBAQEyLp166Rdu3aSJ08emTlzplJn2bJl4uLiIn5+fhIXF6fHaOlbk5ycLE2aNJF+/frplK9Zs0YcHBxk9uzZIiLy119/ybx58yQxMVFERBISEr54rB8rPDxcevXqJRUrVpSJEycq5dq2HDlyREqXLi2nTp3SV4hERDpU2e344sULHD58GEFBQahfvz42btyIM2fOYMKECcifPz9EBB07dkTNmjURExODYsWKwcjIiD2tBuTvv//GqFGjMG3aNPzwww+IjIxEz549YW9vj9mzZwMAfvzxR7Rp0wYmJiZwd3eHmZmZnqOmb0VycjJEBNHR0UhKSgKQcsthMzMzNG3aFMePH8ecOXPQqVMnFC5cWLmMrr3Urhb29vYYNmwYxo8fj/Xr1wMABg8erPQWh4aGws7OjuPJichgqPLOWZGRkThz5gxcXFywe/dutGnTBhMmTMAPP/yAV69eYfz48bh37x7y5csHZ2dnGBkZITk5WVUnlK/Fm3fh0cqePTtq1KiB+vXr4/79+3B1dUXjxo2xdu1a5M6dG+PHj0dAQAAAoEWLFnB0dPySYdM35s3PqZGREYyNjeHq6org4GDcu3cPZmZmShLr4OCAPHnywNzcXGc7Qx4e8C7a5NXV1RXr16/HpEmTAADjxo1DcHAwpk2bhqxZs+o5SiKiFAa/qoC8ZRJAfHw8WrZsCSsrK6xduxYzZ85E586dAQC3b99Gt27d0KNHDzRq1EgfIdP/006ie/ToEf755x/ExMSgevXqyuuvX7+GpaUlBg8ejFu3bmHRokWwsbFB3759sXnzZuTMmRMbNmyAra0tx9TRZ5N6sueFCxcQFxcHa2trFClSBElJSahVqxb++ecf7NixAw4ODjA1NYWPjw+yZcuGlStX6jn6TyciIgLjx4/HuXPnEBcXh/Pnz+OPP/7gHemIyKAYdI9r6kkCz549w5MnTwAAZmZmKFSoEFasWIEmTZooSWt0dDR69OiBpKQk+Pj46C1u0l3c3MvLCy1atEDTpk1Rt25dpY6lpSWAlKWGzM3NldtMJiUloWfPnti8eTOyZcvGpJU+G3lj9YDmzZujZs2aaNq0KVq0aAFjY2P89ttvKFKkCMqWLYvKlSujQoUKePjwIZYtW6bs42ug7XktVKgQnj17hrCwMCatRGRwDL7HFQBGjBiBrVu34vnz52jdujXGjh0LAPDz88OpU6eQO3duODo6KstgnTx5Eqampm9dPJw+v9SLm1euXBk9e/ZEs2bNcPDgQQwcOBCDBw9GQEAAkpKSoNFoMGbMGGzduhUNGjTA06dPsWLFCpw4cYLj6uiLmT59OsaPH4+1a9ciQ4YMuHbtGkaMGAEnJyfs3LkTALBixQpERkbC1NQUHTp00Fnz9Gvy+PFjJCcnw87OTt+hEBGlYZCJa+qlZObNm4dx48Zh8ODBePHiBSZNmoSGDRsiODgY5ubm+PXXX3Hs2DEkJiaicOHCGDx4MExMTL7KE4qavLm4OQA8efIERYsWRb169bB06VKl7unTpzF//nwcOXIEVlZWWLBgAcqUKaOnyOlbk5CQgHbt2sHJyQkjR44EAGVpq9atW6NFixbKuM/UDH3JKyKir5FBZXba8azak8GxY8cQFxeHWbNmoWnTpgCA2rVrw8vLC+3atcOvv/6Kzp07K0MFtNQ2s/drk3pxc1tbW6V88eLFePbsGa5evYpRo0ZBo9GgW7duKFu2LBYuXIiYmBgkJCQot50k+txEBCYmJvjnn3+UiVdAyiSrypUro3Hjxrhw4QISEhJgamqqsy2TViKiL89grqM3b94c586dU55funQJlSpVQv/+/REVFQUg5SRTqVIl7Nq1C9u2bcMPP/yAiIiINPviCUW/tIubt2rVCitXrsS8efMwefJkTJ48GePHj8eoUaPw/PlzbN++HeXLl4eTkxMWL16MjBkzMmmlz+rIkSP49ddfMW/ePDx+/BgajQYajQaNGzfG/fv3sX//fqWuRqNBvnz58Pz5c8THx+sxaiIi0jKYbkkzMzPltp4iAmdnZ4SGhqJdu3YICwtDq1atYGFhARGBu7s7du3ahUqVKqFw4cIYMWKEnqOnNzk4OGDIkCEYP348Zs2ahZs3b2Lnzp2oWbMmAKBevXoAgHXr1uH48eOoWLGiPsOlb8Cvv/6KkSNHws7ODjExMVizZg3WrVuHzJkzw9vbG6tXr8a8efMQGxsLb29vPH/+HNu2bUOhQoWQMWNGfYdPREQwgDGub44T++WXX1C8eHF4eHjA2NgYq1atQps2bfDTTz9h7NixMDU1VYYUXLx4EUWLFuWwAAP28OFDTJgwAQcOHEDbtm3x008/AQDi4uKUNTDftuQZ0ae0cOFC9OzZEyEhIahduzb27t2LoUOHYu/evcidOzcA4OTJk+jfvz8ePXqE2NhY2NraIiEhAadOndI57hARkf7oPXHV0p4UnJycEBsbixUrVsDNzQ3GxsZYuXIl/Pz88NNPP2HcuHEwMTHROYlwIpZh064PeeLECXz33XcYPHgwAE5uoS9j+fLlaNu2LdasWYMmTZoASFk6z9XVFT4+Prh58ybatm2L7777Dg8ePMDNmzdx5MgR5M6dGy1btuRkTyIiA2JwiSsAVKlSBREREQgODoa7u7uSvLZv3x4dOnTA3LlzmfCojDZ5PXPmDGrVqoXRo0frOyT6BiQlJcHLywtXr17FkiVLUKdOHQBAo0aNcOrUKdSoUQPh4eHYv38/5s+fjy5durx1HzzeEBEZBr0nrqnXWk3dq+Hm5oYnT57oJK9LlixBcHAwDhw4wEt2KhQREQF/f3/cu3cPK1eu1FlxgOhziY6OxnfffYdXr15hzJgxCAwMxM2bN7FhwwbkzZsXxsbGaNq0Kf78809cunQJVlZW+g6ZiIjeQS+J6969exEWFobhw4cDeH/y+vTpUwQHByvDBrQ43kydHj58CABc3Jy+CG1vaXR0NBo0aIAzZ84ga9as2LNnDwoWLKgce0aMGIEDBw5g165dsLCw0HfYRET0Dl98Oay4uDisXr0aq1evxpQpU1KCMDJCcnIyAMDExAQJCQkAUtZxtbOzQ506dXDp0iWd/TBpVSc7OzsmrfRZaY8lwP+WxrOyssKWLVtQqVIl2NjY4Nq1a4iPj4eRkRGSkpLw559/olChQkxaiYgMnF56XB88eIDJkyfj2LFjOpN1Uve8pv53nz59MGPGDI4zI6L3Sn3cuHLlCl69eoUiRYogU6ZM0Gg0iIqKQsOGDfH69WuMHDkSnp6e+O677/D333/j3LlzaSZ+EhGRYdHbGNd3zTTXnngePnyI3r17o3Xr1mjUqBEATpIgondLnXD+/PPPWLFiBeLi4iAiGD9+PLy8vJAzZ05ERUWhUaNGiI+PR1RUFBISEnDhwgWYmpryGENEZOD0ducse3t7DBs2DK6urli/fj0mTpyYEpCREcLDw9GkSROcPXsW9evXV7bhCYWI3iYpKUlJWseOHYvFixdj7ty5uHfvHsqVK4cRI0Zg+fLlCA8Ph7W1NTZu3Ii4uDiYmpoqSWtiYiKPMUREBk6vt3xNnbxu2LABU6ZMwdOnT9G6dWs8f/4cly5dgomJic49xImItDZt2gQg5UetiODy5cvYu3cv5s+fD29vb2zfvh2HDh1CkSJFMHbsWCxbtgz379+HtbU1jhw5gpMnTypJK9dpJSIyfHpNXIH/Ja8VKlRAaGgoChYsiIiICJw9e5a9IET0TsuXL0e7du0wffp0ACkTNm1sbNCtWzd4eXnhyJEj6NixIyZOnIg9e/agWrVqCAwMxIIFC/D48WNYWFgoE0OZtBIRqYPeE1cgJXkdOnQonJycUKlSJZw7d469IET0Xm5ubujevTsWLVqEyZMnAwBy5coFT09PmJubY+nSpWjQoAE6d+4MIGVFC1NTU1y9ehXZsmVT9qOdzEVERIbPYLJCe3t7zJw5EzY2NjAyMmLSSkTvlJSUhEKFCqFv376wsLDAkiVLkClTJvTo0QO2trZITEzEw4cPkSdPHmV5rJcvX2L58uWoUKECNBoNVw8gIlIhg8oMs2TJAgC8dEdE7yQiyvChPXv2ICIiAg8fPsSwYcOQnJyMXr16wcTEBPnz58eKFSvw4sULXLt2DS9fvkT58uWh0Wh0ls0iIiL10PstX4mIPsbQoUPx66+/Yty4cYiLi8PmzZvx999/o1u3bhg4cCAA4KeffsKTJ09gamqKefPmcckrIiKVY+JK9H/t3V1IVFsfx/Hf4EkcA0GRIkR7YdRUNFSSsjKEQNMuQqHoRVB7M1+GiMq88EKMCiyKwJSizKCswLkoCDOIoimDNKTMIVQy7MKCpCLzDZ3nIhyYx/M8nHM65t7T9wNzMf+19p697n6z19prw3Tev3+vTZs2qby8XNu2bZMk9fT06Pz583I4HDp06JDKysok/e9XSgMAzIe5MgCmExgYqA8fPujjx4+eWmRkpEpKShQQEKCqqipVV1dL8n74itAKAOZGcAVgaNMPV01PDk1OTiogIECpqanq6urS4OCgp6/NZlNKSoqWLl2qvr4+MaEEAL6F4ArAsJqamlRQUKDu7m59+/ZN0o+XDQQGBionJ0e3bt3ShQsXNDAwIOnHzgEjIyMqLi5WQ0ODZ/cAAIBvYI0rAEP68uWLkpOT9fXrVy1cuFDJyclav369CgoKPH1qa2tVVVWl+Ph4BQcHa2BgQGNjY+ro6PC8TYstrwDAdxBcARjS5OSkKisrtXjxYq1cuVIPHjzQsWPHlJmZqZiYGFVUVMjf319tbW1qbW1VV1eXwsLCVFNTw+4BAOCjCK4ADKulpUVbt27V48ePlZCQoNHRUZ04cULV1dWKj4/Xli1blJubq+XLl3sdx+4BAOCbCK4ADK20tFRut1u1tbWSpLi4OEVFRSk6OlqdnZ1qbW1VfX299u7dK0ksDwAAH8YtCQCGlpiYqIaGBg0NDWnDhg0KDg5WY2OjgoKCNDg4KKfTqc2bN3v6E1oBwHdxxxWA4aWkpKi9vV1paWlyOBwKCQmZ0YflAQDg+9gOC4BhTf+vttvtiouL0+nTpxUSEvKnW1wRWgHA9xFcARjW9LR/enq6Pn36pPv373vVAQC/F4IrAMMLCwtTRUWFTp06pe7u7rm+HADAHGFuDYApZGVlqb29fcbWVwCA3wcPZwEwjemtrni5AAD8ngiuAAAAMAXWuAIAAMAUCK4AAAAwBYIrAAAATIHgCgAAAFMguAIAAMAUCK4AAAAwBYIrAPi4hw8fymKx6PPnz3/5mCVLlujs2bOzdk0A8E8QXAFgjuXn58tisaioqGhGW3FxsSwWi/Lz83/9hQGAwRBcAcAAwsPDdePGDY2MjHhqo6OjampqUkRExBxeGQAYB8EVAAwgKSlJERERcjgcnprD4VB4eLgSExM9tbGxMdntdi1YsEABAQFau3atnj9/7nWuu3fvKioqSlarVenp6erv75/xe0+fPlVaWpqsVqvCw8Nlt9s1PDw8a+MDgH8DwRUADKKgoEANDQ2e75cvX1ZhYaFXnyNHjqi5uVmNjY168eKFbDabMjIyNDQ0JEkaGBhQTk6OsrKy1NnZqd27d+vo0aNe53j16pUyMjKUk5Ojly9f6ubNm3I6nSotLZ39QQLATyC4AoBB5OXlyel0qr+/X+/evdOTJ0+0c+dOT/vw8LDq6upUU1OjjRs3KjY2VhcvXpTVatWlS5ckSXV1dVq2bJnOnDmj6Oho7dixY8b62JqaGm3fvl0HDhxQZGSkUlNTde7cOV29elWjo6O/csgA8Lf8MdcXAAD4ITQ0VNnZ2WpsbJTb7VZ2drZCQ0M97X19fZqYmNCaNWs8tXnz5iklJUUul0uS5HK5tGrVKlksFk+f1atXe/1OR0eHent7de3aNU/N7XZrampKb9++VUxMzGwNEQB+CsEVAAyksLDQM2VfW1vr1eZ2uyXJK5RO16dr033+n6mpKe3bt092u31GGw+CATAylgoAgIFkZmZqfHxc4+PjysjI8Gqz2Wzy9/eX0+n01CYmJtTe3u65SxobG6tnz555Hfff35OSkvT69WvZbLYZH39//1kaGQD8PIIrABiIn5+fXC6XXC6X/Pz8vNrmz5+v/fv36/Dhw2ppaVF3d7f27Nmj79+/a9euXZKkoqIi9fX16eDBg3rz5o2uX7+uK1eueJ2nvLxcbW1tKikpUWdnp3p6enT79m2VlZX9qmECwD9CcAUAgwkKClJQUNCftp08eVK5ubnKy8tTUlKSent7de/ePQUHB0v6MdXf3NysO3fuaMWKFaqvr9fx48e9zpGQkKBHjx6pp6dH69atU2JioiorK7Vo0aJZHxsA/AyL+68siAIAAADmGHdcAQAAYAoEVwAAAJgCwRUAAACmQHAFAACAKRBcAQAAYAoEVwAAAJgCwRUAAACmQHAFAACAKRBcAQAAYAoEVwAAAJgCwRUAAACm8B/CV+PsU9Gl6AAAAABJRU5ErkJggg==",
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",
       "text/plain": [
        "<Figure size 2000x500 with 1 Axes>"
       ]