MachineHach_Used_Car_Price_Prediction.ipynb

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  "metadata": {
    "colab": {
      "name": "MachineHach_Used_Car_Price_Prediction.ipynb",
      "provenance": [],
      "collapsed_sections": [
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      ],
      "authorship_tag": "ABX9TyPfrITOLBPD4RPTuSoGSse+",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ArpanSM/MachineLearningHackathons/blob/master/MachineHach_Used_Car_Price_Prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BtJVGr7VkkRE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        },
        "outputId": "37607838-3c00-4f93-fca9-106411faa2a1"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NbaX-2CUxzqy",
        "colab_type": "text"
      },
      "source": [
        "# Load data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nRcdWNTtfpcE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train = \"/content/drive/My Drive/Work/Datasets/Participants_Data_Used_Cars/Data_Train.xlsx\"\n",
        "import pandas as pd\n",
        "df = pd.read_excel(train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GZ0zBxuWNwRT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "02a08d5c-9cd8-4880-ba56-1db33329e15d"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Name</th>\n",
              "      <th>Location</th>\n",
              "      <th>Year</th>\n",
              "      <th>Kilometers_Driven</th>\n",
              "      <th>Fuel_Type</th>\n",
              "      <th>Transmission</th>\n",
              "      <th>Owner_Type</th>\n",
              "      <th>Mileage</th>\n",
              "      <th>Engine</th>\n",
              "      <th>Power</th>\n",
              "      <th>Seats</th>\n",
              "      <th>New_Price</th>\n",
              "      <th>Price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Maruti Wagon R LXI CNG</td>\n",
              "      <td>Mumbai</td>\n",
              "      <td>2010</td>\n",
              "      <td>72000</td>\n",
              "      <td>CNG</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>26.6 km/kg</td>\n",
              "      <td>998 CC</td>\n",
              "      <td>58.16 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Hyundai Creta 1.6 CRDi SX Option</td>\n",
              "      <td>Pune</td>\n",
              "      <td>2015</td>\n",
              "      <td>41000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>19.67 kmpl</td>\n",
              "      <td>1582 CC</td>\n",
              "      <td>126.2 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Honda Jazz V</td>\n",
              "      <td>Chennai</td>\n",
              "      <td>2011</td>\n",
              "      <td>46000</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>18.2 kmpl</td>\n",
              "      <td>1199 CC</td>\n",
              "      <td>88.7 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>8.61 Lakh</td>\n",
              "      <td>4.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Maruti Ertiga VDI</td>\n",
              "      <td>Chennai</td>\n",
              "      <td>2012</td>\n",
              "      <td>87000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>20.77 kmpl</td>\n",
              "      <td>1248 CC</td>\n",
              "      <td>88.76 bhp</td>\n",
              "      <td>7.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Audi A4 New 2.0 TDI Multitronic</td>\n",
              "      <td>Coimbatore</td>\n",
              "      <td>2013</td>\n",
              "      <td>40670</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>Second</td>\n",
              "      <td>15.2 kmpl</td>\n",
              "      <td>1968 CC</td>\n",
              "      <td>140.8 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>17.74</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                               Name    Location  Year  ...  Seats  New_Price  Price\n",
              "0            Maruti Wagon R LXI CNG      Mumbai  2010  ...    5.0        NaN   1.75\n",
              "1  Hyundai Creta 1.6 CRDi SX Option        Pune  2015  ...    5.0        NaN  12.50\n",
              "2                      Honda Jazz V     Chennai  2011  ...    5.0  8.61 Lakh   4.50\n",
              "3                 Maruti Ertiga VDI     Chennai  2012  ...    7.0        NaN   6.00\n",
              "4   Audi A4 New 2.0 TDI Multitronic  Coimbatore  2013  ...    5.0        NaN  17.74\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GWd5N_r6OYiz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "161fc42b-b5bf-4840-f896-11b8687e4614"
      },
      "source": [
        "test = \"/content/drive/My Drive/Work/Datasets/Participants_Data_Used_Cars/Data_Test.xlsx\"\n",
        "dftest = pd.read_excel(test)\n",
        "dftest.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1234, 12)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X3aLNmCXnLax",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "41498c82-7f2d-4d25-dd17-ef75d40d11d2"
      },
      "source": [
        "s = '/content/drive/My Drive/Work/Datasets/Participants_Data_Used_Cars/Sample_submission.xlsx'\n",
        "sub = pd.read_excel(s)\n",
        "sub.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1234, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "10hSWY5OX7V7",
        "colab_type": "text"
      },
      "source": [
        "# Cleaning and Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iTXR0frSlQzl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "outputId": "69c3931f-1561-40af-ddad-98fe033f136d"
      },
      "source": [
        "print(df.isnull().sum())\n",
        "print(\"-------------------------------\")\n",
        "print(dftest.isnull().sum())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Name                    0\n",
            "Location                0\n",
            "Year                    0\n",
            "Kilometers_Driven       0\n",
            "Fuel_Type               0\n",
            "Transmission            0\n",
            "Owner_Type              0\n",
            "Mileage                 2\n",
            "Engine                 36\n",
            "Power                  36\n",
            "Seats                  42\n",
            "New_Price            5195\n",
            "Price                   0\n",
            "dtype: int64\n",
            "-------------------------------\n",
            "Name                    0\n",
            "Location                0\n",
            "Year                    0\n",
            "Kilometers_Driven       0\n",
            "Fuel_Type               0\n",
            "Transmission            0\n",
            "Owner_Type              0\n",
            "Mileage                 0\n",
            "Engine                 10\n",
            "Power                  10\n",
            "Seats                  11\n",
            "New_Price            1052\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fhqz1hseu6Au",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        },
        "outputId": "bb403ce4-9435-4fc4-a49a-bb40c47a3385"
      },
      "source": [
        "del df['New_Price']\n",
        "del dftest['New_Price']\n",
        "del df['Name']\n",
        "del dftest['Name']\n",
        "del df['Location']\n",
        "del dftest['Location']\n",
        "print(df.isnull().sum())\n",
        "print(\"-------------------------------\")\n",
        "print(dftest.isnull().sum())\n",
        "print(dftest.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Year                  0\n",
            "Kilometers_Driven     0\n",
            "Fuel_Type             0\n",
            "Transmission          0\n",
            "Owner_Type            0\n",
            "Mileage               2\n",
            "Engine               36\n",
            "Power                36\n",
            "Seats                42\n",
            "Price                 0\n",
            "dtype: int64\n",
            "-------------------------------\n",
            "Year                  0\n",
            "Kilometers_Driven     0\n",
            "Fuel_Type             0\n",
            "Transmission          0\n",
            "Owner_Type            0\n",
            "Mileage               0\n",
            "Engine               10\n",
            "Power                10\n",
            "Seats                11\n",
            "dtype: int64\n",
            "(1234, 9)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tpzd_xxOlYAW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        },
        "outputId": "b39fbb9c-a42e-4430-96cc-153d910dd15d"
      },
      "source": [
        "#train\n",
        "df['Power'] = df.Power.str.extract('^([-\\d\\.,\\s]+)')\n",
        "df['Power'] = df['Power'].astype('float64')\n",
        "df['Engine'] = df.Engine.str.extract('^([-\\d\\.,\\s]+)')\n",
        "df['Engine'] = df['Engine'].astype('float64')\n",
        "df['Mileage'] = df.Mileage.str.extract('^([-\\d\\.,\\s]+)')\n",
        "df['Mileage'] = df['Mileage'].astype('float64')\n",
        "#test\n",
        "dftest['Power'] = dftest.Power.str.extract('^([-\\d\\.,\\s]+)')\n",
        "dftest['Power'] = dftest['Power'].astype('float64')\n",
        "dftest['Engine'] = dftest.Engine.str.extract('^([-\\d\\.,\\s]+)')\n",
        "dftest['Engine'] = dftest['Engine'].astype('float64')\n",
        "dftest['Mileage'] = dftest.Mileage.str.extract('^([-\\d\\.,\\s]+)')\n",
        "dftest['Mileage'] = dftest['Mileage'].astype('float64')\n",
        "#train\n",
        "df = df[df.Mileage != 0]\n",
        "df = df[df.Seats != 0]\n",
        "df = df[df.Power != 0]\n",
        "df = df[df.Engine != 0]\n",
        "df = df[df.Price != 0]\n",
        "print(df.isnull().sum())\n",
        "print(\"-------------------------------\")\n",
        "print(dftest.isnull().sum())\n",
        "print(dftest.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Year                   0\n",
            "Kilometers_Driven      0\n",
            "Fuel_Type              0\n",
            "Transmission           0\n",
            "Owner_Type             0\n",
            "Mileage                2\n",
            "Engine                25\n",
            "Power                103\n",
            "Seats                 30\n",
            "Price                  0\n",
            "dtype: int64\n",
            "-------------------------------\n",
            "Year                  0\n",
            "Kilometers_Driven     0\n",
            "Fuel_Type             0\n",
            "Transmission          0\n",
            "Owner_Type            0\n",
            "Mileage               0\n",
            "Engine               10\n",
            "Power                32\n",
            "Seats                11\n",
            "dtype: int64\n",
            "(1234, 9)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FnSuJJfQr82f",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 947
        },
        "outputId": "0adf8e46-f0cb-4dd2-8486-8134f6c65626"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "fig, ax  = plt.subplots(nrows=2,ncols=2,figsize=(20,16))\n",
        "#fig.set_facecolor('xkcd:salmon')\n",
        "ax[0,0].scatter(df['Mileage'],df['Price'])\n",
        "ax[0,1].scatter(df['Engine'],df['Price'])\n",
        "ax[1,0].scatter(df['Kilometers_Driven'],df['Price'])\n",
        "ax[1,1].scatter(df['Power'],df['Price'])\n",
        "ax[0,0].set_title('Mileage')\n",
        "ax[0,1].set_title('Engine')\n",
        "ax[1,0].set_title('Kilometers_Driven')\n",
        "ax[1,1].set_title('Power')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1152 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SSQIRaiVqSSF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = df.drop(df[(df['Mileage']<10) & (df['Price']>100)].index).reset_index(drop=True)\n",
        "df = df.drop(df[(df['Power']<300) & (df['Price']>140)].index).reset_index(drop=True)\n",
        "df = df.drop(df[(df['Kilometers_Driven']>60000)].index).reset_index(drop=True)\n",
        "df = df.drop(df[(df['Power']>450)].index).reset_index(drop=True)\n",
        "df = df.drop(df[(df['Engine']>5000)].index).reset_index(drop=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lQuyA5O9XA90",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 934
        },
        "outputId": "da3097b2-a2a9-43c7-cabd-fb810aef7b48"
      },
      "source": [
        "fig, ax  = plt.subplots(nrows=2,ncols=2,figsize=(20,16))\n",
        "ax[0,0].scatter(df['Mileage'],df['Price'])\n",
        "ax[0,1].scatter(df['Engine'],df['Price'])\n",
        "ax[1,0].scatter(df['Kilometers_Driven'],df['Price'])\n",
        "ax[1,1].scatter(df['Power'],df['Price'])\n",
        "ax[0,0].set_title('Mileage')\n",
        "ax[0,1].set_title('Engine')\n",
        "ax[1,0].set_title('Kilometers_Driven')\n",
        "ax[1,1].set_title('Power')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fhHpunXT3YYZ7k+GCBiFiTJrNbBYaQmA5GBQ+DSFyuZ8tNjHSzBVEgPTGHQpmQgghhKQliw4f1V1Uhp1Qn/Vdd08L8yKROkODEDGmaE8FmUDRXS8S2+R8RRvTiL/QuEMIIYSQIsmqw4e6S1z/Bfr1WZ9197SUUVSHkCKhQYhYUeRmtq3wqml74FVjWy2KYT+EEEIIIaQsXOjwSfqsz7p7WtJUiCWkStAgRLzFZ6+aNMn56BlCCCGEEEKqjE6f9Vl3T0vRRXUIKRoahIi3+OxVw5xAhJAqw3wIhBAy3OQhB3zW3dNCnZ/UHRqEiNf46lVTxxMQQshwwHwIhBAy3OQpB3zV3dNCnZ/UnZGyG0BIFZmeaOOVp8bRHmtBYDU2+pWnxmslAAkh9USXD4EQQkj9oRwwhzo/qTv0ECKEEEIKwodQLeZDIISQ4YZyQI1KTtMAROoKDUKEpIAhF4QQW3xZN5gPgRBChpux0SY+udWTXh9mfJHThBQJQ8YISQFdbQkhtviybszs245Ws9F3jfkQCCFkeAgCu+vDgi9ympAioYcQISmgqy0hxBZf1o06VoEhhBBizs3FQe8g3fVhwRc5TUiR0CBESAoYckEIscWndYP5EAghZHjxSR75BPuFDCMMGSMkBQy5IITYwnWDEEKID1AeyWG/kGGEHkKEpIAhF4QQW7huEEII8QHKIznsFzKMiMCD7GGTk5PB2bNny24GIYQQQnJCCHEuCILJsttB+qEORgghhNQbnQ5GDyFCUtCZ7/L0QIJP/eJTWwghhBBCqspzf/hXOPX+jbV/Tz2yCa//1i+X2CJCiCtoECLEks58Fy+9c3GtLGV3YREvvXMRAIba4OBTv/jUFiKHBjtCCCHEf+LGIAA49f4NPPeHf0WjECE1gEmlCbHk8PEra4aGkMXeMg4fv1JSi/zAp37xqS1kkNBg111YRIB7BrvOfLfsphFCCCEkQtwYlHSdEFIt6CFEpPD0Xs1HknKUuuvDgk/94lNbyCA6gx3XGUIIIaS6cA9BSLWghxAZgKf3eh4ca1ldHxZ86hef2kIGocGOEEIIqR/cQxBSPWgQIgMw3EbPzL7taDUbfddazQZm9m0vqUV+4FO/+NQWMggNdoQQQkg1uG+dfLsou849BCHVgwYhMgBP7/VMT7TxylPjaI+1IAC0x1p45anxoXeH9alffGoLGYQGO0IIIaQa/P7Tv4AR0X9tRKxej8M9BCHVgzmEyAAPjrXQlSzcPL2/x/REm8YFCT71i09tIf2E48IcA4QQQojf2Mhs7iEIqR40CJEBZvZt7yvZDfD0nhDiFhrskmFiTkIIIT5gKrNd7yEoBwnJHxqEyAA8vSeEkHIJE3OGSnWYmBMA12JCCCFe4nIPQTlISDHQIESk8PSeEELKQ5eYk2szIYQQX3G1h6AcJKQYmFSaEEII8Qwm5iSEEDLMUA4SUgw0CBFCCCGeoUrAycSchBBChgHKQUKKgQYhQgghxDNm9m1Hq9nou8bk/oQQQoYFykFCimHocwgxez0hxcB3jRBzmNyfEEKKgzqKnjL6h3KQkGIYaoMQs9cTUgx81whZxUapZnJ/QgjJH+ooesrsH9dykIY/QgYZ6pAxXfZ6QogZnfkupuZOYNvsMUzNnUBnvjvwN3zXCLmnVHcXFhHgnlIte2cIIYQUA3UUPXXpH8pgQuRkMggJIcaEEG8LIS4LIf5GCPHLQohNQoj/KIT4wd3/3+iqsa5h9npCsmEqXPmuEVIfpZr4QdV1MEJ8gTqKnq6iH1TXfYUymBA5WT2E/gDA/xUEwQ4AXwHwNwBmAfxFEAQ/D+Av7v7bS5i9ntQVE68dF5gKV75rhHDTQZxTaR2MEF+gjqKnIYTVdV8xlcFF6dCE+EJqg5AQYgOA/wLAHwFAEAR3giBYAPDrAP747p/9MYDprI3MC2avJ3WkSJdYU+Ga5l2jQCZ1g5sO4oo66GCEuCat3sD9gJ7lILC67ismMphhZWQYyeIhtA3AdQD/QQgxL4T434UQDwD4mSAIPr77N38P4GeyNjIvpifaeOWpcbTHWhAA2mMtvPLUOJOLkUpTpEusSriOjTb7lDIAVu8aBTKpI9x0EIdUXgcjxCVZ9AbuB/S0Fbqe6rqvmMhghpWRYSRLlbF1AH4RwL8JguCMEOIPEHNNDoIgEEJIzcdCiG8A+AYAbN26NUMzssEqLqRuFBmWMrNve1/lCQBoNgQ+vb2ET271ANxTyl55ahynZvcO/Ias4oNOIPN9JVWFJXSJQ2qhgxHiiqx6A/cDavbs2IzXTl+VXq8SJjKYod1kGMliEPo7AH8XBMGZu/9+G6vKyD8IIb4UBMHHQogvAfix7MtBEHwbwLcBYHJyslo+h6RSDFuJyQfHWtJEf3mEpciE62efL2Fhsdf3dyqlTFXKNK7UhVAgk6rDTQdxBHUwQiKk2cgPm36YlmPf/1h5/dD0eMGtyUaSDC5ShybEF1KHjAVB8PcArgkhQj+7XwHw1wDeBfD1u9e+DuBPM7WQkAzIXIhn3r6AXQe/U9vcNEWHpUxPtHFqdi8+mHscp2b34mbMGBQiU8pUJ3qqRIUUyIQQQh2MkDi2OdoYmm5O6PFtel1FFXJDMrSbDCNZPIQA4N8AeF0IsR7ADwH8t1g1Mr0phPhNAB8C+I2M9yAFUNdTEpnBobccrHmwhAoAgFo8L1B+WIrN6Yrq5G45CNBqNvrGjgKZEEL6oA5GyF1kIew6vYGh6cWi8ggH/NC/o/ugsdEm7ls3gpuLvVrtiQhRkckgFATBeQCTko9+JcvvkmLxfZHOgkmIUR0VgDLDUmyUMpXxqB3JJVQ3IyUhhLiAOhgh97A9DGOuGHPGWs2BVADhdVN8NsDF90Gf3Oqh1Wzg1Wd2ld42Qoogq4cQqQE+L9JZURkc4lABcIeNUqYzHjHXCiGEEEJMsdEbmCvGnANP7sTMWxfQW7mXbqw5InDgyZ3Gv+GzAa7O+yBCTKBBiHi9SGdFZnCQQQXALaZKWdnhbYQQQggZPmxDzIYZF7qazwa4Ou+DCDGBBiHi9SKdlbgQGxtt4tPbS32nHFQAyoWeQIQQQggpEh5I2ZFVV/PZAFfnfRAhJtAgRLxepF0QF2J1TaDtA+zb4YLjTQghpKpkMXIMs/xL8+w+G+Dqvg8iJAkahIjXi3QeFO2R4oPSUEQb6pycnAzC8SaEEDKMdOa7fTl1uguLmHnrAoD6y78sst9Xj/Bh2wf5jg/7pmFDBEGQ/Fc5Mzk5GZw9e7bsZhANfDnTERecwOqpwytPjRfWf0W1YWruhLJi2KnZvc7uQ/yA401sEUKcC4JAVhWLlAh1MEIG0em9uw5+R1l16/zLXy26qYVC2U/yxId9U13R6WAjRTeGVI/w5ewuLCLAvdOAzny37KZ5j65yQd3awKR8wwXHmxBCSB1J0ntlxiDd9TpB2U/yxId90zDCkDGSiM/lGH32XOrMd5Ul77MKTpvnLkp4MynfcMHxJoQQEsVnncwGn/XestjfuYg3zlyDKq6Esp+4gAbHcqCHEEnE15fTZ8+lsG0qsghO2+dW3WtECKd9NbNvO1rNRt+1OiXl68x3MTV3Attmj2Fq7oQX86xM9uzYbHWdEEJIfenMdzHz9oU+3WTm7Qtey0qVXE/Sex9Y35B+rrpedfZ3LuK101exrEgzUiddj5SLas9Cg2O+0CBEEvH15fTZrVDWthCd4DQxOtg+t8xQAwDLQeDUgDY90cYrT42jPdaCwGo8eV1ifn02PpbFycvXra4TQgipLwffu4Tecr/BoLcc4OB7l0pqkR6dXE/Se5sN+fZJdb3qvHHmmvKzuuh6PPTzg7ofLvsKQ8ZIIr6WY/TVcympDSrBaVq5wfa5w+9+880LA6c7rl2gfa0gkRW6jw/i8/tHCCGkWD65Jc+fo7peNjq5nqT33lTkClJdrzoqzyAAtUgkzaqp/sCKb+VQT1M2sUZnGffV88NXzyVdG9pjLWW/mXr+pHnu6Yk2VhQCnRv4ZGj8GMTn948QQgjRoZPrSXrvsMm/hhDKz+rgTeNzxMEwMj3RxqnZvfhg7nGcmt1b+n5zGKBBiBiFw/j4cvrsVpgmv4qp0SHtcw+bAuMS9t0gPr9/hBBCimWs1bS6XjZJcl2n9w6b/Hv2sS3Kz+oQQq8qAKO6TkjdoEGIVNYynsVzKe9Y4TT5VUyNDmmfe9gUGJew7wbx1XOQEEJI8Rx4cieaI/2eJM0RgQNP7iypRXqyyPVhk3+Hpsfx/O6tSk+hKuwZdKieS+cZRUidYA4hUulwmDQ5a4qIFU7Tpza5mtI8N+Ny08O+k1PXnFGEEELsqJqczNreYZN/h6bHcWh6HA/PHpN+XmVvGlWOJF3uJELqBA1CBA+OtaQLeR7hMJ35bunKQhEJgtP0aRHK1LApMDYkzU32HSGEEKKGcrL+NISQGkqq7E3TVujs7SFIC+DDvoyUDw1CpLAqYr5k8S/CIyptn1KZKgdf5iYhhBBC8odyPx119KbxtZpy3vAdICHMIUQKi4X2JVdREQmChy2+vOr4MjcJIYQQkj+U++lQec1U2ZtmWHV2vgMkhB5CBEAxnim+5Coq6iRgmLx9qu5y6svcJIQQQkj+DKPcd6Gr1dWbZph09pBhfAeIHHoIkcLwpXT3sJ4E5EXoctpdWESAapYg9WVuEkIIISR/hk3uu9LVqEPXh2F7B4gaeghZUnVPiDLx6VShiJOAYZkrRSTpzhuf5iYhhBBC8mVm33bMvHUBvZV7uW+aI6K2ct+lrjaM3jR1hLovCaFByAIm38pG1UqSZmGY5kodXE6HaW4SQgghBEC8MFZ1C2UloioLX+Vy8SQb1H1JCA1CFtTBE6JshuVUYZjmyoOKcp10OSVkkGHxHCSEkKKxWV8PH7+C3nJ/ZazecuCVnuZSXrgsF085Vh+GZV9G9NAgZEEdPCFIMQzTSUwdXE6HyaOLlIfpPKOyTQghdtjKcd91etd6iaty8dSX8oOyn5QFk0pbwORbREVnvoupuRPYNnsMU3MnoDpwSXMSkyfxdqdJBF2HBIMsvUmKwGSe1SFJOyGEFI2tHFfp7gGAh2eP4ZGX/hz7OxddN9MY13qJq3Lx1JfygbKflAkNQhbM7NuOVrPRd61qnhDEPbJFXHXgYnsSkycuhc/0RBunZvfig7nHcWp2b6WMQYD/J4WkHpjMMyrbhBBij60cl+n0UZaDAK+dvlqaUci1XuJqD0N9KR8o+0mZ0CBkQR08IYh7ZIu4CtuTmDzJW/i48D4qCnr/1Qtf557JPKOyTQghcnRru60cj+v0Kt44cy1Lk1PjWi9xtYehvpQPlP2kTJhDyBIm3yJxTBdr37zJ8hQ+VYsxr0MeJLKKz3PPZJ4xSTshhAyStLankeNRnf7h2WPSvynLszsPvcTFHob6Uj6MjTbxya2e9DoheUMPIUIyotqobRxteu1NlucpT9VcX+n95x9pvXx8nnsm80zm1i+wuvnxyduJEEKKJGltzyrHVTkey8r96KteMj3RxtOPttf6pSEEnn60OoflvnoQq+yOHmWaIDWGHkKEZER1WvLyEzu9FpB5nvJU0fWV3n/pyKMqRhYvH9/nXtI8Cz87fPwKuguLEFhNcgr45e1ECCFFYrK2Z5Hjzz62Ba+dviq9Xhau9RIX8roz38XRc901z6nlIMDRc11MPrTJe7nkswfxzcVB7yDddUJcQg8hQjLi6ylOEnm2O633ka8nN0ROXlUxsnj51CG/QZikvT3WQvxw0BdvJ0IIKZK81/ZD0+N4fvfWPs+X53dvxaHpcSe/Xzad+S5m3rpefVPpAAAgAElEQVTQJ69n3rpgLa999sJNwue210F3IdWFHkKEOKCq3iV5tTuN95HPJzdEjk65yjJmWbx86pTfwHdvJ0IIKYoi1vZD0+O1MQDFOfDuJfRW+o8YeisBDrx7yUpeV1ku+dz2OukupHrQQ4gQ4pw03kc+n9wQOXkpV1lOyqrqsSeDJ4aEELJKndb2MlhQhB6prquoslzyue2c36RM6CFECMkFmfeRLn7d55MbIievilhln5TlkRcpDWX3AyGE+IRP3thFyAlfZFGUKsulLG0vYix8mt9kuKBBiBBSCEkhYWmMCz4qS8NEXophNLGy7diahB7q5o1PoYtZ+oEQQkg+FCEnXN9jo6Ks+UbLsuZVlktp216UXkCdlpQFDUIEABchMojrOZGUb8bWuODTxn1YyVMxjJ+UhQnHk+6TNM+S5k1eeZHSwhNDQghZxRddtQg54foej//Cl6RV1B7/hS+lbmMVSSNTixhv6rSkTGgQIlyEyAB5zImkkDBb44JvG/dhJQ+DRVzp37NjM46e6xrNx6R5ljRvGLpICCH+4ZOuWoSckHlM667r6Mx38caZa9LPTl6+bv1bvoxDURQx3tRpSZnQIFRhXJ2UVHkR8uW0SIbPbdPRme/im29ewHLQX40i65wwCQmzMS5w415PZMrm66evKsuvx+dL0jxLmjd55UUqgqquOYQQkoRPumoRcqIhxIAeFl63IZSpst8C7HUmk3GooizStTnP8Q7vqzL0UaclRcAqYxUlXOC7C4sIcM9C35nvWv9WVTfWLvsg6T5TcyewbfYYpuZOGP1+UW1zjWvFIcrMvu1oNRt917Lkm/G5WgRJj0zZlM9G+Xzcs2Oz9G/D60nzxvU8LYqqrjmEEGKCT7pqEXJCpYeprquQydQoY5Y5hJI8l6ooi5LanNd4R++rYkPLbnwISQMNQp5ga3RwWaK7qhvrIsqUpxVsVS2hnqQ4ZJkTrktqVnXjTvTYKPey+ahyfw+vJ82bqpZ+reqaQwghJvikqxYhJ9qK51JdV5EkUy3tS1A5KIXXqyiLktqc13gn6dyAur9JvUjjfOAShox5QJp4XJcnJVUtIelzTK9PJ1k26NrnqnqUK4WpypUuiBqVa7ZAv6eQaj4mnV6azJsqJnKu6ppDCCEm+Kar5i0nXD2vSqaGLCwOVh7ToTIghderKItM2pzHeJv0yYKkMhypFz7k5aJByAPSGB1cxrNWdWNdRAx3WsGWd9vyis9WtbshhJdeElXcuBM9KiX46UfbOHn5euKcN8m7UMd5U+XcR4QQksT0RBtnP7yBN85cw3IQoCEEnn60fmt5iCvdXCZTo9jmJEqiirKorDYnGeuKaAMpHx/yo9Eg5AFpjA6uT0qquEEq4rQorZDIs215WpJV7fbRGETqSVYl2FXeharh2+k5IYS4pDPfxdFz3bW1fDkIcPRcF5MPbaqtfuJCNw+//8KR89LPbWXjWKsp9Soau5vrpoqyqKw2JxnrfO834gYfvOqYQ8gD0sRFVzXPhUuK6IO0eWrybFue8dmcV8QHpifaODW7Fx/MPY5Ts3ut5p+rvAtVg+8uIaTOVDE3jS9MT7SdycYDT+5Ec6Tfq6g5InDgyZ1r96qaLCqrzfH7jrWa2DjarEy/ETf4kB9NBB6cmk5OTgZnz54tuxmlEff4AOReGVUs41gHfOv3bbPHpFWXBIAP5h4vujmEeIXpekqKRwhxLgiCybLbQfoZdh2MVIO8dR/fdD3XuJSNde8rQoqkKL1Vp4MxZMwDTEIkfEg4VUdCodZdWFzLPdKO9b9v4XRVjM8mpCiyhpxR0SWEEP/IQ/eJ6oDRwgV11LFd5gs9++EN/P3N2wgA/P3N2zj74Y3a9BMhReNDLl8ahDwhyejgQ8KpuhE3soVx1D4pArLNqe/x2UVtqLlxJ4B6HqSZCzS8E0KIn7jWfTrzXcy8fQG95VXdL+59ZKpjV0kXcWHI2d+5iNdOX13793IQrP370PS4y+YSMjSU7XzAHEIVwYeEU3VDZmQL8SEuPdycdhcWEaB/c+prfLaqzZ35biXvQ/zG9TxgjgpCCPET13leDr53ac0YpCJJx66SLhIacqJJuV87fRX7OxetfueNM9esrhNC/IceQhWBYULuSRL0ZRvbdJvTpES7ZZ1YJXmy7e9c7CsZ++xjW1KdKFXNYy7LeCR9t0qnky7pzHfxzTcvDFRIWewt46V3vr/2mc08K9vwXuZYDus8IoQMJ5/cGqyUFSdJx66SLqIz5Ew+tMl4/c9aybNsWZP2/q70V0J8hAahiuB7mFAVURnZop+XSdrNaZlhL7o2u3QzLnvjbkOW8Uj6rurzsx/ewMnL12u7uQ+fW6WALvZW1v7bZp4lGd7zVGTLfG8ZKkcI8Z2i1ykTHVulQ+p0y7LQGXKK6teyZU3a+zNMjtQdhoxVhCqWcfQdWUn5kKzGts58F1NzJ7Bt9him5k6kch9OW4awzLAXXZtduhn7UKLRlCzjkfRd1eevn75aCRf2tOjCPVWYzDPZmhCuBXmHBpT53jJUjhDiO67XqaZmB2SqYzeEsLruK0Wt/2XLmrT3Z5gcqTs0CFWI6Yk2Ts3uxQdzjyeGDJFkokY24J4Az2psc7Vx1G1OdZTpPaNrc1Y3Y9P7+EaW8Uj6rupzVXLMupBmLpvMM53hPW9Ftsz3tkoed4SQ4cT1OqVKHzQiYKxju9RrfCOP9b9sWZP2/nUeZ0IAhoyRu5Qd05sHJs+ky+qetk9UG8cD716y6tO0ZQjLzDela7Ms3wuQ7iTNhxKNpmQZj6TvJoU9RkmjcPm6LqieuyEEAgRYkehopvNMtSbkrciW+d4yRx0hxHdcr1MyOaG7LqOtaFO75LVTJrtVbW0IIdXN8lj/y5Y1ae+v6qOqeYIRooIeQqRSVRJMyfpMWb6v2iAuLPas+zSNV9ieHZutrrtG1eZnH9si/XvV9bT3cYmL0L8s3kyy7wqszsepuRPYs2Oz9HMZtgqXz+uCqk//l9/4Cv7VY1ul30k7z0LyDlMs0+utSh53hJDhRLVO7dmxOZWcVslKmy2+j2unSnbL9IVWs4FnH9vi7BmSxqDs/kp7/91f3mh1nZCqQYMQKT2mNw+yPlOW7+s2iEX06cnL162uF8XkQ5sGFpyRu9d9xJVBJEv+r3hYo8C9cLDuwiKOnuvi6Ufbfb/93O6tThSuvNeFLMY2XZ8emh7H87u3rp3cNYTA87u34tD0eKZ75q3IlpknjjnqCCG+Mz3RxtOPtvvW9l/cugFHz3VTyWmVI5BNEJCPa6dKdp+8fH2g/55+dFVmunqGpDEou7/S3v9HP5Ef9KquE1I1GDJGSo/pzYOsz5Tl+zP7tuOFI+cz3T8Lvo7n4eNXsBK7tnL3uo8bT5flZHWhiabfnZo7MeDqHCp5p2b39l23KSGrIs955KLSiK5PD02PD1T+yHrPIsIUs8yTKt+bEEKS6Mx3cfRcdy10ZzkI8J/ev6HMmVekkcGntVMlo8NDpGj/HT3XxeRDm5w/g24Myu6vNPf3Va8mxBU0CNWELLk+yo7pdZmnJPwt1QmP6TNl6ZPpiTYOvncJn9zqpb5/FsoezyjRsVWNia8C1TcFwKY9KoXH5l3Lcx65NLYVec+8FFlfczURQogvyNbwLHpFXfPC6HLsFSl3fdXt0uCTXl13qA+VA0PGakDW0JYyY3pd5imJ/pYMm2fK2icvP7Fz6POBxMdWha8C1bfS9lnbY/uuJeUvin7PNhSrDGObbwa+EJ9zNRFCiC/YrNUmcvHLm0etrlcFlQ6oqohlWpgiZH3DzGDmq26XBl/06rpDfag8aBCqAVlzfZQZ0+syT4nst0JsnylrnzAfiH48QnwWqL4pAFnbY/uuJeUvCoV0GgFehrHNNwNfSB1zuBFCiGvGRptGf2cqF394/ZbV9aog0wGfflSt/9l6RD1wX3Jwic+6XRp80avrDvWh8mDIWA1wcfJdVkyvy1N71XcEMJBjxYSsfTLs+UB0YygA711BfSttb9oelbttmnctKX9RKKRt3dBn9m3vy+cD5K9A2tyzSJdlXz2XCCHEJxQOLmg1R7Dpgfvw0cIiNrSaEAJ48ch5HD5+Rbt2qzxmVNerRFwHnJo7ofxb2+ddkKRDCAl1uz07NuPw8St48cj50nUnV/igV9cd6kPlQYNQDahybKvLtle5H+qIajzaY61UBroy8E0BSGqPLnFylvdDJ6TT5HAow9hmY1DLmvDaBq5bhBCSzM1FuSHidm8Fp2b3Gq3dUWO/iqrnEJKhe962paxJ0u2KlqGkPlAfKg+GjNUA30JbbHDZ9ir1Q5by11WhSuMB+DsmNu3SudtmGQ+VMN7QakKlOvsowKcn2jg1uxcfzD2OU7N7pcpp0S7LVXtPCCFERZ5yNCnsN2ntNs1r+OxjW5y01ydUfScAY1kTjq1sw95siLXfYdgPSQv1ofKgQagGVDm21WXbq9IPw5I0rSrjAfg7Jrbt0nnyZBkPlZAWQl7lJUnJ9LW/geJdlqv0nhBCiIq81/WkzWLS2m2S13AEwORDm7I31jNURSKe273VSNYkFW2JKgIM+yFpoT5UHgwZqwm+hbbY4LLtVeiHMkpul0UVxgPwd0xs25Xkbpt2PFThVi8eOS/9+wB613Bf+xsox2W5Ku8JIYSoyHtdTwr7TVq7TQwSK3d/v27rcdYw7SRjWm8lWOs3hv2QLFAfKgcahAgpGJ6e+IdvYxLmOVCdxqnalWeyZpmQVrUxKSeBb/0dpYyE14QQUnWKWNd1m8WktVtlqIjjgxzyDZM+Cf+GMpSQ6kGDECGGuKo8ZHt6klfFoyIrKfmOTyda8YSMMlTtKjpZc1rFz6f+juNbdTlCCKkCea7rJvpK0totk1cyAqxW5arTup810bOJMS3qiQxQhhISx+d9V2aDkBCiAeAsgG4QBL8mhNgG4P8E8EUA5wD86yAI7mS9DyFl4rJqgm356zyqNbAKRD8+nWgluWYntatId9u0ip9P/S2DLsukKlAHI76Q17puo6/o1u64vBobbeLT20vorQxmwqubTpQ1nC/JmBYfZ8pQQvrxfd/lIqn0fw/gbyL//n0ArwZB8HMAPgHwmw7u4RRfqwmVCftEj8uqCTZJ0/Kq1sAqEP1kSWTn+t1JKg/rU4K9tKcdTBxIiDMqp4ORepLXum6jryTJ42ilyfnf+yqe+aUtyjLzddKJVHqFSQgdMDi2G0ebGLtbZZTym5BkfN93ZfIQEkL8LIDHAfxPAH5bCCEA7AXwr+7+yR8DOADgf8tyH5dktdD57O6VFt+tlj5gGhtvOj9MT0/yisn3OYdLWaQ50crj3VG5ZrfHWjg1uzfVb+ZB1me36e+i1906rvOkflRRByP1Jg/PkCR9JZpzT+BewatQJp398AZOXr4+sJ535rs48t1rWA7URejrohOp9AqB1f5zLbMJIf34vu/KGjL2LQD/I4B/dvffXwSwEATB0t1//x0Ar1aPLG6TdTWcqPrkwLuXuCm6i0lsfJHGgfh9bccp7xwuw7KhPvjeJedVVYoIp4oq0A0hsBwEaDuoOpJHpbCi1926rvOkllROByPElrHRJj651ZNe78x38dtHzmPl7rW4aWext4zXT18dMBIBq/K7t6w2BgF+5LVzQVgVNP60AepZVY0Q3/A5dyaQIWRMCPFrAH4cBMG5lN//hhDirBDi7PXr19M2w5osFjrf3b3Sonr2hcUeuguLCHBPiA5rKNnMvu1oNRt91+KbdNfzozPfxWefLw1cj9433LzajpPJ80TbYRMSlbZNVaMz35UqqYDcc8y0D/MOp4qOD4C101HbcSrqtKPodbeu6zypF1XVwQixZUEhZxdu9fDSO99fMwapkBmJDh+/opTfIT7ltcvK9ER7oB9CfPFQIKTO2Oy7yiCLh9AUgCeFEL8K4H4AXwDwBwDGhBDr7p5Q/SwA6e4iCIJvA/g2AExOTupN9A7JYqHz3d0rLaalOPM4/a8KJslzXc4PVaWpjaNNvPzEzr72pPHSME0GnMZboijPkbLRGQjiHlwzb11YS1zZXVjEzFsXAKj7MM9wKl3SaptxcnXakdT+otfdtPcbFq844g2V1MEIsUU1OQMAi70kc5CcpPXc1mO2Cut/23MPBULqjO/V91IbhIIgeAnASwAghPgXAP6HIAieE0K8BeBrWK1y8XUAf+qgnc7IEo7hu7tXWkxLcQL5G798FqqyTXq0vSN3Q2/ipJkfqk376Pp1zjbLJkaHNMaduhpO4+ieJ7qeHHj30kAVk95KgAPvXso8t9MY7JLGwXScXIS2mbS/6HU3zf0YZkaKpqo6GCFFEs0pFOXBsRY++3wJC4uSULRW0yhfX1LuIsCv9X/Pjs147fRV6XVCSP74nIfLRZWxOL+D1eSGf4vVePY/yuEeqckSjuG7u1daZH2ycbQp/ds8jV9VCzWKt1dmDEo7P0yNKqrxcDVOaYw7ebfJF1TPM9Zq9q0nMoVTd92GNOFNSeNgOk4uQttM2l/0upvmfgwzIx7htQ5GSFG0x1p4bvdW5Xp+4MmdaI70VxhrjggceHJn4m/HQ69VYWk+cfKyPDRUdZ0QMjxkTSoNAAiC4C8B/OXd//4hgF9y8bt5kdZCZ+Lu5bOHi454n8hClvI2fuUZapRmXJK+o/LiaQiBlSDQ3ifpt029FEy9NNLOyzTeEkUkRfYB1XOaKJOu0JWSnZo7IR1nnUdgc0Tg1p0lbJs9ZjRPsp52mBgc07jZZlmH09xvWLziiJ9UTQcjpCgmH9qEyYc2Kdfzt85exan3b6z9/ciIwItHzuPw8SvadV8Xeh3i2/pPOUUIUeHEIDRM6DZAdQobKCPWMS9hlWZcTL6jatdKEOCDuccztcfUqGJqpEw7L9MYd3yPk3WF6XNuVFRIUXnh2aDL/6Ua52i7o1XGxlpNfHZnaa2tpu9JlnE2NTja5lTKug7bGrrqGk5MCCFVJVz7X3lqXBoCtr9zsc8YBACfL630fReQyw2TvJuj6xuJf1MklFOEEBU0CDmkbsl0ZV5DU3Mnctvk5yWs0oyLyXfSttfkt22MKkmb1yzzMq1xx+c4WZeYPOfLT+zEzNsX+srbNhsCLz+R3ZNoZt/2gd+OohpnWbun5k4MhLHp5okLw0se3mRlrMPD4hVHCCFFo0qGbIJu7X/jzLXU320ockZGuXUnOS9nkTCHECFEBQ1CDqmzO2YR3k95barSjIvJd9K217Q9rowqWeelz8adKoRo5u4xlVAfyHScbeeJC8NLHn1Txjo8LF5xhBBSNCpDhimqtT/JoJP1u76V7mMOIUKIChqEHFJnd8wiTt3z2lSlGReT76Rtr8lvuzR01HVe2hgpyzYc5WVUO3z8ykAFszim42w7T1wZXlz3TVnz3WfDKSGEVJWsBgvV2m/i5TOmCO028VpqCKH9vGjqfGhNSBUoey+igwYhh9Q5bKAoQZK0qYq/THt2bMbJy9e1L1eacbHJ4WP7Mif9tmtvrLrOS1MjZZ1ye8VJev9sxlk2TwTUCap9NTTWdb67wGdlhBDiF76sF1n0TN3a/+xjWxI9j1T2Il1xhpDdX95o3M4iyCqzfZkPhFQR3/cieZSdH1pclGH2FR9KicvK0r92+mpimfo04xL9DrB60hMaG+K/b0tSe1yXsK7rvDQ1Uta5JLju/bMd5/icF7jn8i57t4ouB29KXed7VmTrp2y9JIQQn9YLnYfPWEtdnCFp7T80PY7nd2/VevLE8+qFxOWljB/9xC/PG1WuIJMcQj7NB0KqiO97EXoIOaauYQM+nLqblPm0SaKbRPj3eVh0de3JwxurjvPS9LSrzm7SqvcyrQEknCdTcycG+jZL4vOi8X2+l3HSWreiB4SQ/PBpvdDJOQCZZOCh6XEcmh7Hl186Bln09Ygm6iuUM9tmj0nzBfmmY2TJIeRqPtDLiAwrvu9FaBAiAJIXaR82f1mT46ZBJQRfOHIeh49f6euDNOFsMnwNxfENUyOlqj8DQBoKlYaylJy83ksXic/LVPzyvHfW39a5DQOrY9ldWFzLb9EueEwJIcSn9WJ6oo2zH97AG2euYTkI0BACTz96T/b8ryd/gB/8+LO1v//Zjfcbr5fheq5KxZeQog9AvjqbS1mWZUxVf6MKKZfhe8gMIXni+96OBqEhQSdUTBfpvE/dkwSf6mWK4/Ll0gnK7sIiXjxyHi8cOY+No018entpLcFvGM4W/VtTwVekN1aVT2tMjSEz+7Zj5q0L0uTLLhSSspWcPN5LF7kGsvRJlnmZ53i4+G2VkfnAu5fw+dLK2mdhslNX7fddGSGE+INP60Vnvouj57pra+JyEODouS4mH9qEt85e7TMGAcAPfvwZnvvDv8Lrv/XL0t8KZcuGVhP/eLtnZPTRyaSHvyjvq4e/mK2vXMuyLGOq079N2+WT1xkhReNDpI0O5hAaApJif32IazSJT5blLInj+uVKEpShHvHJrV5itSfTPi0qB0odYsKnJ9o4NbsXH8w9jlOze6V9ND3Rxk/dr7Z9Z53reb0/nfkupuZOYNvsMUzNnSh0XLLmB8rSJ1nnZZ7rmYvfVhmZFxZ7ypBYF+33NecTIcQ/fFovdOvuqfdvSL8jux6XLQuL5sYgmUza37mIqbkTyjac/uEnyT+uwbUsyzKmSfq3Sbt88jojpGh8z29JD6EKYXpqHv+7W3eWtFb5shfpznwX33zzwkD5z/DUPPosTz/a7gvDShuWZYpJJQkbwj41CdErK6dIvM+r5DWkYuGWPDFkSJa5nvT+pPF28cHrCEgfipZlTcl6iuhiPVONmYvfNvV0zHIPGT6E/RJCqkHa9SIPr2NXOqpJHkoZB9+7JJVJr5++Ks0dFJJU0j4JF2FaUbLIgOh3VfIraTx88jojpAx8zm9Jg1BFMN0gyv5ORbh4l7lIh+1VCc6Fxd5alYfuwiKOnusWalE1EYI2jI02S9/sh+g8FaJ97nOMt6nymbQJzzLXde9P2rH2wbU6i+DKsqZkVf7HRpv4RGIAHBvtr0ajmju6MXOxVqrchkcE8Nkd9WbFxXrsszJCCPEL2/UiL91mQ6uprPZlQ1qjukyeANAagwBoq5eZ4CJMyyW6ohNAsozyPWSGkGGGIWMVwdR11OYEJFy8y3QNtj2xKaNEXxiW9K1ndiWGrCURBH6E6AHmG0yfyiJGsQkt0rk7Z53ruvcn7ViX7bUXkjZsLcuaopqXpvNVdSgbva6bO7oxc7FWqtyGmw21OKbSTAjxnbx0G5Vdxdb/pmhPlGcf25Lp+y7CtKLo5J6NrE8rB30PmSFkmKGHUEUw3SDabBj37NgMoNxQgjQb3LLijePeQgL9CkmzIfDA+nW4udhTKio3F3u4qTjpKvq5bMLhfIzxtvGiiY+dywpOuvfnxSPnpd/xzbVa5i0DIPVpb5Y1Jespour9il7XzR3dWutqrZSdvL+gmCsAqDQTQrwnr4OMpJBvU1ymAIjrfzIOTY9nuoeLMK0oKrl38L1LuN1bMZb1WUPPKMsI8Q8ahGL4WnXJdINok5/i5OXra/9d1iKtam9DCHyhtU7qqqvbFOc9ftF+0t0ryaXWhzhqmVC/dWfJus/jFPUO2Sqfec5x1W+bvLey/iq60pzM8HPfupFEg5turNP2d1aji0mf6+ZO0vfzmkehkVJ23QcZRAjxhyLkrO098jrISJt3LZ5nJy5bRhRrroyxVhMP3LeuL3/l0XNdpXFJYLX/so5J1jCtKCq5J9P5kkLUadghpF7QIBTBl9wuMpI2iKHglnmuqCjD6yOuYMiEaqvZwCtPrZ6s2GyKix4/nUBMGi/bzX50fPP0bhkbbaI5IvoqptkYIoocgyokKDR5b2X99cpT43jlqfFCDGuqU0OVohtNlp3XWGdRNk2Mabq5U1aeA9XGJGtiUkJIvShCzqa5R15r554dm/Ha6avW35O1OSpbts0eM/qdVrOBA0/uHHjuyYc24Xff+T5u9VYGvhMATnP+uehbW8Oaj57hhJB8YA6hCL7kdpGhi72NxgUDq4IoDLluj7WwMZZMNcTVxtk09lgWv3z0XBdPP9qWPpdtvLFP46dru+1zxcc33CB2FxbxwpHz2HXwO6lLksfH5JNbPUCsnoalifEucgx8KourImmsk8LeTs3uxQdzj+PU7N7cjNK2Sl+4bvj0vkUxeb/27NiMeFqKcO6UleegrViPVdcJIcNJEWtvmnvktXYe+/7H2s9//qcfUCZw1rX5/mbyFkgIrB1QxvXc6Yk2Nj5wn/K7tpUtdXq0i75V6UxjrXz3CIQQ/6GHUARfErmqUJ2aywR3gFWBcWp278BJD+Bu42xziqRSME5evo5Ts3ulv2/jKeDb+MnaHveQevWZXYnPl5R4e2Gxl/p0UPbbveUAD9y3Dudf/qrVbwHFjkGZua9s0M1hH+as6tRw42izL68A0L9u+NB2Fbo+78x3cfRct8+LUgB4+tF73ynDHV51Ch7meiOEEKCYtTftPfJYO1VVvkL+v0/v4D/bcL+2IpeMz5cGPXvitNatGo1Ueq6uP0wNKqZ6dNa+VelMgL3XOiGkXtAgFKEKISgykgT39EQbZz+8gTfOXMNyEKAhRN/mJws2iX3TKBg2Mew+JOKNti0pPM7UzdtEyUtbkty1Yln0GBS9cc+St0H2XR/WHJUr+stP7AQAHHzv0ppCft+6eyeqPrQ9DSoDejSnWhmo7l92uwghflHE2lvEPVzlQfrkVk9rNIp6D+3vXFzThU241VvR6rmqfhKAsUHFRo/Og6ocrhFC8oMhYxGqEIIiI6lMc3giHgrA5SDA0XPd1GFGUWxOZGzLSduUFQeKHb+ktsk+f/30VanQf+HIeW2onakClsaIk7XEd5yqvkMmyMZ05q0LmPh338HDs8fwyEt/jocV7t6q+bJnx+bc+ss0lDPJFf12JD9C6I3WmaOyNwcAACAASURBVO9Wdqx99WzytV2EEL8oYu1Ne48sKQRU+p0qpMmUUPfd37mI105ftc7LptNzZf0kADy3e6t33u26Pi8qRJ0Q4ic0CEUoK3dEVpIEd57x5qq4bdl1WwXDtt1Fjl9S21ReCCp0ypCs32SkMeK4VizT5EcyUR59QBpetxKsnUxGczvFx1IXLvn0o+2198WV956tMVWlDB5495L25LKK66WJAb2MOenaOEsIqSdFrL1p7mEjd2z0uwNP7kRzRK5rmhDmYXvjzDXr745AreeOCHk/vfrMLquS80Wt/b7m/SOElA9DxmJUsZRikrtnnqcPNpVxbN1STdotczmO5iMKN3fRsK2Tl69ndotNaluavlW5CEf7rbuwCCGAePfGjTimrtiuXYVtXMB9ruonw2ZMQ8+vF46cx8bRptKdPUysHvfem3xo01ofpHGrd+GC3pnvYmFR3u5oOKqPY6VDV62lzDnpoopMEaWoCSHlY7L2Zl0PbNf3vFIIhN994ch547aERNfQNBUbV4BBhSv8LAB2HfwObi72sKHVxNhoEx8tLK4ZWEz7rqjKlr54oVJOEeIfNAjVBJ3gzjMWvK34bVVlHJNEy6FwSGp30uZN9nk0aWuWzd6YYpM/dreimy6uXKeSqARzvN90AtV2U2ur9KnubXvfsuPmbbEt2RqSlNtA1wdpDRSqedRdWMS22WNGOa9u3VlS/n6eXit5K4s6I+jU3InS5mRW42zVDKyEkPwoYz2wMTiMrm/gszuDxTJG18u9oc9+eMO6Pe3YGtoQIpVRSKXnAlg7NIkennQXFjHz9gUAZn1dVA6fDa2m9JBnQ4qQvLRyen/nIl4/fXVND6acIsQPaBAaAvI8fcj62zqlZc+OzX2CI/7bSQaFpOpc8b+3QaVThNdV/fL0o22cvHxdqVyYbrR1Rpw8DS268TK5b1SJUKllvuZMkY1pFlrNhvK3wj5IO5Y641XUlR9QG0915JUnqKhNjOr9KfsENYvHVdUMrISQ9CRtyMtYD2wOH2XGIN31NOFe8eq1zz62RVrJMYmZfdutvZN6ywEOvnfJuK+L8LZVRL4pr6tIK6c7890BnR6gnCLEB5hDaAjIM94862+rlJYD715KLA3tKmwrzWbvpiKUJryu6pdD0+M4NbsX33pmV25JIfPc1OqUzKT7xvMLqPA1Z0p8TNMkugz1rnA+qDzpwj5IO5YmeaeScl6p2DjazE1xKzvHQZXz+JRtzCKEFINJrp4y1oM8k12n8eyJc2h6HM/v3qrMCSTj53/6AUxPtFPJe513cBksKNqjuq4irZw+fPxK5Q4CCRkW6CE0JLg4fVCdSGX5bZUQkLm1xktDJ51GmYb4pNnsmZyE6folTxfhPEMEdUpm0n1NjA7NEYFbd5aUYU1lEx/TXQe/o8yzIyPAqjEoenKp87BLO5bx+ZWkhJkqY9Fy9HmQ1ybG1L29iFwOeYXEFVEmmhBSPibeP2WsB0WFPmXh0PT4WsJnE/l9685qlc1f+8qXUnkXqSgjj46rOZFWTus+p5wipFzoIUSMsK1apPudaAUf29jlqEBJOo3as2Nz4u+l3ey5OAlTVXbKSp6ndDoPClV/h9d1ysCax41YPVXLMseK5MCTO40qwEWJ9kOSh12WsYzOryRPJNW4jrWahVYRy8NDx2btyrt6j6t1VEbS+0cIqQcmG3IXekCaioumes196+TbD9V1W0abyb/za1/5UuLffLSwiM58F0e+ax+ypvIqylMO6HClG4a5Mk2vh6jkuEB+YeiEEDPoIUSMcFW1KB533GwINEcEeiv3fBhazQbub45I3W3jHjhh22SnLFFvoigNIbASBJlOZXw+CcuzbXt2bJaekoXV22SE11WnU6HHzNTciYHTujB8MMxzEz7T2GgTQbAaoldE3+u844D+vt6zYzP+7MLHypPHESHQme+ufbcIT7IkzxfV5wee3FnonM7DQ8d27cozl0OeeT2S3j9CSD0w9VAG/E1S32o28PnSivS6DF1iZxm6ALNQnpt6kB8+fgW9ZfkvqhJVN0cEDjwp96YtK9+bK30iKYemCpl8FwCe273VC92ZkGGGBiFihItQDpkQ7C0H2DjaxOj6dX0CCtCH0oREN2+hkH/xyHltuNhKEOCDuceN261CVvkrWuK+TANRXpta3aYzaY4kbfZ14YP7Oxdx9Fx37btRY2FY0ePAu5dyMRAlKcayvj40PT7wvZDlILBSrF2MZZIi6IuBM492mKxdebvvJ21AXORPYA4hQoYDU8O5z0nqk/IwxpnZtx0zb19QGmbiLPYGjU3AoDxPQnUIFrISBPjR3ONWMqTMtdqFPmE7dtF7A+XrGYSQQWgQIgCSN0QuYo+VG/5bPcz/3lelnx1879La5l/nSizbtKtKvJu22UbA+1byOa8NbpYcQknKgM6I98aZa9qkkr3lYM0jx3Xf2yrG0b7f0Gri86VlrMSavthbxsH3LhmNkauxTFIEi6hyEifJ88rF744oTnDDeSl7d2feuoCD713Cwq3sBkaTDUi0LWnHmjmECBkOithYq2SxjZeOjjTrlakxSMeBdy9ZVQo99v2PlboksFqyfX/n4pqO0hACe3Zs1o5F1dfqLO23le9F5loqI68TIb7AHELEKJ7ZRexxmvwgtyOnPAuLPWWctWzTHuBeVSfbNtvGeJddHSlKnvHpujGc2bcdzZH+Hm+OiL7+1uUX0OU6sa0w4rLvbU7z4n2/sNgbMAaFfHKrlzhGZeUaKIL9nYt48ch5588W7zPZ3ImuA1LPxZXAWS6rpGTqYVuyjnWeucMIIX6RVw7CEFUlLpsKXTps1qtwbbRBdoDYme9aFYEA7uU0VHHrzhJeO311Tc4sBwFeO30Vz/3hXym/U/W1uqj2F6n/1FnXIsQEGoSIsTEjKmA3jjb7kq2aJB+0FSI2RhbVpj2s6mSbINbWwONTuEaexqnEMYzriha6459d+Dhb42K46nsbQ6ZN+fY44RhF36VvvnnBG0OjSzrzXbx2+uqAou3i2VRj0BBiYB3ozHeNTryztEs3D6Ntyfre5p0QmxDiD2kSPtugOoRxUf4dsFuv0sjVO5L8RHnIzTsKr6VT79/AroPfkY6PT2t12sThadtvc78iD1p9OtQlpAwYMkYSjRmykIeo545puJStm7ONkSUpYbEttgYen1yA8zRO6cZwau7EgEt3bzkwyjmwv3PR+uQuCVd9b5PoOGsfh+9OeC+V8l31vDAH3r2k/Czrs6m+H88dZnvqnLZdpmuTixANn93xCSFuKCJEXZXEWVWxMg2m61WatVcmOdOu4SMCSk9fHbow9jJCtONkmUdp2m97vyIPWn061CWkDGgQIonGjKQcKjY5VmyEiI2RxXV1IlsDj+r+e3ZsLjzRdFbjVNImUTWGaQVqZ76L1zVJG2UIAWy4v4mbiz2MjTbx6e2lgUp1tmNvU0lMNY6qvm81R3BnKVjLMbB+nZAmvWwIYXQSGh9Llxv7IowEOuOfaT4d1eem89/21NmkXbLPTNcmVbUaVyEacXzLe0YIMaOIKlWyJM7NhiglrEmXX1CFbN1M8zuA3hgkRHJ1LaCYKmImJOXXy7OdtvO2yINWnw51CSkDhowRZe6W8HrSRj+vyjkmOWlCXLvg2oa3ye7/9KNtHD3XTRWTnMUdPEt8t2kctax9Y6NN6W+qroccPn5FG6MvJQDOv/xVfDD3OOZ/76s4/C+/kmnsk57bNF+DrO+bIwJLK0FfjoGllWBgbreaDSN3/PhYJrXdZi7ZxtHnEbZgkk9H93nSehaiW5+ajcGxSWqX6jMARmtT3iEacegiT0g1KcybIb705LMUJSKTq8Cq584X7pOXqf/y5tGBa7o8hTpURnkhgJ/b/IDx75TtbdKZ72Lm7Qva/HqAu8Thpr+rul5krqWq53UiJCv0ECLaUuKA3nLeme+mquZl7IVgkZPGpQtumioe8ftPzZ1IdYqX9eQ+SwUSkxMcdfvkykXSfjaNkhSfW1nH3tWJq6zvb91ZWquUF9JbDrBxtInR9ev6xkhVmrwhBFaCQDqWSRt7m7lk0w9Z5unG0eZAnwD3Xu8sXokq4uucLpQrHAtZWKTuvqrPVEbE6Dqo8hByGaIRhS7yhFSTIrwZDh+/0ud1C6wm2y/DyyW83+++833cinjWrgTAP34u9/L84fVbA9fS5ilUGU6CQH4fFWV7mxx875JRpba8vFJtvWCLLFNf5L0I8REahEjixmBm33bMvHWhTzkIPXVU3h3i7vdkmG4kDx+/kjonjQuyGhnSbrhMjTJpwrpctFnVPhU3E3ID6dy4R8SqspA1HCwJl5vjeN9vmz0m/buFWz3M/95XB67Lwot0Hk+6ttsaumz6Iem3dXP05Sd2DoQjAKsmRV2J9rAdacYr/pkulMtlWGT8s7BfuguLfcb0pGpoackaWkcI8QvXIfIy8i47nwZZmLWK5SBAZ77bJ4vS5inU5VMy7Q8fvE1khzAybL1STQ9403jBFplryYe8ToSUBUPGKkReVSWMqigpPHVUwjCA2kvA9HS/yifYnfkuRhSnHkkbLtMk30WXlU9qn+1vhqjcwVvNEfz739iVORwsSxtdbI5tfjtN6KPu99MkRze9rvttkxC8w1/7ivRkcLG3rDwxDNuha6fpM7jua5P7RvsFkPvUyaqhpWV/5yJePHJeOg50kSekmhRRpSrvsvO2pAktf+mdi9jfudi35qZBt1Yqw8mQrsKtD2xMCPOPYqOPqrxd8/KCJYSYQw+hipBnAtCk0yadp06aRKimm9SqnmCHY5X2xF/13GEunjwTSpqcPKrat3G0idu9FetTSxNX3bwVqTxPXG1/2/aUSvf7qhA02+Tosrbq3k+TOTo90caLR85L27EcBGg1G8p2JLUz6RniJ5qvPrPLqM+z3tckkXW8GlpaOvNdvCZJ1h4NYwvbRBd5QqpF3t4MRec0SyLNQeBibxlvnLmWqc0Ceh3lrbNXcer9GwPf++ePbMLrv/XLxvcpopjDWKtp5CVl0102+mgRnm2EkHTQIFQR8jQCJG3IdQYcldzQCWBdJaawIteGVhN3lgY3TlUQHqpNX0MIo1MiWXUPAPj09hI6893SyspH2ycT6i8/sTPxu7r7lrkRzTN+PO/Y9KTftzVGmbZVp9ypDD2mRl9dHh/TduoqgWUptZv2vrLnl+HK4H3wvUvKz8J2lP3eEUL8RFU9y6WDkI0RJG2FsKwGrPubq4EUqrXyRz+Rt+mvP/4n4wqzRVV8PPDkzoH0DzKSwvyj2OijzNNDiL+IoCRrf5TJycng7NmzZTfDa7bNHlPm6nFxmqxjau6EctP22edL0hOH9lhr7QQ6TljpwCS5XZSNo028/MRO74WHi7HadfA7yn4F5KF6siTFefVV2pLgab9XZ/J+5jx/f3/n4toJbEMIPPvYFhyaHteuGdF1Ia4IA8k5k7KialvYvjznnO7eQPZn78x3cfC9S4m5ImTrcxHvnhDiXBAEk05/lGSGOhiJ87Ai9x0A/Eijx5iuIzI9sNkQOPy1r0j/fn/nIl4/fXVAt2oIwFKVHEDnOZOkt6n0vTi6td1UXrogqey87X2LbDshJBs6HYweQhWhzPAplSfAnh2bceS71wb+XlUaPmR6oo0D716yTvA3un6dsw1KnpsfF2OlOqH5aGERrz6za2A8mg2BT2/fq2aV1wlTiM6zQHXadfbDGzh6rqs8BZN978Uj5/HCkfNroYltC+NSFSjiZDAvL5DOfBdHz3XXFMrlIMDRc11MPrTJ2DW8jBNDnZdO3u+NrF/CxNJZjVGd+S6++dYFLCec/obtiH+3iBNqQkh9sVlHZBWvessBDr53aeBvQ1kjW9myGoMAaHXRJL3N1HPJVTGHrET1gf2di9Kw4j07Nhv/HsPACKkHNAhVhCyLbtaNs2rTJsstBAA/dX+y4cbGJTXElXDMe/PjQkDqjEqy8ZB5aqUNKcw6X1ThjbJY/qRS4vEKTKbGpargKhQ07ZipPHyytt0mP43KYJWlbTqSFHhXobgy8jSAHXzvkpExqHU3BCIaznDrzlJuIcmEkOqRJmTMRp6pvBhl101yr+VBs9F/uCmTs6oQfxm6Yg5lHPievHzd6rqMcFyjnqn3rWO9IkKqBg1CFSHtRsKV8UO2aVPlCVlICFcA0sWDuxKOeeZjAtxs+lSeBOHJTXw8VKXNbY1oLuaL6p6qWP6kUuJxTIxLVcHFyWDaMYufDi4Hwdq/TQwvSW3P4pmUtW06ZO9WnDwrGeblsWVSUrg5IvD0oz87MF9UVKGiIyHEPapsErosE3l5upS2DkWeVSVnn360LS8XKcFFMQeXuByv272Vtf9eWOxV8oCOkGGGBqEKkWYjkafxI8uphsmmLIpL4ViEe27WTd/0RBtnP7zRFzMfAGshOfHfVo1FAOCRl/68z7tC53lhMl903iid+a4yLl1Vke7BsZb2ezKSjEtVwcXJYNp3/I0zg+Ge4fUko4tuvFwYbrO0LUQ1T6MGW5UxxPdKhmmIJuo2XXfr2A+EkGTamoT/KmzkmSpvz1hrsOR52oTSISMCMHCeHKC3Emg9mHVVzMJQ4BBXxRxckkX/SMpFVMUDOlId6pAywjdoEKo5eRo/dLmFkqoryATgnh2bcfLy9bUqY0KsehsVleNnRAh05rveLConL18fOHiyKecZEvWuAKD1vEiaLzpvFGC1opVMOWo1G/jC/Q38wz/dGfhsdP2I8nsqdMaltLgUMKa/pRq3Tz77HLsOfgc3Fwfnf/y3VYpy0juetqxwOAdU4+zCcJu15HGS11T4P1VS66rlP+jMd7Wff+uZXWvzR+XZGaeK/UAIccOeHZut88vYeLrIKl41RwQOPLlT+rsm1bFUNITAiACWUnw/lK+2ns9hXjhTfcKl52gW/cNk3Y/Lzboc0JFqwJyH+UCDUM3JMzZZZdRJyu0S91B5bvdWJ7lBTFFtwpeDwKtFJW05T5WBQOV1EX52aHo8cb7ovFHC/5bxi1s34NT7N6Sf/eDHnynbJaM5IrB+3Qg+u9N/ryRFJsmzyZWAsfktWfw9ANzqreDWXRdsmdHNJNwn6R1XGdUaCXWFVR4mDSGsKmTpxiNt23RtlBlTVSezAIxLBvtA+P7JeH731r62q97xsVYTD9xXTJVCQojfHPv+x8rrKn3NxtPF1ismS/n4tIYk4J7MsfVSaghRSpWtNPqH7UGYqZcpPUxJHuSd9mNYoUGo5uQdmxw/1ZiaO6F9UfPMDWLTZgD45psXvHZztTXmhWOhKherU6jCz5LmS1qPs9M//ET7uYrQbT00EIy1mvjsztKAMWis1cSBJ3daVz4DoHUHf+HIeRw+fsVqc2wrrML763LAmBjdosSTYcp49rEt0hPgZx/bov2eaqxXgsDKGKQbj7RtS2qjypgabXcVT5907198XVW947r3hxAyXNgkfY5i4+li+rf/9k8upgr5ckGSbqSSx1kMWFlI0j9kBzG2hisTzx96mJK8KLIq3zBBg1DNKTo2OelFdZEbxAXTE21l6IQPi0pnvovPPl8auB4K2SzeFbrPkjwmVCpOaKRSnaClUY7aY60BRWVq7oQ078AD9+kr2yUpSS5LkacRViZzzmZePrD+Xn+o5sqh6XF8cP3TPs+tqUc2Jb6HunxV+zsXjd7jpPEIfyNtlbHR9Y0Bo2HYxqm5E33hqfH3p+jTJxehiqoxkeX7qItXFCHDTJk5NFytE6bPIFvLiyJJN1J5ZetyLeWJTv9wddihkjcNIbASBJQfJFfKqspXd2gQGgLyqmojI+lFzZobxCU2i0oa5SutwibLawIAG0ebePmJ1fh6nVDf/eWN0vCs3V/eiG2bfyrR8yLJYyJO9CToxSPnpUYjlZEKWDVCfO/qTSMvNpWyk+TKrfueztAVYmMQSCOsTNzRA+j7McrNu0azpJxP37t6s+9737t6U5lHK5zP3YXFgYSZIabefiZGs0PT46mMxPs7F7UbiO7CYt87EH9/VOOQJampClcKuq0naB28oggZVsp+X8O1sKjQ6jKJylvZunnjs8+l33v4i+VsTnX6h6vDDpW8sQkZJyQtZVXlqzsjZTeA1IuZfdvRajb6rkVfVFUOENPcIC5JamtIqLh0FxYR4J7iokvkmuY7Iar47NG7Xh9JeXx+9BP5xvVHP1nEoelxPL9760B/n7x8Xdk2Xbz4WKuJ+5sjePFuaNU/f2QT4iPZajbw7GNb0BwZHOMRAfzLya145alxtMdaEFg9WVMpFiqjioA+sa7ue6YbfVMPHdN5lfQdGaaG0w13K7Wo5so337yAg+9d0s6jKNH5DOir7OpyVYWMjQ5WktFdt8Hk/nGizy2ZptrrWUh6l02ZnmgnvkOd+S6m5k5g2+wxTM2d6HtfXLWDEJI/Pr2vae9b1DPkpVreM2itSD//T+/fMNL3XKPTP1wddpjIG0LygvMvH+ghRJySFKKWNTeIS0zD6dKcqmQ5iUnynsj6+aHpcUw+tMn4dE5nCPl8aaXvN258dgfP7d4qDcc59v2PB/IPrASrfXVqdq/RYj6zb7vUCykAtH0rO1FQebmoMHVHlc2rh7/YwjffvIAXjpyXhj/FvzM22kQQQBoeZ0KoBOtC+FS5IGTjbVOqPDRa6TzkVHYtF46Cab0Nw+dW5arII4dFkueajdu9zhM06TSeMfmEVAff3tfofaOepKFHa1uiWxX1DHk5nyfJxCSdJC90eq0sbyaQ7kC2yMgDQuJw/rmHBiEPKTM23AW6FzVrbhDXmCwqLnPCmCg7SSFHWT8H7AxWunAm2W+cvHxdmqRwwcIAoWJ6oo0XUuR+kilJulOxeLJIW3fU6LxSJVL/4Pqn+NFPFrXJHVUJwpMI+9o0xCyKzPBlM0YCwMS/+06fwSlugLipMHSprtuQ5pmBcuLPdfPQZQhF0vs+NtqUGghdeGwRQtziWw6N8L6qcuSytWxDqyk98Ai9W6O0E+R1GZi0pywDnUqv9SllQ9X3OYTUDYaMeUaWUKOqcGh6HO+/8qv40dzjeP+VXy3NGGSKSskaG20qQzBU34kqTqrvJoUcZf0csDNYmYYzJf12Up+YsjFluNH0RBunZvfig7nHcWp2rzbp433rRrBxtOnEHVUVwnTq/Ru5vedJObtUqAxfNmM0MiKkxoVoOICruSBj95c3Wn8n+tyqs9I8Ig+S3i1XIRQ6o9PU3Amlt1hJhXIIIRr27NhsdT1Pomunzmsmvpb1luWhVrLrPuYGMfGq8S3JrUrnKToB9jDscwipGjQIeYZPseFkFdmmrdkQ+PT2klKg6RS2JGGYFB+b9XPAbkMe/T0TVL+dJreODFfhRrrN+MJiD7d7K3j1mV3G4WwqTI0ytu/5mOQkFejvU9XfRH/DJA5bNXZTj2xaU4wbQuCB9Q0sa2KrQmOhTf4uleFUhSqHVkOIted8fvdW5XOrWp+HbcTk3XJxMq7bvOh+P23IIiEkP/7ke/J1UHU9DQ+sl8vG+9aNKNfOJI+Y6OeqxP+y69MTbTy/e6thy4shSa77mOTWlQ6WFe5zCPEPhox5hsu4al9cMn1pR1pk4Uaffb40sFmKhmCcvHxd+lsnL1/HycvXE8O1kkLZsn6uy9KvGq/piTa2zR7TboyTKhsByTmbknAVbhRtj2xTHCZfDknbbpsQpvh7rvpuQwicf/mrAPTvl+4Qs9Vs4MCTO42eQ1eq/NJH/4SFxR6WgyCxPHDoxWUyF5Ly3qieW7VWrgQBPph7PPFZVeEJodHG9XoWvluPvPTnzvI7xCkjJIAQ4p7OfNfKmJKWZmMEwODv3VlaUa59SaHYWTxmJh/aJM0/mTcNIbBt9phUtqqWVVnOJB9wpYNlxbccWIQQGoS8w1VsuC8lPX1pR1biBpZtitwupomdbT9zjW6DrxsvncJnogS5SATnMn9C2B5Vrp7lIMDMWxcAAfSW1fkQdKgSqcuIP8PuL2/EqfdvDPxdNCxK16eqUCAA1mFwspK7M29dQM8i23JUgU6aC0mniKp5mnV+JBlL81rP8szv4GMODkKIHeH6UwQq78BwNZKtfbK1MyR+WDSmyCEk82oNZU0W0q6BqjxIumVZlkPRF3xIxutbDixCCEPGvMOVS2ceLplpwjfq6hqaFHKl+zzP/Ck2xHPqmJS0V83PbzkIrTIl6zsim8c6L4zeSrBmDAoxmcPhfV4/fRUPrG+s5aBpCIGpRzYZPYMq/El1PY7quRpCOElUbGMMAuy8uHRGVd08zTo/dCGXea5nqvC+pLA/E2b2bU+VA8mFdxIhxA1Jla1cvq0m737oRRvKUgD4xa0bBv5OFo584MmdaI7036M5InDgyZ0D308ja+L3PzW7N/NaarrWMxeOHl9C1wgh96CHkGe4cul07ZKZ9mS8rq6hOi+CpM/PfnhD6jFSRkLIOEnj5YPLsU0b4uE9e3ZsxtFz3YF5nMYLQzeH4+/LZ3eW0Wo2+pRik9AjXTJgE3ReJ535bqZxS/MO2xg9daeIunlqGo6m+1x1iprneqbaf7mwyeiq8+l49rEt2W9OCHFC0jrznMM8O6YyMepB89tvnofMbrNnx+aB9dRGjmdZX6N6mYucaGFbVB5OACrpBW+Cq3BpH/RIQkg/NAh5iG9hNYBdmfI822FLkgBLK+CSBJruc9UJkyrvUBrSPpfJeGWZn/s7F/HGmWtYDgI0hMCzj21JVWVO1gYT44/MELfYW3ZWnj3E5H0x6UddDiETdG7yWZVWXfjgxtEmbvdWlAZTE/bs2IzXT1/ty1kV/oYq71M4JtG+DefFi0fOr82LI9+91hcC+MKR82sGk42jTbz8hDy3ksn7kfbdW1CE96mu26KaCw0hsBIEWDcC9CIFfqYe2eR9BUhChgndmuv6fU0TYqVy4nnt9FVMPrRJahTKopckEYaxn/3wRl8+wCyEa/2BzI/vCgAAIABJREFUJ3cqDWBxWZ/FkOIyZ13WdrgMl/YhdI0Qcg+GjNUU1y6ZaU/GVRW6Pvt8ySr0LA1J1byylr6UhVyZfJ6311SW58rTlXd/5yJeO311zcCxHAR47fRV7O9kz4kge+bXT1/VutdHWQ6CAff1JHQeXa7GOGtemYe/qDZaZQ11mtm3XdpnzYbAy0/sTKx0p6Mz38WR714bSGD+i1s3YHqibVWlLD4vXjt9dSAEMMont3qYefuC9H1Juq/Ju6cKvU0TSmoTxquar88+tgWvPrML6xr9z/W9qzcZ+kCIR4yuV6vsrt9XXRXONGQpKz6zb3uqzcqp2b1rHtkucrHFZYxJ9cYs+pjL8uxZf6uu6R8IIavQIFRTdHkw0pA27028HRtHm0Cw6robCqUXj5x3YhSIkyTA8hZwLjd+NmR5rizzJmlz+saZa9Lvya7b5quSPbON+tcea+Gn7pc7TKrsRDqPLldjrMsBZMLpH36i/TyLEXJ6oo1nfmlLX0jTaHMEh7/2lbXTP53BVMfB9y5JjTan3r+xFupmMk+Tcm6o6C0H0vcl6b5J755OKbc1xtoq+LrKh1T2CfGfH/z4M+Vnrt9X2Vr3/O6ta/+2zS+WpX3TE21sGE2X/0eld9jSEAJPP9ruW+t1eY3C/smytrpcl7P+Vl3TPxBCVmHIWI1x6ZKZlDPHtB1TcycGKh8FAF5XuBRnIUmAqT7vLixKy4zaoHOvzdKXJmQV3GnmjYk7sam3SxrXZBc5BlQ5VlQ6X9SFPSlcLXofG7J6CCX9XRYjZGe+i6Pnun3VVgJHaU111dFCV3zdPA3HI0tlLdWc0t036d3TKeVhZRpTl37bMF7fKx8SQrKR9/s6+dC9sLS4nDYhS/t0MkGHC8+g8HeOfPfamp6a9CzhfU3XXVk4l0sjjE7fNaHM9A8uw+YIIXLoIUSMcOVxpBJKAdB3im5bzUxG2kpgYXuyuOcmbdZcem/F2aCopKG67gKT0ydTb5c0J1mqsTQxT7zy1DjOfjhY2j2JsN0yT42j57p4+tF25jFuK55LdV3VRhlZjZBleZUkKbDR8chCGkU3ac0xSdo+s2/7WuLsw8evKNcf283CmOKEfWy0qf2MEFIOcV0oCZeb8yQPRJke88B6fYhZ2val1QG3zR5L9T0VveUAB9+7BCD5WdoJemY875ysr1U6m0vZJGDWv2VVBnMZNkcIUUODEDEmSwhIiE6QfbSw6HTxTxJgJjHyaTe4Jhu/rH2pIs9qRSpMNqeqikXx62lOxVRj/dxdF3cVz+/eiumJdiq38vAEUGUYOXn5euYxzqqEqfr8gfUNqYHKxhirG6esRl1deeCkaZw2TCxKsyG0oVqqZ9uzY/NA+6LjlbQ5sFn/bMMSVQflQaD/jBBSPLK1QLf2ud6cq+RavMx8VMbduqNed7O0L+0hQx7LV+ippNMfk/TMeF+o+loIODPCzOzbLp0/0cNYHa4OMl2kA2A4MyHuSR0yJoTYAuD/APAzWF1Tvh0EwR8IITYBOALgYQA/AvAbQRDoE1mQoWFm33a8eOS8VFA/ONZKXc1Mhm0lMJXykLa8dlHutXF3WpVrtatqRTJMnjd0NU+qMpam70zKmOoqnOncylXVVtqGXh9ZyFqe1bTPAXmo3ot3q2+1JfdVjdOGVjNVNZLoPNZ5piQp+bp+b4+1cOvOkjb8QFdlTBfOCGA1hC7y9wLoyzuRFC5qs/7Zhp7eVJRIVl1P+oyUC3WweqPKi7e+IXAnll9Ntj5nRbWORsvMx9d1lUxoCJHJC9rH0NWobO4uLK5VBI2PhYkMVz3fwq0eXn1ml7NS76qw+DzTCURxmQ7AxzlBSJXJkkNoCcA3gyD4nhDinwE4J4T4jwD+GwB/EQTBnBBiFsAsgN/J3lT3DEtcqk/POT3RxtkPbyjLSb+YUWDJ7qd71nh+I1dGnKx5gkzHTCZgBeSb5qgXguv5IHtegcHKRoemxxNL49r2Xfx5Xn1ml/R5Jh/ahJOXr+OjhUV8obUOf3bhY7x++ioeHGthRMhzBTWEULZnz47NmJo7oTRQuChFDqRXwmzvqUvMLVPcZvZtx8xbF/oSazZHBISAtVE3Po/T5osA1JuS9lgLp2b3SnNftJr9HlPhKWa875JOK2X9F03mnLQ5sFF+ZRuSaFvifa0ztH72+RIWJMafIvJDkNRUXgcjalQhr3eWA/xo7vHc7z822kxch+Pr+sNflK8xIyKbr07asvOmqHQm6d9G3GxMZXPS3+nWZpe5QFWHW0Wt82kOfMvMXUTIMJHaIBQEwccAPr773/8khPgbAG0Avw7gX9z9sz8G8JfwUBlJY6n2GdXmL8tz5mVIOjQ9jsmHNg0k4T18/IrR5jovXCZ7zuLZYTNmqk18XMEJnyOveS8z9AVY9ZiwSRYezrnF3rLyxC3+9zqPjajHyae3l9aMF1FFt7uwqIydffaxLdKxlCWNjiIrRV7kWpNHYm6p4hb3QRdqY47u923CvDYm5LVJeo+T3k1d37lIzKxT8FXKb4BVg3X8PQj/W9besx/eWDOA6hKd79mxGW/8P/KQSVWpelI+VdfBiB6VkSLHyO8+bhuux9H17dT78lx8vRVI5Y+pjqnzLHfBc7u34ui5v8NibyXxb/MIo8270EjR91GhMurpjH1lt5mQYcFJlTEhxMMAJgCcAfAzdxUVAPh7rLoze4fL0KSy0W1g0j5n3pvY6KZof+figMdQlKIW/6zhObLfS/NdmzHTJelu301MG32OqbkTuc37k5evD4yhzW/H59xyEKyNvW2lpQPvXsLnSyvGHicrAFrNEdxZCqShVfGxlPVjSNyAVcZao7rnC0fO48C7l3DgycGQKJNT2Oh8O3z8ykBpeFmp+BBdGJiNB+DLT+zUfm7yHuveTd14JZ1Wmpxk6jZBMuU3RLUGq9r72umrfd8NE51HjUQz+7bj8PErWFaU0lOVqid+UUUdjOhRraShcThvj28T4whgflgXl3k2OqYu3MkFx77/MV556hcGPF5lmBZ0sMG17lnEfdIcGIeHfLLrRbSZEKIms0FICPFTAI4CeCEIgn8UkRc7CIJACLmvqBDiGwC+AQBbt27N2gxr6hSXqtvApH3Oojaxnfmu1hiUR2y8DhsjTl4eVDZjlhQek+W3bTH5bV2fpZlzytj7FLlPbvdW8IGhK77qvgIY6Pc0p2JZ0Y3nwmIPM29dANCveOuMESFR5d92znx6ewmd+a50LE1DAkSkzbq5lMXNXjde33pml/a0MukkM2kTFA8DiyN7H0zHIZroPIoqTNfmt0l5VFUHI+kJ14ayPdubIwK37ixh2+wxI8NQ/EDBRt6rwrpd8Mmt3oDhYUOric/uLPUdcuR5OOkyNCzv+6Q9MFblatTlcAx/kwYgQvIlU5UxIUQTq4rI60EQvHP38j8IIb509/MvAfix7LtBEHw7CILJIAgmN28u3i3dtkKLz+g24mmfsyiDmS5MLNxc+ygI8iyFaTNmtlWo8pz3WSsopZlzLt/XaDuTqmDY9KPq9Ctatj5LRS7TdkTprQQDVTqmJ+5VEQEGQxPi88q272X3DJ89qYJOyHO7t659b+btC31zaebtCwN9Z9q30b/TEe2jeKUV3WchJhVTpidWKxCq+iP+PtiMg8qorKKK8nCYqLIORtyQV8WlkYQFWWB1I//Jrd7aGpyEyYGC7HpnvpvKGNQea1l59IRr7wdzj+P8y1/F4a99JXNVLd9Jo3+krfylGos8vK5IevLQSYn/pDYIidVjqD8C8DdBEPz7yEfvAvj63f/+OoA/Td+8/MhaztkndJvTtM9ZlMGsqM2+a/IshWkzZiab0LS/bYsq30h4PanPVCFFulAjXelXG+I5lqKGhhePnMf+zsW+v7fpR92pmIlhMY1wNumXaHn4h2eP4ZGX/nzNLf9bz+zCq8/s0s6rNH0f9xYLnx24l/sKd+/38z/9QN93px7ZtBbCd/C9S9JwtYPvXZL+vs5oG/+7JKKbhrjBWvdZ/PmTrpuuwTbjoDIqNxuDu7/miKikPBwWqq6DET1jLX2utCh5ePLpDDCtZgOt5oiVkSYuH23kfVq9ambf9kw6QtJ6XnXSHmymPTCu076rruR52E38JkvI2BSAfw3gohAi9Dn/XQBzAN4UQvwmgA8B/Ea2JuZDneJSdUnX0j5nUYncVKEi4m4bkiirgppOIGZtk+2Y2bjT5jnvVflGwutJSoTKa1jnTZwUYmNCNCxRlhsoAPD66at9ybFt+lFXtj7Jbd7ENTterj0IVsuFj402cXtpWdl/Y6PNgZxN0Xu88tS4NOwwJN4HI4r8AFGiBglVQvRwPKLJwQHge1dvroWcqXJCRa8nGSBt2g3o8xyYYlMxxXQNDsfhm29e0D6HzqgMAP/2Ty7iszur9xIAnvmlLZWUh0NEpXUwoufAkzuNctoA+RyebVRUGRsRwCtPjVvn9IkfKNjI+7QGr8PHr2Bm33a88tS4VkcICxX4VJG3CNKmhkhb+atO+666Uqf8usSOLFXG/m+oCx78StrfLZK6xKUmLbJpnrOohVtVrvy53VsT7+Ui8XVaBUAlEDe0mpnbFP5tXnMzr99OMvgkKRE3FXl/VNdDwmdJyn+jImr00CXpjgtE037UbexV+VvCdtgajKIK/Ce3emiOCKwIDCQNbo4IBMFgmXTZPXRE+0BWzj1K3CChmy8ulBLV74fvZNwQlsSzj20x+jsdSUae+HokSwIte/7pidUqf9FE0lFMcrFFp0iaCoGkWOqggxE1Mh1MVSkwDw8L1bL4hfubqZI8x9cRG3mftux8/HAjDDWOepc2GwIvP7GzdpWH///23j5IqvO+9/w+3XMGesYxPfjiXLsNArEu2FAjGAsH7Ll1yygVkZhAxsgSUaS9qd29SdXu3qrVS83W4LBmlEvM7FK2dG9tam9tcu/W3rKsAJJuLzK6i70Lt7aKBGSUmYHgiEgyCNxWItYwxGIGpqfn2T/6PD1Pn37ezulz+vX3qZqC6ek+/ZznnPM8v+f7/F5cqMfTJ+qGcaesuzqVTsqvS4QjlipjxBLN2mFIYpCN65i25K/AkoeHqEJw5p2b2gS0gnoXjfUYALoEvHfuFWsMqbliCc8fq03imyTNuA9tgo/NiIi66wTYy5brElIGS5ibDM+oE6JJXNXtWopztk3OtvMuLnJkMx6YVA6esfLrtsTbtvNV3WNiJzboraS6B03X25aIO5vxlO2Xwyx0x0kzFko4DFacsz1bruNd8O+q8ei1twtVO+v5yQK2vPCDyrkP9Hk4uLtcMU7noadLMC9Du4IE0XqobLCtD6xsyNyuE2xm5orITxa0HkQ6RK44YePpKk6lGKux/XZsXKUVu22IqqOiLy98cAuvnL9RacO+L67WegfXOwa2uscRefoQQeqxw4n2hgShGOnGHQYbLn2i8vCQ3weoJ556lewoiyB5gl+R8ZBiqIRZAPpdtRLnDbsXwoYZRZ3Mg8ew7V7ajIjRnRtqXORd85iYrnnGS+Oxh3M4+qMbyp1BGeG1o7qMKcYq1VTiMn6iimTZPq9iYNu4M1fE1YldVg+eICYDQHeP2cLMZEznbgp/yk8WlOEUXophfM+myntm5xdqPpvx0k7n76UZ+nt7cGeuiH+8Yjm2PrDSeN4AKqKOvAMtkl2Lv4t/VfeObjx6/tg0nj06hWyfhzuzRcjFoG/PFivHr6eaXTMq4REEYUY3TzfCnjQJ81FKwItjiXHdlFsvaK/oxG5XhIgFAEffulHVhqNv3cDWB1bGPga6zBXNFlTI04cI0qh0IUTrQYJQjCS9y9oKE0hYXPtE977xE5dxf2FROam6KNmmPgsrKAUn+Jm5olNlJPl8GrHjHkdeGhs6bwZbiIvViAh2KFv6PtO9b/IGER4WLjurYgfx5XPXa0ShYI4d8X4bLv2ta5duZ/TObNF5d1Y8DzZvIhmbARDHWGc6d9OCQwhPRx7f7ORpI8hmPIzv2aT1ymJA5ZoXS0seVPL1sp23Kdm1rV90446473TXu1gqV2/T7bi75D6Szz34OkEQjaeZIfEAsPZT4cK00oxhkXOnxPw2gnNJHOEqR05dwd37CzU5mYqLHOMnLms+5U6wr2fnF4w57Fph85g8fYggdE90LyQIxYhNYKhncm5X7yNX0UX3PlVYiJhUXfJxmPosrGukLgluGBoRhxslzCjsYl53jO9Pf4j+Ze7DivxMqJL7isX0vaJaFJS9i1T3ghxu47qjdWhksEo8UrUr2F+mZ7ue/tbtjC4qX61Ffh5c7700Y3js4XI427NHp5RjVVxx5rprokvEDSz1na7qi0746l/WU3n/c8emakIIe9KsRswJfqftvE3JrocnThvH/BWaMDgXfjajr5DmkiNJ9444FncEQYSnmSHxAHDuJ7dDtdc1F5sr8lgbNYeQjOnztnHX5hms6msdceXIiwvy9CGC0D3RnUQuO0/UYioTXG8pvyTLnCeJa+nksPGpP5uZw8iQueS6rc/ClsCMQ8xpRByurZxrHIt5k4Dneo8HnwmdQXl7tmi99233QlhGhpbKzS5q2iULvaPHp6vO+5mjU/jP/8f/iHVjJ405iWzjQth7bqDPq+TREblyjpy6gvxkwfneE270cptGj09XXUfX5zoqtlLBpn6x3d8XPrilzCelE4Pkz9vGeBMifGzLCz/AurGTGJ44XfWZeoqYfTab0ZapDlO+miCI1qCeeTo/WcDzx6brshnjFnjCIo+1ozs3wEs1z1/RZs+88MblUKHYlLiXIIhWgwShGDEJDDZxIj9ZwPDEaeVCAWjfzO+uoovufcGkvwJhLMgL96DHgK3PwooIrovdjJfG09vX1Bgwcj6cA/lLWL//TawdO4n1+9/Egfwl1aGcke+fGY2XgrDvdIJRMNTOdD+69oXJAA0TwqQieH1N90I92MSP8ROXlaWB54qLRg+Lz1rKzpu+W0Uum8HkNx/F+J5NyHjpmhC3HRtXGUUWAQOsbvUuz7XpHrLdX+LZ1IU7mfrFdr1eOX9D+1kTYoc4eN5eimFmdt4pr4YIRVMtMHTPrQujOzdoBaX5hZKxr4Ha5Oq21wmCWMI2nkXBtrFjasv+1y9pBZ0wnqKNIvhNwblkZCiHTyxvfkCDyp7JTxacw7fFeSW9oUIQBBEWEoRixCQwmMQJF++huCeQJAwYFa6ii+59B3dvCuXFI+PSZ2FEBJvXAlDejT+8txxypMuHcyB/Cd89d70qseF3z12PLAoF7x+dCHHHT6z48b3aZLtemtWE2pnuR5e+EMieNPI9V68LeKOMJ9XupCzuRQnzEfewTbTcsXGV8u/BgVt+JnQi05l3blY9YwN9Xs15Zby09v6Rz9P2XJvuoTDekp/M1C4CbM+/TayKsvMtPh8872zGAwKJ5cMQVfyTGegrl4HWCUqzxUVrXx/cvQleOnCPK5KuEwRRTb3e3zp0w5Rt+LJttKzIeE6235PbVrs21QmduJzx0nhq+xqrjViPYB4nwTnb5HGVzXjK8wrrnU4QBJE0zZfcOwxd7KUpX41LPHGcmd+jxJbXk//INR7V9L4o3x13tnw5FE0nZszMFXHk1BXMzi8ok8seOXUFf3fnnvKzr5y/USltHQZXTxtxr6m8Wfp7e6rOz3Y/qhLPzc4vKHfK5JBJ+Z7TJbINkmLAsp609TommXQ9KCLU404vcvSMDNnLzutyCK3o89DX2xM6WXrwGVP1mWsFGdvzavJ8st1fwWpdApEY2nRdbUkRdcmXg4j7M6coHS/+PzxxOnLeH4G4/qrxyksxfGJ5D2Zmi1iR8XA3MK5kvHRFtMk6loFW5aoYGcrh+IXrOPv+rcprv7p2gPIIEISFpPLB6MYV+XXV+G3yAPJSDL+4v1CVMD9YBVEct97KXkHuFRfx0r4tAKLZc3HkEYqDFYEQXFN/6+aqZibubcfiNARBJA8JQg3CJE48q1mAyRNNnBNIWAOm2QmtoyY4S2LSFW1ZN3ZSK2bYEgq6JH8NM2m7uIDb7rU7kpHpWn5VJS7o7nFdQm4XUWiRo1xVytAfSd6j4ycu1+ScWeTl10eGchhwXIgLSpzjtbcL2PrASqtoqbsWt2eLmPzmo8q/hUmWrnq2XnjjsvJ8woQPRSnhK/9NVa0LKOfZqfd6PrlttbJy2/D6lbj287lQY0UcIbsM5fvXZbzSjQs6zz/Xdh/IX6oSgwDg7Pu3cCB/KZJITRDdQhzh/Krn2lY1UDfnmYThVIqhuFBdkiBYBVFXpbFebMUAbITZrEiSYCSdrhhAxktZNy4aLcQ025YnCKJ1IUGoQZiMfZuXgHyMOAbtsAZMK1VECEs9fWYSZaLuVrl8TiQpFp48IqkvoJ60XY4pXJVd7rWo5atN97hOiBIeGLpqXvD/Hjy28DSJo5KXDdtO7cHdm5RVq0zIxjEQ3pvFdC1UIhMAzM4vVAkPOg7u3lTjnRM2fMjWbts5map1CQ7kL+GV8zdQ4hxpxvDkttU4NDJoNXqFwKH6bFji2LXmQOU+VY1X+clClUiXzXh4cd+WqvfpPP9M7ZbR5VWK6rVIEN1C2GqlQXTjlc6LUbyum/OW9aS0Gy33F9T1KeVxtd7cfibqEdBVXozNIBi6ppuKlzuG1Asa4bnTzrY8QRDJQoJQA9GJE3GHNtkIa8C0a0LrerAtKnWLbhPimtp2uVRJikVSX9f7R0YWVFzuNZshaiJsyGQum6mIIiYPI9v1aOY9Ks53/MTlikjU35uGl07hjp9A2NQ2k2gZ5Vqo2gOUjX6X3cAwnnU6IzZKu8OE4Yk8XPJnxe9n3rlpNXoPjQzGInS4jgMi1E337Bdm5jA8cVrp+RMU52bmijUCsek+Dy4OVXNLPc88QXQz9dpvukW6TlTP+Xaa7pk3zTkuJDln1pv379rPm29zBs9Bl9soTM6jRnnudKMtTxCEG5RUugVwTbwcF2ET2nVjRQRbDhT5munQJRTUeXeI111yB8iItujKSwvPkGC7dfdaxlMPC6ZzteFyz5naFrUaV5h7VJdo3bUCU/+ynkq7//hrg5g6+CiuTuzS9ptomynBu+6ztmsxMpRD/7Javd+17PDIkD3ZuimZqqndLudkK6Fu8mjRGbcFP4F/WEzXR3XPPh1IkPrSvi2YOvgoRoZy2nNnfvuC/Xjk1BVl6FxxkVddR9N9LrzwTHOLbUwiCEJNvfabbrwqcW6cM01zXj1zdVJ2XcZLY8fGVXUVM2m2cKGyk+OwPWz2TVx0oy1PEIQb5CHUIjQynjhsbp1GezC1Ai47KeKa6TxbdAkFdTlM6qnqIbfF5hliutcO5C9hrljrVp4C6rrervecrm0u1bhePne9xhNCGKAuni6yJ4acbNMWQhXFm8zk+fTM0Sm88Mbl8vfW8ewlvRtoMmJt7bad0/ieTVVhk0A5Ier4nnKfmzxaspqcDuJ7AfddV5ed2zBjt6pfVOEdoh9N10r+m8nzMM1YxQtPRxJjEkF0C/XYbybvWZF/TzV/2cZY1fjpElaqS27v8lldqBpj5eIQ8hgTxQvGNXF+nKRYubKbzn6Iwz5ulOdON9ryBEG4QYJQlxLGgFEt5ndsXIUjp67g2aNTVkEpbGx0K1RBCJuYF3AX2Gw5THRJioVHiql/hDdNcEHsGieu87xADMl8dflRXPpNdz1SjGHt2EmlIfq5geV47e2CVmyRv0eVxFgk2xTJm3XttMXlm+6P4YnTynAjIeI99nAOLHBmwd911JvbwoatmhlgfiZs48mRxzdHyq1kcmoJmy8h7pwLqn7R5SCy/V2+jiNDOa0gVOIc6/e/iRLnNRXTBIdGBnH15sdV+TmG16+k/EEEkTCjOzcoxRvxnOrGGdMYm58sKMPOXQgeV1Q3tJEzjFWcA3fna+e5sGNpMyJYFzlwbWKX9u9h7T8VSc/VgjjaShBEZ0KCUARaQbBoNLJhEibeOWxsdKtUQQizkxLlftj6wEqceecmfjYzh3+8Yjm2PrCy8jeTR4pL/9Sz26TzvFjk5RLbcd7rYa61LleLaK+q1e9+dFf5vapcOi5JjHWE8SZz/SxQNpZVXhuzxUVlqWCZ/GQBd+/XGvFx7gbajFjbYsY2nhzeO6j1bNn+4IAyuej2BwfwF5ako2F2XZPYuQ32y/DEaW0/ju7cUDMWAGqPPdOCTDwnumcsP1nAX12/U/WZv7p+xykJOUEQdRIUsf3fgx6/A31e1WZGcBwVonq9uol83OGJ01qPS5mzY49g6I9+ENqDpzAzh3VjJ51sJ5d2NIN6Pfwb6bnTyGgEgiDaBxKEQtIqgkUzCbNrHnaHvVWqILjupES5H2yfCetREuyfenabdJ4XcjuPX7iOcz+5HalCkyyeqaqK6a51sE90FclcCXNP2a6Xzo29rzdtDVeLWqWqWOKV9gcFyR0bV1V5RgmCi4mwuHxPGCNWHE91/rbro0sueu3nZq8aINyuq8kzzXURY8O0GBgZyuHCB7dqhMF0utYNyjXBtapvW2XcJYhuQ5UnTHinfnxvocqz5/ZsUbkZkFSpeACh5qd7Eb9fzp0G6G0nXUhakujy2cnUu0lssze7cROaIIjG0pWCUD2DazsZzklNImF2zcPusJsSwrrkgokTl50Ul/sheB1m5xesnwnrUSL3z4qMBy/NqoxM14W6LpeI3E7ZM0Ou7iSLQqp7D6jOHWMSnlSLbblP1o2dtJ6LDdnQ1eWdyWY86zXW6VJ350u4Oz9X+a5nj07hmaNTVWE7OzauMva3rf0qsSqYS0kmTO4ck/gjvuc/+3Q/3vvobuX7Uo45iF0WMKZn3jR+vLhvi9KrBgi/62rzTHPxeFQ9B8HXDu8d1I7VZ965WXNcWRAUCPFIhKKaCPYfVZ8hiOagE1x0njaqZz/JUvEpVvYQdkGVfzAMNlu6GTUPf2vzZ4x/j2uTWGfzNWoTmkQnguhuuk4QqncesP9IAAAgAElEQVRwbRfDOWxYV5iJIIwHSlhvFd37RRUe27k0Gtv9oLoOYY8l49I/QWEjjGdIML+RK6+cv1H5rO7eW9aTcjZabTuGrp41ph1F5rd1ZCinTGIMlPtS56YurtcdRzd2cWSRsDqY/DtM2wXPH5uuuU66z9yeLTqFALmKTBy1YXl350t45ugULnxwy+g15rqA0T3z1nFF0Ql9Xgrf8qv/uI55Lp5pOgG4MDNXdQ0ricr5Uj4PVXicHPphus+Dr+cnC3jt7YLTcyuPv/nJgtbjjqrPEERyRKl6CNQ++0nany5ikK4SZxSieMwmiUqQl0l6k7gRm9AU+UAQRNeVna+3vGO7lG3UneczR6eqyn2aSkfrCFO2PmyJe9X7TVV4Gk2wBHVWYwiJ+yHMzp3LPeTaPzL3Qu7aHRoZxPuHvxqqdG2JcwxPnMaB/CU8f2xaee9Fif/XXWdVPwTJeGk8tX2N1uWbA5VjjwzlcOTxzZVzdnF0EddrhYNLeZBiiVv746nta+ApQoNkwobNuTwzqns27M7sy+euG8eQKAsY+V4wjStHTl1RJlAVu9f5yQJGj09XjXmjx6e17R0ZyuHs2CO4OrELi5r+lgVgcWygtt+KJV7TNvm8VOOxjmBJeNexRh5/xfep7iOqPkMkRXAejSqMtDvjJy5r/2YKVQp6Yjbb/hTDh6ODqBGTl6mrB2qc2OaqpDeJG7EJ3aiy9wRBtC5d5yEUdnCNO29GUgTbaVpIyOp/lN0HVRUKxoBnj05VSk7LYT7ye20eSMH3m8qMNtorS7WL4qWYMTzLtY2u91CYKkWCuWIJ4ycuO8ej6zwcbBRm5iKHP5lQ9aGqH3ZsXFVJ1C2f16GRQazVhJhpq6JY2iRfr2KpPjd5HYdGBrH1gZV47tiUs8u+DZf7MY7niqO82NHdZ1FzJ4m2mcYVXbUtIQDevb+grMAjPyM6bJ5J4ycuO1fzkRHnFUZALnFe9SybvlXkBhvo88D50litCl8V7z/se1MRRJy0ojdCo8NlxPeZNgV+a/NntPNpcIjRJZ9vFMJLNo5vNw2fpr+ZkurXQ1BsC94rKzTh5nGJdI2oQNYukQ8EQSRH1wlCYQZXleHy2tsFPPZwTrnwVNEIQ0PVTttCXog+UScCEe+s+u7R49N44Y3LmJktVs5ZVy3I5dg64kzsCtivlWqxVlzkyGY89C/rCbXwNX3GhmuVIpmZuWJlF1Y2HCthLP5xg9eTY8kDKZfNYO2nMsrqTi4M9Hm4V1wMnetAZ/ioKqzo+tNUplx83iUpJwNqjq8qpxsHoqrbd57YEiphqJdmAOdQOYaJvlQldBYJwqOKNUHkcDuRQ0mEktlyJ+nGr2CpddUzY0qMbhJORFtN95KtGkzUKjjivMIY4IzBaRGYy2ZwduyRsvdS4NnXscg5iUFEIrRaHsZGC1Suc83rb/801HGTEoMYs5d7j9tDSZc7Tif6iLx8cVRYkwlu1Ck3BdMMXopVbQTEuUnciApkjSp7TxBE69J1glCYwVVnuJx552aVwCHcn4MTWKMMDV2Ih00UMnmYuE4EOpFEePXUc8623XLXxK4u1FPO/c5cEVMHH1X+TXe/je+JXu3J5TtUCI8AXUWTkaGc9l4Si0oAOJC/FDrHUMZL4+DuTc5Jb+XP2Qwfl2un+z7xuotnhtwH4plPMt+BnF9GJB12+b5SiYOlap9+L8UwunODdkEiEoQPr1+JW3fnY09SylEOJdv6wEpjXoZcnZ6YpnvLJnbp7qULH9yqbAJk+zws60nhzlyxxjvSRjrFUFJsc6/9VAbDE6eN+a5qcjhx+yLQS7NKn73wxmXnRSMtBIikaDVvhLgEKtfNP1cvwFlDqHfGq872sP/1i87tDIvLVC3GGF1xBqCcn8LVl1aXO85kvx85dSUWMUjeAHPaFCyVPS/7eqNt8NkI62UfhUaWvScIojXpOkEozODqYriYFqNhDI16PIl07RSTmm4BJL6nnonAxYiTzznMeYYxEMOWEQ+2weVa6RaTHEveHMHvjzqZ5ycLeOGNyxVhLZvxKiKSqv0ugoHJO0J8j8s9Lxb0YcSQw3vLCYZdk96qPHF0uFw73XOQ8VJYv/9Na5tUeVeSquoiI87j7NgjSpFZxSKg9K0vLnI8c3TKKhSf+8ltfPuJzdrQq3oQYVu6+4wBVWK7EA/TjOGxh908wnTXmqFs+MrPlcxAn76inJxU+/ZssZKf6uiPbjh53Ih23b2/oFwwmbzuMl4ajz2cCy3CAkB/b0+lX3Sht6rvo4UAkRSt5o0Qh0AVZvMvDuErFcgfVm91L6A+G1S8T1ecQdgv33j9olHoUjFXLFU2rEz21LOG+erp7Wsqgr4Q8XXjYXADTEZ37WZmi5j8pnpTMA5cKt66YrrOVGWMILqXrhOEAPfB1cVwMS1GXQ2Nej2JdO2UQwV0ok+9E4FreMnPZtQlsk3nGTZ0RZSpltuvygEVXMSZwi7ka2XyxDGdi+1+s7URKIeiPOeH3ATLfwerFA390Q+Uxo5Lf+ri4UXi5KhiyMhQDsMTp50+pzPGdLg8Z6prl4KbIR3cKUyyxK8K+TyCz2uUHVHbZ0p+uJBJEDKFZZn+Brh5JuYnCzj61pIAIryXTl78ELse+ozyGQDK/aMLR/vy+pVLoV+BZ95LMxzcvUm7qFAltVdVXlPhpRmOfH0zRoZy2lxWOuR77+UI+blcQ9jENQsKbwQRN63mjRCHQBVm8083xwYxCfdJhCnrbLMwmOzJ/GTB6KFomjfkCpk6e8pkB+sqXq4bO6nsY51N0WpiZlhsNnic4z6VsSeI9qIrBSEdYRJIq3JwyNgWPfJ36UoZuyQ5BewGlk30qWcicA1Xyhp233XJZ3XntdxLaXd3RK6SZ45OYaDPw8f3FqpKPKsWiiYjJZizBID2ukfxUlKVptYtNBcBvHz+eo0Lt7yDBgAHd2/S3g+6UueiookuQbLYkDSJITqDTlTuchH3oiwMXIw01TPw4Z05ozqSYsB3ntiSyA5vGFZkvJqQVCGYuXg3hUXkVdK5/2czXiWJqApbe3SeiQzAjo2rAOiTM9+eLSqfD/nZO3nxQ+X3/vjDXwAo3wty6GKaMez74upKyKSrCG06y1w2oxxrbWKZTNBbKmpuJ7GQMoVzyMLba28XsPWBlWS8E4nQat4IcQhUYbyMXIsQZLyU0ZtGPNdxoRO0Usxeel5ui86e1FV/FJQ4N4pgNttKuenDgJ/dmasS4rMWDyH4bVi//008uW11lZjUamJmWBqVv6sVE8cTBGGGBCEf1wTSOzau0i6qZUzhWDs2rqp6XbdAEImAXRJWr8h4WO6lqhI5y5+LW/2XjwvoRRLBx/cWtBNwMPnsc8emao4tG44AjCKUHNpRD6qJXvRj2J0lGVXiZhmT7aVbSwZ30AC9wR106fZSDON7NiE/WdDuPMo5oXR8+4nNSs8LIaDaQpVUMfsuuBppwWfA5q2xyIHjF67XtCeupMsueCmGu/MLVc+HbFjFLQYBwPYHBwCo3f/FvRJGOJGRPROPX7heFSrFgYoYYRpfdWcsnj3dcy9ez08WqkIXZRFkdOcGZchDWHQebmGuV3DXWXWfeymGTyzvMY51YmPBVLVIppkJfonuICl7JApxCFS9PSncX6gVb3p7UjWvuXr32LxXo3jwhOVn/oaVDZfxwmW+sOW8NBG8jjpBzdVrUnilAqiIQq0mZoZFdw3itmdaLXE8QRB2SBDycUkg7RouYwvHChNyohtAg22ZmSvntXhxX61HQxiScvMMs8Ba5MA3Xr9odA8G7CJUPdjECRevFF1fJhVy9PyxpWphun4zGTTDE6e1xxZeIzoPhxTTJK31f7UlfHQJE5O9qkQ7xHUSOZRM923werjsfJ59/1aNKOvqFeeCnNtACM7y77PztUKqbFglUWr32s/tpd0BhM4x1Oel8C2/lHl+soC/UOTNER6DUagnEb6cq0mXY8gVBmirHw70eU7HFvmOZGzXQydwigWQKZF3kIIf4kvGO9EN1CtQqcQg0+su2DYexJiVJGFSAgh0do/LfGtri4xc3EJUyDw0Mli5juv3vxn9yyReOX+jykuolcTMsNiqrcZFqyWOJwjCDglCPi4DmGslIls4lin5nWu7klDgo7h5JpVg15Z4UPRrEpWeRKiGrnocYPdKMfVlPZNin8GNvMS5k1uu6DthuIkKSaZ+lL0pVCxytWdGcZEb82kJRP4nIYgERZ9g+GawwpycQ0mF6nrU7t2qCT5TYfL4ZLw0Uky9KzzQ52lzGwjWaRb5oj/j8mhRHRvQG7+2HEMqZouLuPDBrYooair/brrPVbi67ZvGC3HeMwbBRiQ71yWHBqrDP4PPo6uDEIf6Ga5nMRJ23CEXf4JoHms/ZRdj4lxgZ7y00p5xGefl3G86u8d1ihro83CvuGj0+D2Qv1Tl7ajy5onLe7bEuVbgl8WvbJ8HzstVZ4PvbZV8OrZqq3HR7rmWCKIbcV0TdTy6gUp+3Tb5Ci8H20AfZlDUvTcJBd4kMoX5jCu5GCaH0Z0bkPHSzu/3Ugwpy2aIyPG0//VLKPgLfmHY5CcLAMqLpMN7B5HLZsBQPpfDewerxAJdX9quv6l539r7UCXfj4rg9RKi1rqxkxieOF1pv+r8TN+b8/skCgWLaCK/77vnrlcMCVn0efncde195rJTqroernKDSIgu9yNQFg1f3LfF+NnDewfxtS+ox4NdD33G+t0u45KTT78C3XOQ8VLKeyZIlF3FV87fAGAfp5Z5aXhp/fFTKC8cVM/eQJ/++TAtsES1wKzm87lsBlcnduHs2CMY37PJadwJ3pum3EvB75LRPccyusshXg9rjDfCA4EgCDWmyoOCz2YzsS2yTfaMDSHWRLEhZTJeGgd3bzK2JSgGyYj5BYjX60VlAwZtqNuz5dQHwffabMlGorO547DFZVR2eVDUc5nTCIJoHOQh5OOSh8TkPuuyQ61LJAyUhQqw6gTHpmMmocCHFZnyk4W6vHPOjj2CdftPKnfNg3O5aYdluZfSigUpBnxyuVe1awNAex1En7t4YJl26019+eK+LcqEuqLcqalPxfeZvLLEd6t260TCbRU60cZLsUreq2ZhE5RsAkM9QumKjIfnj0+jJCUnf/54OTzPZugKbxgVLiE8tnHpyKkrxqToQeT7XbdjO1tcxKzfXyYvwSe3rXbKSSMjRD5bKMLMbBEv7tui9VxbBNDX26Ms9Xtw96aq6xWGwswcvBRDOsVqPq+qYiiPSS75GVxCMFSGs+45lj1SdZu84vUo3mSNypVFEJ2KKjlxHIhx4sIHt0KPwyriCIWKulEpPC+D3vVBTGIQsDS/HMhfwmIC+fXkIii2sVEWwloln06jkmLbwpsp6XRr0yoebURjIUHIxyVZnC53yECfh4O7NxkfGFUiYVkEkBd4uu8Puqd6KVZl3Nc7sIcRmcT56Egx4DMr7IufLz+4UrkT9uUHV9Z8V3DyCJZgV5Hx0uhf1oM7c0XMzi9g/MTlijj0ku/doepznWjiukDS9SVHOcmr7J2RzXgY37N0/+jCWsQujnjf88emla6+4nqpRK2wJlLGS+Hw3ocaXmo9LDYh1FaxRf+5sht9UBwoLXInV3qXECUTtnEprNAVxUTWGa+HRgZx9ebHTjvZAnHb2/IwuQjbunMfGco5Jf7XYRJMCjNzGD0+jRfeuFxJ4C/ytumuNUN5gSJCIVX096YxO1+q5JESoZwiPE33HMuGtE5MTjO2tPsactM83swSBNH+qBZLJlThTHEgvGZawYtPJK7vSQGqadY271yd2FX1u25BKnsAqUgzZhWNwoYjB5GLoNgwzc/NyKczMlRbYfOxh5MrOGPK/9kqIhlRDYl13QsJQhK2HZJ6KgzoFufBZLquuXpuzxbhpVmlBLSIXxb5YJKs1qQ7H5lFDmP8uUi8qjMURIlo3XfNFUuVSc3E3fkS7s7XVh8Su+xPbV+jzD1Tb/I904I3aEwEE0+6XAedp5B434H8pVh29+cXOJ49OhVJSGgU4pyDRqScoDlK+xmAL6xZEUrwCKLyQhMI0eNA/lJVKfX+3jT++Gtu7vqNqnimMl7zkwW8dfV2qOP09ZbdyIVhqiohLyox2rxZTKKRa2hWFIqLvKrqnqiKOLpzg/JZ4YDyPKvew3nlWsrvdbm2wpDWjTkit9hyLxXKm0y0nSCIMvnJQtW4JARiF753/nrsXkJAPMKCKXeEpxF5ZIRNU4fOUsHUxzZ778ltq7WiUZoxfPuJzQ31dBZzVKvk0zFV2GzkYp+STrcuJNZ1LyQIhSSqW229A6DqIS2WOPqX9WB8z6ZYFF1Z8BJJfcVAcOGDW1XVj2wLlVw2Y6wWZFtoBMUbFfUmwuMAvnvuOr4//WHFa0iICC7J90xulWLBG6XMcxjhUQ6XE55GcbmQA/EnG0wChlpvHZGPqB44oKyEFeU4QRjKbdz0zf+rJuH03fkSnvFDgrIZD3fnFyoL+eCzvWPjqtiutQmV8Tp+4nLoZNaz/rkKwzT46RQDHns4h+9Pf2g8ts0bslFCGbBUFfHH//I3Q4djCuQwvShP3M9m5oxjzlyx1NIefgTRbFzCJFRjnusYKN7GmHtyeRNiHohjrMv06vOh1VEoLRL7X7+o7GPhWa3r7qe3r8GhkUHtfFjivKGezvIc1YgwLRdaZbFPSadbFxLruhcShBpEVlNuWJfANIjpIY1zkK9U0Xp1umoRKk+yJq8HYGmHP46F6oG8fjdH58UTFrG75SIiiIS1+clCTR+NvrpU9h0IX+Y5WNEsTNUsYMnTyOZW3YowACmDV5bpOtfj/s0Y8FlDaGNScpg4rqr6mIzKNV1+tsPcY654aVblScIA7Ni4yqltNlJ++JLOMF/ksIaBCuHzT868WyXAfP7T/fjhc18BoPay89IM/b09kUPJTIh70JYDLCmEIX3y4oexHteUoJsgWpkweTDykwU8d3SqUmigMDOH5/yxRf5MHGNHXHsssmdg2IqPQUzzUCPF9fxkAXOa+dzU9yksheOZRCPbeWQzHvqX9Si9jD+bzWB2fkFpxwPlsVJXZQyo3eADoK1imxStsthvVC4jIjwk1nUvJAgFOJC/VBVfKycDzE8WqnJTuOQOEuiMgJnZorakpYzpIa0nR4mKF964bA0t0P21z0vhW3sH645rZyj398sagYah7B5sWzzGzcf3ytde1UfFEscLb1yuXMOwRpR4v4uH1zdev1hjOAkDsdW8ejJeCveKiwYBMYW/+Ze/ibWaEutJns9T28q7iqZcP62IeLaTMOR6UgwLJV65XhyIza28xO0hiLbn+e79BRx+88f4+1/MV73+7kd38evf+U8VUUjO08UA7Pviauu1ZiiLhBHyUQNQG7om8dyVgT4P/zC3oH0WhGCnW6y4EEyknU4xHNy9KfLxCKJZhM2Dsf/1izVVJxf91+XqVq1GwfcMrFcQMuGyuReXcBzVbpSv3bKelFZUsm1kyrkcVag24jJe2lqRLRhZ0Kw8LXEu9utJPFxP+g0iWUis6166UhDSDWTBZHRyMkAANZPi7dlijVeIDt3uhio5qOpYuod0x8ZV2vwU8iAfZvCuZ1Fxf6HcknoXqhzlyUI3eXOUd4S2PrCycl46D5M4KS4CQ3/0A20fidfrLaE5Vyzh+WPqe+tA/pLWKybpnZ4BjaebicN7H8LIUE4r+MwVF7FO8zcgPk+w2uMu7SqO7txQ5fHV6ohnO4ndW5UxrfI4jBr6UG8PFxd5jRgkePeju9jywg9qxlsO4OhbN7D1gZXW0vNRzolhabd3RcbDci+F27PFyr1bjygkSjED+uqCQrCLSjbj4e79BchHNuUVIYhWJqzXtE5AEK/nJwuxheb296atnqGuMFa/rWHDxetQjE9Pb18Tup/6pXC1OOyXewaPYbmYiwzzQ5VtdnxcQkazQrfiWuzHIWjFUdWOiB8S67qXrhOETAOZLtTGNMEVS7xqENcJLy6LWiECiOoyqhKcwWObRBOxa9zI3QiRwFQXIudKmjHjwk2uuCX3fSMSILucVxyVP0RfAuXzFPeWqV/6YjQ2g2S8NO5F8MaSvaZ0mK5ZiXOkgJod3Hop8bK4JkShdhGDvDTDjo2rGu7VFDTWW8wRrYJOfC86VoaLgsgJJb7fS7Gq0DvdQsSG8LiUnx9VdUGxmIhaQWd+oaTM20GJJIl2JM7QGJHkOA6GJ05X8qjFAecw5mqMAxd7R4wRWx9YGVoQ+toXlsaXODY4bMfgqN3Y4tzdCzYOIaNZoVtxVRlrlVxERDKQWNeddJ0gpBvIXnjjcmQvBDH5mIQX12OL96lEG9VD+qxhgSMmON056zxQshmvrlj5uWIJy3pSde2K2/pL3tGQRbhykuUGZ0FUENfELiZZQO8dIJOUGMRQ3kGLskt62w+LrIekrugr52/g0Mgg9r9+MaFviJ9iiTckkXSQYL6zKN5inYiqCo8q0WyUsXC2uIhvvH6xapNgUTM2FmbmInv1NMvjkCCSQLchxRgqIfpyfhgTUZLn60hCwE8iJ5qcOiEMUcSpkxc/rPLUjSLa93lLI5+pwitQ3mxUnVYjBY1m5WmJq8pYq+QiIggiPrrOK1w3YNWzsBGpKnTCy/iJyxWPljCICSo/WcDwxGmsGzuJ4YnTVS7CpglEfF53zsIDJehyPL5nEzw5AUcE7swV8eX10UMYbMhx/c8enULBLy0+V1xECtX5QxrN2rGTsXopFTSJwxsJR7gk2arPtyLCMIpbREw38f5LipnZYmWsyE8WcIfEIADxlFo2Mevn3xKbBCsy+nwdcTelz1B9iCBaFZ2OschReZa+e+56xW4wkYTg0sqI1AlRNkij9JXYMBqeOB36s0DZ1vvW3ocqv48M5XB47yCymnGyxLm2nY0SNEZ3bkDGqx5bG5GnxeTZEwbduoMSDxNE+9J1gpBrVa8wyHmAVMzMFbFj46qaCcAFsQgQhov4XSzMRndugGntKXJa6FBNBiNDORx5fDNy2Yzx2CaWeyn81fU72r+nWX0r5uGJ0ziQv6TMn7SI6ElhWxGG1th5aYU2JEFUQ9REm0SfhYIDeOboFNaNncQzUkUeonHMFUtgDJHmkijEGd5CEI3iTheJOP0xi7bNqFIqbFuTd1Cfl6rZWE0zht/dtqbGu2VkKIepg4/ipX1bQtmwQUHDtBlbD0K0EjZ2LpuxJqaOg7g8e5olaBEEkRxdJwjdT8jLIj9ZME48Z965WTUBuAoiDLVVd2QRZ2QoZ9zhyvZ5uDu/YPwO1WQwMpTD2bFHcHVil1M7g9wrLmo9WjJeGt9+YnMkrylBYWZOm0y70+BojZ36JMTUVqCdqou1At3wzIWhvzcdWTiPwu3ZYsO8BelaE+1IN3kqzC/EK80nVZhDDuuKQm9PukaIECFPOqHGZh/LBAUNkQJCtxlbL7KNfXbskYaEquk2h02bxiqaJWgRBJEcXZdDKErCTRv9vWk8f2zaOPEERZflXsop34vumIWZOeQnC9aE1Zzbk+XaqpFFqfJkeveynhSePToVehIK8x2tSD3VspLKDRQGyhdDNIKkqsolRSs8mwRBLGHLI9NJxJXfSFDP+KvLP5nNeBjfswmjx6cjt/fOXDH2ZMa5bEZbSakTEyfr9qGjOOx3YuLhMNWYCaLT6DpBKAppxrDIOVZkPPzi/gJK0oSWTjHMLyxaJ9AVGa+qrHUciwiXhNU212mxK3Igfwkvn79eFXtvc+GNijAYui02v8Q5Ml66K4zUViOJKmVE/GRaJCk8QRDtS7Aqa6sUm2gHnty2OnLRApXo46UYxvdsqrkmYWWhz/rijQpTyJOu+MFAn4ezY49oP9eJiZNnNJt6ute7iUZWYyaIVqQrQsbkOGATqiR0Irzp6sQuTB18FE/+6upKuFeaMfSmmXXHI+OlUSwtxl7WWuxW6MLP0owZXafTjOHw3kFc+OAWvnvuesuWke4U0qxcqave/ElEeBYB/PIv9Ta7GYQFWrQRBBEHckhOqZOSCibMoZFBPL19TSQ7JZh/MpfN4Mjjm6sq5UZNRbBj4yrnZMayzX9PsQHnpRkO7t5k/L5OTJzciecUF3El3CaIdqXjBaFgHLCJ8T2b8NK+Ldq4WFXJRtsCJuOlcHjvYGJhBYWZOa2HUIlzY5K3Rc4xMpRrShLBbqTEEbl6B1E/f/+L+WY3gSAIgmgw852Y5T9BDo0M4v3DX8W1CMJNUrlxzrxz0ymZcdDmD9roDMC+L662tkv3XTs2rkok0XQjoGTQejrRI4wgwtDxglCYct3CPVA3mUUp/b2yf1ndE2LUijK5bAYjQzkMaJIBc5RLpNcjUMRd4SIOmllyniAIgiAIohNoFY/mwsycUzJjm53OURaXggQrigGo+a7HHs7htbcLiSWaThpKBq2HvKeIbqfjcwiFUXdtCeOiKMXiM7pkezZEWNf+1y+GCqfwUgyz8wtYN3YSmTqrO5hoxbLE5B1OEARBEARRH7YNw0ZtwAlhypbM2MVOD75Hlz/m8N7BqjxDwxOn2z7RdCcmg44DVSJ68p4iuomO9xAKq+6aJhPdsQb6PG2WflGqe3zPJniamdNUfr3kh3Wt7F+mfU/Nd2Y8gJWrQnEkU1lNQNoLQRAEQRBE9/G729ZY3xP0vslPFrSe6zpKnDuFarnY/MH3uOaPobCizoW8p4hup+M9hKKUH13rJ58e6PNwcPdSdQSdgnxw9yatB49IaBessBAsaTg8cRoFxaSSZgzrxk46Cy9CXGpEBa8UI28cgiAIgiCSh8pCJ8daqehKzrFvn96+BodGBo3v0XnfsAjbicJGNlWAstn8Kq8PV6Hns9mM0k6nsKLOgLyniG6m4z2EgqqvLR5anqJuzxYx+up0ZSfCpCDrwrnmiot4cP9JrB07iSOnrmB05wZlfiJVsjegvCsSZtrcsXGVcsJKAiF0x8UAABhtSURBVJedIYIgCIIgiHoIJgtut/wtSdGXQEoA1761iUGA3vumXs91XQWooJ2ezXhlL37ovT5c88dQUmaCIDqVjvcQAqpV3wP5S/juuevOny2WeFV8cBQFWXjRmHY1gh5EKcYiJXv+/vSHoT8TFsaAp7aVd4bC9CVBEARBEERYdMLC88emAdTaVIJOF4y+tfchPHN0KvbjxlVyO8lwKpdj9y/rsXo7ueaPsXn6EwRBtCtdIQjJqKoL2HDxuBno83B71h6mNVcsYfzEZYwM5ZTuzyKB3TrJfTcMjQgV++yKTGVn6POf7se7H91N/DtbCQbKnUQQBEEQJqKEeB3IX8Ir52+gxDnSjOHJbatxaGRQa4eVOMezviAiHzs/WcD4icsNsYmayZ+ceTexY8ch5ujCrOJA5dmTnyxg9Pg0iv5ObGFmDqPHzaJhGKGHwooIguhEuk4QijLBuZTdPLh7E0ZfnUaxZJcKZuaKeOpP/xJ/8f6tirAQ9B5KchIVeCmGI49vxpFTV0J9V2FmDk/96V/i2s/nGhaeFie9aYZ0KhUqr5TM1YldVfH2BEEQBEEscSB/CS+fu661cXSfkb2OS5xXfjdtxHAAzxydwvPHpvHkttXY+sDK0Lkj25UkN+Si2KFBEXDHxlV47e1CjffN/YWScw7KFCvb4UXpA7pQrfETl6veBwDFRV7ZiNVBQg9BEN1MxwpCup2pKBOcHLqlO66YSJ4/Nu0U6nX2/Vs1r8kuunfvL4RqYxQW/J21KN4uqva3C4xF9/HpTbOOd0EnCIIgiKjkJwvKcHIR4nX8wnWc+8ntGi+g751Xh6B/7/x1pxlbCEhH37qOBIurdgVCcNEVTMkochepEki/9nYBjz2cw5l3blbZzaYwt3SKoSSJOukUw74vrq45hkrA0XmEzcwVMTxxmkK8CIIgFHSkIKSragCUY4VdPXlk1o6dxECfh4/vLVS5oso7XpVqZJK7aljEMRuxsxUhRVFHcH8huqXY19sTS1w9QRAEQXQi4ycua/9W4rxqQ0n2AtKZTWHNqVYUg7w0C213NhM5+bJKvDm896Ga13R5ns68c7OSDkHw3LEp7XUtBT18Slx5jLC4eKkJdKGLBEEQnUhHVhnTTUqVhXzEOfn2bLFG6FEl3uvtid6taca6ws25XbkzV0w0SaJDdCJBEARBtCxR8va8cv5GAi1pHY58fbNT+oFWQRZMvFR1u4O/C1zLtwPhRT5Xu0vTtAouybJF6KLw9hei5YH8Jac2EARBtBsdKQiZJqUjp65E9t6xfV9+soDRV6dxdz66oBOlshjRODiSTSjNOfD09jUJfgNBEARBtBadbPtkMx5GhnJtc445KVmzymYuLnKlqOJavj0KrsdwMe9t4pJOnOx00ZIgiO6lIwUh06RkmggY3BJI677vyKkrbeUSTLQeuWy5gttL+7ZUGWUEQRAE0Q4M9HnNbkJLMTNXbKtCFDs2rqr8P4zXz+jODch46arXdMmfsxn3e0R3DBUudpNNXNIJd+0i6BEEQYSlIwUh06Skmwhy2QyuTuzCt5/YjDCSkDxRJRVKFDyXRpBierdgIjnEvTQylMPozg1kWBMtSZqGBoIgNBzcvanZTSA0ZDMeBvo84wbomXduVv4fxutnZCiHw3sHkctmwFC2q+VcRDLjezZpbUwvxSptNB1Dhcr+l3ERl3T90k4hfwRBEGHoyKTSYuJQVQMDUJO0WZ4gRoZyxuoHXpqhv7cHd+aKNcdNqlT84b2D2tLwA30e+np7Yv/e7zyxBQBCl6QnotPnpSr3UrBkL0G0Er09acp1RhCEkuMX1NXCiObCAEwdfLTy+zqN15K8ublj4yplxTjZi0jGtXy7bKcXZuaQZgwlzpEzVBBzIWj/r8h4YAyYma212XU8uW218pyf3LY6UpsIoh501a0JIk46UhAC9JOSTSwCUJmYVBz5+mbtg+hawSzjpSs7HsMTp62Ci0nIEjtxcVYmy2Uzle8cGcph/f43E3GVzXhpLJRKsVYEkQW7nlT0aiPD61dWVUJpBLN+Y/OTBa0YNNDn4fZs+ISdBBEnJAYRBKEj7NyZ8UhgbgQrMh6GJ05XbN++3rQy52VW8kyWvYVkdK+HwVU8avRxRTUxqjJGNBtT1WwShYg46VhByIRtsjCJH6bPib/94X+4VJlkGYAvr1+Jaz+fUwpQozs3GMUcEWftImQFd1oYwidAVrnTxikG5fw8TqL9APDc0SnEoQnJO0v5yQKePzaNqCmgX/79L+GpP/3LhotCQPk66lo9M1tEb5phvgm5qrIZL1L1GIIgCIJQIeZtk2c2AVyb2FWzOATgbOd5KYa78wuVOdy0EXmvWKoIR7pjJ1lttRU4NDJIAhDRdExVs0kQIuKkKwUhGzlN6Jdrkl+5ygEH8Na12+jvVXe1eKBfeONyjeeHl2IY37Op6r26AUD1N5XxkPHSeOzhHL4//WHFMGCsXN1K56oblxCQy2ZwduwR5d/qNQblY4vzrkfIEsbQQJ8Hzsvl5rN9Hu4XSxVPnigwBvR56l05kS/IZKjZEqMnycxcsSmeU0Rn4aVZRybff3r7GmWYAUEQesS8bbMB0gzowGHDCWF7qjYGXUL6c9kMZucXnL2L54qL1uPGUTmMIAgzYZK6E0Q9dGRS6XoJUykhiErNLZY4ZuaK4Fhy98tPFip/HxnKYfKbj1YqS4lEekce14enuaBL8HdoZBBTBx/FtYlduDaxC1cPl/89O/aI8vvC5NEb6PPw9PY1ofpvZCgXqqJWMA9h8NiqaxDEdErpFEPB3xm7PVvE/YVFvLhvCya/+SgG+pc5t1PV7hef2II//togvEBWXi/NKuF/psSFpsToSZNmDC///pfw9PY1oT/bzIppGS+N4fUrlX97evsavLRvS6TjphkLlYA+CdotyeXw+pXY98Xk8jA0qzf6vBQOjQyiz0tmSm2vq0x0O70JZJ3/9hPRxul2x0uxKvtmZCiHs2OP4Kpvs9nm1mzGw9mxRzATY6h5mKpfBEFEJ0xSd4KoBxKEFISplBDERbUV7n4ySSUNCxoPUY5pMiTkPnrJF00OjQyG7j+dCDe8fmVl0ZtmDE9vX4PvPLHFeGyXa8AB9PeqK1GUFqu3IeXrVY8qL85jZCiHI1/fXFVBTPYgs4Us6vrq6e1rEhVeRLsOjQzipX1bQlW/03mG1csnl6WN7UgzhsN7BytCVvBeOjTiXr1ExksxfPuJzbg6sSsxEcDGtYldeP/wV9tGLPBSwONb1+C1twv2N0ekXgeCXDajHRcEqv5e5qWxbuwk5hbU3oMDfR6uTeyK3C6nkJA0o8qQREuQlAdgcCOl08l4KevGoKmqluxlrltAhunRKFW/CIKITj0OCgQRBgoZ0xA1KZ2rC68sLLR60jDdOYkwLSFmPXt0CkdOXamIWWHa7pIjSfX+MO0Ntv3v7txzbp+4XqZjeymGTyzvwcxsESlFYvLiIq+K+70nhZ7NzBUr19wWsmjrq7WayiGAe74BFbLYpGrDrbv3MacIpxN5sHTn5YIu0fsvZXoxunODMuRSTt4ORM8J8NK+LVXHz2Y8jO/ZVDlub09aG0Zo628RrhlE9LVLf5mez2ZVCAyet7gWLt57NpJKQCsEQl3lHaDs4fT41jVVFWzuyqEYmostRPWoYS+6aykWc3JONlXVnh0bV+F7569jsUtDbojGEnfF1Vw2gyOnrnRkqGkQBoTaFHSt1qXKVylSCJx552ZlHteFlplC/gmCSIawayOCiEoighBj7DcA/CsAaQB/xjmfSOJ7WhFbkmiBvFvT6knDdIbE6M4NsYpZcVWcsF0D0fYweYvE9dIde6DPw8HdSyKBrZyr6Zqb+ltg6iuToORyfw70ebhXXDR+v6oN+ckCRo9PoyitOuUdSpfrEjROxcRn6k/Rjnq87D7/6X68+9Fd5eu2+/KOIb/Wi/u2VAz1IAzAU9vKHjO6vtbdo7J3mel+sd3jSYkrHLUJ5EeGcng2ZK4wsbgJLnKi5hzTPRsZP+QLKFfYUS2IlvWk8PLvfwnA0tg2PHHaKb+aGD8+mQlfJbDPS5UrWAaeLQDoSbOaype6ezVKfiNyIW5Pmm2Dje7cgGePTtXtsRc8nop6NjmiktR3RhVdXGwn14WlLvckeSQQRHNIqhofQcjELggxxtIA/gTArwP4KYAfMcZOcM5/HPd3tSLBSTfb5+HjewtVhnxwcm31pGEmQ2J44nTLiVmqayASQ8ttf/7YtFPiafl6uRpVuh1SsTA0XfN6dwRMAkFwN1HlySFyGYX9flu7Xa+LClt/iuNHved++NxX8Ovf+U9VotDnP92PHz73FetnTR46oj2qyjBP+R4pWx9Yqe2zCx/cqlnIy/mmAHO/m4QTIa7oBCvhWSJK7wpECd4z79zUegHoFjaungPCW0eHqc2m4+uejcN7l75LNySowjJcxmh5/DCF3760bwueOzZV5cWTYsC39j6EkaGc0guuWOKJjrWd74/RebSCDTYylMP4ictWsdQ1B9rIUE77zMftjWRDzAtl4eRixSs2xYDf3bYGV29+7FR4ISjGN0J0iVM4IgiCIDqHJDyEfhXAe5zznwAAY+zPAfw2gK4QhAC154RpcnVZ7DYbnSHRqmKWi+Hz5LbVyl3z4fUrce3nc9rr5XJsm5eP7ZrXI264CDPi/6Z7M8r329od9bxcvKbqxUX8UWFrW5jrEcQmGAl0xxjQeLsM9HlVgo1JQNQJM/nJAkZfna4J4wgmQZVR9VWKlYUHzpfEJlton6nPnz02pRR1GHNb7Og8vlSv657jNGNY5Lzm+C7ioa5tOjHJdaw1VYvUhWS20hxEONMSNpjJc1Lw5LalBPMv7duiFLBF0n/TM+8iPvWkGEqLHBzl+337gwNV8/yOjatqvDXFhomqAqttLhueOK0VqhhDJYS2FUUX8kggCILoLpIQhHIAbki//xTAtgS+p22wTa6NWOwmRTuIWTrEolN4QLguRl2wLe6SvuauBl27GH6tvGvp0rZ6Bb6onz24e1ONaBPGw8jWLgDG/Eq6z9R7HU3HUXlVAeXwPPFZ0/eFGdNMHkeq73ARD+vxkjMxvmeTMqTzyOObAehFQaLtaAkbzMVzR55rw3qY1sypipBKwF1kBuAkvrsyunNDjcef4Klta9pm7iUIgiA6H8YdQmZCHZCxrwP4Dc75P/d//y8AbOOc/4vA+/4AwB8AwJo1ax7+4IMPYm1Hu5FUlbGk0cWbUxUKO+16zYn2ohvvswP5S5GF3rBjWtj+jXo94hhrTd/diPuEMfY253xrrAclqmgVG0x1v8rYwkKjfF+rjXP5yQK+8frFStEBxspiUJznTRAEQRAumGywJAShLwEY55zv9H/fDwCc88O6z2zdupVfuHAh1nYQjaMVDTGCIIiotOqY1qrtcoUEoeRpJRtM3K+yp1CcnrgEQRAEQbjRaEGoB8DfAvg1AAUAPwLwu5zzy7rPkCBEEARBEJ0NCULJQzYYQRAEQRBBTDZY7DmEOOcLjLF/AeAUyiVP/53JECEIgiAIgiDqh2wwgiAIgiDCkERSaXDO3wTwZhLHJgiCIAiCINSQDUYQBEEQhCupZjeAIAiCIAiCIAiCIAiCaCwkCBEEQRAEQRAEQRAEQXQZJAgRBEEQBEEQBEEQBEF0GSQIEQRBEARBEARBEARBdBkkCBEEQRAEQRAEQRAEQXQZJAgRBEEQBEEQBEEQBEF0GSQIEQRBEARBEARBEARBdBkkCBEEQRAEQRAEQRAEQXQZJAgRBEEQBEEQBEEQBEF0GSQIEQRBEARBEARBEARBdBkkCBEEQRAEQRAEQRAEQXQZjHPe7DaAMXYTwAcJHPofAfj/EjhuJ0N9Fh7qs/BQn4WH+iw81GfhSbLPHuCcr0ro2EREErTBCDdonGpf6Nq1L3Tt2he6dtHQ2mAtIQglBWPsAud8a7Pb0U5Qn4WH+iw81GfhoT4LD/VZeKjPCKKx0DPXvtC1a1/o2rUvdO3ih0LGCIIgCIIgCIIgCIIgugwShAiCIAiCIAiCIAiCILqMTheE/rdmN6ANoT4LD/VZeKjPwkN9Fh7qs/BQnxFEY6Fnrn2ha9e+0LVrX+jaxUxH5xAiCIIgCIIgCIIgCIIgaul0DyGCIAiCIAiCIAiCIAgiQEcKQoyx32CMXWGMvccYG2t2exoNY+zfMcY+Yoz9tfTaSsbYDxlj7/r/DvivM8bYv/b76iJj7AvSZ37Pf/+7jLHfk15/mDF2yf/Mv2aMscaeYfwwxlYzxs4wxn7MGLvMGPvv/dep3zQwxpYzxt5ijE37ffaC//o6xth5/zyPMsZ6/deX+b+/5/99rXSs/f7rVxhjO6XXO/JZZoylGWOTjLHv+79TnxlgjF3zn50pxtgF/zV6Ng0wxrKMsVcZY+8wxv6GMfYl6jOCaDwsJpuMaCwsRruQaCwsRvuUaA5x2MlECDjnHfUDIA3gfQAPAugFMA3gV5rdrgb3wT8F8AUAfy299j8DGPP/Pwbgf/L//1UA/xEAA7AdwHn/9ZUAfuL/O+D/f8D/21v+e5n/2d9s9jnH0GefAfAF//+/BOBvAfwK9ZuxzxiAT/j/9wCc98/vGIDf8V//NwD+G////y2Af+P//3cAHPX//yv+c7oMwDr/+U138rMM4DkA3wPwff936jNzf10D8I8Cr9Gzae6z/wPAP/f/3wsgS31GP/TT+B/EYJPRT1OuWyx2If005drFYp/ST1OvYV12Mv2E++lED6FfBfAe5/wnnPN5AH8O4Leb3KaGwjn/fwHcCrz82ygvEOD/OyK9/u95mXMAsoyxzwDYCeCHnPNbnPPbAH4I4Df8v32Sc36Ol5++fy8dq23hnH/IOf8r//+/APA3AHKgftPin/vH/q+e/8MBPALgVf/1YJ+JvnwVwK/5XgW/DeDPOef3OedXAbyH8nPckc8yY+xzAHYB+DP/dwbqsyjQs6mBMbYC5UXovwUAzvk853wG1GcE0XBissmIBhOjXUg0mBjtU6IJxGQnEyHoREEoB+CG9PtP/de6nV/mnH/o///vAPyy/39df5le/6ni9Y7BdzccQnlHgfrNgO/SOQXgI5QXi+8DmOGcL/hvkc+z0jf+3+8A+BTC92W78xKA/wHAov/7p0B9ZoMD+AFj7G3G2B/4r9GzqWcdgJsA/nff5frPGGP9oD4jiFYh7LNINJE67UKiCcRknxLNIQ47mQhBJwpChAV/R5fKyylgjH0CwGsAnuGc/4P8N+q3WjjnJc75FgCfQ9k7ZWOTm9TSMMZ+C8BHnPO3m92WNuOfcM6/AOA3Afx3jLF/Kv+Rns0aelAOUflfOedDAO6iHNpQgfqMIFoDehZbG7IL2xOyT9sTspObQycKQgUAq6XfP+e/1u38vXBd9f/9yH9d11+m1z+neL3tYYx5KE/6L3POX/dfpn5zwA9HOQPgSyi7Sff4f5LPs9I3/t9XAPg5wvdlOzMMYA9j7BrK4VyPAPhXoD4zwjkv+P9+BOA/oGzc0bOp56cAfso5P+///irKAhH1GUG0BmGfRaIJxGQXEk2kTvuUaDxx2clECDpREPoRgM/72ch7UU4wdaLJbWoFTgAQFWJ+D8D/Kb3+z/zqCNsB3PFdYU8BeJQxNuBXUHgUwCn/b//AGNvux2j+M+lYbYt/Lv8WwN9wzr8j/Yn6TQNjbBVjLOv/PwPg11GOsT8D4Ov+24J9Jvry6wBO+7trJwD8jl8pYB2Az6OcsLbjnmXO+X7O+ec452tRPp/TnPOnQH2mhTHWzxj7JfF/lJ+pvwY9m1o4538H4AZjbIP/0q8B+DGozwiiVQj7LBINJka7kGgwMdqnRIOJ0U4mwsBbILN13D8oZ/r/W5TjRf+w2e1pwvm/AuBDAEWUd4r/a5TjKf8fAO8C+L8BrPTfywD8id9XlwBslY7zX6GcrPY9AP+l9PpWlBdk7wP4XwCwZp9zDH32T1B2+70IYMr/+Sr1m7HPHgIw6ffZXwP4pv/6gyiLE+8BOA5gmf/6cv/39/y/Pygd6w/9frkCqVpRJz/LAL6CpeoJ1Gf6fnoQ5Wpp0wAui3OiZ9Pab1sAXPCfzzzKVcKoz+iHfhr8g5hsMvpp+HWLzS6kn4Zfu9jsU/pp6nX8Cuqwk+nH/Yf5nUkQBEEQBEEQBEEQBEF0CZ0YMkYQBEEQBEEQBEEQBEEYIEGIIAiCIAiCIAiCIAiiyyBBiCAIgiAIgiAIgiAIossgQYggCIIgCIIgCIIgCKLLIEGIIAiCIAiCIAiCIAiiyyBBiCAIgiAIgiAIgiAIossgQYggCIIgCIIgCIIgCKLLIEGIIAiCIAiCIAiCIAiiy/j/Ae++m9octy6yAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1440x1152 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bsSPX8DSa4XB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "2ae51566-cdef-490b-c69f-9bc8c40f4d6c"
      },
      "source": [
        "df.dropna(inplace=True)\n",
        "df.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Year                 0\n",
              "Kilometers_Driven    0\n",
              "Fuel_Type            0\n",
              "Transmission         0\n",
              "Owner_Type           0\n",
              "Mileage              0\n",
              "Engine               0\n",
              "Power                0\n",
              "Seats                0\n",
              "Price                0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fpUmH2AtbTg0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "outputId": "4c03b091-ce12-46e1-f038-fc794157f233"
      },
      "source": [
        "df.describe(include = 'all')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Year</th>\n",
              "      <th>Kilometers_Driven</th>\n",
              "      <th>Fuel_Type</th>\n",
              "      <th>Transmission</th>\n",
              "      <th>Owner_Type</th>\n",
              "      <th>Mileage</th>\n",
              "      <th>Engine</th>\n",
              "      <th>Power</th>\n",
              "      <th>Seats</th>\n",
              "      <th>Price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>3529.000000</td>\n",
              "      <td>3529.000000</td>\n",
              "      <td>3529</td>\n",
              "      <td>3529</td>\n",
              "      <td>3529</td>\n",
              "      <td>3529.000000</td>\n",
              "      <td>3529.000000</td>\n",
              "      <td>3529.000000</td>\n",
              "      <td>3529.000000</td>\n",
              "      <td>3529.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1894</td>\n",
              "      <td>2379</td>\n",
              "      <td>3130</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2014.590819</td>\n",
              "      <td>36420.135449</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>18.748850</td>\n",
              "      <td>1550.574384</td>\n",
              "      <td>112.644795</td>\n",
              "      <td>5.176254</td>\n",
              "      <td>10.910298</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.655116</td>\n",
              "      <td>15233.810474</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.974467</td>\n",
              "      <td>558.077827</td>\n",
              "      <td>53.635686</td>\n",
              "      <td>0.674354</td>\n",
              "      <td>12.231428</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1998.000000</td>\n",
              "      <td>171.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7.500000</td>\n",
              "      <td>624.000000</td>\n",
              "      <td>34.200000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.450000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2013.000000</td>\n",
              "      <td>25000.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16.200000</td>\n",
              "      <td>1197.000000</td>\n",
              "      <td>78.900000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>3.950000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2015.000000</td>\n",
              "      <td>37822.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>18.600000</td>\n",
              "      <td>1396.000000</td>\n",
              "      <td>88.800000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>6.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2017.000000</td>\n",
              "      <td>49663.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>21.190000</td>\n",
              "      <td>1968.000000</td>\n",
              "      <td>138.020000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>11.560000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2019.000000</td>\n",
              "      <td>60000.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>33.540000</td>\n",
              "      <td>4951.000000</td>\n",
              "      <td>450.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>97.070000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Year  Kilometers_Driven  ...        Seats        Price\n",
              "count   3529.000000        3529.000000  ...  3529.000000  3529.000000\n",
              "unique          NaN                NaN  ...          NaN          NaN\n",
              "top             NaN                NaN  ...          NaN          NaN\n",
              "freq            NaN                NaN  ...          NaN          NaN\n",
              "mean    2014.590819       36420.135449  ...     5.176254    10.910298\n",
              "std        2.655116       15233.810474  ...     0.674354    12.231428\n",
              "min     1998.000000         171.000000  ...     2.000000     0.450000\n",
              "25%     2013.000000       25000.000000  ...     5.000000     3.950000\n",
              "50%     2015.000000       37822.000000  ...     5.000000     6.200000\n",
              "75%     2017.000000       49663.000000  ...     5.000000    11.560000\n",
              "max     2019.000000       60000.000000  ...    10.000000    97.070000\n",
              "\n",
              "[11 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "33E69h5qpzdv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "23c7e51a-ab23-4a5c-e5bb-7c5dd011f8c2"
      },
      "source": [
        "#test\n",
        "dftest['Engine'] = dftest['Engine'].fillna((dftest['Engine'].mean()))\n",
        "dftest['Power'] = dftest['Power'].fillna((dftest['Power'].mean()))\n",
        "dftest['Seats'] = dftest['Seats'].fillna((dftest['Seats'].mean()))\n",
        "mean_mileage = dftest['Mileage'].mean(skipna=True)\n",
        "dftest['Mileage']=dftest.Mileage.mask(dftest.Mileage==0,mean_mileage)\n",
        "print(dftest.isnull().sum())\n",
        "print(dftest.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Year                 0\n",
            "Kilometers_Driven    0\n",
            "Fuel_Type            0\n",
            "Transmission         0\n",
            "Owner_Type           0\n",
            "Mileage              0\n",
            "Engine               0\n",
            "Power                0\n",
            "Seats                0\n",
            "dtype: int64\n",
            "(1234, 9)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2w5BhKQUpSyJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "outputId": "e7132904-967c-43d5-c4e4-21653b827102"
      },
      "source": [
        "dftest.describe(include='all')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Year</th>\n",
              "      <th>Kilometers_Driven</th>\n",
              "      <th>Fuel_Type</th>\n",
              "      <th>Transmission</th>\n",
              "      <th>Owner_Type</th>\n",
              "      <th>Mileage</th>\n",
              "      <th>Engine</th>\n",
              "      <th>Power</th>\n",
              "      <th>Seats</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1234.000000</td>\n",
              "      <td>1234.000000</td>\n",
              "      <td>1234</td>\n",
              "      <td>1234</td>\n",
              "      <td>1234</td>\n",
              "      <td>1234.000000</td>\n",
              "      <td>1234.000000</td>\n",
              "      <td>1234.000000</td>\n",
              "      <td>1234.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>647</td>\n",
              "      <td>905</td>\n",
              "      <td>1023</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2013.400324</td>\n",
              "      <td>58507.288493</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>18.365316</td>\n",
              "      <td>1593.584967</td>\n",
              "      <td>110.380420</td>\n",
              "      <td>5.284546</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.179700</td>\n",
              "      <td>35598.702098</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.051387</td>\n",
              "      <td>562.054549</td>\n",
              "      <td>50.870564</td>\n",
              "      <td>0.821931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1996.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7.940000</td>\n",
              "      <td>624.000000</td>\n",
              "      <td>34.200000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2011.000000</td>\n",
              "      <td>34000.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15.400000</td>\n",
              "      <td>1198.000000</td>\n",
              "      <td>76.200000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2014.000000</td>\n",
              "      <td>54572.500000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>18.250000</td>\n",
              "      <td>1461.000000</td>\n",
              "      <td>98.600000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2016.000000</td>\n",
              "      <td>75000.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>21.100000</td>\n",
              "      <td>1968.000000</td>\n",
              "      <td>126.300000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2019.000000</td>\n",
              "      <td>350000.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>32.260000</td>\n",
              "      <td>5998.000000</td>\n",
              "      <td>616.000000</td>\n",
              "      <td>10.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Year  Kilometers_Driven  ...        Power        Seats\n",
              "count   1234.000000        1234.000000  ...  1234.000000  1234.000000\n",
              "unique          NaN                NaN  ...          NaN          NaN\n",
              "top             NaN                NaN  ...          NaN          NaN\n",
              "freq            NaN                NaN  ...          NaN          NaN\n",
              "mean    2013.400324       58507.288493  ...   110.380420     5.284546\n",
              "std        3.179700       35598.702098  ...    50.870564     0.821931\n",
              "min     1996.000000        1000.000000  ...    34.200000     2.000000\n",
              "25%     2011.000000       34000.000000  ...    76.200000     5.000000\n",
              "50%     2014.000000       54572.500000  ...    98.600000     5.000000\n",
              "75%     2016.000000       75000.000000  ...   126.300000     5.000000\n",
              "max     2019.000000      350000.000000  ...   616.000000    10.000000\n",
              "\n",
              "[11 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aQgIA9TYrBOJ",
        "colab_type": "text"
      },
      "source": [
        "# Box Plot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OazNXIG3tlFz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "fig, ax  = plt.subplots(nrows=2,ncols=3,figsize=(20,16))\n",
        "ax[0,0].boxplot(df['Mileage'])\n",
        "ax[0,1].boxplot(df['Engine'])\n",
        "ax[0,2].boxplot(df['Kilometers_Driven'])\n",
        "ax[1,0].boxplot(df['Power'])\n",
        "ax[1,1].boxplot(df['Price'])\n",
        "ax[0,0].set_title('Mileage')\n",
        "ax[0,1].set_title('Engine')\n",
        "ax[0,2].set_title('Kilometers_Driven')\n",
        "ax[1,0].set_title('Power')\n",
        "ax[1,1].set_title('Price')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s--gGetcv6Y2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "i = df[df.Kilometers_Driven == 6.500000e+06].index\n",
        "df.drop(i,inplace=True)\n",
        "i = df[df.Price == 160.000000].index\n",
        "df.drop(i,inplace=True)\n",
        "i = df[df.Price == 120.000000].index\n",
        "df.drop(i,inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rjuQIY4OYQFn",
        "colab_type": "text"
      },
      "source": [
        "# Target Variable"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zv-ElpJsYWDE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from scipy import stats\n",
        "from scipy.stats import norm, skew \n",
        "import seaborn as sns\n",
        "color = sns.color_palette()\n",
        "sns.set_style('darkgrid')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Or6Jddm2YN-y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 623
        },
        "outputId": "afc25a3d-55d1-487b-87a0-44bedb4d348b"
      },
      "source": [
        "sns.distplot(df['Price'] , fit=norm);\n",
        "# Get the fitted parameters used by the function\n",
        "(mu, sigma) = norm.fit(df['Price'])\n",
        "print( '\\n mu = {:.2f} and sigma = {:.2f}\\n'.format(mu, sigma))\n",
        "#Now plot the distribution\n",
        "plt.legend(['Normal dist. ($\\mu=$ {:.2f} and $\\sigma=$ {:.2f} )'.format(mu, sigma)],loc='best')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('SalePrice distribution')\n",
        "#Get also the QQ-plot\n",
        "fig = plt.figure()\n",
        "res = stats.probplot(df['Price'], plot=plt)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            " mu = 10.91 and sigma = 12.23\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c7k_SaXHY_KT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 623
        },
        "outputId": "389059be-b4c1-4f1a-d644-370196ee8d10"
      },
      "source": [
        "import numpy as np\n",
        "#We use the numpy fuction log1p which  applies log(1+x) to all elements of the column\n",
        "df[\"Price\"] = np.log1p(df[\"Price\"])\n",
        "sns.distplot(df['Price'] , fit=norm);\n",
        "# Get the fitted parameters used by the function\n",
        "(mu, sigma) = norm.fit(df['Price'])\n",
        "print( '\\n mu = {:.2f} and sigma = {:.2f}\\n'.format(mu, sigma))\n",
        "#Now plot the distribution\n",
        "plt.legend(['Normal dist. ($\\mu=$ {:.2f} and $\\sigma=$ {:.2f} )'.format(mu, sigma)],loc='best')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('SalePrice distribution')\n",
        "#Get also the QQ-plot\n",
        "fig = plt.figure()\n",
        "res = stats.probplot(df['Price'], plot=plt)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            " mu = 2.14 and sigma = 0.76\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y0ps0udyZRJl",
        "colab_type": "text"
      },
      "source": [
        "The skew seems now corrected and the data appears more normally distributed."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B5nMi5mt-ZRk",
        "colab_type": "text"
      },
      "source": [
        "# Correlation Map"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZoSJ9m5DbLmM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Correlation map to see how features are correlated with SalePrice\n",
        "corrmat = df.corr()\n",
        "plt.subplots(figsize=(12,9))\n",
        "sns.heatmap(corrmat, vmax=0.9, square=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9vvNNSvZb3RO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 47
        },
        "outputId": "4a24a18a-7532-4247-c738-cec91def16f2"
      },
      "source": [
        "df_na = (df.isnull().sum() / len(df)) * 100\n",
        "df_na = df_na.drop(df_na[df_na == 0].index).sort_values(ascending=False)[:30]\n",
        "missing_data = pd.DataFrame({'Missing Ratio' :df_na})\n",
        "missing_data.head(20)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Missing Ratio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [Missing Ratio]\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GcOj4vma-O5k",
        "colab_type": "text"
      },
      "source": [
        "# Scaling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "crIac-EGrRFF",
        "colab_type": "text"
      },
      "source": [
        "## Train"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eF3A3_sGv47E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Robust Scaling\n",
        "from sklearn.preprocessing import RobustScaler\n",
        "robust = RobustScaler()\n",
        "#Kilometers_Driven\n",
        "df['Robust_KD']=robust.fit_transform(df.Kilometers_Driven.values.reshape(-1, 1))\n",
        "#Engine\n",
        "df['Robust_Engine']=robust.fit_transform(df.Engine.values.reshape(-1, 1))\n",
        "#Power\n",
        "df['Robust_Power']=robust.fit_transform(df.Power.values.reshape(-1, 1))\n",
        "#Mileage\n",
        "df['Robust_Mileage'] = robust.fit_transform(df.Mileage.values.reshape(-1, 1))\n",
        "#Seats\n",
        "df['Robust_Seats'] = robust.fit_transform(df.Seats.values.reshape(-1, 1))\n",
        "\n",
        "#MinMaxScaling\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler(feature_range=(-1,1))\n",
        "#Kilometers_Driven\n",
        "df['MM_Kilometers_Driven'] = scaler.fit_transform(df.Kilometers_Driven.values.reshape(-1, 1))\n",
        "#Engine\n",
        "df['MM_Engine'] = scaler.fit_transform(df.Engine.values.reshape(-1, 1))\n",
        "#Power\n",
        "df['MM_Power'] = scaler.fit_transform(df.Power.values.reshape(-1, 1))\n",
        "#Mileage\n",
        "df['MM_Mileage'] = scaler.fit_transform(df.Mileage.values.reshape(-1, 1))\n",
        "#Seats\n",
        "df['MM_Seats'] = scaler.fit_transform(df.Seats.values.reshape(-1, 1))\n",
        "\n",
        "#MaxAbsScaling\n",
        "from sklearn.preprocessing import MaxAbsScaler\n",
        "maxabs = MaxAbsScaler()\n",
        "#Kilometers_Driven\n",
        "df['MA_KD']=maxabs.fit_transform(df.Kilometers_Driven.values.reshape(-1, 1))\n",
        "#Engine\n",
        "df['MA_Engine']=maxabs.fit_transform(df.Engine.values.reshape(-1, 1))\n",
        "#Power\n",
        "df['MA_Power']=maxabs.fit_transform(df.Power.values.reshape(-1, 1))\n",
        "#Mileage\n",
        "df['MA_Mileage'] = maxabs.fit_transform(df.Mileage.values.reshape(-1, 1))\n",
        "#Seats\n",
        "df['MA_Seats'] = maxabs.fit_transform(df.Seats.values.reshape(-1, 1))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RwTRLZHCclfL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "outputId": "8e4cf441-092f-4579-92c4-18050ff36564"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Year</th>\n",
              "      <th>Kilometers_Driven</th>\n",
              "      <th>Fuel_Type</th>\n",
              "      <th>Transmission</th>\n",
              "      <th>Owner_Type</th>\n",
              "      <th>Mileage</th>\n",
              "      <th>Engine</th>\n",
              "      <th>Power</th>\n",
              "      <th>Seats</th>\n",
              "      <th>Price</th>\n",
              "      <th>Robust_KD</th>\n",
              "      <th>Robust_Engine</th>\n",
              "      <th>Robust_Power</th>\n",
              "      <th>Robust_Mileage</th>\n",
              "      <th>Robust_Seats</th>\n",
              "      <th>MM_Kilometers_Driven</th>\n",
              "      <th>MM_Engine</th>\n",
              "      <th>MM_Power</th>\n",
              "      <th>MM_Mileage</th>\n",
              "      <th>MM_Seats</th>\n",
              "      <th>MA_KD</th>\n",
              "      <th>MA_Engine</th>\n",
              "      <th>MA_Power</th>\n",
              "      <th>MA_Mileage</th>\n",
              "      <th>MA_Seats</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2015</td>\n",
              "      <td>41000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>19.67</td>\n",
              "      <td>1582.0</td>\n",
              "      <td>126.20</td>\n",
              "      <td>5.0</td>\n",
              "      <td>12.50</td>\n",
              "      <td>0.128857</td>\n",
              "      <td>0.241245</td>\n",
              "      <td>0.632612</td>\n",
              "      <td>0.214429</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.364857</td>\n",
              "      <td>-0.557199</td>\n",
              "      <td>-0.557480</td>\n",
              "      <td>-0.065284</td>\n",
              "      <td>-0.25</td>\n",
              "      <td>0.683333</td>\n",
              "      <td>0.319531</td>\n",
              "      <td>0.280444</td>\n",
              "      <td>0.586464</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2011</td>\n",
              "      <td>46000</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>18.20</td>\n",
              "      <td>1199.0</td>\n",
              "      <td>88.70</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.50</td>\n",
              "      <td>0.331590</td>\n",
              "      <td>-0.255512</td>\n",
              "      <td>-0.001691</td>\n",
              "      <td>-0.080160</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.532000</td>\n",
              "      <td>-0.734227</td>\n",
              "      <td>-0.737855</td>\n",
              "      <td>-0.178187</td>\n",
              "      <td>-0.25</td>\n",
              "      <td>0.766667</td>\n",
              "      <td>0.242173</td>\n",
              "      <td>0.197111</td>\n",
              "      <td>0.542636</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2013</td>\n",
              "      <td>40670</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>Second</td>\n",
              "      <td>15.20</td>\n",
              "      <td>1968.0</td>\n",
              "      <td>140.80</td>\n",
              "      <td>5.0</td>\n",
              "      <td>17.74</td>\n",
              "      <td>0.115477</td>\n",
              "      <td>0.741894</td>\n",
              "      <td>0.879567</td>\n",
              "      <td>-0.681363</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.353825</td>\n",
              "      <td>-0.378784</td>\n",
              "      <td>-0.487253</td>\n",
              "      <td>-0.408602</td>\n",
              "      <td>-0.25</td>\n",
              "      <td>0.677833</td>\n",
              "      <td>0.397495</td>\n",
              "      <td>0.312889</td>\n",
              "      <td>0.453190</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2016</td>\n",
              "      <td>36000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>First</td>\n",
              "      <td>11.36</td>\n",
              "      <td>2755.0</td>\n",
              "      <td>171.50</td>\n",
              "      <td>8.0</td>\n",
              "      <td>17.50</td>\n",
              "      <td>-0.073876</td>\n",
              "      <td>1.762646</td>\n",
              "      <td>1.398850</td>\n",
              "      <td>-1.450902</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.197713</td>\n",
              "      <td>-0.015022</td>\n",
              "      <td>-0.339586</td>\n",
              "      <td>-0.703533</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>0.556453</td>\n",
              "      <td>0.381111</td>\n",
              "      <td>0.338700</td>\n",
              "      <td>0.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2018</td>\n",
              "      <td>25692</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>21.56</td>\n",
              "      <td>1462.0</td>\n",
              "      <td>103.25</td>\n",
              "      <td>5.0</td>\n",
              "      <td>9.95</td>\n",
              "      <td>-0.491830</td>\n",
              "      <td>0.085603</td>\n",
              "      <td>0.244418</td>\n",
              "      <td>0.593186</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.146869</td>\n",
              "      <td>-0.612665</td>\n",
              "      <td>-0.667869</td>\n",
              "      <td>0.079877</td>\n",
              "      <td>-0.25</td>\n",
              "      <td>0.428200</td>\n",
              "      <td>0.295294</td>\n",
              "      <td>0.229444</td>\n",
              "      <td>0.642815</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Year  Kilometers_Driven Fuel_Type  ...  MA_Power MA_Mileage  MA_Seats\n",
              "0  2015              41000    Diesel  ...  0.280444   0.586464       0.5\n",
              "1  2011              46000    Petrol  ...  0.197111   0.542636       0.5\n",
              "2  2013              40670    Diesel  ...  0.312889   0.453190       0.5\n",
              "3  2016              36000    Diesel  ...  0.381111   0.338700       0.8\n",
              "4  2018              25692    Petrol  ...  0.229444   0.642815       0.5\n",
              "\n",
              "[5 rows x 25 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k5VFbOLrrdDa",
        "colab_type": "text"
      },
      "source": [
        "## Test"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QJu2Qkv6rmWr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "aa774bf2-c714-49a8-c9ec-c04136b08054"
      },
      "source": [
        "dftest.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Year</th>\n",
              "      <th>Kilometers_Driven</th>\n",
              "      <th>Fuel_Type</th>\n",
              "      <th>Transmission</th>\n",
              "      <th>Owner_Type</th>\n",
              "      <th>Mileage</th>\n",
              "      <th>Engine</th>\n",
              "      <th>Power</th>\n",
              "      <th>Seats</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2014</td>\n",
              "      <td>40929</td>\n",
              "      <td>CNG</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>32.26</td>\n",
              "      <td>998.0</td>\n",
              "      <td>58.20000</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2013</td>\n",
              "      <td>54493</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Manual</td>\n",
              "      <td>Second</td>\n",
              "      <td>24.70</td>\n",
              "      <td>796.0</td>\n",
              "      <td>47.30000</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2017</td>\n",
              "      <td>34000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>13.68</td>\n",
              "      <td>2393.0</td>\n",
              "      <td>147.80000</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2012</td>\n",
              "      <td>139000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>23.59</td>\n",
              "      <td>1364.0</td>\n",
              "      <td>110.38042</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2014</td>\n",
              "      <td>29000</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>18.50</td>\n",
              "      <td>1197.0</td>\n",
              "      <td>82.85000</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Year  Kilometers_Driven Fuel_Type  ...  Engine      Power  Seats\n",
              "0  2014              40929       CNG  ...   998.0   58.20000    4.0\n",
              "1  2013              54493    Petrol  ...   796.0   47.30000    5.0\n",
              "2  2017              34000    Diesel  ...  2393.0  147.80000    7.0\n",
              "3  2012             139000    Diesel  ...  1364.0  110.38042    5.0\n",
              "4  2014              29000    Petrol  ...  1197.0   82.85000    5.0\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vJSSKFAgrfBt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Robust Scaling\n",
        "from sklearn.preprocessing import RobustScaler\n",
        "robust = RobustScaler()\n",
        "#Kilometers_Driven\n",
        "dftest['Robust_KD']=robust.fit_transform(dftest.Kilometers_Driven.values.reshape(-1, 1))\n",
        "#Engine\n",
        "dftest['Robust_Engine']=robust.fit_transform(dftest.Engine.values.reshape(-1, 1))\n",
        "#Power\n",
        "dftest['Robust_Power']=robust.fit_transform(dftest.Power.values.reshape(-1, 1))\n",
        "#Mileage\n",
        "dftest['Robust_Mileage'] = robust.fit_transform(dftest.Mileage.values.reshape(-1, 1))\n",
        "#Seats\n",
        "dftest['Robust_Seats'] = robust.fit_transform(dftest.Seats.values.reshape(-1, 1))\n",
        "\n",
        "#MinMaxScaling\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler(feature_range=(-1,1))\n",
        "#Kilometers_Driven\n",
        "dftest['MM_Kilometers_Driven'] = scaler.fit_transform(dftest.Kilometers_Driven.values.reshape(-1, 1))\n",
        "#Engine\n",
        "dftest['MM_Engine'] = scaler.fit_transform(dftest.Engine.values.reshape(-1, 1))\n",
        "#Power\n",
        "dftest['MM_Power'] = scaler.fit_transform(dftest.Power.values.reshape(-1, 1))\n",
        "#Mileage\n",
        "dftest['MM_Mileage'] = scaler.fit_transform(dftest.Mileage.values.reshape(-1, 1))\n",
        "#Seats\n",
        "dftest['MM_Seats'] = scaler.fit_transform(dftest.Seats.values.reshape(-1, 1))\n",
        "\n",
        "#MaxAbsScaling\n",
        "from sklearn.preprocessing import MaxAbsScaler\n",
        "maxabs = MaxAbsScaler()\n",
        "#Kilometers_Driven\n",
        "dftest['MA_KD']=maxabs.fit_transform(dftest.Kilometers_Driven.values.reshape(-1, 1))\n",
        "#Engine\n",
        "dftest['MA_Engine']=maxabs.fit_transform(dftest.Engine.values.reshape(-1, 1))\n",
        "#Power\n",
        "dftest['MA_Power']=maxabs.fit_transform(dftest.Power.values.reshape(-1, 1))\n",
        "#Mileage\n",
        "dftest['MA_Mileage'] = maxabs.fit_transform(dftest.Mileage.values.reshape(-1, 1))\n",
        "#Seats\n",
        "dftest['MA_Seats'] = maxabs.fit_transform(dftest.Seats.values.reshape(-1, 1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PXLcOlKi9IEL",
        "colab_type": "text"
      },
      "source": [
        "# Categorical Features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MQCyqIf4u6dZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "one_hot = pd.get_dummies(df['Transmission'])\n",
        "df = df.drop('Transmission',axis = 1)\n",
        "df = df.join(one_hot)\n",
        "one_hot = pd.get_dummies(df['Fuel_Type'])\n",
        "df = df.drop('Fuel_Type',axis = 1)\n",
        "df = df.join(one_hot)\n",
        "one_hot = pd.get_dummies(df['Owner_Type'])\n",
        "df = df.drop('Owner_Type',axis = 1)\n",
        "df = df.join(one_hot)\n",
        "\n",
        "del df['Automatic']\n",
        "del df['CNG']\n",
        "del df['Fourth & Above']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WjOGU_eL9Pii",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "outputId": "054be09f-6370-4a0a-b793-35e58264a7df"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Year</th>\n",
              "      <th>Kilometers_Driven</th>\n",
              "      <th>Mileage</th>\n",
              "      <th>Engine</th>\n",
              "      <th>Power</th>\n",
              "      <th>Seats</th>\n",
              "      <th>Price</th>\n",
              "      <th>Robust_KD</th>\n",
              "      <th>Robust_Engine</th>\n",
              "      <th>Robust_Power</th>\n",
              "      <th>Robust_Mileage</th>\n",
              "      <th>Robust_Seats</th>\n",
              "      <th>MM_Kilometers_Driven</th>\n",
              "      <th>MM_Engine</th>\n",
              "      <th>MM_Power</th>\n",
              "      <th>MM_Mileage</th>\n",
              "      <th>MM_Seats</th>\n",
              "      <th>MA_KD</th>\n",
              "      <th>MA_Engine</th>\n",
              "      <th>MA_Power</th>\n",
              "      <th>MA_Mileage</th>\n",
              "      <th>MA_Seats</th>\n",
              "      <th>Manual</th>\n",
              "      <th>Diesel</th>\n",
              "      <th>LPG</th>\n",
              "      <th>Petrol</th>\n",
              "      <th>First</th>\n",
              "      <th>Second</th>\n",
              "      <th>Third</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2015</td>\n",
              "      <td>41000</td>\n",
              "      <td>19.67</td>\n",
              "      <td>1582.0</td>\n",
              "      <td>126.20</td>\n",
              "      <td>5.0</td>\n",
              "      <td>12.50</td>\n",
              "      <td>0.128857</td>\n",
              "      <td>0.241245</td>\n",
              "      <td>0.632612</td>\n",
              "      <td>0.214429</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.364857</td>\n",
              "      <td>-0.557199</td>\n",
              "      <td>-0.557480</td>\n",
              "      <td>-0.065284</td>\n",
              "      <td>-0.25</td>\n",
              "      <td>0.683333</td>\n",
              "      <td>0.319531</td>\n",
              "      <td>0.280444</td>\n",
              "      <td>0.586464</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2011</td>\n",
              "      <td>46000</td>\n",
              "      <td>18.20</td>\n",
              "      <td>1199.0</td>\n",
              "      <td>88.70</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.50</td>\n",
              "      <td>0.331590</td>\n",
              "      <td>-0.255512</td>\n",
              "      <td>-0.001691</td>\n",
              "      <td>-0.080160</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.532000</td>\n",
              "      <td>-0.734227</td>\n",
              "      <td>-0.737855</td>\n",
              "      <td>-0.178187</td>\n",
              "      <td>-0.25</td>\n",
              "      <td>0.766667</td>\n",
              "      <td>0.242173</td>\n",
              "      <td>0.197111</td>\n",
              "      <td>0.542636</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2013</td>\n",
              "      <td>40670</td>\n",
              "      <td>15.20</td>\n",
              "      <td>1968.0</td>\n",
              "      <td>140.80</td>\n",
              "      <td>5.0</td>\n",
              "      <td>17.74</td>\n",
              "      <td>0.115477</td>\n",
              "      <td>0.741894</td>\n",
              "      <td>0.879567</td>\n",
              "      <td>-0.681363</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.353825</td>\n",
              "      <td>-0.378784</td>\n",
              "      <td>-0.487253</td>\n",
              "      <td>-0.408602</td>\n",
              "      <td>-0.25</td>\n",
              "      <td>0.677833</td>\n",
              "      <td>0.397495</td>\n",
              "      <td>0.312889</td>\n",
              "      <td>0.453190</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2016</td>\n",
              "      <td>36000</td>\n",
              "      <td>11.36</td>\n",
              "      <td>2755.0</td>\n",
              "      <td>171.50</td>\n",
              "      <td>8.0</td>\n",
              "      <td>17.50</td>\n",
              "      <td>-0.073876</td>\n",
              "      <td>1.762646</td>\n",
              "      <td>1.398850</td>\n",
              "      <td>-1.450902</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.197713</td>\n",
              "      <td>-0.015022</td>\n",
              "      <td>-0.339586</td>\n",
              "      <td>-0.703533</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>0.556453</td>\n",
              "      <td>0.381111</td>\n",
              "      <td>0.338700</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2018</td>\n",
              "      <td>25692</td>\n",
              "      <td>21.56</td>\n",
              "      <td>1462.0</td>\n",
              "      <td>103.25</td>\n",
              "      <td>5.0</td>\n",
              "      <td>9.95</td>\n",
              "      <td>-0.491830</td>\n",
              "      <td>0.085603</td>\n",
              "      <td>0.244418</td>\n",
              "      <td>0.593186</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.146869</td>\n",
              "      <td>-0.612665</td>\n",
              "      <td>-0.667869</td>\n",
              "      <td>0.079877</td>\n",
              "      <td>-0.25</td>\n",
              "      <td>0.428200</td>\n",
              "      <td>0.295294</td>\n",
              "      <td>0.229444</td>\n",
              "      <td>0.642815</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Year  Kilometers_Driven  Mileage  Engine  ...  Petrol  First  Second  Third\n",
              "0  2015              41000    19.67  1582.0  ...       0      1       0      0\n",
              "1  2011              46000    18.20  1199.0  ...       1      1       0      0\n",
              "2  2013              40670    15.20  1968.0  ...       0      0       1      0\n",
              "3  2016              36000    11.36  2755.0  ...       0      1       0      0\n",
              "4  2018              25692    21.56  1462.0  ...       1      1       0      0\n",
              "\n",
              "[5 rows x 29 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ChLFMerV-Dlr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "one_hot = pd.get_dummies(dftest['Transmission'])\n",
        "dftest = dftest.drop('Transmission',axis = 1)\n",
        "dftest = dftest.join(one_hot)\n",
        "one_hot = pd.get_dummies(dftest['Fuel_Type'])\n",
        "dftest = dftest.drop('Fuel_Type',axis = 1)\n",
        "dftest = dftest.join(one_hot)\n",
        "one_hot = pd.get_dummies(dftest['Owner_Type'])\n",
        "dftest = dftest.drop('Owner_Type',axis = 1)\n",
        "dftest = dftest.join(one_hot)\n",
        "\n",
        "del dftest['Automatic']\n",
        "del dftest['CNG']\n",
        "del dftest['Fourth & Above']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ozB91Ija-FNh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "outputId": "84d3b5fa-5e38-41a2-a825-3af3d12c4b18"
      },
      "source": [
        "dftest.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Year</th>\n",
              "      <th>Kilometers_Driven</th>\n",
              "      <th>Mileage</th>\n",
              "      <th>Engine</th>\n",
              "      <th>Power</th>\n",
              "      <th>Seats</th>\n",
              "      <th>Robust_KD</th>\n",
              "      <th>Robust_Engine</th>\n",
              "      <th>Robust_Power</th>\n",
              "      <th>Robust_Mileage</th>\n",
              "      <th>Robust_Seats</th>\n",
              "      <th>MM_Kilometers_Driven</th>\n",
              "      <th>MM_Engine</th>\n",
              "      <th>MM_Power</th>\n",
              "      <th>MM_Mileage</th>\n",
              "      <th>MM_Seats</th>\n",
              "      <th>MA_KD</th>\n",
              "      <th>MA_Engine</th>\n",
              "      <th>MA_Power</th>\n",
              "      <th>MA_Mileage</th>\n",
              "      <th>MA_Seats</th>\n",
              "      <th>Manual</th>\n",
              "      <th>Diesel</th>\n",
              "      <th>LPG</th>\n",
              "      <th>Petrol</th>\n",
              "      <th>First</th>\n",
              "      <th>Second</th>\n",
              "      <th>Third</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2014</td>\n",
              "      <td>40929</td>\n",
              "      <td>32.26</td>\n",
              "      <td>998.0</td>\n",
              "      <td>58.20000</td>\n",
              "      <td>4.0</td>\n",
              "      <td>-0.332768</td>\n",
              "      <td>-0.601299</td>\n",
              "      <td>-0.806387</td>\n",
              "      <td>2.457895</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.771181</td>\n",
              "      <td>-0.860811</td>\n",
              "      <td>-0.917497</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.50</td>\n",
              "      <td>0.116940</td>\n",
              "      <td>0.166389</td>\n",
              "      <td>0.094481</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2013</td>\n",
              "      <td>54493</td>\n",
              "      <td>24.70</td>\n",
              "      <td>796.0</td>\n",
              "      <td>47.30000</td>\n",
              "      <td>5.0</td>\n",
              "      <td>-0.001939</td>\n",
              "      <td>-0.863636</td>\n",
              "      <td>-1.023952</td>\n",
              "      <td>1.131579</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.693450</td>\n",
              "      <td>-0.935988</td>\n",
              "      <td>-0.954967</td>\n",
              "      <td>0.378289</td>\n",
              "      <td>-0.25</td>\n",
              "      <td>0.155694</td>\n",
              "      <td>0.132711</td>\n",
              "      <td>0.076786</td>\n",
              "      <td>0.765654</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2017</td>\n",
              "      <td>34000</td>\n",
              "      <td>13.68</td>\n",
              "      <td>2393.0</td>\n",
              "      <td>147.80000</td>\n",
              "      <td>7.0</td>\n",
              "      <td>-0.501768</td>\n",
              "      <td>1.210390</td>\n",
              "      <td>0.982036</td>\n",
              "      <td>-0.801754</td>\n",
              "      <td>2.0</td>\n",
              "      <td>-0.810888</td>\n",
              "      <td>-0.341645</td>\n",
              "      <td>-0.609488</td>\n",
              "      <td>-0.527961</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.097143</td>\n",
              "      <td>0.398966</td>\n",
              "      <td>0.239935</td>\n",
              "      <td>0.424055</td>\n",
              "      <td>0.7</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2012</td>\n",
              "      <td>139000</td>\n",
              "      <td>23.59</td>\n",
              "      <td>1364.0</td>\n",
              "      <td>110.38042</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.059207</td>\n",
              "      <td>-0.125974</td>\n",
              "      <td>0.235138</td>\n",
              "      <td>0.936842</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.209169</td>\n",
              "      <td>-0.724600</td>\n",
              "      <td>-0.738122</td>\n",
              "      <td>0.287007</td>\n",
              "      <td>-0.25</td>\n",
              "      <td>0.397143</td>\n",
              "      <td>0.227409</td>\n",
              "      <td>0.179189</td>\n",
              "      <td>0.731246</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2014</td>\n",
              "      <td>29000</td>\n",
              "      <td>18.50</td>\n",
              "      <td>1197.0</td>\n",
              "      <td>82.85000</td>\n",
              "      <td>5.0</td>\n",
              "      <td>-0.623720</td>\n",
              "      <td>-0.342857</td>\n",
              "      <td>-0.314371</td>\n",
              "      <td>0.043860</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.839542</td>\n",
              "      <td>-0.786751</td>\n",
              "      <td>-0.832760</td>\n",
              "      <td>-0.131579</td>\n",
              "      <td>-0.25</td>\n",
              "      <td>0.082857</td>\n",
              "      <td>0.199567</td>\n",
              "      <td>0.134497</td>\n",
              "      <td>0.573466</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Year  Kilometers_Driven  Mileage  Engine  ...  Petrol  First  Second  Third\n",
              "0  2014              40929    32.26   998.0  ...       0      1       0      0\n",
              "1  2013              54493    24.70   796.0  ...       1      0       1      0\n",
              "2  2017              34000    13.68  2393.0  ...       0      1       0      0\n",
              "3  2012             139000    23.59  1364.0  ...       0      1       0      0\n",
              "4  2014              29000    18.50  1197.0  ...       1      1       0      0\n",
              "\n",
              "[5 rows x 28 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YBENJ-Cb93rU",
        "colab_type": "text"
      },
      "source": [
        "## Rest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VI6I0n3lvj2T",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Train\n",
        "del df['Transmission']\n",
        "del df['Fuel_Type']\n",
        "del df['Owner_Type']\n",
        "#Test\n",
        "del dftest['Transmission']\n",
        "del dftest['Fuel_Type']\n",
        "del dftest['Owner_Type']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fFJBtC9mcjIV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3d19ca92-4968-4ade-f9bf-a75cac879d37"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "cols = ('Fuel_Type', 'Transmission', 'Owner_Type')\n",
        "# process columns, apply LabelEncoder to categorical features\n",
        "for c in cols:\n",
        "    lbl = LabelEncoder() \n",
        "    lbl.fit(list(df[c].values)) \n",
        "    df[c] = lbl.transform(list(df[c].values))\n",
        "\n",
        "# shape        \n",
        "print('Shape df: {}'.format(df.shape))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape df: (3529, 10)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d7WKTuoEr8bb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        },
        "outputId": "d7d5c2ff-fa28-4325-f9ea-98adfb026e30"
      },
      "source": [
        "print(df.head(2))\n",
        "print(dftest.head(2))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   Year  Kilometers_Driven  Mileage  ...  MA_Power  MA_Mileage  MA_Seats\n",
            "0  2015              41000    19.67  ...  0.280444    0.586464       0.5\n",
            "1  2011              46000    18.20  ...  0.197111    0.542636       0.5\n",
            "\n",
            "[2 rows x 22 columns]\n",
            "   Year  Kilometers_Driven  Mileage  ...  MA_Power  MA_Mileage  MA_Seats\n",
            "0  2014              40929    32.26  ...  0.094481    1.000000       0.4\n",
            "1  2013              54493    24.70  ...  0.076786    0.765654       0.5\n",
            "\n",
            "[2 rows x 21 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_bIfwzfj9Ozv",
        "colab_type": "text"
      },
      "source": [
        "# XGBoost Modelling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pPP46XA4hNb7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn import preprocessing\n",
        "import xgboost as xgb\n",
        "from xgboost.sklearn import XGBRegressor\n",
        "from sklearn.model_selection import GridSearchCV, train_test_split"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5eUBXhwifz0R",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Organize our data for training\n",
        "X = df.drop([\"Price\"], axis=1)\n",
        "Y = df[\"Price\"]\n",
        "X, X_Val, Y, Y_Val = train_test_split(X, Y,test_size=0.3)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kVB0r1RWBmfb",
        "colab_type": "text"
      },
      "source": [
        "def **XGBRegressor**(*max_depth=3, learning_rate=0.1, n_estimators=100, verbosity=1, silent=None, objective='reg:linear', booster='gbtree', n_jobs=1, nthread=None, gamma=0, min_child_weight=1, max_delta_step=0, subsample=1, colsample_bytree=1, colsample_bylevel=1, colsample_bynode=1, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, base_score=0.5, random_state=0, seed=None, missing=None, importance_type='gain', **kwargs *)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IzWxtiP1FBEq",
        "colab_type": "text"
      },
      "source": [
        "*'colsample_bytree': 0.7, 'gamma': 0, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'n_estimators': 500, 'nthread': 4, 'objective': 'reg:linear', 'scale_pos_weight': 1, 'silent': 1, 'subsample': 0.8*\n",
        "\n",
        "* n_estimators 100,500,1000,2000\n",
        "*   'max_depth':range(3,10,2)\n",
        "*   'min_child_weight':range(1,6,2)\n",
        "*   'gamma':[i/10.0 for i in range(0,5)]\n",
        "*   'subsample':[i/10.0 for i in range(6,10)],\n",
        " 'colsample_bytree':[i/10.0 for i in range(6,10)]\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rquBpAdufz2T",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Various hyper-parameters to tune\n",
        "xgb1 = XGBRegressor()\n",
        "parameters = {'nthread':[4], #when use hyperthread, xgboost may become slower\n",
        "              'objective':['reg:linear'],\n",
        "              'silent': [1],\n",
        "              'learning_rate': [0.01],\n",
        "              'n_estimators': [8000],\n",
        "              'max_depth': [3],\n",
        "              'min_child_weight': [4],\n",
        "              'gamma':[0.01],\n",
        "              'subsample': [0.8],\n",
        "              'scale_pos_weight':[1],\n",
        "              'colsample_bytree':[0.8],\n",
        "              'reg_alpha':[0.01]}\n",
        "\n",
        "xgb_grid = GridSearchCV(xgb1,\n",
        "                        parameters,\n",
        "                        cv = 5,\n",
        "                        n_jobs = -1,\n",
        "                        verbose=True)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W_CluYtUfzyM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 154
        },
        "outputId": "70220110-1646-43bf-c969-4073006bb081"
      },
      "source": [
        "xgb_grid.fit(X,Y)\n",
        "#print(xgb_grid.best_score_)\n",
        "print(xgb_grid.best_params_)\n",
        "print(\"--------------------------------------------\")\n",
        "from sklearn.metrics import r2_score\n",
        "print(\"R2-Score\")\n",
        "print(round(r2_score(Y_Val, xgb_grid.best_estimator_.predict(X_Val)),4))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 1 candidates, totalling 5 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done   5 out of   5 | elapsed:  1.3min finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "{'colsample_bytree': 0.8, 'gamma': 0.01, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 4, 'n_estimators': 8000, 'nthread': 4, 'objective': 'reg:linear', 'reg_alpha': 0.01, 'scale_pos_weight': 1, 'silent': 1, 'subsample': 0.8}\n",
            "--------------------------------------------\n",
            "R2-Score\n",
            "0.9015\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BpdCaO_gy4XI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# get feature importances from a model\n",
        "import matplotlib.pyplot as plt\n",
        "def plotFeatureImportances(model):\n",
        "    #first print all features importances in descending order\n",
        "    feature_importances = pd.DataFrame(model.feature_importances_,\n",
        "                                   index = X.columns,\n",
        "                                    columns=['importance']).sort_values('importance',ascending=False)\n",
        "    #print(feature_importances)\n",
        "    # Next plot feature importances to get idea about where the curve breaks\n",
        "    # in the graph i.e. select top appropriate features\n",
        "    features = X.columns.tolist()\n",
        "    importances = model.feature_importances_\n",
        "    indices = np.argsort(importances)\n",
        "    plt.figure(figsize=(10,10))\n",
        "    plt.title('Feature Importances')\n",
        "    plt.barh(range(len(indices)), importances[indices], color='b', align='center')\n",
        "    plt.yticks(range(len(indices)), [features[i] for i in indices])\n",
        "    plt.xlabel('Relative Importance')\n",
        "    plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J8xbLO7by5pY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "plotFeatureImportances(xgb_grid.best_estimator_)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "curVfLgvso4l",
        "colab_type": "text"
      },
      "source": [
        "## XGBoost Test"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3FTZwoZXm_Nn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Save the file\n",
        "dftest['Out'] = xgb_grid.best_estimator_.predict(dftest)\n",
        "sub['Price']=dftest['Out']\n",
        "sub.to_excel(\"output.xlsx\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZIxGLWl7F56Q",
        "colab_type": "text"
      },
      "source": [
        "# RandomForrestRegressor Modelling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NYGRC0CSF-UC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "param_grid = {\n",
        "    'bootstrap': [True],\n",
        "    'max_depth': [120],\n",
        "    'max_features': [3],\n",
        "    'min_samples_leaf': [3],\n",
        "    'min_samples_split': [8],\n",
        "    'n_estimators': [100]\n",
        "}\n",
        "\n",
        "rf = RandomForestRegressor()\n",
        "grid_search = GridSearchCV(estimator = rf,\n",
        "                           param_grid = param_grid,\n",
        "                           cv = 3,\n",
        "                           n_jobs = -1,\n",
        "                           verbose = 2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ej7Ff6icGtKi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 154
        },
        "outputId": "f8de46c0-c96d-45c3-de26-07f37c0d38cb"
      },
      "source": [
        "grid_search.fit(X,Y)\n",
        "#print(grid_search.best_score_)\n",
        "print(grid_search.best_params_)\n",
        "print(\"--------------------------------------------\")\n",
        "from sklearn.metrics import r2_score\n",
        "print(\"R2-Score\")\n",
        "print(round(r2_score(Y_Val, grid_search.best_estimator_.predict(X_Val)),4))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 3 folds for each of 1 candidates, totalling 3 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done   3 out of   3 | elapsed:    0.8s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "{'bootstrap': True, 'max_depth': 120, 'max_features': 3, 'min_samples_leaf': 3, 'min_samples_split': 8, 'n_estimators': 100}\n",
            "--------------------------------------------\n",
            "R2-Score\n",
            "0.8536\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PJNYmNXrD_zv",
        "colab_type": "text"
      },
      "source": [
        "# GBM Modelling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lE43WpLYEDtA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "parameters = {'loss':['ls'],\n",
        "              'learning_rate':[0.25],\n",
        "              'n_estimators':[100],\n",
        "              'subsample':[1.0],\n",
        "              'criterion':['friedman_mse'],\n",
        "              'min_samples_split':[2],\n",
        "              'min_samples_leaf':[1],\n",
        "              'min_weight_fraction_leaf':[0.0],\n",
        "              'max_depth':[3],\n",
        "              'min_impurity_decrease':[0.0]\n",
        "              }\n",
        "\n",
        "model_gbr= GradientBoostingRegressor()\n",
        "grid_search_gbr = GridSearchCV(estimator = model_gbr,\n",
        "                           param_grid = parameters,\n",
        "                           cv = 3,\n",
        "                           n_jobs = -1,\n",
        "                           verbose = 2)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xmkRrbt2JbcM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 154
        },
        "outputId": "0315139a-50bb-45e0-94ea-5b2a1ffc28d2"
      },
      "source": [
        "grid_search_gbr.fit(X,Y)\n",
        "#print(grid_search_gbr.best_score_)\n",
        "print(grid_search_gbr.best_params_)\n",
        "print(\"--------------------------------------------\")\n",
        "from sklearn.metrics import r2_score\n",
        "print(\"R2-Score\")\n",
        "print(round(r2_score(Y_Val, grid_search_gbr.best_estimator_.predict(X_Val)),4))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 3 folds for each of 1 candidates, totalling 3 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done   3 out of   3 | elapsed:    1.3s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "{'criterion': 'friedman_mse', 'learning_rate': 0.25, 'loss': 'ls', 'max_depth': 3, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'subsample': 1.0}\n",
            "--------------------------------------------\n",
            "R2-Score\n",
            "0.8793\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X1Mgz07syaXb",
        "colab_type": "text"
      },
      "source": [
        "# Bagging Regressor Modelling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I5nUfhra99H5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn import tree\n",
        "from sklearn.ensemble import BaggingRegressor\n",
        "model_br = BaggingRegressor(tree.DecisionTreeRegressor(random_state=1))\n",
        "\n",
        "param_grid = {'n_estimators':[10],\n",
        "              'max_samples':[1.0],\n",
        "              'max_features':[1.0],\n",
        "              'warm_start':[False],\n",
        "              'n_jobs':[-1],\n",
        "              'verbose':[1]}\n",
        "\n",
        "gscv_bg = GridSearchCV(estimator=model_br,\n",
        "                       param_grid=param_grid,\n",
        "                       cv=5,\n",
        "                       n_jobs=-1,\n",
        "                       verbose=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_mSScBHMxUTb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "outputId": "8bed8dee-2a5d-40f4-aa6b-b6e9e16afe03"
      },
      "source": [
        "gscv_bg.fit(X,Y)\n",
        "print(gscv_bg.best_params_)\n",
        "print(\"R2 Score\")\n",
        "print(round(r2_score(Y_Val,gscv_bg.best_estimator_.predict(X_Val)),4))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 1 candidates, totalling 5 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "{'max_features': 1.0, 'max_samples': 1.0, 'n_estimators': 10, 'n_jobs': -1, 'verbose': 1, 'warm_start': False}\n",
            "R2 Score\n",
            "0.8529\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Done   5 out of   5 | elapsed:    1.1s finished\n",
            "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.2s remaining:    0.0s\n",
            "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.2s finished\n",
            "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
            "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.0s finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bZ2ZubbcI0w3",
        "colab_type": "text"
      },
      "source": [
        "# Adaboost"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ap2qE9W599GH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.ensemble import AdaBoostRegressor\n",
        "model_adb = AdaBoostRegressor()\n",
        "param_grid={'n_estimators':[50,100],\n",
        "            'learning_rate':[1.0],\n",
        "            'loss':['linear']}\n",
        "  \n",
        "gscv_adb = GridSearchCV(estimator=model_adb,\n",
        "                        param_grid=param_grid,\n",
        "                        cv=5,\n",
        "                        n_jobs=-1,\n",
        "                        verbose=2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_mdq-Wrjzl9e",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "outputId": "049594c9-339c-439f-8a80-9444543d97ca"
      },
      "source": [
        "gscv_adb.fit(X,Y)\n",
        "print(gscv_adb.best_params_)\n",
        "print(\"R2 Score\")\n",
        "print(round(r2_score(Y_Val,gscv_adb.best_estimator_.predict(X_Val)),4))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 2 candidates, totalling 10 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  10 out of  10 | elapsed:    3.0s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "{'learning_rate': 1.0, 'loss': 'linear', 'n_estimators': 50}\n",
            "R2 Score\n",
            "0.6312\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tjc8hesnK4Ul",
        "colab_type": "text"
      },
      "source": [
        "# LightGBM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5dcONp8I989T",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import lightgbm as lgb\n",
        "train_data=lgb.Dataset(x_train,label=y_train)\n",
        "params = {'learning_rate':0.001}\n",
        "model= lgb.train(params, train_data, 100)\n",
        "from sklearn.metrics import mean_squared_error\n",
        "rmse=mean_squared_error(y_pred,y_test)**0.5"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "osF_VajvLBd9",
        "colab_type": "text"
      },
      "source": [
        "# CatBoost"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tb_vG0NX987s",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install catboost\n",
        "import numpy as np\n",
        "from catboost import CatBoostRegressor\n",
        "model=CatBoostRegressor()\n",
        "categorical_features_indices = np.where(df.dtypes != np.float)[0]\n",
        "model.fit(x_train,y_train,cat_features=([ 0,  1, 2, 3, 4, 10]),eval_set=(x_test, y_test))\n",
        "model.score(x_test,y_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3NyHGCBb98wR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-ZvLjvbCyzBn",
        "colab_type": "text"
      },
      "source": [
        "# Skewness"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lgM_BYeI98tE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "numeric_feats = df.dtypes[df.dtypes != \"object\"].index\n",
        "# Check the skew of all numerical features\n",
        "skewed_feats = df[numeric_feats].apply(lambda x: skew(x.dropna())).sort_values(ascending=False)\n",
        "print(\"\\nSkew in numerical features: \\n\")\n",
        "skewness = pd.DataFrame({'Skew' :skewed_feats})\n",
        "skewness.head(15)\n",
        "\n",
        "skewness = skewness[abs(skewness) > 0.75]\n",
        "print(\"There are {} skewed numerical features to Box Cox transform\".format(skewness.shape[0]))\n",
        "from scipy.special import boxcox1p\n",
        "skewed_features = skewness.index\n",
        "lam = 0.15\n",
        "for feat in skewed_features:\n",
        "    #all_data[feat] += 1\n",
        "    df[feat] = boxcox1p(df[feat], lam)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WhQlubbhzhwy",
        "colab_type": "text"
      },
      "source": [
        "# Misc"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wLdPNM1oqRGB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sT6Dbxkpp6xG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Source:- https://www.kaggle.com/serigne/stacked-regressions-top-4-on-leaderboard\n",
        "from sklearn.linear_model import ElasticNet, Lasso,  BayesianRidge, LassoLarsIC\n",
        "from sklearn.kernel_ridge import KernelRidge\n",
        "from sklearn.pipeline import make_pipeline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C0m090iNbebt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# get feature importances from a model\n",
        "import matplotlib.pyplot as plt\n",
        "def plotFeatureImportances(model):\n",
        "    #first print all features importances in descending order\n",
        "    feature_importances = pd.DataFrame(model.feature_importances_,\n",
        "                                   index = X.columns,\n",
        "                                    columns=['importance']).sort_values('importance',ascending=False)\n",
        "    print(feature_importances)\n",
        "    # Next plot feature importances to get idea about where the curve breaks\n",
        "    # in the graph i.e. select top appropriate features\n",
        "    features = X.columns.tolist()\n",
        "    importances = model.feature_importances_\n",
        "    indices = np.argsort(importances)\n",
        "    plt.figure(figsize=(10,10))\n",
        "    plt.title('Feature Importances')\n",
        "    plt.barh(range(len(indices)), importances[indices], color='b', align='center')\n",
        "    plt.yticks(range(len(indices)), [features[i] for i in indices])\n",
        "    plt.xlabel('Relative Importance')\n",
        "    plt.show()\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import metrics\n",
        "import numpy as np\n",
        "from sklearn.metrics import mean_squared_log_error\n",
        "# We have created a function to print accuracy metrics which can be used\n",
        "# to get accuracy metrics of all models in upcoming steps\n",
        "def print_accuracy_report(y_test, y_pred,X_test, model):\n",
        " print('R Squared(Accuracy)', metrics.r2_score(y_test, y_pred))   \n",
        " print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))  \n",
        " print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))\n",
        " print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))\n",
        " print('Root Mean Squared Log Error',np.sqrt(mean_squared_log_error( y_test, y_pred )))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R88Sl0yaQ_55",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        },
        "outputId": "af84a950-0dd2-43fd-8e82-721a8f686970"
      },
      "source": [
        "#define a function for random forest regressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "import numpy as np\n",
        "\n",
        "def RandomForestRegressorModel(X,y):\n",
        "    X_train, X_test, y_train, y_test = train_test_split(X, y,random_state=42, test_size=0.3)\n",
        "    rf = RandomForestRegressor(random_state=42)\n",
        "    rf.fit(X_train, y_train)\n",
        "    y_pred = rf.predict(X_test)\n",
        "    print(print_accuracy_report(y_test, y_pred, X_test, rf))\n",
        "    return rf\n",
        "\n",
        "randomForestModel = RandomForestRegressorModel(X,y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "R Squared(Accuracy) 0.8025900530441361\n",
            "Mean Absolute Error: 2.408346454603899\n",
            "Mean Squared Error: 23.563801989756616\n",
            "Root Mean Squared Error: 4.854256069652343\n",
            "Root Mean Squared Log Error 0.29256690669402813\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cAfDzYqJSOZm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 924
        },
        "outputId": "747a9b42-de00-4459-e9a1-ef0ca1ea7cfe"
      },
      "source": [
        "plotFeatureImportances(randomForestModel)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                      importance\n",
            "MA_Power                0.200544\n",
            "MM_Power                0.185552\n",
            "Power                   0.167950\n",
            "Robust_Power            0.156524\n",
            "Robust_KD               0.055071\n",
            "MM_Kilometers_Driven    0.054219\n",
            "Kilometers_Driven       0.052916\n",
            "MM_Mileage              0.023629\n",
            "Robust_Mileage          0.022843\n",
            "Mileage                 0.022829\n",
            "MM_Engine               0.016594\n",
            "Robust_Engine           0.015143\n",
            "Engine                  0.014778\n",
            "Robust_Seats            0.003846\n",
            "Seats                   0.003811\n",
            "MM_Seats                0.003749\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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dkhzQxtsE2JbetoZrq+q2sQqoqlOAUwCSubUqHlKSJGl1MtSufg9V1ez+hiRL63sl8DbgacB7gVcBB9ALuwAB3l5VF48abx/ggZVXsiRJ0prN7QdrhnnAIfDbQPrTqvplVf0Q2AzYtqpuBb4FHEMv7AJcDLw1ycx277OSbLC6i5ckSRo0V2rXDMcBpyZZCDwIHNZ37Rpg7XY8D/gwvXAL8FlgFnBdesu9i4FXroZ6JUmS1iipckvldNbbUzs06DIkSVrljDzdl2S4quaOdc3tB5IkSeo8Q60kSZI6z1ArSZKkzjPUSpIkqfMMtZIkSeo8Q60kSZI6z/fUTnNz5sCQb/SSJEkd50qtJEmSOs9QK0mSpM4z1EqSJKnzDLWSJEnqPEOtJEmSOs9QK0mSpM7zlV7T3PAwJIOuQpI0nVQNugJNRa7USpIkqfMMtZIkSeo8Q60kSZI6z1ArSZKkzjPUSpIkqfMMtZIkSeo8Q60kSZI6z1ArSZKkzjPUSpIkqfOmZKhNsiTJgiQ3JPlKkk3H6X95krkrYd6jk6w/Tp/bkyxKsjDJJUn+YEXnlSRJmu6mZKgFHqqq2VW1A/Bz4G2rad6jgWWG2mbfqtoJGALesyoLSs9U/XuWJEkCpm6o7fcdYCuAJLOTXN1WSc9L8sS+fof2re7u1vofl+SYkQ7t2qwkGyT5apLrW9uBSY4CtgQuS3LZBGu7EnhmknWT/HtbwZ2fZN8231eT7NSO5yd5Xzv+uyRvbsfvTPLd9kzHt7ZZSW5OcjpwA/DU/kmTHJFkKMkQLJ7s91OSJGmNM6VDbZK1gf2A81vT6cC72irpIuD9fd3Xr6rZwJHAqeMM/VLgrqraua0GX1RVJwF30VuF3XeCJf5Jq+NtQFXVjsDBwGlJ1gXmAXsl2QT4DbBHu28v4Mok+wPbArsBs4E5SfZufbYFTq6q51bVHf2TVtUpVTW3qubC5hMsVZIkac01VUPtekkWAHcDTwYubcFw06q6ovU5Ddi7754zAarqSmDjcfbhLgJenOSEJHtV1b2TrO+yVt/GwIeBPYEz2vzfB+4AnkUv1O5NL8x+Fdiw7dndpqpuBvZvX/OB64Dn0AuzAHdU1dWTrEuSJKmTZgy6gFXkoaqa3QLgxfRWQk8b554a4/w3/G7wXxegqm5Jsivwx8AHk3yjqv5uEvXtW1U/HTlJsrR+3wXmArcClwKbAW8GhkduBT5cVf/Sf1OSWcADk6hHkiSp06bqSi0AVfUgcBTwDnoh754ke7XLhwJX9HU/ECDJnsC9bfX1dmDX1r4rsE073hJ4sKrOAE4c6QPcB2y0HKXOAw5pYz8LeBpwc1X9Gvgh8Fp6e4PnAcfQ24sLvcD+piQbtnu3SvL7yzG/JElSp03Vldrfqqr5SRbS26t6GPCZtoJ7K/DGvq4PJ5kPzATe1NrOBd6Q5EbgGuCW1r4jcGKSR4FHgLe29lOAi5LcNYl9tQAnA59Osoje6vDhVfWrdm0esF9VPZRkHvCU1kZVXZJkO+A7bbX3fuD1wJJJzC1JktR5qRr9U3dNJ8nc6r1ZTJKk1cPooeWVZLj3QffHm9LbDyRJkjQ9TPntB4OS5BpgnVHNh1bVokHUI0mSNJUZaleRqnr+oGuQJEmaLtx+IEmSpM4z1EqSJKnzDLWSJEnqPPfUTnNz5sCQb/SSJEkd50qtJEmSOs9QK0mSpM4z1EqSJKnzDLWSJEnqPEOtJEmSOs+3H0xzw8OQDLoKdV3VoCuQJE13rtRKkiSp8wy1kiRJ6jxDrSRJkjrPUCtJkqTOM9RKkiSp8wy1kiRJ6jxDrSRJkjrPUCtJkqTOM9RKkiSp8wy1Y0iyJMmCJDck+UqSTcfpf3mSuSth3qOTrD9On9uTbNaO5yS5LckuSQ5PsjjJ/CT/leTiJC9c0ZokSZK6wFA7toeqanZV7QD8HHjbapr3aGCZoXZEkp2ALwIHVtX81nx2Ve1SVdsCHwG+lGS7VVOqJEnSmsNQO77vAFsBJJmd5OokC5Ocl+SJff0O7Vvd3a31Py7JMSMd2rVZSTZI8tUk17e2A5McBWwJXJbksnFq2g74MnBoVV07Voequgw4BThiuZ9ckiSpIwy1y5BkbWA/4PzWdDrwrqraCVgEvL+v+/pVNRs4Ejh1nKFfCtxVVTu31eCLquok4C5g36rad5z7/y/wf6rqW+P0uw54zhjPdUSSoSRDsHicISRJktZ8htqxrZdkAXA38GTg0iSbAJtW1RWtz2nA3n33nAlQVVcCG4+zD3cR8OIkJyTZq6runWR9Xwf+vIXuZclYjVV1SlXNraq5sPkkp5YkSVrzGGrH9lBbdd2aXjCcyJ7aGuP8N/zu93hdgKq6BdiVXrj9YJL3TbK+/9P+PHmcfrsA35vk2JIkSZ1jqF2GqnoQOAp4B/AAcE+SvdrlQ4Er+rofCJBkT+Detvp6O73wSpJdgW3a8ZbAg1V1BnDiSB/gPmCjCZT2KPC/gOck+buxOiT5Q3r7af91Is8qSZLUZTMGXcCarqrmJ1kIHAwcBnymvXbrVuCNfV0fTjIfmAm8qbWdC7whyY3ANcAtrX1H4MQkjwKPAG9t7acAFyW5a7x9tVX1cJI/Ba5I8hN6ofvAFqrXB24DXlNVrtRKkqQpL1Wjf2qu6SSZWzA06DLUcf5nRJK0OiQZ7n0m6PHcfiBJkqTOc/vBGirJNcA6o5oPrapFg6hHkiRpTWaoXUNV1fMHXYMkSVJXuP1AkiRJnWeolSRJUucZaiVJktR57qmd5ubMgSHf6CVJkjrOlVpJkiR1nqFWkiRJnWeolSRJUucZaiVJktR5hlpJkiR1nm8/mOaGhyEZdBXqqqpBVyBJUo8rtZIkSeo8Q60kSZI6z1ArSZKkzjPUSpIkqfMMtZIkSeo8Q60kSZI6z1ArSZKkzjPUSpIkqfMMtZIkSeo8Q60kSZI6b9xQm6SSnNF3PiPJ4iQXtPPDW58X9fV5ZWs7YBnjXp5kbjveJsl/JXlJkrlJTuob+5Mr8oDjSXJ0kvVX4fiHt+/X/PaMFyd54TL6vyXJG1ZVPZIkSVPRRFZqHwB2SLJeO38xcOeoPouAg/rODwaun0gBSZ4CXAS8o6ourqqhqjpqIveuJEcDkwq1Sdae5BxnV9UuVbUt8BHgS0m2G2PcGVX1mao6fZLjS5IkTWsT3X5wIfCydnwwcOao6/OA3ZLMTLIh8ExgwQTG3QK4BHhvVZ0PkGSfkVXgfklmJflmkoVJvpHkaa39c0k+neTqJLe2+09N8r0kn+u7f/8k30lyXZJzkmyY5ChgS+CyJJctrV9rvz3JCUmuA16b5KgkN7V6zprg95Gqugw4BTiijXt5kn9KMgT8RZLjkhyT5DlJrh31/Iva8ZwkVyQZbiu/W/SNdUKSa5PckmSvsWpIckSSod6ciydauiRJ0hproqH2LOCgJOsCOwHXjLpewNeBlwCvAM6f4LinAZ+sqi9OoO8ngNOqaifg88BJfdeeCOwO/GWb++PAc4Edk8xOshlwLPCiqtoVGAL+qqpOAu4C9q2qfZfWr2+en1XVrlV1FvBuYJdWz1sm+LwjrgOe03f+hKqaW1UfG2moqu8DT0iyTWs6EDg7ycz2vTigquYApwIf6htrRlXtRm8F+v1jTV5Vp7T55sLmkyxdkiRpzTOhUFtVC4FZ9FZpL1xKt7PobUE4iMev5C7N14HXT3BP6+7Af7bj/wD27Lv2laoqetsgflJVi6rqUeDGVvcLgO2Bq5IsAA4Dth5jjvH6nd13vBD4fJLXA7+ZQP39Mur87DF7wRfohVnan2cDzwZ2AC5tNR4LPKXvni+1P4fpPbskSdKUN2MSfc8HPgrsAzxp9MWqujbJjsCDVXVLMjq3jekfgEOBc5K8oqomGw5H/Kr9+Wjf8cj5DGAJcGlVHTzOOBmn3wN9xy8D9gZeDrw3yY6TqH8X4HtLGbff2fS+N18Cqqr+q32Pb6yq3Zdyz8jzL2Fyf7+SJEmdNZlXep0KHF9Vi5bR593AeyZZw9HAL4F/y7KT8Ld57MNoh9DbxztRVwN7JHkmQJINkjyrXbsP2GgC/X4ryVrAU9v+2HcBmwAbTqSQJH9Ibz/tv47Xt6p+QC+c/i2PrebeDGyeZPc23swkz53I3JIkSVPVhFfyqupH/O4+1rH6fG2yBVRVJTkMuIDeyu1Xl9L17cC/J3knvU83vXEScyxOcjhwZpJ1WvOxwC30PrR1UZK72r7apfXrtzZwRpJN6K3unlRVv1hGCQcm2ZPeWxZuA15TVd9bRv9+ZwMnAtu0Z/l1e1XaSW3+GcA/0dtqIUmSNC2ltxVV01Uyt3qfh5Mmz/98SJJWpyTDvQ+6P56/UUySJEmdt8o/SJTkPNqPzvu8q6ouXtVzr05J3gj8xajmq6rqbYOoR5IkaTpx+8E05/YDrQj/8yFJWp3cfiBJkqQpzVArSZKkzvPl/NPcnDkw5O4DSZLUca7USpIkqfMMtZIkSeo8Q60kSZI6z1ArSZKkzjPUSpIkqfMMtZIkSeo8X+k1zQ0PQzLoKtQV/gYxSdKaypVaSZIkdZ6hVpIkSZ1nqJUkSVLnGWolSZLUeYZaSZIkdZ6hVpIkSZ1nqJUkSVLnGWolSZLUeYZaSZIkdd7AQm2S+/uO/zjJLUm2TvKWJG9o7Z9LcsAqrGHTJEeuqvHbHJ9LcluS69sznp7kKcvo/9kk26/KmiRJkqaaga/UJtkPOAn4o6q6o6o+U1Wnr6bpNwUmFWrTM9nv2zuramfg2cB84JtJnjDG2GtX1Z9X1U2THF+SJGlaG2ioTbI38K/An1TVD1rbcUmOGaPvfknmJ1mU5NQk67T225N8OMmCJENJdk1ycZIfJHlL3/3vTPLdJAuTHN+aPwI8o9174tL6JZmV5OYkpwM3AE9tK7A3tHr+ciLPWz0fB+4G/qiNfX+SjyW5Htg9yeVJ5rYV6xP76j88ySfb8euTXNvq/pcka/eN9aG2Knx1kicv5ft+RPteDcHiiZQuSZK0RhtkqF0H+DLwyqr6/rI6JlkX+BxwYFXtCMwA3trX5b+rajYwr/U7AHgBMBJK9we2BXYDZgNzWqB+N/CDqppdVe9cRj9a+8lV9VxgM2Crqtqh1fPvk3z264DntOMNgGuqaueq+lZfn3OBV/WdHwiclWS7drxHe+YlwCF9Y13dVoWvBN481uRVdUpVza2qubD5JEuXJEla8wwy1D4CfBv4swn0fTZwW1Xd0s5PA/buu35++3MRvYB4X1UtBn6VZFNg//Y1n8cC5bZjzLOsfndU1dXt+Fbg6Uk+keSlwC8n8Az90ne8hF6A/R2t/luTvCDJk1otVwH7AXOA7yZZ0M6f3m77NXBBOx4GZk2yLkmSpE6aMcC5HwVeB3wjyXuq6u9XYKxf9Y35q772R+k9Y4APV9W/9N+UZNaocZbV74GR86q6J8nOwEuAt7TneNMk6t0F+EY7friqliyl31lt7O8D51VVJQlwWlX9zRj9H6mqasdLGOzfryRJ0moz0D21VfUg8DLgkCTLWrG9GZiV5Jnt/FDgiklMdTHwpiQbAiTZKsnvA/cBG02g3+9IshmwVlWdCxwL7DqRItqHzI4CtgAumsAt5wGvAA6mF3ChF4YPGKkrye8l2Xoi80uSJE1VA1/Jq6qftx/hX5lkzE8tVdXDSd4InJNkBvBd4DOTmOOSthf1O72FTu4HXl9VP0hyVZIbgK+1fbWP60dv1bPfVsC/970FYaxV034nJvlbYH3gamDfqvr1BOq+J8n3gO2r6trWdlOSY4FL2vyPAG8D7hhvPEmSpKkqj/20WtNRMrdgaNBlqCP8z4UkaZCSDPc+6P54A39PrSRJkrSiBr79YKpI8ilgj1HN/1xVk33dlyRJkibJULuSVNXbBl2DJEnSdOX2A0mSJHWeoVaSJEmdZ6iVJElS57mndpqbMweGfKOXJEnqOFdqJUmS1HmGWkmSJHWeoVaSJEmdZ6iVJElS5xlqJUmS1Hm+/WCaGx6GZNBVDEbVoCuQJEkriyu1kiRJ6jxDrSRJkjrPUCtJkqTOM9RKkiSp8wy1kiRJ6jxDrSRJkjrPUCtJkqTOM9RKkiSp8wy1kiRJ6rxpGWqTVJIz+s5nJLUd+/YAACAASURBVFmc5IJ2fnjr86K+Pq9sbQcsY9zLk/x38tjv6Ery5ST3t+Mtk3yxHe8zMp8kSZJWzLQMtcADwA5J1mvnLwbuHNVnEXBQ3/nBwPUTGPsXwB4ASTYFthi5UFV3VdVSQ7EkSZKWz3QNtQAXAi9rxwcDZ466Pg/YLcnMJBsCzwQWTGDcs3gsDL8a+NLIhSSzktww+oYkGyQ5Ncm1SeYneUVf/3lJrmtfL2ztayU5Ocn3k1ya5MKRFeQkc5JckWQ4ycVJthhjviOSDCUZgsUTeCRJkqQ123QOtWcBByVZF9gJuGbU9QK+DrwEeAVw/gTH/Qawd5K16YXbsydwz3uBb1bVbsC+wIlJNgD+B3hxVe0KHAic1Pq/GpgFbA8cCuwOkGQm8AnggKqaA5wKfGj0ZFV1SlXNraq5sPkEH0uSJGnNNWPQBQxKVS1MMoveKu2FS+l2FnAUsAnwDuA9Exh6CfAteoF2vaq6vW+L7dLsD/xpkmPa+brA04C7gE8mmd3GfVa7vidwTlU9Ctyd5LLW/mxgB+DSNufawI8nULMkSVKnTdtQ25wPfBTYB3jS6ItVdW2SHYEHq+qWCYTTEWcB5wHHTbB/gNdU1c2/05gcB/wE2JneqvrDExjnxqrafaKFSpIkTQXTefsB9H48f3xVLVpGn3czsRXafvOAD/P4fbpLczHw9pG3JiTZpbVvAvy4rcgeSm/lFeAq4DVtb+2T6YVygJuBzZP8djtCkudOsnZJkqTOmdahtqp+VFUnjdPna1V12bL6jHFPVdVHq+qnE7zlA8BMYGGSG9s5wMnAYUmuB55D760NAOcCPwJuAs4ArgPurapfAwcAJ7R7FgAvnEztkiRJXZSqGnQNWg5JNqyq+5M8CbgW2KOq7p78OHMLhlZ+gR3gP31JkrolyXDvg+6PN9331HbZBe09uE8APrA8gVaSJGmqMNQuhyTnAduMan5XVV28umqoqn1W11ySJElrOkPtcqiqVw26BkmSJD1mWn9QTJIkSVODoVaSJEmdZ6iVJElS57mndpqbMweGpucbvSRJ0hTiSq0kSZI6z1ArSZKkzjPUSpIkqfMMtZIkSeo8Q60kSZI6z7cfTHPDw5AMuorVp2rQFUiSpFXBlVpJkiR1nqFWkiRJnWeolSRJUucZaiVJktR5hlpJkiR1nqFWkiRJnWeolSRJUucZaiVJktR5hlpJkiR1nqFWkiRJnde5UJtkSZIFSW5I8pUkm47T//Ikc1fCvEcnWX+cPrcnmTeqbUGSG9rx3CQntePDk3xyReuSJElSB0Mt8FBVza6qHYCfA29bTfMeDSwz1DYbJXkqQJLt+i9U1VBVHbUqipMkSZrOuhhq+30H2AogyewkVydZmOS8JE/s63do3+rubq3/cUmOGenQrs1KskGSrya5vrUdmOQoYEvgsiSXjVPTF4AD2/HBwJl9c+yT5ILRNyTZPMm5Sb7bvvZo7bsl+U6S+Um+neTZrX39JF9IclN71mtGVqOT7N/uuS7JOUk2HGO+I5IMJRmCxeN9jyVJktZ4nQ21SdYG9gPOb02nA++qqp2ARcD7+7qvX1WzgSOBU8cZ+qXAXVW1c1sNvqiqTgLuAvatqn3Huf9c4NXt+OXAVybwOP8MfLyqnge8Bvhsa/8+sFdV7QK8D/j71n4kcE9VbQ/8LTAHIMlmwLHAi6pqV2AI+KvRk1XVKVU1t6rmwuYTKE+SJGnNNmPQBSyH9ZIsoLdC+z3g0iSbAJtW1RWtz2nAOX33nAlQVVcm2XicfbiLgI8lOQG4oKrmLaPvWH4G3JPkoFbfgxO450XA9klGzjduK6ybAKcl2RYoYGa7vie9IExV3ZBkYWt/AbA9cFUb6wn0VrMlSZKmtC6u1D7UVl23BsLE9tTWGOe/4Xeff12AqroF2JVeuP1gkvctR41nA5+ib+vBONYCXtD2Cs+uqq2q6n7gA8BlbcX45SM1LkOAS/vG2b6q/mw56pckSeqULoZaAKrqQeAo4B3AA/RWR/dqlw8FrujrfiBAkj2Be6vqXuB2euGVJLsC27TjLYEHq+oM4MSRPsB9wEYTLO884B+AiyfY/xLg7SMnSWa3w02AO9vx4X39rwJe1/puD+zY2q8G9kjyzHZtgyTPmmANkiRJndXF7Qe/VVXz24/eDwYOAz7TXrt1K/DGvq4PJ5lP78f3b2pt5wJvSHIjcA1wS2vfETgxyaPAI8BbW/spwEVJ7hpvX21V3QecANC3pWBZjgI+1Z5lBnAl8BZ6wfi0JMcCX+3rf3Jrv4nevtsb6YX1xUkOB85Msk7re2zfs0mSJE1JqRr9k3mt6dqH5GZW1cNJngF8HXh2Vf168mPNrd7nyaYH/7lLktRdSYZ7H3R/vE6v1E5j69N7vdhMevtoj1yeQCtJkjRVGGqXQ5JrgHVGNR9aVYtWx/xte8MK/5Y0SZKkqcJQuxyq6vmDrkGSJEmP6ezbDyRJkqQRhlpJkiR1ntsPprk5c2Bo+rz8QJIkTVGu1EqSJKnzDLWSJEnqPEOtJEmSOs9QK0mSpM4z1EqSJKnzfPvBNDc8DMmgq1h9qgZdgSRJWhVcqZUkSVLnGWolSZLUeYZaSZIkdZ6hVpIkSZ1nqJUkSVLnGWolSZLUeYZaSZIkdZ6hVpIkSZ1nqJUkSVLnGWpXoiSV5Iy+8xlJFie5oJ3/aZJ3t+PjkhwzqFolSZKmEn9N7sr1ALBDkvWq6iHgxcCdIxer6nzg/EEVJ0mSNFW5UrvyXQi8rB0fDJw5ciHJ4Uk+OfqGJM9IclGS4STzkjyntb88yTVJ5if5epInt/bNk1ya5MYkn01yR5LN2rXXJ7k2yYIk/5Jk7VX+xJIkSQNmqF35zgIOSrIusBNwzQTuOQV4e1XNAY4BTm7t3wJeUFW7tHH/urW/H/hmVT0X+CLwNIAk2wEHAntU1WxgCXDI6MmSHJFkKMkQLF7Ox5QkSVpzuP1gJauqhUlm0VulvXC8/kk2BF4InJNkpHmd9udTgLOTbAE8Abitte8JvKrNd1GSe1r7fsAc4LttrPWA/xmjxlPoBWmSuTWpB5QkSVoDGWpXjfOBjwL7AE8ap+9awC/ayuponwD+sarOT7IPcNw4YwU4rar+ZlLVSpIkdZzbD1aNU4Hjq2rReB2r6pfAbUleC5CendvlTXjsg2aH9d12FfC61n9/4Imt/RvAAUl+v137vSRbr+jDSJIkrekMtatAVf2oqk6axC2HAH+W5HrgRuAVrf04etsShoGf9vU/Htg/yQ3Aa4G7gfuq6ibgWOCSJAuBS4EtVuhhJEmSOiBVbqnsmiTrAEuq6jdJdgc+vZTtCxMYa27B0MotcA3mP3dJkroryXBVzR3rmntqu+lpwBeSrAX8GnjzgOuRJEkaKENtB1XVfwG7DLoOSZKkNYV7aiVJktR5hlpJkiR1nqFWkiRJnWeolSRJUuf5QbFpbs4cGJo+b/SSJElTlCu1kiRJ6jxDrSRJkjrPUCtJkqTOM9RKkiSp8wy1kiRJ6jxDrSRJkjrPV3pNc8PDkAy6iomrGnQFkiRpTeRKrSRJkjrPUCtJkqTOM9RKkiSp8wy1kiRJ6jxDrSRJkjrPUCtJkqTOM9RKkiSp8wy1kiRJ6jxDrSRJkjrPUAskqSRn9J3PSLI4yQXt/PDW50V9fV7Z2g5YxriXJ7k5yYL29cUVqPGzSbZf3vslSZKmMn9Nbs8DwA5J1quqh4AXA3eO6rMIOAj4ejs/GLh+AmMfUlVDK1pgVf35io4hSZI0VblS+5gLgZe144OBM0ddnwfslmRmkg2BZwILlmeiJJ9LclKSbye5dWS1N8laSU5O8v0klya5sO/a5UnmtuP7k3woyfVJrk7y5Na+eZJzk3y3fe2xlPmPSDKUZAgWL88jSJIkrVEMtY85CzgoybrATsA1o64XvVXalwCvAM6f4Lif79t+cGJf+xbAnsCfAB9pba8GZgHbA4cCuy9lzA2Aq6tqZ+BK4M2t/Z+Bj1fV84DXAJ8d6+aqOqWq5lbVXNh8go8hSZK05nL7QVNVC5PMordKe+FSup0FHAVsArwDeM8Ehl7a9oMvV9WjwE0jK630Qu45rf3uJJctZcxfAxe042F62yUAXgRsn2Sk38ZJNqyq+ydQpyRJUmcZan/X+cBHgX2AJ42+WFXXJtkReLCqbukLj8vjV33Hkx3okaqqdryEx/4e1wJeUFUPr0hhkiRJXeP2g991KnB8VS1aRp93M7EV2uVxFfCatrf2yfTC9WRcArx95CTJ7JVYmyRJ0hrLldo+VfUj4KRx+nxtksN+PslD7finVfWiZfQ9F9gPuAn4IXAdcO8k5joK+FSShfT+bq8E3jLJeiVJkjonj/0UW2uCkT2wSZ4EXAvsUVV3r7r55has8BvHVhv/uUqSNH0lGe590P3xXKld81yQZFPgCcAHVmWglSRJmioMtStBkvOAbUY1v6uqLp7sWFW1z0opSpIkaRox1K4EVfWqQdcgSZI0nfn2A0mSJHWeoVaSJEmdZ6iVJElS5xlqp7k5c3qvyerKlyRJ0lgMtZIkSeo8Q60kSZI6z1ArSZKkzjPUSpIkqfMMtZIkSeo8f6PYNDc8DMmgq1g633ggSZImwpVaSZIkdZ6hVpIkSZ1nqJUkSVLnGWolSZLUeYZaSZIkdZ6hVpIkSZ1nqJUkSVLnGWolSZLUeYZaSZIkdV7nQ22SJUkWJLkhyVeSbDpO/8uTzF0J8x6dZP1x+tyeZFGrb0GSk1ZgvgvHezZJkqTpqvOhFnioqmZX1Q7Az4G3raZ5jwaWGWqbfVt9s6vqqOWdrKr+uKp+sbz3S5IkTWVTIdT2+w6wFUCS2UmuTrIwyXlJntjX79C+1d3dWv/jkhwz0qFdm5VkgyRfTXJ9azswyVHAlsBlSS6bbJFttfiEJNcmuSXJXq19/SRfSHJTq/makVXltuq7Wavpe0n+NcmNSS5Jsl7r84wkFyUZTjIvyXOWMv8RSYaSDMHiyZYvSZK0xpkyoTbJ2sB+wPmt6XTgXVW1E7AIeH9f9/WrajZwJHDqOEO/FLirqnZuq8EXVdVJwF30VmH3Hef+y/q2H/xlX/uMqtqN3orvSG1HAvdU1fbA3wJzljLmtsCnquq5wC+A17T2U4C3V9Uc4Bjg5LFurqpTqmpuVc2FzccpX5Ikac03Y9AFrATrJVlAb4X2e8ClSTYBNq2qK1qf04Bz+u45E6Cqrkyy8Th7VRcBH0tyAnBBVc2bZH37VtVPx2j/UvtzGJjVjvcE/rnVdkOShUsZ87aqWtB/f5INgRcC5yQZ6bfOJGuVJEnqpKmwUvtQW3XdGggT21NbY5z/ht/9fqwLUFW3ALvSC7cfTPK+Fa6451ftzyVM/n8uftV3PHL/WsAv+vbvzq6q7VZCnZIkSWu8qRBqAaiqB4GjgHcADwD3jOxVBQ4FrujrfiBAkj2Be6vqXuB2euGVJLsC27TjLYEHq+oM4MSRPsB9wEYr+TGuAl7X5t0e2HGiN1bVL4Hbkry23Z8kO6/k+iRJktZIU2H7wW9V1fz2I/uDgcOAz7TXbt0KvLGv68NJ5gMzgTe1tnOBNyS5EbgGuKW17wicmORR4BHgra39FOCiJHeNs6/2siRL2vHCqnrDMvqeDJyW5Cbg+8CNwL3jPvhjDgE+neTY9mxnAddP4n5JkqROStXon8RrUNqH3WZW1cNJngF8HXh2Vf161c05t2BoVQ2/wvznKUmSRiQZ7n3Q/fGm1ErtFLA+vZXdmfT2Bx+5KgOtJEnSVGGoXQmSXMPj3zRwaFUtmsw4VXUfsMK/7UySJGm6MdSuBFX1/EHXIEmSNJ1NmbcfSJIkafoy1EqSJKnzDLWSJEnqPEPtNDdnTu+1WWvqlyRJ0kQYaiVJktR5hlpJkiR1nqFWkiRJnWeolSRJUucZaiVJktR5/kaxaW54GJJBVzE2334gSZImypVaSZIkdZ6hVpIkSZ1nqJUkSVLnGWolSZLUeYZaSZIkdZ6hVpIkSZ1nqJUkSVLnGWolSZLUeYZaSZIkdZ6hdhVLsiTJgr6vd6/AWN9embVJkiRNFf6a3FXvoaqavTIGqqoXroxxJEmSphpXagckye1Jjk9yXZJFSZ7T2jdPcmmSG5N8NskdSTZr1+5vf+6T5PIkX0zy/SSfT5J2bU6SK5IMJ7k4yRaDe0pJkqTVw1C76q03avvBgX3XflpVuwKfBo5pbe8HvllVzwW+CDxtKePuAhwNbA88HdgjyUzgE8ABVTUHOBX40OgbkxyRZCjJECxeGc8oSZI0UG4/WPWWtf3gS+3PYeDV7XhP4FUAVXVRknv+//buPVyyqrzz+PcnVxEEIpgHRAUJxlHAA90goih4TxRhAgoMUYlXHEceR4maQR1hmDGMZkzUJzI9k4iOCoiKQYwwoA0qyqVb+gIYlIvOCIwBL4SbKO07f+x1pDyc7nM453RX7erv53n2U7v2XmvttddZVfWeVWvXXkveK6vqxwBJVgC7Ar8A9gQuagO3mwC3Tc1YVUuAJV3exfUwz0eSJGnkGNQO1/3tcQ0P/29x/8D6ZP4A11bVMxegbpIkSb3h9IPRcxnwSoAkLwK2fxh5rwd2TPLMln+zJE9b+CpKkiSNFoPa9W/qnNq/nCH9ycCLklwDvAL4f8BdszlQVf0KOBI4LclKYAXgLyZIkqSxlyqnVI6SJFsAa6rqgTbi+vGF+kmw6Y+3uGDZ+ip+XuyakiRpUJLlVbV4un3OqR09TwA+l+QRwK+ANwy5PpIkSSPPoHbEVNUP6H6uS5IkSbPknFpJkiT1nkGtJEmSes+gVpIkSb1nUCtJkqTeM6jdyC1a1P101igukiRJs2VQK0mSpN4zqJUkSVLvGdRKkiSp9wxqJUmS1HsGtZIkSeo9g1pJkiT13qbDroCGa/lySGZO509sSZKkUeZIrSRJknrPoFaSJEm9Z1ArSZKk3jOolSRJUu8Z1EqSJKn3DGolSZLUewa1kiRJ6j2DWkmSJPWeQa0kSZJ6b6yD2iRrkqxIck2SLyfZbob0lyRZvADHfVuSrWZI89okq5OsavU7bI7HOjjJgXOrqSRJ0ngY66AWuK+qJqpqT+BnwFs20HHfBqw1qE2yC3AS8Oyq2hs4AFg1x2MdDBjUSpKkjdq4B7WDvgM8DiDJRJLL2yjpuUm2H0j3qoHR3f1b+vcnOXEyQdu3a5JHJflKkpVt21FJTgB2BpYmWbqWujwWuAu4G6Cq7q6qm1vZuye5IMnyJN9M8pS2/dAkVyS5OsnFSX4/ya7A8cC/b3U+KMkrWl1WJvnGdAdP8sYky5Isg9vn3KCSJEmjYtNhV2BDSLIJ8Hzg79qmTwFvrapLk5wC/Ee60VWArapqIslzgL8H9lxH0S8Bbq2ql7bjbFtVdyZ5O3BIVd2xlnwrgZ8ANyf5GvDFqvpy27cEOL6qfpDkGcDfAs8DvgUcUFWV5PXAO6vqHUlOB+6uqg+1OqwGXlxVt6xtukVVLWnHIVlc6zg/SZKkXhj3oPaRSVbQjdB+D7goybbAdlV1aUvzSeCcgTxnAlTVN5I8eoZ5uKuBv0pyGnB+VX1zNpWqqjVJXgLsRxdsfzjJIuBDdFMJzkkymXyL9rgLcHaSnYDNgZvXUvxlwBlJPgd8cTb1kSRJ6rtxn35wX1VNAE8Ewuzm1E4duSzgAX63rbYEqKrvA/vSBbenJnnfbCtWnSur6gPA0cAR7Ri/aPOAJ5d/1bJ8FPhYVe0FvGmyDtOUezzwHuDxwPIkj5ltnSRJkvpq3INaAKrqXuAE4B3APcDPkxzUdr8KuHQg+VEASZ4N3FlVdwI/pAteSbIvsFtb3xm4t6o+DXxwMg3dfNlt1lafJDu3ciZNAD+qqn+hm5LwipYuSZ7e0mwL3NLWXzOQ93eOlWT3qrqiqt5HN2H28etoGkmSpLEw7tMPfquqrk6yCjiGLig8vf3s1k3Anw0k/WWSq4HNgNe2bV8AXp3kWuAK4Ptt+17AB5P8Bvg18Oa2fQlwQZJbq+qQaaqzGfChFhT/ki74PL7tOxb4eJL3tHRn0c3BfT/dtISfA1+nBdbAl4HPt58EeyvdRWN70I1Mf63llSRJGmup8jqhjVl3odiyGdPZTSRJ0rAlWV5V095TYKOYfiBJkqTxttFMPxiWJFfw4C8YTHpVVa0eRn0kSZLGkUHtelZVzxh2HSRJksad0w8kSZLUewa1kiRJ6j2DWkmSJPWeQe1GbtGi7ue6ZlokSZJGmUGtJEmSes+gVpIkSb1nUCtJkqTeM6iVJElS7xnUSpIkqfe8o9hGbvlySGZO5y8gSJKkUeZIrSRJknrPoFaSJEm9Z1ArSZKk3jOolSRJUu8Z1EqSJKn3DGolSZLUewa1kiRJ6j2DWkmSJPWeQe0QJTkpybVJViVZkeQZcyjj8CRPXR/1kyRJ6gvvKDYkSZ4JvAzYt6ruT7IDsPkcijocOB+4biHrJ0mS1CeO1A7PTsAdVXU/QFXdUVW3JlmU5NIky5NcmGQngCRvSHJVkpVJvpBkqyQHAi8HPthGendPckKS69ro71lDPD9JkqQNJlU17DpslJJsDXwL2Aq4GDgb+DZwKXBYVd2e5CjgxVX12iSPqaqftrynAj+pqo8mOQM4v6o+3/bdCuzWRn+3q6pfTHPsNwJv7J49YRH8aMb62k0kSdKwJVleVYun2+f0gyGpqruTLAIOAg6hC2pPBfYELkoCsAlwW8uyZwtmtwO2Bi5cS9GrgM8k+RLwpbUcewmwBCBZbLgqSZJ6z6B2iKpqDXAJcEmS1cBbgGur6pnTJD8DOLyqViY5Djh4LcW+FHgOcChwUpK9quqBBa66JEnSSHFO7ZAk+cMkewxsmgC+B+zYLiIjyWZJntb2bwPclmQz4NiBfHe1fSR5BPD4qloKvAvYlm5UV5Ikaaw5Ujs8WwMfTbId8ABwA9081yXAR5JsS/f3+WvgWuC9wBXA7e1xm1bOWcD/SHICcDTwdy1vgI9MN6dWkiRp3Hih2Eaum1O7bMZ0dhNJkjRs67pQzOkHkiRJ6j2DWkmSJPWeQa0kSZJ6z6BWkiRJvWdQK0mSpN4zqJUkSVLvGdRKkiSp9wxqN3KLFnW/QTvTIkmSNMoMaiVJktR7BrWSJEnqPYNaSZIk9Z5BrSRJknrPoFaSJEm9t+mwK6DhWr4ckpnT+QsIkiRplDlSK0mSpN4zqJUkSVLvGdRKkiSp9wxqJUmS1HsGtZIkSeo9g1pJkiT1nkGtJEmSes+gVpIkSb1nUCtJkqTeM6idQZJK8umB55smuT3J+e35cS3NCwbSHN62HbmOcl+W5OokK5Ncl+RNc6zfRJI/nkteSZKkcWFQO7N7gD2TPLI9fyFwy5Q0q4GjB54fA6xcW4FJNgOWAIdW1dOBfYBL5li/CcCgVpIkbdQMamfnH4GXtvVjgDOn7P8msH+SzZJsDfwBsGId5W0DbAr8FKCq7q+q6wGS7JjkC0muasuz2vb9k3ynje5+O8kfJtkcOAU4KsmKJEcleW5bX9HSbrNQjSBJkjSqDGpn5yzg6CRbAnsDV0zZX8DFwIuBw4Dz1lVYVf2spflRkjOTHJtk8m/xN8CHq2o/4Ajgf7bt/wQcVFX7AO8D/ktV/aqtn11VE1V1NnAi8JaqmgAOAu6bevwkb0yyLMkyuP3htYQkSdII2nTYFeiDqlqVZFe6Udp/XEuys4ATgG2BdwD/YYYyX59kL+AFdIHoC4Hj2vOnJplM+ug2+rst8Mkke9AF0ZutpejLgP+W5DPAF6vqx9Mcewnd9AeSxbWuekqSJPWBI7Wzdx7wIR469QCAqroS2AvYoaq+P5sCq2p1VX2YLqA9om1+BHBAG3mdqKrHVdXdwH8CllbVnsChwJZrKfMvgdcDjwQuS/KUWZ+hJElSTxnUzt7fAydX1ep1pHk3M4zQAiTZOsnBA5smgB+19f8NvHUg7URb3ZYHL1A7biDvXXRzdCfT796C5dOAqwCDWkmSNPYMamepqn5cVR+ZIc1Xq2rpLIoL8M4k1ydZAZzMg4HqCcDiJKuSXAcc37b/V+ADSa7md6eNLKWbrrAiyVHA25Jck2QV8Gvgq7M9R0mSpL5KlVMqN2bdnNplM6azm0iSpGFLsryqFk+3z5FaSZIk9Z6/frCeJTkX2G3K5ndV1YXDqI8kSdI4Mqhdz6rqXw+7DpIkSePO6QeSJEnqPYNaSZIk9Z5BrSRJknrPoHYjt2hR93NdMy2SJEmjzKBWkiRJvWdQK0mSpN4zqJUkSVLvGdRKkiSp9wxqJUmS1HsGtZIkSeo9g1pJkiT1nkGtJEmSes+gVpIkSb1nUCtJkqTeM6iVJElS7xnUSpIkqfcMaiVJktR7BrWSJEnqPYNaSZIk9Z5BrSRJknrPoFaSJEm9Z1ArSZKk3jOolSRJUu8Z1EqSJKn3DGolSZLUewa1kiRJ6j2DWkmSJPWeQa0kSZJ6L1U17DpoiJLcBVw/7HqMgB2AO4ZdiRFgOzzItujYDh3boWM7PMi26GzodnhiVe043Y5NN2AlNJqur6rFw67EsCVZZjvYDoNsi47t0LEdOrbDg2yLzii1g9MPJEmS1HsGtZIkSeo9g1otGXYFRoTt0LEdHmRbdGyHju3QsR0eZFt0RqYdvFBMkiRJvedIrSRJknrPoFaSJEm9Z1A7RpK8JMn1SW5I8u5p9m+R5Oy2/4okuw7s+4u2/fokL55tmaNqrm2R5IVJlidZ3R6fN5DnklbmirY8dsOd0dzMox12TXLfwLmePpBnUWufG5J8JEk23BnNzTza4diBNliR5DdJJtq+cewPz0ny3SQPJDlyyr7XJPlBW14zsL13/QHm3hZJJpJ8J8m1SVYlOWpg3xlJbh7oExMb6nzmap59Ys3AuZ43sH239jq6ob2uNt8Q5zIf8+gPh0x5j/hlksPbvnHsD29Pcl3r+19L8sSBfcN/JfnufwAACQlJREFUj6gqlzFYgE2AG4EnAZsDK4GnTknzb4HT2/rRwNlt/akt/RbAbq2cTWZT5igu82yLfYCd2/qewC0DeS4BFg/7/DZQO+wKXLOWcq8EDgACfBX4o2Gf6/pqhylp9gJuHPP+sCuwN/Ap4MiB7b8H3NQet2/r2/exPyxAWzwZ2KOt7wzcBmzXnp8xmHbUl/m0Q9t391rK/RxwdFs/HXjzsM91fbbDQJrfA34GbDXG/eGQgfN7Mw9+ZozEe4QjteNjf+CGqrqpqn4FnAUcNiXNYcAn2/rngee3/5gOA86qqvur6mbghlbebMocRXNui6q6uqpubduvBR6ZZIsNUuuFN58+Ma0kOwGPrqrLq3u3+hRw+MJXfUEtVDsc0/L21YztUFU/rKpVwG+m5H0xcFFV/ayqfg5cBLykp/0B5tEWVfX9qvpBW78V+Gdg2rsb9cB8+sS02uvmeXSvI+heV6PeJxaqHY4EvlpV966/qq5Xs2mHpQPndzmwS1sfifcIg9rx8Tjg/w48/3HbNm2aqnoAuBN4zDryzqbMUTSfthh0BPDdqrp/YNsn2tdI7+3B16zzbYfdklyd5NIkBw2k//EMZY6aheoPRwFnTtk2bv3h4ebtY3+ABXpvS7I/3YjWjQOb/3P7avbDPfiHeL7tsGWSZUkun/zKne5184v2OppLmcOwUJ91R/PQ94hx7g+voxt5XVfeDfoeYVArTSPJ04DTgDcNbD62qvYCDmrLq4ZRtw3kNuAJVbUP8Hbgs0kePeQ6DU2SZwD3VtU1A5s3pv6gKdoI1P8C/qyqJkfv/gJ4CrAf3dew7xpS9TaUJ1Z3e9R/A/x1kt2HXaFhaf1hL+DCgc1j2x+S/CmwGPjgsOsyyKB2fNwCPH7g+S5t27RpkmwKbAv8dB15Z1PmKJpPW5BkF+Bc4NVV9dsRmKq6pT3eBXyW7quaUTbndmhTUX4KUFXL6UaintzS7zKQvw99Yl79oXnICMyY9oeHm7eP/QHm+d7W/sH7CnBSVV0+ub2qbqvO/cAnGO8+MfgauIlujvk+dK+b7drr6GGXOSQL8Vn3SuDcqvr15IZx7Q9JXgCcBLx84JvMkXiPMKgdH1cBe7SrTjen+xA+b0qa84DJKxKPBL7e5ricBxyd7grw3YA96CZ2z6bMUTTntkiyHd2H1bur6rLJxEk2TbJDW98MeBlwDaNtPu2wY5JNAJI8ia5P3FRVtwH/kuSA9nX7q4F/2BAnMw/zeW2Q5BF0H1i/nU87xv1hbS4EXpRk+yTbAy8CLuxpf4B5tEVLfy7wqar6/JR9O7XH0M0bHNs+0frCFm19B+BZwHXtdbOU7nUE3etq1PvEQnzWHcOUf3zHsT8k2Qf473QB7T8P7BqN94j1dQWay4ZfgD8Gvk83qnZS23ZK63wAWwLn0F0IdiXwpIG8J7V81zNwZeJ0ZfZhmWtbAO8B7gFWDCyPBR4FLAdW0V1A9jfAJsM+z/XYDke081wBfBc4dKDMxXRvzjcCH6PdmXCUl3m+Ng4GLp9S3rj2h/3o5rzdQzfidu1A3te29rmB7iv33vaH+bQF8KfAr6e8R0y0fV8HVrf2+DSw9bDPcz22w4HtXFe2x9cNlPmk9jq6ob2uthj2ea6vdmj7dqUbfXzElDLHsT9cDPxkoO+fN5B36O8R3iZXkiRJvef0A0mSJPWeQa0kSZJ6z6BWkiRJvWdQK0mSpN4zqJUkSVLvGdRK0ohJsqbdfveaJF9uv5+8rvTvT3LiDGkOT/LUgeentB9Rn29dz0hy5MwpF06StyXZakMeU9LoM6iVpNFzX1VNVNWewM+AtyxAmYcDvw1qq+p9VXXxApS7QbWbgrwNMKiV9DsMaiVptH0HeBxAkt2TXJBkeZJvJnnK1MRJ3pDkqiQrk3whyVZJDgReDnywjQDvPjnCmuQlSc4ZyH9wkvPb+ouSfCfJd5Ock2TrdVU0yQ+TfKAdY1mSfZNcmOTGJMcPlP+NJF9Jcn2S09td20hyTJLVbYT6tIFy707yV0lW0t0oZmdgaZKlbf/H2/GuTXLylPqc3Oq/erK9kmyd5BNt26okR8zlfCWNFoNaSRpRbVTy+Tx4q8olwFurahFwIvC302T7YlXtV1VPB75Hd6enb7cy/ryNAN84kP5i4BlJHtWeHwWc1W59+h7gBVW1L7AMePssqv1/qmoC+CZwBt3tUg8ATh5Isz/wVrqR492BP0myM3Aa8DxgAtgvyeEt/aOAK6rq6VV1CnArcEhVHdL2n1RVi4G9gecm2XvgWHe0+n+8tRnAe4E7q2qvqtob+Po8zlfSiNh02BWQJD3EI5OsoBuh/R5wURs1PBA4p7uFOgBbTJN3zySnAtsBW9Pdk32tquqBJBcAhyb5PPBS4J3Ac+mCzsva8TanGzWeyWQAvprutqB3AXcluX9gbvCVVXUTQJIzgWfT3Xr2kqq6vW3/DPAc4EvAGuAL6zjmK5O8ke4zbadW71Vt3xfb43LgT9r6C+juaz/ZBj9P8rI5nq+kEWFQK0mj576qmmgXQ11IN6f2DOAXbRR0Xc4ADq+qlUmOAw6exfHOAv4d3fzdZVV1V7rI7qKqOuZh1v3+9vibgfXJ55OfOVPvzz7T/dp/WVVrptuRZDe6Edj9WnB6BrDlNPVZw7o/8+Z6vpJGhNMPJGlEVdW9wAnAO4B7gZuTvAIgnadPk20b4LYkmwHHDmy/q+2bzqXAvsAb6AJcgMuBZyX5g3a8RyV58jxPadL+SXZrc2mPAr4FXEk3dWCHNu3imFav6Qyey6OBe4A7k/w+8EezOP5FDFx8l2R71u/5StoADGolaYRV1dV0X6UfQxekvq5dMHUtcNg0Wd4LXAFcBvzTwPazgD9PcnWS3accYw1wPl1AeH7bdjtwHHBmklV0X8U/5MK0OboK+Bjd1IqbgXOr6jbg3cBSYCWwvKr+YS35lwAXJFlaVSuBq+nO9bN05z2TU4Ht2wVpK+nm567P85W0AaRqpm99JElaGEkOBk6sqpcNuy6SxosjtZIkSeo9R2olSZLUe47USpIkqfcMaiVJktR7BrWSJEnqPYNaSZIk9Z5BrSRJknrv/wPakAeHlsIcswAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vPVrdAYE6QPu",
        "colab_type": "text"
      },
      "source": [
        "### Check distribution of target variable"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yDpIde9k6Z4b",
        "colab_type": "text"
      },
      "source": [
        "We should check the distribution of the target variable in a regression problem using distribution plot. If it is skewed then application of log, exponent or sqrt transform can help in reducing the skewness to get a normal distribution."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6V0wlw28bxay",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 379
        },
        "outputId": "e503acc1-f079-42b8-8a97-8486b9696dd3"
      },
      "source": [
        "fig,ax = plt.subplots(nrows=2, ncols=2,figsize=(20,8))\n",
        "ax[0,0].hist(df['Price'],bins=100,alpha=0.5, label='Price')\n",
        "ax[0,1].hist(df['Robust_Price'],bins=100,alpha=0.5, label='R_Price')\n",
        "ax[1,0].hist(df['MM_Price'],bins=100,alpha=0.5, label='MM_Price')\n",
        "ax[1,1].hist(np.log1p(df[\"Price\"]),bins=100,alpha=0.5, label='Log_Price')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x576 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "riOo4qIK-VmJ",
        "colab_type": "text"
      },
      "source": [
        "## Log Transformation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wz1wkdpVfjsD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X =  df[df.columns.difference(['Name','Price','MM_Price','Location','Year','Fuel_Type','Transmission','Owner_Type','Robust_Price'])]\n",
        "y = np.log1p(df[\"Price\"])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vsoI2fze74Q5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 0, test_size = 0.25)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V0R8boEa-xfy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        },
        "outputId": "43dc7ce5-3b8c-41b2-8dcf-820544e1e251"
      },
      "source": [
        "randomForestModel = RandomForestRegressorModel(X,y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "R Squared(Accuracy) 0.8456793854342743\n",
            "Mean Absolute Error: 0.21269288487577825\n",
            "Mean Squared Error: 0.08522446296190488\n",
            "Root Mean Squared Error: 0.2919322917422889\n",
            "Root Mean Squared Log Error 0.09906300429120905\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9I6sSO20-yB0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 924
        },
        "outputId": "f4909566-8487-48bf-9f1d-7725a2d8f181"
      },
      "source": [
        "plotFeatureImportances(randomForestModel)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                      importance\n",
            "Power                   0.201764\n",
            "MM_Power                0.181749\n",
            "Robust_Power            0.177649\n",
            "MA_Power                0.167243\n",
            "MM_Kilometers_Driven    0.042630\n",
            "Kilometers_Driven       0.039682\n",
            "Robust_KD               0.039505\n",
            "Robust_Mileage          0.032656\n",
            "Mileage                 0.029889\n",
            "MM_Mileage              0.029653\n",
            "MM_Engine               0.017371\n",
            "Robust_Engine           0.016448\n",
            "Engine                  0.015058\n",
            "Robust_Seats            0.003462\n",
            "Seats                   0.002804\n",
            "MM_Seats                0.002438\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wSc4kuV5AyKV",
        "colab_type": "text"
      },
      "source": [
        "The distribution of numerical features can be checked using a histogram plot while the distribution on categorical features can be checked using a bar plot or box plot."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DKicl3_8BLS3",
        "colab_type": "text"
      },
      "source": [
        "In case of categorical features check if any of the column values can be combined together while in case of numerical features check if distribution can be normalized i.e. evenly distributed."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DJvEZba9BL_4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "outputId": "b1084d0c-bdeb-4a92-cc3e-c55a97e4b285"
      },
      "source": [
        "df[\"Year\"].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2014    792\n",
              "2015    738\n",
              "2016    735\n",
              "2013    637\n",
              "2017    584\n",
              "2012    555\n",
              "2011    450\n",
              "2010    330\n",
              "2018    297\n",
              "2009    189\n",
              "2008    159\n",
              "2007    105\n",
              "2019     99\n",
              "2006     66\n",
              "2005     42\n",
              "2004     25\n",
              "2003     14\n",
              "2002     11\n",
              "2000      4\n",
              "2001      4\n",
              "1998      3\n",
              "1999      2\n",
              "Name: Year, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h6GJ1zjJDno1",
        "colab_type": "text"
      },
      "source": [
        "In categorical features and clubb together last less value valued data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8jsuJISSDy1w",
        "colab_type": "text"
      },
      "source": [
        "You can find histograms of numerical features in the jupyter notebook and make necessary transformations if required."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q8-9AcInD6cz",
        "colab_type": "text"
      },
      "source": [
        "## **Model application and hyper-parameter tuning**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "opt0GW-bEeAS",
        "colab_type": "text"
      },
      "source": [
        "Hyperparameter tuning is the process of choosing a set of optimal hyperparameters for a learning algorithm. The two common approaches to do hyperparameter tuning are GridSearchCV and RandomisedSearchCV."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EVS7ihiKEisB",
        "colab_type": "text"
      },
      "source": [
        "Although GridSearchCV is exhaustive, RandomisedSearchCV is helpful to get a range of relevant values quickly."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yMV8ZeSSCydG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Source: https://towardsdatascience.com/hyperparameter-tuning-the-random-forest-in-python-using-scikit-learn-28d2aa77dd74\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "from sklearn.model_selection import ShuffleSplit, train_test_split \n",
        "from sklearn.ensemble import RandomForestRegressor \n",
        "\n",
        "# Number of trees in random forest\n",
        "n_estimators = [644]\n",
        "\n",
        "# Number of features to consider at every split\n",
        "max_features = ['auto']\n",
        "\n",
        "# Maximum number of levels in tree\n",
        "# max_depth = [int(x) for x in np.linspace(10, 110, num = 11)]\n",
        "# max_depth.append(None)\n",
        "\n",
        "# Minimum number of samples required to split a node\n",
        "min_samples_split = [2, 5, 10]\n",
        "\n",
        "# Minimum number of samples required at each leaf node\n",
        "min_samples_leaf = [1, 2, 4]\n",
        "\n",
        "# Method of selecting samples for training each tree\n",
        "bootstrap = [True]\n",
        "\n",
        "# Create the random grid\n",
        "random_grid = {'max_features': max_features,\n",
        "               'bootstrap': bootstrap,\n",
        "               'n_estimators': n_estimators\n",
        "              }\n",
        "rf = RandomForestRegressor()\n",
        "# Random search of parameters, using 3 fold cross validation, \n",
        "# search across 100 different combinations, and use all available cores\n",
        "# Fit the random search model\n",
        "\n",
        "def RandomizedSearchCVRandomForestRegressor(X,y):\n",
        "    X_train, X_test, y_train, y_test = train_test_split(X, y,random_state=42, test_size=0.3)\n",
        "    estimator = RandomForestRegressor()\n",
        "    cv = ShuffleSplit(n_splits=5, random_state=42, test_size=0.3)\n",
        "    n_jobs=4\n",
        "    rf_random = RandomizedSearchCV(estimator = rf, param_distributions = random_grid, cv = 3, verbose=2, random_state=42, n_jobs = 8)\n",
        "    rf_random.fit(X_train, y_train)\n",
        "    print(rf_random.best_params_)\n",
        "    y_pred = rf_random.best_estimator_.predict(X_test)\n",
        "    print(print_accuracy_report(y_test, y_pred, X_test, rf_random.best_estimator_))\n",
        "    return rf_random.best_estimator_"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dWwLWt2MFGp9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 250
        },
        "outputId": "382a9882-703d-444c-9603-8aa38d48d5ab"
      },
      "source": [
        "randomForestRegressorModel2 = RandomizedSearchCVRandomForestRegressor(X,y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 3 folds for each of 1 candidates, totalling 3 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:281: UserWarning: The total space of parameters 1 is smaller than n_iter=10. Running 1 iterations. For exhaustive searches, use GridSearchCV.\n",
            "  % (grid_size, self.n_iter, grid_size), UserWarning)\n",
            "[Parallel(n_jobs=8)]: Using backend LokyBackend with 8 concurrent workers.\n",
            "[Parallel(n_jobs=8)]: Done   3 out of   3 | elapsed:   29.7s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "{'n_estimators': 644, 'max_features': 'auto', 'bootstrap': True}\n",
            "R Squared(Accuracy) 0.8480539968223175\n",
            "Mean Absolute Error: 0.21087551354126183\n",
            "Mean Squared Error: 0.08391306991918851\n",
            "Root Mean Squared Error: 0.2896775274666444\n",
            "Root Mean Squared Log Error 0.09820944404361752\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EtzFg8L-QADE",
        "colab_type": "text"
      },
      "source": [
        "A similar approach can be applied for ExtraTrees and XGBoost Regressor Models."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O6u4Nnj7QCF6",
        "colab_type": "text"
      },
      "source": [
        "# **Stacking**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BEA5BfvEnMbG",
        "colab_type": "text"
      },
      "source": [
        "## Simplest Stacking approach : Averaging base models"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JQBecHVAkI9z",
        "colab_type": "text"
      },
      "source": [
        "We begin with this simple approach of averaging base models. We build a new class to extend scikit-learn with our model and also to laverage encapsulation and code reuse "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3pbjO38dkMxh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.base import BaseEstimator, TransformerMixin, RegressorMixin, clone\n",
        "class AveragingModels(BaseEstimator, RegressorMixin, TransformerMixin):\n",
        "    def __init__(self, models):\n",
        "        self.models = models\n",
        "        \n",
        "    # we define clones of the original models to fit the data in\n",
        "    def fit(self, X, y):\n",
        "        self.models_ = [clone(x) for x in self.models]\n",
        "        \n",
        "        # Train cloned base models\n",
        "        for model in self.models_:\n",
        "            model.fit(X, y)\n",
        "\n",
        "        return self\n",
        "    \n",
        "    #Now we do the predictions for cloned models and average them\n",
        "    def predict(self, X):\n",
        "        predictions = np.column_stack([\n",
        "            model.predict(X) for model in self.models_\n",
        "        ])\n",
        "        return np.mean(predictions, axis=1) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4WnquQFzkdZi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "averaged_models = AveragingModels(models = (grid_search_gbr.best_estimator_,xgb_grid.best_estimator_, grid_search.best_estimator_))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pTLtTY-pk2HA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "averaged_models.fit(X,Y)\n",
        "from sklearn.metrics import r2_score\n",
        "print(\"R2-Score\")\n",
        "print(round(r2_score(Y_Val, averaged_models.predict(X_Val)),4))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y698SvxJoPXI",
        "colab_type": "text"
      },
      "source": [
        "## Less simple Stacking : Adding a Meta-model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "223aH1ZroXwY",
        "colab_type": "text"
      },
      "source": [
        "In this approach, we add a meta-model on averaged base models and use the out-of-folds predictions of these base models to train our meta-model.\n",
        "\n",
        "The procedure, for the training part, may be described as follows:\n",
        "\n",
        "* Split the total training set into two disjoint sets (here train and .holdout )\n",
        "\n",
        "* Train several base models on the first part (train)\n",
        "\n",
        "* Test these base models on the second part (holdout)\n",
        "\n",
        "* Use the predictions from 3) (called out-of-folds predictions) as the inputs, and the correct responses (target variable) as the outputs to train a higher level learner called meta-model.\n",
        "\n",
        "The first three steps are done iteratively . If we take for example a 5-fold stacking , we first split the training data into 5 folds. Then we will do 5 iterations. In each iteration, we train every base model on 4 folds and predict on the remaining fold (holdout fold).\n",
        "\n",
        "So, we will be sure, after 5 iterations , that the entire data is used to get out-of-folds predictions that we will then use as new feature to train our meta-model in the step 4.\n",
        "\n",
        "For the prediction part , We average the predictions of all base models on the test data and used them as meta-features on which, the final prediction is done with the meta-model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HvwPH-jxFIja",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.base import BaseEstimator, TransformerMixin, RegressorMixin, clone\n",
        "class StackingAveragedModels(BaseEstimator, RegressorMixin, TransformerMixin):\n",
        "    def __init__(self, base_models, meta_model, n_folds=5):\n",
        "        self.base_models = base_models\n",
        "        self.meta_model = meta_model\n",
        "        self.n_folds = n_folds\n",
        "   \n",
        "    # We again fit the data on clones of the original models\n",
        "    def fit(self, X, y):\n",
        "        self.base_models_ = [list() for x in self.base_models]\n",
        "        self.meta_model_ = clone(self.meta_model)\n",
        "        kfold = KFold(n_splits=self.n_folds, shuffle=True, random_state=156)\n",
        "        \n",
        "        # Train cloned base models then create out-of-fold predictions\n",
        "        # that are needed to train the cloned meta-model\n",
        "        out_of_fold_predictions = np.zeros((X.shape[0], len(self.base_models)))\n",
        "        for i, model in enumerate(self.base_models):\n",
        "            for train_index, holdout_index in kfold.split(X, y):\n",
        "                instance = clone(model)\n",
        "                self.base_models_[i].append(instance)\n",
        "                instance.fit(X[train_index], y[train_index])\n",
        "                y_pred = instance.predict(X[holdout_index])\n",
        "                out_of_fold_predictions[holdout_index, i] = y_pred\n",
        "                \n",
        "        # Now train the cloned  meta-model using the out-of-fold predictions as new feature\n",
        "        self.meta_model_.fit(out_of_fold_predictions, y)\n",
        "        return self\n",
        "   \n",
        "    #Do the predictions of all base models on the test data and use the averaged predictions as \n",
        "    #meta-features for the final prediction which is done by the meta-model\n",
        "    def predict(self, X):\n",
        "        meta_features = np.column_stack([\n",
        "            np.column_stack([model.predict(X) for model in base_models]).mean(axis=1)\n",
        "            for base_models in self.base_models_ ])\n",
        "        return self.meta_model_.predict(meta_features)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8zJF4naDqgyF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.ensemble import RandomForestRegressor,  GradientBoostingRegressor, ExtraTreesRegressor\n",
        "from sklearn.linear_model import ElasticNet, Lasso,  BayesianRidge, LassoLarsIC\n",
        "from sklearn.kernel_ridge import KernelRidge\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.model_selection import KFold\n",
        "from sklearn.preprocessing import RobustScaler"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rig1X0lrtMKr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Lasso Regression\n",
        "lasso = make_pipeline(RobustScaler(), Lasso(alpha =0.0005, random_state=1))\n",
        "#Elastic Net Regression\n",
        "ENet = make_pipeline(RobustScaler(), ElasticNet(alpha=0.0005, l1_ratio=.9, random_state=3))\n",
        "#Kernel Ridge Regression\n",
        "KRR = KernelRidge(alpha=0.6, kernel='polynomial', degree=2, coef0=2.5)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SJZ5Pa5TRKDI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "stacked_averaged_models = StackingAveragedModels(base_models = (grid_search_gbr.best_estimator_,xgb_grid.best_estimator_, grid_search.best_estimator_),\n",
        "                                                 meta_model = lasso)\n",
        "\n",
        "stacked_averaged_models.fit(X.values,Y.values)\n",
        "print(\"R2-Score\")\n",
        "print(round(r2_score(Y_Val.values, stacked_averaged_models.predict(X_Val.values)),4))"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}