From ec313d904206c0e275acdd840757b6dbec91bf22 Mon Sep 17 00:00:00 2001
From: Diwakar Gupta <39624018+Diwakar-Gupta@users.noreply.github.com>
Date: Sun, 15 May 2022 00:07:31 +0530
Subject: [PATCH] knn solution

---
 22-05-12-KNN/Solution.ipynb | 1365 +++++++++++++++++++++++++++++++++++
 1 file changed, 1365 insertions(+)
 create mode 100644 22-05-12-KNN/Solution.ipynb

diff --git a/22-05-12-KNN/Solution.ipynb b/22-05-12-KNN/Solution.ipynb
new file mode 100644
index 0000000..e8c122b
--- /dev/null
+++ b/22-05-12-KNN/Solution.ipynb
@@ -0,0 +1,1365 @@
+{
+  "metadata": {
+    "kernelspec": {
+      "language": "python",
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "pygments_lexer": "ipython3",
+      "nbconvert_exporter": "python",
+      "version": "3.6.4",
+      "file_extension": ".py",
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "name": "python",
+      "mimetype": "text/x-python"
+    },
+    "colab": {
+      "name": "KNN solution.ipynb",
+      "provenance": [],
+      "collapsed_sections": [],
+      "include_colab_link": true
+    }
+  },
+  "nbformat_minor": 0,
+  "nbformat": 4,
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/Diwakar-Gupta/Data-Science-APRIL/blob/main/22-05-12-KNN/Solution.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "\n",
+        "\n",
+        "**KNN is a supervised machine learning algorithm.It can be used to classification of data set.Here The dataset contains the Age,salary and the decision to purchase a particular Car.We will be use kNN to predict if a particular person will buy a car.**"
+      ],
+      "metadata": {
+        "_uuid": "2640c66758a4a570cb5c2d4e9e9ebbbe647d149f",
+        "id": "p37DkS16WzNf"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "import numpy as np \n",
+        "import pandas as pd \n",
+        "\n"
+      ],
+      "metadata": {
+        "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
+        "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
+        "_kg_hide-input": true,
+        "trusted": true,
+        "id": "rWaGOkYaWzNs"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**1.Importing the Python Modules**"
+      ],
+      "metadata": {
+        "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
+        "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
+        "_kg_hide-input": true,
+        "id": "ULc4kdfBWzNv"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt \n",
+        "import pandas as pd \n",
+        "import seaborn as sns\n",
+        "import warnings\n",
+        "warnings.filterwarnings('ignore') \n",
+        "plt.style.use('fivethirtyeight')"
+      ],
+      "metadata": {
+        "_kg_hide-input": true,
+        "_uuid": "8b6c22aee4d9a0869a4cb78951db5c57b9fbfeb3",
+        "trusted": true,
+        "id": "JnrBKw0eWzNw"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**2.Exploring the Data**"
+      ],
+      "metadata": {
+        "id": "qlDVYqwrWzNw"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "dataset=pd.read_csv('/content/Social_Network_Ads.csv')\n",
+        "dataset.head()"
+      ],
+      "metadata": {
+        "_uuid": "8c5edff5031cb90fca60650af4c905d049c8bdc8",
+        "_kg_hide-input": true,
+        "trusted": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "AJpS4eHMWzNx",
+        "outputId": "0e8e1503-620c-42ca-f4df-eaab9718605c"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "\n",
+              "  <div id=\"df-262e59ce-74ff-4a28-8ac9-ec82153cf1a6\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <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>User ID</th>\n",
+              "      <th>Gender</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>EstimatedSalary</th>\n",
+              "      <th>Purchased</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>15624510</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>19</td>\n",
+              "      <td>19000</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>15810944</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>35</td>\n",
+              "      <td>20000</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>15668575</td>\n",
+              "      <td>Female</td>\n",
+              "      <td>26</td>\n",
+              "      <td>43000</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>15603246</td>\n",
+              "      <td>Female</td>\n",
+              "      <td>27</td>\n",
+              "      <td>57000</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>15804002</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>19</td>\n",
+              "      <td>76000</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-262e59ce-74ff-4a28-8ac9-ec82153cf1a6')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-262e59ce-74ff-4a28-8ac9-ec82153cf1a6 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-262e59ce-74ff-4a28-8ac9-ec82153cf1a6');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ],
+            "text/plain": [
+              "    User ID  Gender  Age  EstimatedSalary  Purchased\n",
+              "0  15624510    Male   19            19000          0\n",
+              "1  15810944    Male   35            20000          0\n",
+              "2  15668575  Female   26            43000          0\n",
+              "3  15603246  Female   27            57000          0\n",
+              "4  15804002    Male   19            76000          0"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "So we have the User ID, Gender,Age,Salary and the data if Purchase made by a used."
+      ],
+      "metadata": {
+        "_uuid": "f8548683194855ae2a4804da8bff1832718acec0",
+        "id": "OTvaGywLWzNx"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Summary of Dataset**"
+      ],
+      "metadata": {
+        "id": "BDsxZJcYWzNy"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print('Rows     :',dataset.shape[0])\n",
+        "print('Columns  :',dataset.shape[1])\n",
+        "print('\\nFeatures :\\n     :',dataset.columns.tolist())\n",
+        "print('\\nMissing values    :',dataset.isnull().values.sum())\n",
+        "print('\\nUnique values :  \\n',dataset.nunique())"
+      ],
+      "metadata": {
+        "_kg_hide-input": true,
+        "trusted": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7VOw_zT_WzNy",
+        "outputId": "f7d7c93c-2828-4616-b22c-a3638b31e541"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Rows     : 400\n",
+            "Columns  : 5\n",
+            "\n",
+            "Features :\n",
+            "     : ['User ID', 'Gender', 'Age', 'EstimatedSalary', 'Purchased']\n",
+            "\n",
+            "Missing values    : 0\n",
+            "\n",
+            "Unique values :  \n",
+            " User ID            400\n",
+            "Gender               2\n",
+            "Age                 43\n",
+            "EstimatedSalary    117\n",
+            "Purchased            2\n",
+            "dtype: int64\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "dataset.describe().T"
+      ],
+      "metadata": {
+        "trusted": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 175
+        },
+        "id": "BT2WQtjqWzNz",
+        "outputId": "d5587932-4e42-4319-a6c4-54723c008c27"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "\n",
+              "  <div id=\"df-f1561399-76e3-45f2-bfa5-0d41d2d20364\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <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>count</th>\n",
+              "      <th>mean</th>\n",
+              "      <th>std</th>\n",
+              "      <th>min</th>\n",
+              "      <th>25%</th>\n",
+              "      <th>50%</th>\n",
+              "      <th>75%</th>\n",
+              "      <th>max</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>User ID</th>\n",
+              "      <td>400.0</td>\n",
+              "      <td>1.569154e+07</td>\n",
+              "      <td>71658.321581</td>\n",
+              "      <td>15566689.0</td>\n",
+              "      <td>15626763.75</td>\n",
+              "      <td>15694341.5</td>\n",
+              "      <td>15750363.0</td>\n",
+              "      <td>15815236.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Age</th>\n",
+              "      <td>400.0</td>\n",
+              "      <td>3.765500e+01</td>\n",
+              "      <td>10.482877</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>29.75</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>46.0</td>\n",
+              "      <td>60.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>EstimatedSalary</th>\n",
+              "      <td>400.0</td>\n",
+              "      <td>6.974250e+04</td>\n",
+              "      <td>34096.960282</td>\n",
+              "      <td>15000.0</td>\n",
+              "      <td>43000.00</td>\n",
+              "      <td>70000.0</td>\n",
+              "      <td>88000.0</td>\n",
+              "      <td>150000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Purchased</th>\n",
+              "      <td>400.0</td>\n",
+              "      <td>3.575000e-01</td>\n",
+              "      <td>0.479864</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f1561399-76e3-45f2-bfa5-0d41d2d20364')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-f1561399-76e3-45f2-bfa5-0d41d2d20364 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-f1561399-76e3-45f2-bfa5-0d41d2d20364');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ],
+            "text/plain": [
+              "                 count          mean  ...         75%         max\n",
+              "User ID          400.0  1.569154e+07  ...  15750363.0  15815236.0\n",
+              "Age              400.0  3.765500e+01  ...        46.0        60.0\n",
+              "EstimatedSalary  400.0  6.974250e+04  ...     88000.0    150000.0\n",
+              "Purchased        400.0  3.575000e-01  ...         1.0         1.0\n",
+              "\n",
+              "[4 rows x 8 columns]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We can see that mean age is arounf 37 \n",
+        "\n",
+        "Mean estimated salary is 69742 $"
+      ],
+      "metadata": {
+        "id": "0sSZuDuZWzN0"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Gender**"
+      ],
+      "metadata": {
+        "id": "3Qei8mhSWzN0"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "f,ax=plt.subplots(1,2,figsize=(18,8))\n",
+        "dataset['Gender'].value_counts().plot.pie(explode=[0,0.05],autopct='%1.1f%%',ax=ax[0],shadow=True)\n",
+        "ax[0].set_title('Purchase by Gender')\n",
+        "ax[0].set_ylabel('Count')\n",
+        "sns.countplot('Gender',data=dataset,ax=ax[1],order=dataset['Gender'].value_counts().index)\n",
+        "ax[1].set_title('Purchase by Gender')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "_kg_hide-input": true,
+        "trusted": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 558
+        },
+        "id": "KkrruaNlWzN1",
+        "outputId": "1d48b37b-458a-4a3e-e0de-e6215eceadff"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 1296x576 with 2 Axes>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Data is almost even between Male and Female with sligh inclination towards female"
+      ],
+      "metadata": {
+        "id": "DV97rDcAWzN1"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Purchase Distribution**"
+      ],
+      "metadata": {
+        "id": "pU2ACbmiWzN2"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "f,ax=plt.subplots(1,2,figsize=(18,8))\n",
+        "dataset['Purchased'].value_counts().plot.pie(explode=[0,0.05],autopct='%1.1f%%',ax=ax[0],shadow=True)\n",
+        "ax[0].set_title('Purchase Distribution')\n",
+        "ax[0].set_ylabel('Count')\n",
+        "sns.countplot('Purchased',data=dataset,ax=ax[1],order=dataset['Purchased'].value_counts().index)\n",
+        "ax[1].set_title('Purchase Distribution')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "_kg_hide-input": true,
+        "trusted": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 558
+        },
+        "id": "tm_a9tXuWzN2",
+        "outputId": "173cfa84-df1d-4970-d38d-bef33e2f91c4"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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7FyXpeKbD7FIAr7PznE13LUzSzCPZZpcCAAAAoAoR6KDSWPOdGrYySaOi01VgWMwuB/BaWXZDL2206tl1qSyYDAAAAFQTBDqoFNuS8nX7/NNanmAzuxSg2ph3PFddf0rStiS2NwcAAAC8HYEOKpRhGJq4O139liTrbD6jcoCqdirLoXuXntMHuzLkNFgwGQAAAPBWBDqoMOfyHLpvWaLe2ZElhwhzALPYDem9nZm6f0WK0vKZggUAAAB4IwIdVIgtifnqNO+01iWy8DHgLtadyVfPRUk6aGXqIwAAAOBtCHRQbjMOZWng0iSl2Ph2AtzNiUyH7l6crOVxuWaXAgAAAKAC8Rc4yswwDL2+IUl/3pwuO99KgNvKtBl6dHWqJsVkml0KAAAAgArCX+Eokxy7U/1/OKkvjzKVA/AETkP6+y8Z+tO6VOXZWSwZAAAA8HQEOii1uMwC3f7dCW1O9ze7FAClNPd4rvotS9aZHNa7AgAAADwZgQ5KJfpMtrrMT9Ape5DZpQAoo53nbOqxMEm/JBeYXQoAAACAMiLQwRWbuS9FA5alKMMIMLsUAOV0Ntep/suSNfdYjtmlAAAAACgDP7MLgGcYuz5Bnx01JIuv2aUAqCB5Dum59WlKzXfq+Ta1zC4HAAAAQCkQ6OCSnIahxxad0LKUQMliMbscABXMkDQqOl3WAqdGta9jdjkAAAAArhBTrnBRdodT/eccLgxzAHi1cTszNSbaKsNgBywAAADAExDooERZeQXqMeugtuQwDQOoLj7fn60/b7LK4STUAQAAANwdgQ6KSc7IUffvDmuPva7ZpQCoYjOP5OjptakqcBDqAAAAAO6MQAdFJKRmqte8EzqqemaXAsAki07m6eFVKcq2Oc0uBQAAAMBFEOjA5USSVXf/EKdTlhCzSwFgsrWn83XfinOy5hPqAAAAAO6IQAeSpAMJKbpn0Vmd9mGaFYBC25NtundZspJzHWaXAgAAAOB3CHSgHbGJGrD8nJJ8aptdCgA3sz/NrvtXpjBSBwAAAHAzBDrV3M/HE/TA6nSl+LCbFYCS7U216aH/nlMWa+oAAAAAboNApxr75UicHonKkNUn2OxSALi57ck2Pbo6Vfnsfr8hNiAAACAASURBVAUAAAC4BQKdaurnQyf05Lp0JfvUMbsUAB5i/Zl8PRWVKruTUAcAAAAwG4FONbR9/1H9ab1VCb5sTQ6gdJbH5emFDWlyGoQ6AAAAgJkIdKqZvUdP6uWNqTrhF2Z2KQA81LzjuRqx2Wp2GQAAAEC1RqBTjZxIOKO/rEnQAf+rzC4FgIf75nCO3tyWbnYZAAAAQLVFoFNNnElO0UtLDmunf3OzSwHgJf61L0vjd2WYXQYAAABQLRHoVANp6Zl6+cdd2uzf2uxSAHiZcTsz9fWhbLPLAAAAAKodAh0vl5WTq/+Zu0WrfK8zuxQAXmrkVqs2nMk3uwwAAACgWiHQ8WJ5+QV66/soLfG5XobFYnY5ALyUzSk9GZWiExl2s0sBAAAAqg0CHS9ls9s1cd5KfW9cJ7vF1+xyAHi5tHxDw1alKL3AaXYpAAAAQLVAoOOFHA6HvvxhpWbkXa0sS5DZ5QCoJg6l2/XHtalyOA2zSwEAAAC8HoGOlzEMQ98tW6vvrA112ifE7HIAVDOrEvL15na2MwcAAAAqG4GOl1kdvVNzEwzt9bvK7FIAVFOf78/Wfw6z8xUAAABQmQh0vMiB4yc1a2ecNvizoxUAc726xapNZ9n5Ct5r4sSJ6tGjh5o1a6ZWrVpp6NCh2r9/f5E2hmFo3Lhxuv7669WoUSP1799fBw4cKNLGarXqueeeU0REhCIiIvTcc8/JarVW5VMBAAAeikDHS6RY0/XvFdFaHthOTgtfVgDmsjmlJ9ekKjaTna/gnTZu3Kg//vGPWrFihRYuXCg/Pz/dd999SktLc7X5+OOPNXnyZI0fP15r1qxRaGio7r//fmVmZrraPPvss4qJidG8efM0b948xcTE6PnnnzfjKQEAAA9jsVqtrF7p4fILbJow8yd9Y9yoJEtts8sBAJc29fy0ZkCYgvwsZpcCVKqsrCxFRERo5syZ6tevnwzD0PXXX68//elPeu211yRJubm5uuaaa/SPf/xDzzzzjA4dOqROnTpp+fLluv322yVJW7ZsUb9+/bR9+3Zdc801pjyXtp9tM+VxAU+178WOZpcAoJpiKIeHMwxD3y76r35ytCTMAeB29qfZ9QaLJKMayMrKktPpVEhI4YYEJ0+eVGJionr27OlqU6NGDd1xxx2Kjo6WJG3btk21atVSp06dXG1uv/12BQcHu9oAAABcDIGOh1uyIVoLzwXpsG+42aUAQIm+OpithbG5ZpcBVKrRo0crMjJSHTsWvlOfmJgoSQoNDS3SLjQ0VElJSZKkpKQkNWjQQBbLbyPYLBaLGjZs6GoDAABwMX5mF4Cy23ngqBbvTdCmIIZ5AnBvL29K080N/dWsFr924H3Gjh2rrVu3avny5fL19a30xzty5EilPwaAK8f/SQCV6VJTsOlZe6gzySmaG7VFK4Nul4NFkAG4OWuBoefWp2lx34by9WE9HXiPMWPGaMGCBVq0aJFatGjhOh4eXjhyNjk5Wc2aNXMdT05OVlhYmCQpLCxMKSkpMgzDNUrHMAydO3fO1aYklb62zn9ZQwcoDbPWuwIAkgAPlJufr+k/rdSGwBuUaqlpdjkAcEW2JBbo/V2Zl28IeIhRo0Zp/vz5Wrhwoa699toi55o3b67w8HBFRUW5juXl5WnLli2uNXM6duyorKwsbdv2W4Cybds2ZWdnF1lXBwAAoCSM0PEwhmFo1tI12mvU1x7fxmaXAwCl8n8xmerWOFBdGweaXQpQLq+99pq+//57zZgxQyEhIa41c4KDg1WrVi1ZLBYNHz5cEydO1DXXXKPWrVtrwoQJCg4O1oMPPihJuu6669S7d2+NGDFCH330kSRpxIgRuueee3jHHwAAXBbblnuYDTv2aMHWvZoV1Fn5Fn+zywGAUmtS00cbB4epflDlrzUCVJbzu1n93qhRozRmzBhJhW/CvP/++/r6669ltVrVoUMHTZgwQW3atHG1t1qtev3117Vs2TJJUr9+/fTBBx9c9PpVgW3LgdJh23IAZiHQ8SBnz6Vq8vcLtbDGrTppqWd2OQBQZn2bBWl27wZmlwGgBAQ6QOkQ6AAwC2voeAib3a5vF63S3qCrCXMqQ3qyNHOM9Oad0sibpfcHSke3l9x2ztvSiLZS1PRLXzPmv9Lnfyq85ujbpEnDpL1rirY5tFl6715pdEdpxmjJXvDbufxs6Z/9pDPsnADvszwuT1P2Z5ldBgAAAOCxCHQ8xA+rNupUga82WK42uxTvk5shffJ44cd/+kwavUh64A2pdv3ibXetkE7tkepefPcRl6M/S9d0LLzmq/OkG7pJ016Rjv1SeN7plL59XbrjYemVWVLcPmnL3N8+f+kn0s39pMasowDv9PdfMhSbaTe7DAAAAMAjsSiyB9h//KR2Ho3VipqdZbew5kSFWzNNqtNQemzcb8caNC3eLvW09OP70vCp0pQXLn/dB8YUvd/3RenAOmnvaqlVByk7rfDW5RHJP1C6sbuUeLyw7cmYwtE7r80v89MC3F2O3dCIzVb9cE9Ds0sBAAAAPA4jdNxcTl6+FqzaqP01WinJUsvscrzTntVS83bSN69Kf+0qffiAtGGmZFywvJTDLn07Urr7eSm8VdkfKy9HqlGn8ONa9aU6odKhTVJBrnR8h9TkusLHmvO29NBbkl9AuZ4a4O6iTudr9tEcs8sAAAAAPA6BjhszDEOzl0XJagRqs6W52eV4r5R4adPswlE5z38pdXtCWjxJ2jjrtzbLJ0vBIVKXYWV/nI2zpPSz0q2DCu9bLNJT/yet/EIaP1i66nqp0/2Fa/NE3FgY+Hz6ZOE6Ossnl+85Am5s7LZ0nctzmF0GAAAA4FGYcuXGovccVOzps1pT4xbZmGpVeQyn1OxGacCIwvtNb5DOnZQ2zpa6PiYd3SZt/7F80592r5QW/p/05ASpfpPfjrfsIP3vnN/uJ5+UtsyTXpsrffas1GWo1L6vNGloYY1t7yp7DYCbSs13amx0ur68q4R1qwAAAACUiBE6biozO1crNv2sk0FX6biFrX0rVZ3Q4tOowlpK1jOFHx/dLmUkS291l15tV3hLOy0tmii93fPy19+1onAHrUffk27scem2c/8uDfxfyeIjxe8rXBQ5KFhq2106Gl2WZwd4hDnHc7UqPs/sMgAAAACPwQgdNzVn5Vo5fP202lKO9VpwZa6+WUo6UfRYcqxUr3Hhx12GSTf1KXp+ynPSzfdKnR+89LV3Lpe+Gys98p7U/p5Lt43+QQqoUdguN6PwmPPXHYDsNslyRc8G8Fgjtli19b4wBfvzXgMAAABwOfSa3dC+Y7GKTUjUJr9WyrIEml2O97vrycJdpf47pXDK064VhYsi3/lI4fnaDQq3Dr/w5uNXuDNW2AXbyM8cU3g7b8dSacYoqf+Iwl2tMpILb9nW4jVkpkgrPpMe/Gvh/Rp1pEatpaivpfgDUsxK6epbKu0lANxBXJZD/9yZYXYZAAAAgEdghI6bsdntWrh2qzJqNNAONbn8J6D8IiKlP3wiLf2ocIHieo2lfn8p3E68NNLOFL2/eU7hCJsf3y+8ndfqNunPXxdt+8M4qfvTUkij3449+p703RvShlnSbYOKjxICvNCU/dl68OqauiWUHd4AAACAS7FYrVbj8s1QVX5cs0k7Dh/TnICOOmupbXY5AFDlbqzvr3UDQ+XrwzxDwAxtP9tmdgmAR9n3YkezSwBQTTHlyo2cPZeqXw4c0T7/5oQ5AKqtvak2fXM4x+wyAAAAALdGoOMmDMPQnJXr5AispQ2WFmaXAwCmem9nhtILnGaXAQAAALgtAh03sXn3fp1Ly9AmnxYqsLC0EYDq7VyeUxN2Z5pdBgAAAOC2CHTcQHZunlZv3aGcoLqKUaPLfwIAVANT9mfpRIbd7DIAAAAAt0Sg4wbmr9ogi8Wi9ZYWMiwsAgoAklTglP72c7rZZQAAAABuiUDHZLGnz+rwyXgl+YXosCXU7HIAwK0sOpmnbUn5ZpcBAAAAuB0CHRMZhqGFUVtUMyhQ6yxXm10OALilt37OMLsEAAAAwO0Q6Jho79FYJael64Slvk5ZQswuBwDc0pbEAi07lWt2GQAAAIBbIdAxidPp1IpNPysoKIDROQBwGe/8kiGnYZhdBgAAAOA2CHRMEr3noDKys3XQEqYkSy2zywEAt3bAatesozlmlwEAAAC4DQIdE9jtDq3dvlsBQUFab2lhdjkA4BE+3JUph5NROgAAAIBEoGOKqJ93Ka+gQLvUWOmWGmaXAwAe4WSWQwtOsJYOAAAAIBHoVLm8/AJt3X1A/oGB2mppZnY5AOBRJu3JlMFaOgAAAACBTlVbvmm7HIZT+xSmLEug2eUAgEfZn2bXivg8s8sAAAAATEegU4Uys3O189AxBfj7a5ulqdnlAIBHmhSTZXYJAAAAgOkIdKrQ4nVb5efjo2OqrxRLsNnlAIBHik4q0Kaz+WaXAQAAAJiKQKeKZOXk6mDsKfn5+SqatXMAoFwmxWSaXQIAAABgKgKdKvLfrTvk6+uj06qteEtds8sBAI+2KiFfu1MKzC4DAAAAMA2BThUosNm098gJ+fv5KZq1cwCgQnzEWjoAAACoxgh0qsCGX/bI4XQqTUE6ooZmlwMAXuGnk7k6lm43uwwAAADAFAQ6lczpdGr7/sMKDPDXdktTGRaL2SUBgFdwGtIne1lLBwAAANUTgU4l+3n/EWXn5CpH/tqjcLPLAQCvMvd4rjIKnGaXAQAAAFQ5Ap1KZBiGNu3Yq5o1grRDjWW3+JpdEgB4lRy7obnHc8wuAwAAAKhyBDqV6MipBKWkZ8iQtNvS2OxyAMArfX2IQAcAAADVD4FOJVoVvUM1awTquOopyxJodjkA4JX2pNq0I5ktzAEAAFC9EOhUkrPnUpWQmCKLxaIYSyOzywEAr/b14WyzSwAAAACqFIFOJVmx+WfVDApQjvx0TA3MLgcAvNqC47nKsrE4MgAAAKoPAp1KkF9gU+zpRPn4+GifwuWw8DIDQGXKshuadzzX7DIAAACAKkPSUAm27Tkop7PwnWKmWwFA1fj6ENOuAAAAUH0Q6FSCHQePKigwQGdUS+cswWaXAwDVwq4Um3ansDgyAAAAqgcCnQqWnGpVUqpVEqNzAKCqfcMW5gAAAKgmCHQq2Nqfd6tGoL9s8tEBhZldDgBUK/OO5yjXbphdBgAAAFDpCHQqkMPh0JFTp+Xr66vDaqh8i5/ZJQFAtZJhM/Tf+DyzywAAAAAqHYFOBdp3/KRy8wr/kNhjCTe5GgConhadZLcrAAAAeD8CnQq0Zdd+1QgKVI78dUohZpcDANXSirg85TuYdgUAAADvRqBTQTKzc5WQdE4Wi0VHVV+GxWJ2SQBQLWXYDEWdZtoVAAAAvBuBTgXZuHOPfH0LX84jloYmVwMA1dtPsQQ6AAAA8G4EOhXkwPFTCvD3V4F8FMt0KwAw1bJTubI5mXYFAAAA70WgUwFS0zOUkp4pSTqherJbfE2uCACqN2uBofVn8s0uAwAAAKg0BDoVIHrPQQX4F25RznQrAHAPC2PZ7QoAAADei0CnAhw9dVoB/n5ySjqm+maXAwCQtORUnhxMuwIAAICXItApp6ycXCWnWSVJcQpRnsXf5IoAAJJ0Ls+pTYkFZpcBAAAAVAoCnXLaeeCoLL9uUX7E0sDkagAAF1rEtCsAAAB4KQKdctp//KSCAgMkSUdEoAMA7mR1AtuXAwAAwDsR6JRDfoFNZ86lSpLOqpYyLEEmVwQAuNDxTIfis+xmlwEAAABUOAKdcth3LFZ2h0OSFKt6JlcDACgJ25cDAADAGxHolMPOg0dVMyhQknTKUtfkagAAJSHQAQAAgDci0Ckjh8Oh00kpslgsckpKUB2zSwIAlGDjWXa6AgAAgPch0Cmj4wlnlZdf+EdComqpwOJnckUAgJLEZzt0LJ11dAAAAOBdCHTKaPehY6pZo3C6VZyYbgUA7mwd064AAADgZQh0yujMuVT5+BS+fHGsnwMAbo11dAAAAOBtCHTKwGa3K8WaIUkyJMUzQgcA3NrGs/kyDMPsMgAAAIAKQ6BTBqfOJMlmK1yPIVnByrP4m1wRAOBSzuU5tS+NdXQAAADgPQh0ymD34WOqwfo5AOBRmHYFAAAAb0KgUwZnklPl++v6OadYPwcAPEJ0EoEOAAAAvAeBTinZ7Q6d+3X9HIn1cwDAU8Sk2MwuAQAAAKgwBDqlFJ+YpPyCwj8KUhWkHEuAyRUBAK5EbKZDGQVOs8sAAAAAKgSBTinFHDmhGkGFIU6SaplcDQDgShmS9qQySgcAAADegUCnlOITz8nP11eSlGQJNrkaAEBpMO0KAAAA3oJApxQcDofOWdNd98+JQAcAPEkMI3QAAADgJQh0SuGcNUN5+b/9MZBEoAMAHiUmpcDsEgAAAIAKQaBTCsfjz8jPr/Aly5ev0hVkckUAgNI4ZLUr32GYXQYAAABQbgQ6pXDydKKCAgoXRE5WsGSxmFwRAKA07Ia0P41pVwAAAPB8BDqlkJqRKcuvIU4y060AwCOxMDIAAAC8AYFOKaRnZrs+TmaHKwDwSCyMDAAAAG9AoHOFsnPzlJOf77rPCB0A8Ex7GKGDCrJp0yYNGzZMN9xwg0JCQjRz5swi54cPH66QkJAit969exdpk5+fr5EjR6ply5Zq0qSJhg0bpoSEhKp8GgAAwEMR6Fyhs+dSZLc7XPcJdADAMx3PtJtdArxEdna22rRpo/fff181atQosU337t116NAh123u3LlFzo8ZM0aLFi3SV199paVLlyozM1NDhw6Vw+Eo8XoAAADn+ZldgKc4cuq0agQVLoicoUDlW3jpAMATnctzKtvmVLA/72mgfPr06aM+ffpIkl588cUS2wQGBio8PLzEc+np6fr22281efJk9ejRQ5I0ZcoURUZGau3aterVq1flFA4AALwCvdkrlJiSJn+/whAnRSW/CwcA8Aynshj9gKqxZcsWtW7dWh06dNDLL7+s5ORk17ldu3bJZrOpZ8+ermNNmzbVddddp+joaDPKBQAAHoRhJlfImpHl+jhdQSZWAgAor5NZdt1Qz9/sMuDlevfurYEDB6p58+Y6deqU3n33XQ0aNEhr165VYGCgkpKS5OvrqwYNGhT5vNDQUCUlJV30ukeOHKns0gGUAv8nAVSma6655qLnCHSugNPplDUrWwH+hS9XhoVABwA82clMRuig8g0ZMsT1cdu2bdW+fXtFRkZqxYoVGjRoUJmve6mOXYX477bKvT7gZSr9/yQAXARTrq5AWkaWCmy/7YrCCB0A8GxMuYIZGjdurCZNmuj48eOSpLCwMDkcDqWkpBRpl5ycrLCwMDNKBAAAHqRUgU5cXJxyc3Mvej43N1dxcXHlLsrdJFvTZRiG6366Ak2sBgBQXifZ6QomSElJ0ZkzZ1yLJLdv317+/v6KiopytUlISNChQ4fUqVMns8oEAAAeolSBzk033aTFixdf9PyyZct00003lbsod3M68ZwCA35ba4EROgDg2Rihg4qQlZWlmJgYxcTEyOl0Kj4+XjExMYqLi1NWVpbefPNNbdu2TSdPntSGDRs0bNgwhYaGasCAAZKkunXr6oknntBbb72ltWvXavfu3Xr++efVtm1bde/e3dwnBwAA3F6pAp0LR6mUxG63y2KxlKsgd5SUZnXtcOWUlK0AcwsCAJTLySxG6FRH48eP1/79+y96/sCBAxo/fvwVX2/nzp3q1q2bunXrptzcXI0bN07dunXTe++9J19fX+3fv1+PPvqobr31Vg0fPlytW7fWypUrVbt2bdc1xo0bp/79++uZZ55R3759FRwcrNmzZ8vX17dczxUAAHi/Ui+KfLHAJj09XatWrVJoaGi5i3I32bl5ruedpQAZXhhaAUB1kl5gKL3AqboBLCVXnbz//vtq2bKl2rRpU+L584HOqFGjruh6Xbt2ldVqvej5BQsWXPYagYGB+vDDD/Xhhx9e0WMCAACcd9me7Pvvv6/69eurfv36slgseu6551z3L7xdffXVmjt3bpEdHbxFdk6e6+Ms1s8BAK/AOjr4vaysLPn7s509AADwDJcdodOhQwf98Y9/lCRNnTpVPXr0UKtWrYq0sVgsCg4OVvv27cu1Dae7ys7Nk34dlJPFdCsA8AoJ2Q61a2B2Fahse/fu1Z49e1z3t2zZIru9eJhntVo1bdo0th8GAAAe47KBzt133627775bkpSdna0//OEPuvXWWyu9MHdhtzuUm1+gGkGFQU4mI3QAwCtYCy69Lhy8w+LFi13r4lgsFk2fPl3Tp08vsW1ISIi+/PLLqiwPAACgzEq1hs5nn31WWXW4razcXDmdTtf9bAsjdADAG6QXOC/fCB7v6aefVt++fWUYhnr27KmxY8e63qi6UHBwsK6++mr5+ZV6eUEAAABTlLrX4nA4tHr1asXGxspqtRbb+cpisej111+vsALNlpmdK7vjt+1t80r/kgEA3JA1n0CnOmjUqJEaNWokSVq0aJGuu+46r9zAAQAAVD+lSid27typJ554QqdPn77oFubeFuics6bLz++3rUMLxDaiAOANGKFT/dx5551mlwAAbi1zxCNmlwB4lNqTvjP18UsV6Lz66qvKy8vTzJkz1blzZ4WEhFRWXW4jKcWqAP/fXiYCHQDwDumsoVMtrV69Wt9+++0lRxrv2rXLpOoAAACuXKkCnX379unNN99Uv379Kqset5OemSU/X0boAIC3YYRO9fPJJ5/o7bffVlhYmG655Ra1adPG7JIAAADKrFSBTpMmTSqrDrdlczhksVhc9wl0AMA7WAl0qp0vvvhC3bp109y5c+Xv7292OQAAAOXiU5rGI0aM0DfffKOMjIzKqsft2C5YEFki0AEAb8GUq+rHarVq8ODBhDkAAMArlGqETlpammrWrKlbbrlFgwcP1lVXXSVf36IBh8Vi0csvv1yhRZrJbrMXuV/ALlcA4BWYclX9dOjQQUeOHDG7DAAAgApRqnTi7bffdn08bdq0Ett4W6DDCB0A8E4EOtXPhAkT9NBDD6l9+/Z6+OGHzS4HAACgXEoV6Ozevbuy6nBbdvvvR+gQ6ACAN8gsMOQ0DPlcsE4avNuTTz6pgoICvfDCCxoxYoQaN25c4kjjrVu3mlQhAADAlStVoBMREVFZdbitC0fo2OQjg44/AHgFQ1Ku3VCwPz/Xq4uGDRsqNDRUrVu3NrsUAACAcmNBmMuw2X4LdBidAwDexcG6yNXKkiVLzC4BAACgwpQq0GnXrl2RLbxLYrFYtGvXrnIV5U7sDod8fAqfs41ABwC8ipNABwAAAB6qVIFOly5digU6DodDcXFxio6O1g033KB27dpVaIFmMgxDNrtdgQFsbwoA3shpkOhUJ5s2bbqidl26dKnkSgAAAMqvVIHO559/ftFze/bs0ZAhQ7xq1wib3S7jgs6+j+j4A4A3YcpV9TJgwIDLjjSWpNTU1CqoBgAAoHwqbA2dyMhIPf3003rrrbe0bt26irqsqQpsdjkvGI9vIdABAK9CoFO9LFq0qNgxh8OhU6dO6ZtvvpHT6dRbb71lQmUAAAClV6GLIoeFhenQoUMVeUlT2R0ORuig2gk07Hrc2Kl6yjO7FKDCRYSH6pF7e7ru+/uww1V1cuedd1703GOPPaZ+/fpp48aNuuuuu6qwKgAAgLKpsEAnNTVV3377rZo0aVJRlzSdxWKR5YLOPiN04PUMQwOMg2qoXLMrASqFj8UgxEGJfHx89MADD2jSpEl64403zC4HAADgskoV6AwcOLDE4+np6Tpy5IgKCgo0ZcqUCinMHfhYfKQiI3QA73ancVKtxdoR8F5OtrXCJaSlpSk9Pd3sMgAAAK5IqQIdp9NZbDFBi8Wi5s2bq3v37nr88cd17bXXVmiBZircrpwROqgeWjmSdHPBUcbmwKv5+/qaXQJMFBcXV+Lx9PR0bd68WZ9++qk6d+5cxVUBAACUTakCnSVLllRWHW6pcMrVb/dZQwfe6qoAm6Ieaqfa/u3NLgUAKk27du0uusuVYRi67bbbNGnSpCquCgAAoGwqdFFkb+NjsejCDIcROvBGQRaHfux/leoEMHIBgHf717/+VeJI45CQEF199dW6/vrrTaoMAACg9Eod6KSlpWnSpElauXKlTp06JUmKiIhQ37599corr6hevXoVXqRZLBZLkY4fI3TgbSwy9NVd9XVNiL/ZpQBApXvsscfMLgEAAKDClCrQiY+PV9++fZWQkKD27du7Fkk+cuSIPv744/9n787jo6zv/e+/r1kyM8lk38nGFgJh3xfZVxUUKa7Vtv6s1epp1Z6enl9te3q35/bW7ranx3rs6bFqT9Eqoq1rXcCNRZQdBNn3LQECJJlktuv+AxuNIZkJJLlmJq/n48EDcs2VyTtAgPnw/b6/Wrx4sV599VUVFxd3StiuZrM1X5NDKTISzT0DXJrby2t1DADoUqFQSBs2bGj2H1PDhg2Tzcbf9AAAIH60a6Dzox/9SKdPn9YLL7ygiRMnNntsxYoVuv766/XjH/9Y//3f/92hIa3y+S1XkmQ3wwoZ/IMP8W9KTlg/HJsT1b2OZS/Idmhv5wYCYoB//pek1AyrY6ATLVmyRN///vd17NgxmZ+cZGkYhvLz83X//fdrwYIFFicEAACITrsGOkuXLtXtt9/eYpgjSRMmTNBtt92mRx99tMPCWe3zW64kyaOAauWyKBHQMYqTAvrzpaWtloN+luP9pXI/9ssuSAVYLzDnapkMdBLWSy+9pFtvvVX9+vXTP//zPzedzLl9+3Y9+uijuvXWW+VyuXT55ZdbnBQAACCydg10fD6fcnJa/x/9nJwc+XyJc+jxuRe7zZfoMNBBvPMYIT0/r0heZ+SVZrb9u+T6w8+6IBUQI9hyk9B++ctfatiwYXr55Zfldrubrk+ZMkVf/vKXdemll+oXDLi8EgAAIABJREFUv/gFAx0AABAX2vUv1/79++uZZ55RY2Nji8f8fr+efvppDRgwoMPCWe38K3SCFqUBLp4hU49OzVLf9LZLkEPBoBqrjsn16+/L8Dd0UTogBrClNqFt3bpV1157bbNhzj+4XC5dd9112rp1qwXJAAAA2q9dK3Tuuece3XzzzZo2bZpuueUW9e3bV9K5UuQ//vGP2rZtmx5//PFOCWoVp6P5Uc5uBSxKAly8b1e6dVnPtkuQQ8GgXn3s97pk7RvKrjvZRcmAGMEKnYTm8Xh04sSJVh+vrq6Wx+PpwkQAAAAXrl0Dnfnz5+u//uu/9MMf/lDf+c53mlavmKapvLw8Pfzww00nXyUKp9OpUCjU9DYrdBCvpuWE9YMIJcimaeq9vy3RgG0fqJhhDroh02aPfBPi1pQpU/TII49o2rRpmjBhQrPHVq1apd///veaOXOmRekAAADap10DHUm67rrrtHDhQq1bt04HDhyQJJWUlGj48OFyONr9dDHP7XSq7jMDnWRW6CAOlSQF9KdLSyPe99H7K+Ve866GVO3pglRADGKFTkL78Y9/rJUrV2revHkaOnSoysvLJZ1babxhwwbl5+frRz/6kbUhAQAAonRBExiHw6HRo0dr9OjRHZ0n5riSnKpr+LRDxG0GpMgHAwExI9oS5KP79ujga3/TlYc/6qJkQAxKovQ+kZWWluq9997Tr371K73++uv629/+Juncf0zdeeeduueee9o8/AEAACCWRPyvyKNHj2r06NG677772rzvvvvu05gxY1RdXd1h4WJBUlLz8li2XCGeGDL1x2lZ6hOhBLn+7Bmt+sv/avaBjXKGQ23eCyQqM8nFQCfB1dXVyefz6f7779cHH3ygo0eP6ujRo/rggw903333yefzqb6+3uqYAAAAUYk40HnkkUd06tQp3X333W3ed/fdd+vkyZN65JFHOixcLHA5m78QZssV4sm/DHTr0rLIJchvLnpC0/ZvUKqfFzLovsyUVKsjoJN973vf0xe/+MVWH7/xxhv1b//2b12YCAAA4MJFHOi89tprWrBggVJT2/6HbmpqqhYuXKhXXnmlw8LFAo/LpXA43PQ2p1whXkzPNfX9MdGVIA/a/qGKzibW6jqgvUxvutUR0MmWLVumefPmtfr4vHnz9Oabb3ZhIgAAgAsXcaCzZ88eDRo0KKonq6ys1O7duy86VCzJSE1RkFJkxJkSV0B/mtMj4n1bVi2XZ827Gly9t/NDATGOFTqJ79ixYyosLGz18fz8fB09erQLEwEAAFy4iAMdwzCarVBpSzgcbjrKPFGkp6YoEPx0oOOVXzJNCxMBbUs2gvrr3CKlRChBPrJ3tw699qImU4IMnONNszoBOllOTo62bdvW6uPbtm1TejortQAAQHyIONApLS3VmjVronqytWvXqrQ08tHI8SQz1dtshY5T4XNDHSAGGTL1x+k56h2hBLnuzGm9//SfNfvgBjkoQQYksUKnO5g1a5Yee+wxrVu3rsVja9eu1WOPPaZZs2ZZkAwAAKD9Ih5bPmfOHD3yyCO666671K9fv1bv2759uxYvXqyvf/3rHRrQasket+y25nOvTPlUK05CQez510EezSlNafOeUDCopYv+pOn7NyjV7+uiZEDsM1NYoZPo7r33Xr3++uuaNWuWZs2apQEDBkiSPvroI73xxhvKy8vT97//fYtTAgAARCfiCp1vfvOb8nq9uuKKK7R48WIFg82P7Q4Gg1q8eLGuvPJKpaam6hvf+EanhbWCx+2S7XPbyDLUYFEaoHUzc03dOzq7zXtM09R7f12iQTs+VA9KkIFmTC8rdBJdfn6+li1bpmuuuUbLly/Xgw8+qAcffFArVqzQtddeq2XLlrXZsQMAABBLIq7Qyc7O1jPPPKObbrpJt912m+666y717dtXXq9XtbW12rlzpxoaGlRYWKinnnpK2dltv6CMN16PWw5H85+mTNMnJVZVEOJcmSugx+dE3u64ZeV78qx9hxJk4DxYodM95OXl6eGHH5ZpmqquPjfYzsnJSbgOQAAAkPgiDnQkadiwYVqxYoX++Mc/6tVXX9W2bdt09uxZpaamasiQIbrssst08803J2SRoM1mkzfZI3/g09OtMsQ2FcSOZCOo5+dFWYL8+ouaf3hrFyUD4gsdOt2LYRjKzc21OgYAAMAFi2qgI0lpaWm6++67dffdd3dmnpjkTfbo5OlPBzqZbLlCjDBk6rHpOeqVFl0J8lUHN1KCDLSGU64AAAAQRyJ26EBKTfbI/MxR5Zms0EGM+L+DPJodoQQ5GAho6aInNGPfenkpQQZaxQodAAAAxBMGOlHIy8pQIPjpqgaXQvKYHF0Oa83MM/XdqEqQn9XgHR+qsPZEFyUD4lM4I8fqCAAAAEDUGOhEobggV43+5gMcTrqClcpcAT0xp0fE+7asfE8p697ToOp9XZAKiF9mcoqUlmF1DAAAACBqDHSikJeZLsNo/lPFtitYJdkI6q/zipTsaPvL9/DunTr8+ouaRAkyEFE4t8jqCAAAAEC7MNCJQro3RU67vdm1LJOBDrqeIVOPT89RzyhKkD94ZpFmU4IMRCWcz0AHAAAA8YWBThTsdruSk93NruWr1qI06M7uHezRrGhKkP/8uKbv30AJMhAlk4EOAAAA4gwDnSilJnuavV3AQAddbHa+qX8dFbkE+d2/LtbgnWsoQQbagRU6AAAAiDcMdKL0+YGOV355zUaL0qC76ekK6LHZkUuQNy1/V961yylBBtqJgQ4AAADiDQOdKJ07ujzY7FqhzlqUBt1JSpQlyId27dCxN1/SpMMfdVEyIHGYeQx0AAAAEF8Y6ESpf68SNTQGml0rMNl2hc5lyNQTM3JUFqEEufZ0jdYsflKzDmyQwwx3UTogMZjuZJkZbW9nBAAAAGINA50oFeRkyeloftJVASt00Mm+N8SjGSVRlCAvelzT9q+XN9DQRcmAxBHOi7ydEQAAAIg1DHSilOR0Kt3b/IU1W67QmWbnm/rOyOhKkIfuXKPC2pNdlAxILJxwBQAAgHjEQKcdstLTmr3tUVDpJisi0PF6uQN6fHbkF5mblr+r1LXvaWD1/i5IBSSmcH6x1REAAACAdmOg0w4l+TlqDHyuR4dVOuhgKUZQz88tksdhtHnfuRLkFzXx8NYuSgYkJk64AgAAQDxioNMOFb1KFPB/7qQrk4EOOo4hU3+akRtVCfKHixdp9oGNlCADFylc1MvqCAAAAEC7MdBph/zsTDmdjmbXCsRJV+g43x/i0fSS5DbvCQYCevPPj2vG/g1KoQQZuCim06lwWV+rYwAAAADtxkCnHZwOhzJSvc2uFeqMbKyQQAe4rED6l2hKkJ97RsN2rlEBJcjARQuX9pUcba+IAwAAAGIRA512ykr3yjTNpreTFFaRzliYCImgtzugR2dFPjp543tvK3X9cg08QQky0BFCvSutjgAAAABcEAY67dSzR4H8nytG7mnWWJQGicBrBPXcvMglyAd3bdfxpS9rEiXIQIcJ9+5vdQQAAADggjDQaad+ZcUKBEPNrvXUKYvSIN7ZFNafZuaqLDVyCfKaZ57U7AMbZWeLH9BhQn0GWB0BAAAAuCAMdNopLytDyW5Xs2sFOiu3GWjlPYDW/WBosqYVU4IMWMH0psnML7Y6BgAAAHBBGOi0k81mU15WRvNrkkrFtiu0z2UF0j+PiK4EefjONSqoowQZ6Eih3qzOAQAAQPxioHMBykuL1Oj3N7vWix4dtENvdzDqEuS09ctVSQky0OHozwEAAEA8Y6BzAYZW9FEw2LzHhB4dRMtrBPX8vB6RS5B3blfVmy9rIiXIQKfghCsAAADEMwY6FyAzLVVp3ua9JxlqUIbpsygR4oVNYf3vzFyVRipBrqnRmmef1KyDlCADnSXUhxU6AAAAiF8MdC5QYU62TNNsdo1VOojkh8NSNDVSCbLfr6WLHtfMfZQgA50lnF8kedOtjgEAAABcMAY6F2hwv16qb2hsdq2nyUAHrZtXKN0zPKvNe0zT1DvPPaPhOz9UPiXIQKehEBkAAADxjoHOBaroWSy7rflPX5lqZGN7DM6jryegP8yMXIK84Z1lyli/QgNOHOiCVED3FaocYXUEAAAA4KIw0LlAHpdL2elpza65FWLbFVpItQX13LxiuSOVIO/Yrqq3XtUlRyhBBjqTaRgKDRlrdQwAAADgojDQuQjFBTkKhZuvyBlgVlmUBrHoXAlynkq8jjbvO3vqlNY++6TmHKAEGehs4dK+MjOyrY6BBLB8+XJdf/31GjBggDIyMvTnP/+52eOmaeqBBx5Q//79VVBQoLlz52rr1uZD+5qaGt12220qLS1VaWmpbrvtNtXU1HTlpwEAAOIUA52LMKJ/X/l8zXt0ynWCF+Ro8sNhKZpS5GnznqDfr2WLHteM/euVTAky0OlYnYOOUldXp8rKSv3kJz+Rx9Pyz/rf/OY3euihh/TTn/5US5cuVW5urhYsWKCzZ8823XPrrbdq48aNWrx4sRYvXqyNGzfq9ttv78pPAwAAxCkGOhehpCBPyR5Xs2suhdRblNlCuiLKEuS3lzyt4TvXKL+O7XpAVwgOHWd1BCSI2bNn64c//KHmz58v2+d69UzT1MMPP6x77rlH8+fPV2VlpR5++GHV1tZq8eLFkqSPP/5Yb7zxhn79619rzJgxGjNmjB588EH9/e9/144dO6z4lAAAQBxhoHMR7Ha7ivNzWxxf3p9tV91eX09Q/x1lCXLmhhUacJISZKArmClpCvfhhCt0vn379unYsWOaPn160zWPx6MJEybo/ffflyStXr1aXq9XY8d+umps3LhxSklJaboHAACgNW0XeyCisYP7608vvilvsrvpWl+dkMMMKWjYLUwGq6Tagnp+XlFUJcjVb72iK45s66JkAIKDRkk2/mxG5zt27JgkKTc3t9n13NxcHTlyRJJ0/PhxZWdnyzA+/fvCMAzl5OTo+PHjrT43q3eA2JJIX5MFVgcA4kxXfP2Xl5e3+hgDnYtUXlqkZHfzbVdJCquPTupj5bbyXkhUNoX155l5Ko5Qgnzm5EmtfXaRrqIEGehSIbZbIQG09Q+7DvH66s59fiDBdPrXZBc6G/kWAJ9h9dc/W64ukt1uV1lhnsKfO+2KbVfd04+Gp2hyFCXIbz35hGbu36DkQGOb9wLoOKZhU3DwGKtjoJvIz8+XJFVVNf/3QFVVlfLy8iRJeXl5OnHiRLOt26Zpqrq6uukeAACA1jDQ6QDjhg5Q/edOu+qjk0oygxYlghXm95DuGhZdCfKInR8qjxJkoEuFe1VIaRlWx0A3UVZWpvz8fC1btqzpWkNDg1auXNnUmTNmzBjV1tZq9epPV8SsXr1adXV1zXp1AAAAzoctVx2gd1GhUpKbr8pwfrLtaqv4H7buoF9yUL+fWRrxvvVvL1XWxhXqf/JgF6QC8FmhIazOQceqra3V7t27JUnhcFgHDx7Uxo0blZmZqZKSEt1xxx361a9+pfLycvXt21e/+MUvlJKSoquvvlqSVFFRoZkzZ+pb3/qWfv3rX0uSvvWtb2nOnDmWL+EGAACxjxU6HcBms6lnj/wW264GmK0XGiJxpNmCen5esVz2tkuQ93+8TSffekUTDlOCDFghOIT+HHSsdevWafLkyZo8ebJ8Pp8eeOABTZ48Wffff78k6e6779Ydd9yh73znO5o2bZqOHj2qJUuWKDU1tek5/vCHP2jQoEFauHChFi5cqEGDBumRRx6x6lMCAABxhBU6HWT8kAH6aNc+pXzmtKs+Oimv2ahaw9XGeyKe2RTWoll56pHS9qk5Z06e1PrnntRVBzdRggxYIJyRrXDv/lbHQIKZNGmSampqWn3cMAzde++9uvfee1u9JyMjQ7///e87Ix4AAEhwrNDpID2LCuT93LYrm6TBOmZNIHSJfx/h1cQeUZQgL6IEGbBScNwMyWh7FR0AAAAQTxjodBDDMNSrqKDFtquh5hEZnzm9AoljfpH0jaGZbd5jmqbefvZpjdz1ofLqWv9fXACdK3jJHKsjAAAAAB2KgU4HmjCsUr6G5isw0tWonuI0o0RTkRzU72f0iHjf+reXKnvjClVQggxYJlTSR+HSPlbHAAAAADoUA50OVJyfq6z0tBbXh5lHLEiDzpJmC+q5aEqQt23Vqbde0fgjW7soGYDzCV4y2+oIAAAAQIdjoNOBDMPQ8AF91egPNLveVyfkNelOSQR2hfVktCXIzz+lmQc3yc6WO8Ayps2m4PiZVscAAAAAOhwDnQ42fkiljM8Vb9okDWeVTkL49xFeXRKhBDngb9SyRY9p1j5KkAGrhQaOkpmRbXUMAAAAoMMx0OlgbleSehXltyxH1hGOq45zC4oM/VOUJcijdq1Vbj0lyIDV2G4FAACARMVApxNMHTVMvgZ/s2spCqi/qixKhIvVPzmoR2YURrxv3bI3lEsJMhATTHeygiMnWR0DAAAA6BQMdDpBSUGusjNaliOPMA9bkAYXK90W1HNXFCspihLkmnf+rnFHtnVRMgBtCY6eIiW5rI4BAAAAdAoGOp3AMAyNHdK/xSqdHjqrHuYZi1LhQtgV1lOz81SY3HYJ8umTJ7T+uac06wAlyECsYLsVAAAAEhkDnU4yemCFXE5ni+vjzf0WpMGF+v9GejW+MHIJ8luLHtes/RvkCVKCDMSCcHa+Qv2HWR0DAAAA6DQMdDqJ0+FQ/94lCgZDza731UnlmbUWpUJ7LCw29PUh0ZQg/0Wjd66hBBmIIcEJsySj7W2SAAAAQDxjoNOJZo4bIX8w2OI6q3RiX//koB6eHrkEee3S15W7caX6nTrUBakARMO0OxSYPt/qGAAAAECnYqDTidK9KSorzGtxhHmFqpVt1lmUCpGk24J6PooS5L1bt+j0O69RggzEmOCYqTKzcq2OAQAAAHQqBjqd7LJLxqj+c+XIhqQJrNKJSXaF9Zc5eSqIogR54/NPa9bBjZQgAzEmcNl1VkcAAAAAOh0DnU5WlJ+j0oJcmZ970d9fVco06y1KhdbcP8qrcQXtKUH2t3kvgK4V7D9M4bJyq2MAAAAAnY6BThe4fOIY1fman35kkzTePGBNIJzXNSWGbh8cuQT5rcWUIAOxKnDZtVZHAAAAALoEA50uUFKYp6K87BardAbqmNJNn0Wp8FkDUoJ6aFp0Jch5G1dQggzEoHBhiUJDx1sdAwAAAOgSDHS6yGUTR6v+PKt0xrFKx3IZtqCenxddCfKZd1/T+KMfd1EyAO3hn3MNR5UDAACg22Cg00V6FRWqICezxSqdwTqmVLPBolSwK6yn5uQpP1IJ8olqbXruac08sFE2SpCBmGOmpit4yRyrYwAAAABdhoFOF5pzySj5Gpqv0rHL1CWceGWZn4xOjb4E+cB6SpCBGBWYPl9KclkdAwAAAOgyDHS6UN+SIuVkZrRYpTNER5Vn1lqUqvu6tsSmrw3KaPMe0zT11jNPacyutcqpP91FyQC0h+l0KjDjKqtjAAAAAF2KgU4XMgxDs8ePbLFKx5A03dxlTahuqjIlqIemF0S8b+2br6lg00qVU4IMxKzg+Fky07OsjgEAAAB0KQY6Xax/rxJlpae3WKVTptMqN6stStW9ZNiCem5esZy2CCXIH23WmXdf01hKkIGYZRqGAnOusToGAAAA0OUY6HQxwzB0+aQx8n3uxCtJmmbult0MW5Cq+3AorL9EU4JcXaVNzz+tmQc3UYIMxLDguBkKF/eyOgYAAADQ5RjoWKCiZ7GKC/IUDjcf3mSqQSN02KJU3cNPRqdqbIQSZH9jg5YtelyzDmygBBmIYabDKf/Cr1odAwAAALAEAx2LLJw5UY2BQIvrl5j75DEZInSG60ptujWKEuS3F/9FY3dTggzEusC0K2TmFlodAwAAALAEAx2L5GSma3DfXvIHgs2uuxTSRHOfRakS1yBvUP85LXIJ8pqmEmRWSgGxzHR7FLjyS1bHAAAAACzDQMdC86aMk81oWcw7TEeUY9ZZkCgxZdqDWhJNCfKWzTpLCTIQFwKXXiszLdPqGAAAAIBlGOhYyONyadKIQWpobL7FyiZpurnbmlAJ5h8lyHmetkuQa6qOa/Nfn9YsSpCBmBdOzZD/0uusjgEAAABYioGOxSaPHCJvsqfFMea9dIpjzDvAz8akakx+5BLktxY9rln718tNCTIQ8wJXfknyJFsdAwAAALAUAx2L2Ww2zZ08Vr7GloOEWeZOucyWxcmIzvVlNt0yMHIJ8lvPPKlxe9Yp23emi5IBuFDhnAIFpl9pdQwAAADAcgx0YkBl7zL1yM1ucYx5qvxsvbpAg7xB/XZq5BLkD15/VT02r1JfSpCBuOD/wi2Sw2l1DAAAAMByDHRixMIZE9XQ2HI1zhAdU0/zlAWJ4lemPajnoihB3rN5k+rfe0NjjmzvomQALkaouLeC42daHQMAAACICQx0YkRedqaG9e/T4hhzSbrU3K4ks+V1tORQWM9cmq/cKEqQt/ztGc08tEk2UYIMxAP/NV+TbPy1BQAAAEgMdGLKlVPHy5XkbFGQnK5GTTH3WJQqvvxsTKpG5bnbvMff2KC3n3yCEmQgjgQHjlRo2HirYwAAAAAxg4FODHE6HFo4c5J8DS2HDMN1RCVmjQWp4scX21OCvHstJchAnDAdTjV++R6rYwAAAAAxxWF1ADRXXlqkgX3L9PHeA0pyflr8aUi6zNyuRzVSQaPt7UTd0RBvUP8xtTTifR+8/qp6bFqlPpQgx7Tf7a7Sf+87ob3154ablalufa9fvuYWpEuSblm7T08caN4tNSYzWSsm92v1Oc/3PpKUbLfpzLwhkqR1NfX62voD2lHbqKk5Xv1xRKmyks79MRk2TU14Z4f+fUCBZueldcjniegE5t4gs6DE6hgAAABATGGgE4MWTJ+oXz6xuMX1TDVokrlXy4w+FqSKXVn2oJZcUSxHhBLk3Zs3yvfe65p6lBLkWFfsSdL9lT1UnuJSWKae2H9KC1fv0eopFRqS7pEkzcj16vERZU3vkxTh1//BwcW6v7JHs2uT392hSdneprdvX39AU3O8WjSqTLetP6AHth/TzwcVSZJ+u7tKFV4Xw5wuFs7rIf+8G62OAQAAAMQctlzFIFeSU1dNn6B6X2OLx0brkApNtgr9g0NhLb6sQDnuyCXIH72wWDMObaYEOQ5cWZiuy/LT1NfrUj+vW/dVFirVYdeqU3VN97hsNhW4nU3f/rGSpjXpTnuz+3fVNWp3vV9f7ZnddM/W2gbdWpatfl63ri/K1Lbac1+D++r9+o9dVfrlJ8MddJ3GL90tJbmsjgEAAADEHAY6Maqyd5n69SxSINj8dCtD0lzzY069+sQvx6VpRG7bL/b8jQ16+8+PafY+SpDjUcg09ZeDp1QbDGt8VkrT9eUnalX4ymYNeGOrbl+/X8cbA+163v/Zd0IDU92a8JnnHJLm0RtVZxUMm1padVaD084VbP/ThgP68YBC5bhY1NiVgqOnKDRkrNUxAAAAgJjEQCeGXT1rsmxGy1+ibPk029xpQaLYclNPu74yIL3Ne8LhsJb9ZZHG71mnLEqQ48qmMz6lv7hRyS9s0J0bDmjxmJ4anHZuu9WcvDQ9NqJMr13SRz8b1EMfnKrXrOW71BgKR/XcpwMhPXO4Rl8ty252/ffDSvTs4Rr1e+MjJdkMfbc8X08dPKWgKU3PSdWVq3ar3+sf6a6NBxUIs9KrM4WTvWq86S6rYwAAAAAxi4FODPO4XLpi6njVN7TcejVQxzXYPGpBqtgwNDWoX0/Jj3jfh6+/qqItq9S75kgXpEJHqvC6tGZqhVZM7qfbe+XolnX7tfmMT5J0XXGmrihM1+A0j64oSNeL4/ro49oGvXwsuqHdnw+cVNiUbirJbHZ9YJpHyyaWa/fsgfrfUT0VNE39YOsR/W5ose7ZdFDD0j3aPKO/Np/x6b/3Vnf454xP+a/7usyM7Mg3AgAAAN0UA50YN7Rfb/UuLlQwGGrx2Cxzp3LMuvO8V2LLtgf07LwoSpA3bZBv+Rsac3RHFyVDR0qy2dTX69LIjGTdX9lDQ9M8+s2uqvPe28PjVLEnSTvqWg4/z+d/9p3QF3pkROzd+dcth/X1njnqneLSsupaXVeUoSSbTVf3yNCy6tp2f06ITrByhIJT51kdAwAAAIhpDHTiwHVzpsqwGTLN5ls8nArrKvMjOc2Ww55E5VBYz1xWGLkE+fixcyXIBzdRgpwgwpIaW9nmVN0Y1CFfQIUuZ8TnWX2qThvONOirZVlt3re06qw2nvbpnj655z6+aSrwydeg3zQVMvl91RlMZ5Iab/621TEAAACAmMdAJw4ku1268fLp8jW0LPQ916fTfVag/CqaEuSGBr216HHN3r9B7lD7inIRG+7dcljvnqjV3vpGbTrj0/c+Oqy3q2t1Q3GmaoMhfWfzIa08Wae99Y16q/qs5r+/W3kuh64q/LRT6eY1+3Tzmn0tnvsPe0+oPMWlqTmprX78hlBYd208qIeHlTStBLsk26v/3F2trWcb9MT+k7rkM8edo+P4v3CLzHxOEwMAAAAi4ciWONGrqFCTRw7We+s2y+1KavbYIB3XfjNDm4wCi9J1jZt62vXlaEqQn16kCZQgx7VjjQF9Zc0+HW0MKt1h1+A0t14c31tz8tLkC4W1+UyD/vfAHtUEQip0OzQ1x6unRvdUqvPTlVv7fS0HoGcDIf3lUI1+UNF2/9K/f3xUl+anaWRGctO1Xw8u0pfX7NOEd7Zrbn6a7uyV03GfMCRJwT6VClx6jdUxAAAAgLhg1NTUsG8gTpimqd8vflHHT56Ww9F8y1FANj1hDFe1kdLKe8e3YalBvfGF0oi9Oav//rJSlj6vsUe3d1EyAB0h7EmR777/kZmT2INpIB4M/N1qqyMAcWXLnWOsjtBhzn7rBqsjAHEl9cFz1k9zAAAgAElEQVQnLf34bLmKI4Zh6EtXzG61T2d+gvbpRFuCvGvjejUsf0OjKUEG4k7jV7/DMAcAAABoBwY6cSbZ7dIXL5t23j6dHPk0J8H6dJwK69nLCpUdRQnythefpQQZiEP+KXMVGj3V6hgAAABAXGGgE4d6F/fQpBGD5GtsOdQZqOMaZ+63IFXneHB8moZFUYL89qLHNWv/erkoQQbiij+vWP6b7rI6BgAAABB3GOjEqVnjR6ooN0vBYMstVpPNvaowqyxI1bG+0suum/pHLkFe+vSfNWHPWmX5znZRMgAdIWR3KHj3/ysltT20BQAAANASA5041dSnY7Ts0zEkzTO3qdCM31OehqWF9KvJbZ9EJEkfvPaKSresUq+ao12QCkBHCnzpLoWLe1kdAwAAAIhLDHTiWLLbpRvnTleDv+XWK4dMLTS3KM1ssCDZxcm2B/Xs3CLZI5Qg79ywTv4Vb2gUJchA3GkcMVHBaVdaHQMAAACIWwx04lyvokLNnTRWvobGFo+lKKCrzc1KMoMWJLsw50qQCyKWIJ88ekTbXnxW0w9u5jcxEGf8GdkKfO27VscAAAAA4hqvhRPA2MEDNGZwfzWcpyQ5V/W6ytwqmxm2IFn7/XpCdCXI7zz1hObs30AJMhBnwoZNwW/+u5TstToKAAAAENcY6CSIuZPGqndxoRoDLQccvXRKM81dFqRqn6/0suvGiuhKkC/Zs06ZDZQgA/HGv/AWhfsOtDoGAAAAEPcY6CQIwzB04+UzlOn1KhhqefLVcB3RKPOgBcmiMyLaEuS/v6yyzZQgA/HIN2yCgvNutDoGAAAAkBAY6CQQh8OuWxdeLpvN1uLkK0mabu5WP7PagmRty7EHtDjqEuQ3NfIYJchAvKkvKFXon/4fyWj76xwAAABAdBjoJJgUj1u3LrhU/kDLImRD0pXmVvU2T3Z9sFY4FdKSywuVFaEE+cTRI9r+wrOafogSZCDe+Dxemfc+KCW13Y8FAAAAIHq8Nk5A+dlZuuHSaec9+couUwvMLSozT1mQrKX/mJChITltv8hr9Pn03pN/0qwDlCAD8SZgcyjwr7+UmZFtdRQAAAAgoTDQSVAVvUo0e/zI8w51HDK10NyiYvO0Bck+dUsfh26oSGvznnA4rGXPLNKEPWspQQbiTFhS7Vf/r2y9K6yOAgAAACQcBjoJbOKIwRo1sOK8Qx2nwrra3KxC84wFyc6VIP98Yl7E+z74+0sq27JKvU5TggzEm5OX3iDnxFlWxwAAAAASEgOdBHfFlHEa0q+3Ghr9LR5zKaRrzU3KN7t25UuuI6Bn5xVHLEHesX6tAiuXauRRSpCBeFM1eJzcN9xudQwAAAAgYTHQSXCGYWjhzEmq7F163qGOWyFdZ25SjlnXJXmcCmnJZYXKdLX9W+/E0SPa8eISTTtICTIQb04W9JTnW/dZHQMAAABIaLxW7gYMw9C1c6aqX1nxeYc6HgV1vblRWWZ9p2f57SUZGhxFCfK7Tz5BCTIQh86mZMj5b7+V7A6rowAAAAAJjYFON2EYhm64bJp6Fxeq0d9ySJKigK43NyrD9HVahq/2cej6fpFLkJc+9b+aSAkyEHcaHC6ZP/gPGd5Uq6MAAAAACY+BTjdis9n0pXkzVVKQe96hTqr8+qK5oVO2X41KD+lnUZQgr371JfXa+r56nj7W4RkAdJ5Gu1O+f/mZbD1KrY4CAAAAdAsMdLoZm82mm6+crcLcbPkDwRaPp8qvG80NKurAI83zHAEtnhtFCfK6NQqselMjju3ssI8NoPP5bXbV3PFDOQcMtToKAAAA0G0w0OmG7Ha7vnrVpcrPypA/0HKljltBXWduUl/zxEV/rCSFtOTyQmVEU4L80hLNoAQZiCsBm12HbvyWUkZPsjoKAAAA0K3w2rmbcjjs+uoXLlN2evp5hzpOhbXA3KJB5tGL+jj/OTFTg7KjK0GefWCDkkItVw0BiE1Bw6btl39ZuTPnWR0FAAAA6HYY6HRjTodDt18zVwU52eft1LFJmmtu11jzwAU9/9f6OnRtedvlqOFwWG8+9SdN2rNWGQ21F/RxAHS9oGHTR9OvVdk1X7E6CgAAANAtMdDp5pwOh25dcKn6lvRQw3mGOpI01dyjaeFdkmlG/byj00P6aRQlyO+/8qJ6f7RaZZQgA3EjZBjaMmWBen3pdqujAAAAAN0WAx3IbrfrxrkzNKxfH/kaGs97zxgd0lzzY9nMcMTny3ME9MzcYtmMyCXIoVVLNfI4JchAvAgZhjZfcqV63/wNGRG+xgEAAAB0HgY6kCQZhqEFMy7RpJFDVN/KUGeQjusL5hY5zVCrz5OkkJ6b2yNyCfKRw9rx0hJNP7RZvCQE4kNYhjaNu1y9b72HYQ4AAABgMQY6aGbWuBG6fOKYVlfq9NEp3WiuV5rZcN7H/3NipgZmJbX5MRrq6/UOJchAXAlL2jh6tvrc/i8McwAAAIAYwEAHLYwfWqmrZ0+Wr6FR5nl6c/JVpy+b61Rknm52/bYoS5CXPvUnTd67jhJkIE6EDEMbRsxUn3/6LsMcoB0eeOABZWRkNPvWr1+/psdN09QDDzyg/v37q6CgQHPnztXWrVstTAwAAOIJAx2c15Dy3vo/V82RPxA871AnRQHdYG7UEPOIJGlMRkg/iaYE+eUX1GcrJchAvPDb7Ppg7OXq883vMcwBLkB5ebk+/vjjpm8rVqxoeuw3v/mNHnroIf30pz/V0qVLlZubqwULFujs2bMWJgYAAPGCgQ5a1auoUF+/Zp5CobBC4ZZlyHaZmtawRV/LPamnL49cgrx93YcKr16mEcd3dVZkAB2o3pGkFROuVOXt35bNxl8XwIVwOBzKz89v+paTkyPp3Oqchx9+WPfcc4/mz5+vyspKPfzww6qtrdXixYstTg0AAOIB/0JHmwpysnTPTQuV4nGrMdD8WPNAMKgeeTn62dxBEUuQqw8f0s6XntO0g5QgA/GgxpWi9ybM14hbvsEwB7gIe/fuVf/+/TVkyBDdcsst2rt3ryRp3759OnbsmKZPn950r8fj0YQJE/T+++9blBYAAMQTh9UBEPtSUzz65vVX6alXl2nH/kNyu5JkmqaSHE595YpZEbdhNNTXa/lTT2je/vWUIANx4FhyhtZOmKcJN9wsm91udRwgbo0aNUq/+93vVF5erurqav385z/X7NmztWrVKh07dm7rcW5ubrP3yc3N1ZEjR9p83h07dnRaZgDtl0hfkwVWBwDiTFd8/ZeXl7f6GAMdRMXhsOvGuTP05vvr9PaajbLbbLrz2ivkdrV9olU4FNLSp/6kSXvWKb2xrovSArhQe1NztWvW9Zp45RfozAEu0qxZs5q9PWrUKA0bNkyLFi3S6NGjL/h52/qHXYd4fXXnPj+QYDr9a7IL0eAFtI/VX/8MdBA1wzA0c9wIFeVlK2yaysvOjPg+q155QX23rlbpmeNdkBDAxdiaWaSTC76qsVOmR74ZQLt5vV71799fu3fv1rx58yRJVVVVKikpabqnqqpKeXmRDxkAAACgGAHtNqB3mQb26Rnxvo/XrJbeX6bhlCADMe/DvD5quPnbGsowB+g0DQ0N2rFjh/Lz81VWVqb8/HwtW7as2eMrV67U2LFjLUwJAADiBSt00CmqDh3Urlf+qqsObqEEGYhhYRl6r6hS+V+9R0V9EmfJOBALfvCDH+jSSy9VcXFxU4dOfX29brjhBhmGoTvuuEO/+tWvVF5err59++oXv/iFUlJSdPXVV1sdHQAAxAEGOuhwDfX1WvGXP50rQQ5TggzEqnqHS2+VDdPA2+5RdkGh1XGAhHP48GHdeuutOnHihHJycjRq1Ci9/vrrKi0tlSTdfffd8vl8+s53vqOamhqNHDlSS5YsUWpqqsXJAQBAPDBqampMq0MgcYRDIb362B80bv2b9OYAMexYSqbeLR+jibfcIW96htVxAMSQgb+jFBlojy13jrE6Qoc5+60brI4AxJXUB5+09OOzQgcdauXLf1PtqWr5kjxWRwHQis05PbVlwBjNuukWJbndVscBAAAAcAEY6KDDbPtwtfZv+0hJbo9W9Ryqam+mxu7bKIcZtjoaAEl+m0PvFg/U2cqRmnP1DXI4nVZHAgAAAHCBGOigQ1QdPKB1y15XkvvTlTk7c0p10pOmqbs+VKq/3sJ0AGrcqXqtZLDyx0/VjOmzZBjUlQMAAADxjGPLcdFCwaCWLX5STlfLrRsnUzL0YuVkHUzLsyAZAEnandlDf+07VoOv/YpGzpjNMAcAAABIAAx0cNFsdrv6jxqrQEODTLNlx7bfkaQ3y8dqTdEAhQx+ywFdJWQYWtGjv1ZVTtSc276hkn4VVkcCAAAA0EHYcoWLZhiGhkycotziEr275BkZNptsdvvnb9LmwnIdSs/TxD1rleU7a01YoJuoc7r1RslgJQ0bp7lz58vu4I97AAAAIJGwXAIdprBnb8277U55vF4F/Y3nvedUcrpeGjBZm/P7iKpkoHPsyirSkj7jVXLFtZo0fyHDHAAAACABMdBBh0r2pury/3ObSioGqNF3/iLksM2uNSUD9feKS3Q2KbmLEwKJq97p0us9R2h575Gaesvt6jditNWRAAAAAHQSBjrocDa7XRPmXaVxl1+pUCCgcCh03vuOp2brbwOnakdOaRcnBBLPrqwiPdt3gmorhmnebXcqM7/A6kgAAAAAOhHr8NFpeg8aoh69+uidJU/rxJEjSvK0PAUraHdoRc9hOpBRoPF718sT9FuQFIhf9U6XVpYO0S53hirHjtfQydM4xQoAAADoBhjooFO5U1I066abtXX1Km189y05nE4ZtpYLww5kFKhq4DSN37dBpTVHLUgKxJ/dWUVant9PSsvQrPkLlVtUbHUkAAAAAF2EgQ46nWEYqhw7XsXlFXpnyV90tqZGSS5Xi/sanC4t6ztGfar3a9SBLXKHAhakBWKfz+HSyrLB2unKUN9hIzRyxmyKjwEAAIBuhlcA6DJpWVm6/JbbteGdZdq6epWS3O7zbg3ZlVOqgxkFGn5oq8qr9lH0BHwGq3IAAAAASAx00MVsNpuGT52h0v6VenfJM2rw1cmZ1HK1TqMjSavKhmp7TpnG7t+kvLpTFqQFYsdpV4o+KB6oPe509R4yTKNmzmFVDgAAANCN8WoAlsguKNQVt92pD157RXs2b5DT7Tnvap2TKRl6pf9E9TlxQCMPbpUn2GhBWsA6jXanNhb208aMHnKlpGrmlQuUW1xidSwAAAAAFmOgA8vYHQ6Nu/wKlQ2o1KqXX1Cjzyfnebp1ZBjalVOq/ZmFGnr4Yw04vkc20+z6wEAXCsvQ9twyrevRT7VBU70HsyoHAAAAwKd4ZQDLFfbqo/lf/6Y2vvu2tn34vuwOh2x2e4v7AnanPiwZpB2fbMMqPFttQVqg8x1Ky9WHJQNVZXPJ7U5hVQ4AAACAFhjoICbY7HYNmzpd/UaN1sqX/qpje/cqyXP+bVinPal6rWKCyk4e1qiDW+T1+yxIDHS8026vPigeqP2p2QoHQyofPlLDpkxnVQ4AAACAFniVgJiS7E3VjOtu0pE9u/X+31+Sr/asklzu8967L6uHDqbnqfL4blUe3cUx54hbjXanNvSo0NbcMjU2NqqouFRjLp0nj9drdTQAAAAAMYqBDmJSYa/euvK2f9JHq5Zry6rlMgzbeVcphOwObSrsp615vTXg2G4NPLZLLgY7iBMhw6btuWVa36NCtYGQ0rypmnrtjcou7GF1NAAAAAAxjoEOYpbNZtOgCZPUd9hIvf/KCzq0a4eSWjkNK2h3aFOPftqW30sDju1RJYMdxLCQYdOOnFJtKizXadMmpzNJY2fOUK9BQ877+xsAAAAAPo+BDmKeOzlZUxZep6qDB7Tq5RdUe7pGSe7zb8MK2J3a2KOftub30oBju1V5bDeDHcSMoGHTjtwybS7oq7M2p8xwWBWjRmvIxCn05AAAAABoF15BIG7kFpdo3tfu0I71a7Rl5XI11NVFGOxUnNuKdZzBDqwVsNm1I+fcIKfe6VJjg0/FPcs09tJ5cqekWB0PAAAAQBxioIO4YhiG+g0fpb5DR2jH+jX6aOVyNdTXtz7YcTDYgXUaHEnalttT2/J6qcGRpEZfvTKzUjX92huVVVBgdTwAAAAAcYyBDuKSzWZTxYjRKh82smmw46urk8vjOe/9TYOd/N7qW31AFVV7ld5Q28Wp0V3UJnm0Jb+PduaUKmCzy+/zKSMzTRPnL1RBWU+r4wEAAABIAAx0ENeaDXbWfaiP3l8pX12tXO5WBjt2p7bm99bWvF4qPFutiuN7VFJzTDaZXZwcieh4SqY+zuupPVlFCstQY329sgoKNWnB1covKbM6HgAAAIAEwkAHCcFms6li5BiVDx91brCzaoV89XWtDnZkGDqSlqsjablK9vtUUbVX5VX75Qk2dm1wxL0Gu1O7s0u0I7dUNZ40hcNhBXw+ZRUWacoXrlVucYnVEQEAAAAkIAY6SCjnHex8Up7c2nHQ9UkerSsaoA2FFSo7dVgVVXuVX3uyi5Mj3hz1Zmt7bpn2ZRYqbLMrFAoq2OBTQWlPDZ82U5l5+VZHBAAAAJDAGOggIX12sLP3o83a9sEq1Rw/JqfLLZvdft73Cdts2pNdrD3ZxcqsP62K43vV++RBOcOhLk6PWOVzJGlXdol25JbpjNsrSQr6/TLNoIrL+2n4tJlKSUu3OCUAAACA7oCBDhKazWZT70FD1HvQEJ0+Ua0N77ylI3t3yQyF5XS5Wn2/U8npWtVzqNYUV6q05oh6njyswrNVspt07XQ3pqQjqTnanlumAxmFCttsMk1TjfX18ni96jN0uAZfMrnVQm4AAAAA6AwMdNBtpGfnaPKCqxXwN2r7mg+1c8M61Z4+JZcnudXtWAGHU7tySrUrp1RJQb9KTx1Rz1OHVXi2WjaGOwmtOjlD+zMLtDezSGfdKZLOrcYJhULK6VGkynETVNSnvNXfOwAAAADQmRjooNtxJrk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+            "text/plain": [
+              "<Figure size 1296x576 with 2 Axes>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "So Most people in the dataset have not brough the car.All our attempts should be towards selling more cars."
+      ],
+      "metadata": {
+        "id": "MiK7VF-aWzN3"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Estimated Salary Distribution**"
+      ],
+      "metadata": {
+        "id": "wrdy1QmpWzN3"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "fig=plt.gcf()\n",
+        "fig.set_size_inches(10,7)\n",
+        "fig=sns.distplot(dataset['EstimatedSalary'],kde=True,bins=50);"
+      ],
+      "metadata": {
+        "_kg_hide-input": true,
+        "trusted": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 493
+        },
+        "id": "aH7wYQIOWzN4",
+        "outputId": "bf759165-ab2a-47c7-bd39-fef131670fc9"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 720x504 with 1 Axes>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We can see like mean salary of the population is arond 75000 $. "
+      ],
+      "metadata": {
+        "id": "GvdQTNzmWzN5"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**3.Generating Array of Features and Target Values**"
+      ],
+      "metadata": {
+        "_uuid": "36f11b194df480984c143155cbc44ab6d4a518b9",
+        "id": "_tHM2NS1WzN7"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "X=dataset.iloc[:,[2,3]].values\n",
+        "y=dataset.iloc[:,4].values"
+      ],
+      "metadata": {
+        "_kg_hide-input": true,
+        "_uuid": "6aceb084194c1340c21265786e8d82253e3900fa",
+        "trusted": true,
+        "id": "grdizdr2WzN7"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**4.Splitting the dataset to Train and Test Set**"
+      ],
+      "metadata": {
+        "_uuid": "fdc82cbeaf87798f141f23a4268921fa2093cfea",
+        "id": "4nSqCpU5WzN8"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.model_selection import train_test_split   #cross_validation doesnt work any more\n",
+        "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=0) \n",
+        "X_train"
+      ],
+      "metadata": {
+        "_uuid": "c8fc2993c2e08057515d8078d8f3d6eb8e07db15",
+        "_kg_hide-input": true,
+        "trusted": true,
+        "id": "BUS-TmMDWzN8"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**5.Feature Scaling**"
+      ],
+      "metadata": {
+        "_uuid": "9c5c6cda2b6a1f79bdff3920fcb4b22205d4881f",
+        "id": "5BCGVgIoWzN9"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.preprocessing import StandardScaler \n",
+        "sc_X=StandardScaler()\n",
+        "X_train=sc_X.fit_transform(X_train)\n",
+        "X_test=sc_X.fit_transform(X_test)\n",
+        "# X_train"
+      ],
+      "metadata": {
+        "_uuid": "4ebcd7d8b31490e2df2e68e7da302780ff80f372",
+        "_kg_hide-input": true,
+        "trusted": true,
+        "id": "IAAXVuUXWzN9"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**6.Fitting K Neighbors into Training set**"
+      ],
+      "metadata": {
+        "_uuid": "1b42d0ffab03955876e877696e4820e6eaf654e1",
+        "id": "3naR-ceFWzN-"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.neighbors import KNeighborsClassifier\n",
+        "classifier=KNeighborsClassifier(n_neighbors=1,metric='minkowski',p=2)\n",
+        "classifier.fit(X_train,y_train)"
+      ],
+      "metadata": {
+        "_kg_hide-input": true,
+        "_kg_hide-output": false,
+        "_uuid": "f1b479179e83b731659d7907c7b739e0cd63ad9e",
+        "trusted": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "oCAhqEAWWzN-",
+        "outputId": "bb70a547-034a-4267-c1d5-8fcd9abba8c4"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "KNeighborsClassifier(n_neighbors=1)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 14
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**7.Predicting the test set results**"
+      ],
+      "metadata": {
+        "_uuid": "937b71d896a886d45bbb18c35b300e0a1fa676fa",
+        "id": "I1hoaY9rWzN-"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred=classifier.predict(X_test)"
+      ],
+      "metadata": {
+        "_uuid": "b9b5e9365f45544cd5622a83b9d4877416aed5fb",
+        "_kg_hide-input": true,
+        "trusted": true,
+        "id": "_aMj-QZsWzN_"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**8.Making the confusion matrix**"
+      ],
+      "metadata": {
+        "_uuid": "ba3033855673dcd26b99e6bde2b34e00a09d9fb0",
+        "id": "IG9g9LDlWzN_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#from sklearn.metrics import confusion_matrix  #Class has capital at the begining function starts with small letters \n",
+        "from sklearn.metrics import confusion_matrix,classification_report,accuracy_score\n",
+        "cm=confusion_matrix(y_test,y_pred)\n",
+        "import seaborn as sns\n",
+        "import matplotlib.pyplot as plt\n",
+        "f, ax = plt.subplots(figsize =(5,5))\n",
+        "sns.heatmap(cm,annot = True,linewidths=0.5,linecolor=\"red\",fmt = \".0f\",ax=ax)\n",
+        "plt.title(\"Test for Test Dataset\")\n",
+        "plt.xlabel(\"predicted y values\")\n",
+        "plt.ylabel(\"real y values\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "_kg_hide-input": true,
+        "_uuid": "3ad5dc3c4d2ffd6f18afecca524b4c5fdc7dcc8b",
+        "trusted": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 383
+        },
+        "id": "Mf0EsYJZWzN_",
+        "outputId": "763e625d-28d1-47d2-9ce0-8ed9a10d3bc6"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 360x360 with 2 Axes>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(classification_report(y_test,y_pred))"
+      ],
+      "metadata": {
+        "trusted": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "KmNviAscWzOA",
+        "outputId": "ba44d829-646a-49c6-a72d-3f68f90fa499"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.95      0.95      0.95        58\n",
+            "           1       0.86      0.86      0.86        22\n",
+            "\n",
+            "    accuracy                           0.93        80\n",
+            "   macro avg       0.91      0.91      0.91        80\n",
+            "weighted avg       0.93      0.93      0.93        80\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(accuracy_score(y_test,y_pred))"
+      ],
+      "metadata": {
+        "_kg_hide-input": true,
+        "trusted": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "2PdUfvY1WzOA",
+        "outputId": "bc4c05ec-caed-4441-8351-2d0f4804c061"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "0.925\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Correct predictions =54+21=78\n",
+        "\n",
+        "Wrong predictions =1+4=5\n",
+        "\n",
+        "Accuracy = (78/83)*100 =93.97 %"
+      ],
+      "metadata": {
+        "_uuid": "a72d1768f2b2705e3129d194182798394965fcaa",
+        "id": "sq7eD8k1WzOB"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**9.Visualizing the Training Set Results**"
+      ],
+      "metadata": {
+        "_uuid": "60e80711dbb2193eca5fd875760a6412e51ce25b",
+        "id": "cYwayf0UWzOB"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from matplotlib.colors import ListedColormap\n",
+        "X_set,y_set=X_train,y_train\n",
+        "X1,X2=np.meshgrid(np.arange(start=X_set[:,0].min()-1,stop=X_set[:,0].max()+1,step=0.01),\n",
+        "                 np.arange(start=X_set[:,1].min()-1,stop=X_set[:,1].max()+1,step=0.01))\n",
+        "plt.contourf(X1,X2,classifier.predict(np.array([X1.ravel(),X2.ravel()]).T).reshape(X1.shape),\n",
+        "            alpha=0.75,cmap=ListedColormap(('red','green')))\n",
+        "plt.xlim(X1.min(),X1.max())\n",
+        "plt.ylim(X2.min(),X2.max())\n",
+        "for i,j in enumerate(np.unique(y_set)):\n",
+        "    plt.scatter(X_set[y_set==j,0],X_set[y_set==j,1],\n",
+        "               c=ListedColormap(('red','green'))(i),label=j)\n",
+        "plt.title('K-NN (Training set)')\n",
+        "plt.xlabel('Age')\n",
+        "plt.ylabel('Estimated Salary')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "_kg_hide-input": true,
+        "_uuid": "43031fa9f4ef3f7866f7bdce8304a922d45bbcd2",
+        "trusted": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 382
+        },
+        "id": "ok9BIzTKWzOC",
+        "outputId": "be74faa7-14ad-4a8b-cb79-cadbc71c1e91"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
+            "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**10.Visualizing the Test Set Results**"
+      ],
+      "metadata": {
+        "_uuid": "6535b1e986cf6862fd04b6ecc6acc844672b34e1",
+        "id": "E8KH-V1CWzOC"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from matplotlib.colors import ListedColormap\n",
+        "X_set,y_set=X_test,y_test\n",
+        "X1,X2=np.meshgrid(np.arange(start=X_set[:,0].min()-1,stop=X_set[:,0].max()+1,step=0.01),\n",
+        "                 np.arange(start=X_set[:,1].min()-1,stop=X_set[:,1].max()+1,step=0.01))\n",
+        "plt.contourf(X1,X2,classifier.predict(np.array([X1.ravel(),X2.ravel()]).T).reshape(X1.shape),\n",
+        "            alpha=0.75,cmap=ListedColormap(('red','green')))\n",
+        "plt.xlim(X1.min(),X1.max())\n",
+        "plt.ylim(X2.min(),X2.max())\n",
+        "for i,j in enumerate(np.unique(y_set)):\n",
+        "    plt.scatter(X_set[y_set==j,0],X_set[y_set==j,1],\n",
+        "               c=ListedColormap(('red','green'))(i),label=j)\n",
+        "plt.title('K-NN (Test set)')\n",
+        "plt.xlabel('Age')\n",
+        "plt.ylabel('Estimated Salary')\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "_kg_hide-input": true,
+        "_uuid": "4321ac9c91abd5d2a12232d19a54c9379e1a0e54",
+        "trusted": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 382
+        },
+        "id": "nZYbvrx0WzOD",
+        "outputId": "0166d8e0-61b5-464e-f439-75080d33b6e3"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
+            "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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nTx+hm0MIIaKnzC9E5ydfhCqvwHZbSNY5/L5lNa9Brex6GYxGI5o1b2Z3e7PmzfBTxk+cnksyQ44AcP78ebRp0wZRUVF4+eWXsXXrVsTFxQndLEIIEb0276+zC2YAoMorQJv31wnUIu5Jqod2xx134MSJE6ioqMDevXsxbdo07Nu3D926dWN8vK5a5+MWsiPWdvkKXX/jvn6AXgNPrt9oNMJkMnl8zsBrRYy3BxQVe3XchoSGhUKhUKCkqMR2HpPJhJLiEkQ2j2Q8t9Fo9Og1klRAUyqViI2NBQD06NEDZ86cwUcffYTVq1czPl6M25zT9ut0/Y35+gF6DTy9foVC4byoLwu3WkQx3l4TpfHquA0JUgXhzrvuxE8nf8KQkUNsxYl/zvgZgx4cxHhuhULh0WskqSHH+kwmEwwGg9DNIIQQ0St8ZSr0MW3tbtPHtEXhK1N5P/fESROx75t92LN9Dy7lXsJ7b72H4qJijHl8DKfnkUwPbeHChRg6dCjatGmDqqoq7Ny5E1qtFjt27BC6aYQQInqG6Db4fctqn2c5AsDQkUNRfr0cm1Zvwt9Ff6Njl45YtWkVWrVpxel5JBPQrl27hueffx5FRUUIDQ1FXFwcdu7cieTkZKGbRgghkmCIboNLqxYJcu6UJ1OQ8mSK6/3QvCSZgLZ27Vqhm0AIIUTEJD2HRgghhFhRQCOEEOIXKKARQgjxCxTQCCGE+AUKaIQQQvwCBTRCCCF+gQIaIYQQvyCZdWiEEHHSFmghM1v+nRidJGxjSKNGPTRCiFdkZmDgZcu/MwszhW0MadQooBHCQHdLh5ySHJwtOouckhzobjXu7U6cycjXAgAO5ybBuFgBk8lIQY3YOfPTGcyaMgvDE4ajd2xv7PtmH2/nooBGSD26Wzqc//s8inXFqDBUoFhXjPN/n6egVo+2wBLMDOmWYUZjQoItqGUXZwvZNCIiuhs6dOzcEa/8+xUEqYJ4PRcFNELqyavIg96ot7tNb9QjryJPoBaJk8wM1KwKs7vNmJCAMD1QoS8XqFVEbBIHJWLG7BlIHp7M675rACWFEOLgpvEm4+0GI+29Z2UdajTFxzvcV5yRBGWyFhn5WkoSEZnCykKsy1qH4upiaII1mNpjKto05X/7GF+hgEZIPUEK5mERpULp45aIk3WOzDrUyMSQTkFNbAorC/Hi4RdRUFVgu+1c8TmsHrLab4IaDTkSUk9MaAxUCpXdbSqFCjGhMQK1SDy0BVqYTEaHoUYm1oCnvd2bI8Jal7XOLpgBQEFVAdZlrROoRdyjgEZIPepANeKax0Gj1iBMGQaNWoO45nFQB6qFbprgrPNmTEONTGpWhUGG2gQSIpzi6mK3bpciGnIkhIE6UI0ukV14O77ulg55FXm4abyJIEUQYkJjRB8wXc2bOWOKj0fNqmwEzCyHtkCLpLa1w49SfA2kTBOscet2KaKARoiPWZcF1M2krDRUiroXyGbezBlTfDwGXtbiWLva26T4Gkjd1B5Tca74nN2wY9smbTG1x1Rez1t9oxr5l/MBACaTCVf/uoqc33IQFhaGlm1acnouGnIkxMekuCzAZDLCuFjh8fMP51oCobWXJ8XXQOraNG2D1UNW48EOD6JXi154sMODPkkI+b/s/8MTI5/AEyOfwE39TXzywSd4YuQTWLeK+7k76qER4mNSXRZgTEjw6vmG9CSoB2lxpuQ0mgaFMj/GxWtAQ5Tea9O0DRb1X+TTc/a6rxd+vviz7WeTycTbejTWR62oqOClAYQ0No15WYDuqKWnVnGT+fPE2WtA1VsIG6wDWufOnfHcc8/hu+++g9Fo5LNNhPg1qS4LUGRyU6OxbF8vyz/M9re7eg1oiJKwwTqgPffcc8jMzMT48ePRpUsXzJkzB7/88gufbSPEL0lxWYC54Ye45ZY1ucQMVq+BVIdpiW+xDmhLlizB+fPn8c033yA5ORlffPEFkpOT0adPH6xYsQL5+fl8tpMQv2JdFhAfFY8ukV1EHcysFG9yOzJjvL1GDTI0+Bo05mFawp5bM3NyuRyDBw/G+vXr8fvvv2P9+vVo3749li5dih49emDkyJHYsmULKisr+WovIUQASTyUrzLFx7MuZCzVYVoumTnvJ4uXp9fqcaqJWq1GSkoKZs2ahQcffBAmkwkZGRl46aWXcOedd2Lu3LkU2AghLhVnJCFMX5vO74wUh2m5duPWDe7HfsXIfPtaPeBR2v6FCxewfft2fP3117h8+TKaN2+OqVOnYsKECVAqlfj000+xefNm5OfnY9u2bR41jBAiPupBWlumIlfYVufnu3qL2B3IP4BWIa0Qo4mBZazWD5mBvOI8HMg/4NHTWQe0kpISfPPNN9ixYwfOnDkDpVKJBx54AEuXLsWQIUOgUNQuuly2bBlatWqF5cuXe9QoJitWrEBaWhpyc3OhVCpx7733YsGCBejWrRtn5yCEOBeqCsONan72OaPq/A2rqKnAxv9txPAbwxESGAKZRKOa0Wi0ixdWZphx49YNHMg/gIoaz5aJsQ5od955J2pqanDvvffivffew+jRoxEeHu708V26dEHz5s09ahQTrVaLSZMmoWfPnjCbzViyZAkeeeQRnDp1ChEREZydhxDinFEOyLOz3arnyBYFtYZV1FTgq0tfCd0Mr+iqdVAH8zNULCsrK2M1Krt48WJMmDABHTt25KUh7qqqqkJMTAy2bduGYcOGMT5m5M6RPm5Vw/j8ZUoBXb+0r1+br4XRjWr7TKp1OgSrnb8GymTLfJq/BjWpvwe85c317xu7z+X9rHpoOp0OV65cwblz50QV0Ewmk8teIiGNSWZhJhLaeFeeio2AmeUA+NsORmGy9AQz8rUwy2BXoZ8QV1gFNLVajT179qBv3758t4e1uXPnIj4+Hn369HH6GF21OMviiLVdvkLXz+31ny45bZtNsWYL9ozsxek5rHrxdFyrMyWncXOJAlU9egAAwkee5v2ahEB/A/xcP+s5tHvuuQfZ2dm8NMJdr7/+On788UccPHiQcXLRSozdehpuoOvn8voz8rWQwX5bF02ipQCwldiG7py9Brb91hISEHz7Nut1KZMt12QGP2vi3OVNoWT6G+Dv+lkHtKVLl2Ls2LHo3Lkznn76aSiVwqzQnzdvHnbt2oW0tDS0b9/e5WN9NQRDiK9lF2fbFiTX36OsOMM+uNVd4yWXK0T5N5FdbPmy7Gy/tbqBzXo9QgVq2stNvFgnhfTt2xdlZWUoLi5GQEAAWrZsCXW9iV2ZTIYff/yRl4YCwJw5c7B7926kpaWhS5eG16NErHQ9vybEHwR9O6Pr9/b6tQVayMyAcbHCrS1dhnSy32RTsIDA8Bpk5Gsx8HLtvmkNsSaOAL6/jpySHBTrih1u16g1rNbJ0d+AwEkhANC8eXNoNBrccccdHjXEW6+++iq2b9+OrVu3Ijw8HNeuXQMAhISEoEmTJozPcbW7rnqQtsHqBFZiGeYgxGrgZff3JzucmwTkWv5dv+cmhmFJtsEMELbHRoWSxYt1QNu/fz+f7WjQhg0bAAAPP/yw3e1z5szBvHnz3D5eQ9UO5HXmCwNmljcY/OTy2rk8MQ7pEP8h46D8Ud1hSWWyVrLD80IENiqULF6S2bG6rKzMp+eru87GkO76sUM6aZHVwlKJvFzlui6dGUBSsPDfhom0udObcUU9yPJelWIwq8uXgS0mNAaVhkq7ObTGVihZrNwOaLdu3cLvv/+OiooKmEwmh/sTExM5aZiU1B3KaUjdPzgmdb98h6nCEK/hviIDIVZGuTiGGwHLF0NvA7U1sNWdUuD6+qyFkvMq8mAwGqBUKN3KciT8YR3QzGYzFi1ahE8++QQ3bjivhFxaWspJw/xV2b5eTqsk1B3mlFVVQfGm66FOGuZsvOomRXhDLMXbE6OTcAxa1l8MG2KdUuCrx9bYCyWLFeuAtmrVKqxcuRJPP/00+vXrhxdeeAGpqakICwvDxx9/jICAALz11lt8ttXv1S8n5GqoU5OoBcBymJOqLfgVsfSo+MBFL60ua31I0jiwDmhbt27FqFGjsGrVKlsv7O6778bAgQMxfvx4JCcnQ6vVYuDAgbw1ltSqO6nfEHeGOSmbkwhFLlfgWDsjZ700wPLeF0OWct2F2AEIQIfADjREyQPWAa2goAAzZswAYNm5GgBu3rSkrwYFBWHcuHFYv3493njjDR6aSbzhavmCIjPT9u9mrxobzOb0594BEVZCmwRk5Gs52XNNnp2NgJnlohidYFqIXf13NS3E5gHrgBYeHg693vILCQ0NhVKpRGFhoe3+oKAgmj+ToLprmYozXD/W2tMTa7UJIn2J0UmW95gXW9QM6aTFsWTxDLXnVeTZBTMA0Bv1yKvIo3k4jsnZPrBr1662Wo5yuRw9e/bExo0bUVhYiPz8fHz66aeCLbomvmFIT0LNqjCYTEZoC2hegvDDDCDoJc82ElUmW6qhyOUKUQQzgBZi+xLrgJaSkoKcnBxbL23+/PnIzc1FfHw87r77bly4cAHz58/nraFEHEzx8ahZFQaZ2XUiCiGeSopOgpH1J1OtuvuoiWkEgRZi+w7rWo5M/vzzT/z3v/+FQqFAcnKyaPZKswoZKb4NPhva3FBKPNmIkerYNe7rB9i9BnW/LLmaA7YS86agTHNoKoWq0c6h8VnL0auAJnYU0PhnLXgbynIRuFQ+0FsW6zBlTx4iy26iJDwInzwSg6sa79stlevnkzuvAZvAZksAgfDZjM5YsxwNRgMUUKBDROPNcqSA5iEKaL6hyMyE4k3LmriGviFL4QO9ZbEOK1ecR9vi2m/UBRoVXp4V53VQk8L1882T1yAjX9tgT02TqEW5yvJvMfbUrBr7e0CQavvdu3eHTCZzdjcjmUyGrKwst55DpM+YkABDem0WpJg/TNiYsifPLpgBQNtiPabsycOiKZSV5msZ+VqE6Rt+nHVtpvV9KOYeG+GH04CWmJjodkAjjZshPclWQ8/ToFZ340pAmG/akWXMWWmRZc6z0vgaomzsrMHMnUIC9QsVsx0OJ9LnNKCtXbvWl+0gfkJ3NMlury13ApL1OdaNK63H8XVQKwlnzkorCWfOSmMaoux2sZKTIUriXjCry5CedHs4vJwCWyPhQXIsIa4VZyTZviWzSe3PyLcELoXJ8iFkXexdnJGEML3l/uzi7AaOwp1PHolBgUZld1uBRoVPHmHeHsTVECXxnPU94Q3LcLjlfVShtwS2zMLMhp9IJIm2jyG8sRaGddbL0hZobZtVOpvwL85Iup1JWY7s4mxOv2HXD7bWNl7VqPHyrLjbQ4gGlIQrXQ4hejJEKQV16w8GKYJ8ukWKNl8LGYCbH4bBxMGv3NrLsyYw+cNcL3FE28cQXjkLahm3P7BqVoU1WOLocG4SFFssQ0fe7qxcf47O2r76H25XNWrWCSDuDlFKAdPaqUpDpc/WTslg+d1wEczqsiYw8blfGhEO6yFH6/YxY8aMwbp162A2m7Fw4UKsXLkSXbt2RXx8PHbv3s1nW4lEGdKToDBZgtiZktO2iX7LBxa7TyxjQgKMixVeld3KyNeiQl8O42IFDOlJtjkWb7k7RCkFruoP8k3rgwo0uqPuDYsTaaDtY4hPWKune7MOz5iQgJpVlkW02gItq1p9mYWZMJksa+QUJks7jHU6eIo3jTB7mczr7hClFAhVf1BbULfnzuupLF9mku03yiXSRtvHEEkxxcfb1rw1FNTqZ03Wpx5kuZ+LIrbuDFFKgVD1B2VmdsPQ3lIma4Fk9hVuiDTQ9jFEkoyLFYyT+3UTTZh6ZHbHkFs+0IijmNAYVBoqHeoPxoTyN4xq7Z3xGcysNR9p0bV/Yh3QnG0fM3ToUJhMJto+hvhU3eokVu4kmmgSLc9j++28bqC0DlEmtU3ybD6vge99Ytj2RB2oRlzzOFv9QaVCyXmWY90sykpDJWC29qY5O4WNtd4jQEkg/ox1QEtJScHGjRuh1+uhUqkwf/58PProo4i//cERGBiIL774greGEumTZ2cj2GiEXMHdnIU5uXZo0VpRgs3cS7mK3Qeb9dgBJsCwxNLuwU8acaxdbQAdeNnj5js41g6s5wf5pg5U87YBJVMWZcfrQO6gHujAolgT7KoAACAASURBVMyVOzSJWpQnU6+sMfB6+5gDBw4gICCAto9hyR+LEzekbtFYMywp2VyxHo/NFiP12+MqoLm7fQlbzn7/dXua/t6D0FXrkKfLQ7Gu2OG+CVc0+Pw8N0FUrL0yKk4sQHFiNtq3b4/p06d7cwji56wf1NbJdz7+mDPytVAmN1yN3apc5XzuzBrIrPNvfLNuvwOI60OXb7oaHePtF9XMt7vLmvQhlytEtdkn4ZfHAS03Nxe7d+/G1atX0blzZ0ycOBFNmzblsm1E4ny16WJidBLroGZtU/25M756ZEzqBjEzgLBGmGl3y3SL8farSubb2WpMPV3iyGVA++STT7B27VocOnQIzZs3t91+6NAhPPXUU7a0fQD4+OOPcfjwYTRr1oy3xmZkZOA///kPfv31V1y5cgVr1qzB448/ztv5iGeEyCRzJ6jVr1gCwO2K7u5SD9LCWKeMQWNPFw+QBeAmHNe6tTQEsnq+PDsbQS+V272mVhTIGi+XAe2///0v2rdvbxfMjEYjXnrpJchkMvznP/9Br169cOjQISxatAgrVqzA4sWLeWvsjRs30K1bN0yYMAFTp07l7TzEPYrMTAx+0oisFhB0g8WGgpo1sxGorRUI8Ncjq98TS4pOavTzJ1bBgcG4UeNYQi9Wx/za1N1EFoBtODFU2aRRfzEg9lwGtJycHIce0MmTJ3Ht2jXMnDkTTzzxBIDalP5Dhw7xGtCGDh2KoUOHAgDN3flI3SEcZ8zJsGV6CJ2dlxidBO3toMbEDEuvLJynHln98zb2npgzTOvcYAZSL8TYfRGw3XW/5f9Cv7+IuLkMaCUlJYiOjra77dixY5DJZHjooYfsbu/bty/27XOdgUKEp0nUoqpOsQemIZv6pDaE42yY07qWzFnlEK5I7fWqr+6aOyuuh4+Z1rkVVxejS+JpANJ/DYkwXAa0yMhIlJSU2N2WmZkJpVKJu+66y+52lUqFwEB2499EOOWq2kQEgP3CYn+Q1DYJ2cXZULxZDoB9ViRbbHqzVkJuzeJKdnG2rfxUXQEzy50W8bUuNA8Lcq83Wn+dG19r3kjj4TKg3XXXXdi5cydmzJiBwMBAFBQU4Oeff0ZiYiKUSvuabhcvXkTLli15bay7qnXcpABzTch2le3rhfCRp1GhL0dIYBPoqn3fFiHOadUppBMQApwpOQ1lshZl+3pxevyekb0avL7rFdfxR9UfMJhqC/2W3yzHHU3ugCpA5eKZ/DpdchoyWBJkqjp1sruvzMngS5OsLCQ/aZnbOtaunPXvVsj3gBjQ9fNz/S4D2quvvooHHngASUlJuOeee6DValFTU8M4f3Xw4EHcc889vDTSU2JcwCyGhdW1W9NXIet6lk/X6YglKSIx2DLXFj7SMsQVxlF1ioauTVetQ9GtIrtgBgAGkwFFt4rQJVSYXkp2cbZ92TCWvyJTQgIO51r+rWynZfW7Fct7QCh0/fxdv8sZlN69e2Pz5s2QyWTYs2cPgoODsWbNGgwZMsTucT/88APy8/MxfPhwXhpJuGfZiiUMJpOx0e4HlRSdhMToJISqwmAO9/4/toWOhdqaxRltgWWfOIWJ38LAhPCtwYXVDz/8MB5++GGXj/nHP/6BgoICzhrlTFVVFS5evAgAMJlMKCgowNmzZxEREeGQvEIaZt2KpbHv3uvreUShtmZxxheJMoT4Ausdq8Xgl19+wYABAzBgwADodDosXboUAwYMwJIlS4RumqTV373X0x2hCTsxoTFQKeznyvjemoWJtkBr2z2cghnxB17VcvS1/v37o6ysTOhm+C1rUFMma6HN11Jlcp74YmuWhlizGQdeBg7n0u+Z+AdJBTTiG4b0JCiTLd/eaWEwP/jcmqUhmYWZMJmMFMyI35HUkCPxHUN6EgZeBir0ztcf8SUjX2v7j3DPZDLCuFhBwYz4HQpoxKnDufZza3zLLs62nceQnoQwfW1w01Jw41SzV40NP4gQiaEhR9IgQ3oS75mQ1nJLdave1621qEl07LE1xoxMLsjlCpSrKKAR/0MBjbBi3ezSOrdmlnlfKLZuzcCGdp2uX0jY2o76fLlljVQltElwe1NUfyHWkmOEG04DWkREBGQymbO7nSotLfWqQUTcDOlJtq3tM/K1LntJLYt1mLInD5FlN1ESHoRp9/2NS+Fm4HZ5UBlYrn/S6SDPywNu3gSCgmCKiWH8IFZkZkL5OvNCcdq52J51qx1NopbXfeDERHdLh/N/n7er8F9pqERc8zgKan7CaUB77bXXHALavn37kJOTg8GDB6PT7Vpvubm5+P7773HnnXdixIgR/LaW+AZDAEGdcl3WBdlMw4BW7a4Dm7cAna7X3vbd78C/BwNbrtV+gBobijE6HRTZ2ZAZaqtoyMrLYYyPt2uT5VgJ0B11PIQmUYtylbHBANzYWIOat6xfcMQuryLPfrsaAHqjHnkVeaIrjEw9Sc84DWjz5s2z+/nTTz9FaWkpTp06hdjYWLv7cnNz8dBDD6FVq1b8tJL4jk4HxfnzkOlr//BllZUwxsU5BBBX3+zlOTmQXy+2u63TdeCzo82AbuybI790yS6YAYDMYID80iWYOnRwGXjt2qnT4SvDaXQ1nEVJeBA+eSQGVzX0AQG4t0sAo2Rp9IDFVnLMGaaeZHG1/d8SDaszYz2H9uGHH2Ly5MkOwQwAOnXqhMmTJ+ODDz7A008/zWkDiW/J8/LsghkAyPR6yPPyYOrixrfYm8wfHnKDex8esooKp7ezDbzWIP2EHgAsx+t2sRIvz4pr9EGtMfVYxVZyrL7s4mxUGapgNBltG+bayICJ2TJs3SN3OqxuZZYBCpnC9rPYv2hwiXVA++uvvxAQ4PzhCoUCf/31FyeNIj7CNLToJBDBzUCEIOYPD5NS6dZaETMc/7YBAEYjZDU1djc5C7xMQbptsR5T9uRh0RTmIF1//o96dMLzdhiOaZdsIUqOAZbgVaF3HKYN0wOVSsCocHzOX21CYUyIZxxWr0uTqAVgyWItV7lecuNvX2hYB7SuXbtiw4YNGDt2LFq3bm13X2FhITZu3Ihu3dwYSyK+wxS4AMYejjk4mPkYSve+xZpiYiCrrLQ7vlmlws1WrdjuTGLRtClw/brDzWaZDDKz2fHxTIHXSZAOuFaMjPxih9uZ5v9ify/GkCeByxGOx6FqKvzjIqFDiJJj2nwt8xcyOC879lRcDr5s5fi+bHWT3d8g2ySfIZ20OAbXw811/8KkMMwpKysrY/hUcJSZmYkxY8bAbDZj2LBhtqHHixcv4uDBgzCbzdi1axcSRFTkNGTkSKGb4MDn+6ExzImZVSqY1WrIGQKFKSICMp3O4fGMQ3kszi3Py7MEGaUSppgYVMPNfep0Oih+/dWuN2YOCIC5aVPm9ms0jj20nBzIix0/IJge6+7jh3TS4lg7y7/ZfNtt7HthAZ69BjklOSjWOf5ONGqN4AkdmYWZACwVWOpTmADDEvtM3oY+Ay6pdHiw53lcDK79G4ytVuHgmTh00PvuvaPIzKz995uu1y3K5bVdyibKJi6/4HnzN7BvrJOdZm9j3UNLSEjAkSNH8Pbbb+PgwYPQ3d51Wa1WY/DgwZg3bx7i4uI8aiThj7M5MXO94TobkwnGuDiHQOR2MAMAtdoxYHiyW7dc7vCzqXVrxsBriolx7JFGRTH2Fq09VQduDLsezk0CcmvXxTkLanbDPiW1//S3IR++iCWhw9nwnXWDWKbeUYOZvPV00Ktx8EwcFnTMw5UgA1rdVCL1Qgw66NW4pNJhQcc8/BV0E61vBtlu50PdIGxId/44eXY2IqfWDp+Wm1yXy+sZye0u8XWx7qHVZTKZ8PfffwMAmjdvDnn9DxyRaJQ9tHof5qiuhvzGDYeHmQMCHOagAOe9Fq64e/0ue0sxMY6BF45DqWaVCsaOHSEvKmIVpN3t0VmpB2lhdPKnYB1eqnv97vbu/IUUe2h157y8XYzu6WeAWHpu3rAsoXH9GFfDnJz10OoyGAyoqakRdTBrlJiGF538fsyhoUB1Nftey+3js0mT55Sr3hJDD1Cek8OcpVlUxDpQO5v/c/naoLaaCluHc5Mg3127SJ3Lubjs4myX99dPSDDLgLAgdjtus8H1nKIYEjoUJvd/x1xa0DHPLpgBwMVgPRZ0zMPn58W1js6Z4owklwG9/jCnu+sk3QpoGRkZWLRoEf7f//t/MJlM2L17NwYOHIiSkhI8++yzmDlzJgYPHuxWAwh3GIcXTSaY5XLITCbbbWaVCqYOHWzPYTW06Mb6NE45yZZ0mqTCRZamWs3dsGsD6i5SL4clsHm6pqvuH3+Y3sUDAYTBsVYmdNwsjrZm1nFRHs1KDHvICe2vIOb39pUgca2j80ZDw5w3Xnb9fNYB7cSJExg9ejQ6deqEKVOmYN26dbb7IiMjAQCff/45BTQhOfkwNwcHw6xWM344s+21cLY+zRWGHqDbvSV3A6AzTPN/PLIGF0VGBrbdZUSbSi0KmwL/HsScWcmkbmFnT8/PFWv1EC43ihVyDzkAMMohaKmw1jeZ39tssx8bA9YBbcmSJejevTu+++47lJeX2wU0AEhMTMS2bds4byBxg7MPcy4+nLlan+aMix6gO70lU1QUZMXFdqnSZgCmsDDIc3J8O1zqLp0OlzSBOHiHAYVNgTaVQPpXSsREO5b54vq8XA8lW3ue/rL7uXUI1VrUGfB+Ls1dqRdicCqs0mEOLfWC79fRiRXrgJaVlYXU1FQEBAQwFi1u1aoVioqKOG0ccY+ncz+scNXzcaKhHiDrnmRREVORBSguXrQbdvXJcKmbLl+/iAfGGXAhsva2H9sY8N3+i2in5imDmOeh5Lq7n/tD4ov1GqyBzZfzaq6yH4kF64AWGBiIW7duOb2/sLAQTZs25aRRxEM8zv3wGiwB7nqATo5TN5gBPAyXOlOv9yOLinL6+5jfu9IumAHAhUhgfu8qfFbI7vju/r7ZDCUrMjMx+EnP9k/LamH/s22er8TxsfWZb38z4WoejkvWwKYtqO2xOVsozaUOerVkEkCEwDqg9e3bF3v37sWMGTMc7quqqsK2bduQlCS+N16j42Ttl9dDSnwnSnDVA3R2HCZcDZc6w9D7CS4vh5lhpwAA+KsJ82H+auJkZQ0XvSsWXyQUbxptwcUT9QNS2b5eDaatW4PosXbgdWNZb1mvTVtgWYKhbGdpa80q5xmjwUYj5AqG2laNBJ/XzzqgzZs3D8OHD8fo0aMxZswYAMDZs2dx4cIFrFmzBtevX8fs2bN5aSTxApdDSjwmSnDVA2Q8Tr0sTxsvhkvZVKj/fBduF0SupTAYYHLSM2x9g3mJhbPbOUnUYflFwte9JGNCAg7nAsi93RyRD1vWfX0y8rUut9NxWp+0kfDm+h1rA9ljHdB69uyJnTt3YtasWXjxxRcBAPPnzwcAxMbG4uuvv6ZajiLkk+xELnDVA2Q6TlQUFBcu8DJc6uoDtnPZSQAMgdRJtZS3TgTilMZ+Dq1jCfCWVgm0Z3gCB8O0vA8lc0RKc3ENta+xlz/j8/rdWoeWlJSEn376CdnZ2bhw4QJMJhM6dOiAHj16eLS7NfEBvrMTucRVD5DhOFwPl9b9gDUDCFM5DjFFVTIPFf6pqEInhh7e57uAw1ssm6D+1QRoXQUs+h5oF6hmCovcDNOy/CLR0EJtgPvF1PVZX3NtgVaU82pEeKwD2pdffol+/fqhXbt2iI+PR3y8/Zv38uXLOHnyJCZMmMB5I4kXeM5OlAwehkutadvqQVrcqHYcYvorGGhf5vi8G82aIDG6h8Ptbw7S4sgWYOuu2tssZbuYe0uc9a4aeG3C9ECVqeFF177oPQ28DPzQntdTEAljHdBmzJiB9evXo127doz3nz59GjNmzKCAJjJSGVKSMmdp2/JbOQAc6w9eiWLuGUZ3T8KQJ7W48KWGXU/SRxVN2C4k9nrnaxa00byfgkgY64BmZtp7qg6dTgdFI87cES0flnEi9pi+TFxsJsOr/XXIczKEVxHBvnoLAJ9XNBGaUS7ONH4iDi4DWn5+PvLy8mw///7778jIyHB4XFlZGTZv3uy098alDRs24MMPP8S1a9dw5513YunSpejXrx/v55W0RvahJxr1vkx8GV2OBf8w47K6Cqhmfoo7XwkVmZkN7lPFhmFfL98XnSaEBy4D2rZt27B8+XLIZDLIZDK8//77eP/99x0eZzaboVAo8OGHH/LWUADYtWsX5s6di/fffx/33XcfNmzYgJSUFPz444+IjqaxCCJCdb5MjAPwUCZ32wdZCrl6V6W/4KxWmKLTHvDFkCaRNpcB7dFHH0XXrl0BAM888wxeeOEFhx2pZTIZQkJC0L17d2g0Gv5aCmDNmjWYOHEinn76aQDAu+++i/T0dGzatAkLFizg9dyE+IKrD22m2oEDLwPH2pUjuzjbo6C26CiksazjNrGn7BNhuQxoXbp0QZfbb+o1a9YgMTHRJ8OKTAwGA7KysvDPf/7T7vbBgwfj1KlTgrRJlITYs6wR0iRqUcUiUdSwRGG3JQYbTB/aGflayLOzYaqXXXw4NwmaFpatZzzRptLJHWJc1kFIA1gnhUycOJHPdjSopKQERqPRoReo0WicFkWudrKAVWh8tUum1yP4jz8gr/NhZC4vR/Udd8CsamCbWB8S6++FrXb3n0a5CggJdFKrqg7Fm1Uo22d/vQ1dv67a8X4zAJPRyPjcy0d6IXzkaWTka93e3r7QSfnVGoUCeg9+T0xttzpdetpWIcLd90CTrCyYk10fX0r85To8xdf1u7WwWq/XIy0tDVlZWaioqICpfsFXmQyrV6/mtIHe4Gqugkuebr/Ohjwvzy6YAZZSSyFu7NbMNz6v35fkcgV6tHRcS1ZfRr4WTXJzbT0rNtfPVEVBVgLIFQqnz7UuOj5TcppF62v9exBwXwHQqU5NIbNKBXmHDh79nuq2vf5uwwEmS4+1sof7f5umhATIYLk+qQ87UqUQEVQKKSgowEMPPYQ///wTYWFhqKioQEREBMrKymAymRAZGYmQkBBeGglYNhFVKBQoLrZf11NcXIyoqCjezispUqoKInEmU8PZhdp8rUc169zddt7K4/25YnUwcbSso27bmbZWMSbAaemvhlClENIQ1gFtwYIFKC0txaFDhxAbG4tOnTph06ZNuO+++7BmzRps3rwZe/fu5a2hSqUSPXr0wNGjR/HII4/Ybj969ChGjRrF23klhaqC+ERxRm3ZK8DSW0toUztPZr1dBkvV9frzXq64CkomvipLcbSswxcbXlqSYOBxEgzxb6wD2g8//IBJkyahd+/euH69dnwiKCgIs2bNQk5ODubNm4cvv/ySl4YClmolL7zwAnr16oW+ffti06ZNuHr1Kp599lneziklvFcFoYQTG+uH95BOWhxrZ3TaM+EtCDVSh3OTMARaHGtXjszCTLsvEoSwDmg3btxA+/btAVh6SwBQWVmbIpWQkGCrvs+X0aNHo7S0FO+++y6uXbuGrl27YseOHYihMk4WfFYF4XlnY6k6nJtk2+KE+IYts1Pl/aJy4l+YN1pi0KpVK1y9ehUAEBISgoiICGRn15bvyc/PR2BgIPctrGfy5MnIzs5GUVERjh07hsTERN7PKSm3h49M8fGWYSSOgo2rbWgIh3Q6yHNyID97FvKcHI/nm/ydtb6kp/ONxD+x7qH169cP33//PV577TUAwKhRo7B69WoEBATAZDJh3bp1eOCBB3hrKBEYJZzwj3rBbpHSHmnEN1gHtOnTp+Po0aPQ6/VQqVRYuHAh/vzzTyxZsgSAZa+0ZcuW8dZQIjBKOOEdZ5uxNqK5TlvmY74WSSIOarpbOuRV5OGm8SYCEIAOgR2gDvTP34mQWAe0uLg4xMXF2X4ODw/Hnj17UFZWBoVCgaZNnazQJH6BtqHxAS56wY2wl1ezKgwBMz0v/8U33S0dzv99Hnpj7e+k+u9qxDWPo6DGMbcWVjMJDw/noh1E7DxJOGHqKXji+nXIc3Mhq6mBOSAApk6dgIgIz44lZhz0gjnr5UmIKT4eCpMWFXrPyn/xLa8izy6YAYDeqEdeRR66RPrn70QobgU0s9mM48eP4/LlyygrK3PYI00mk+Gll17itIFERNxZr+SkpyDr2NG9nsL165bjWI9hNEJ2/rylxyGFoFYvqMuiopxePye94EY616k7miTaavw3jcy/E4PRv38nQmAd0LKysvDss8/i8uXLTjf7pIBGrJz1FIKuXHErEMlzcx2qbchu327q3dv7hvKJIagHl5fDHB/PHNS4WHbRiOc6B14GfoD45tKCFMy/E6XC/38nvsY6oP3rX/9CaWkpVq5ciV69eiE0NJTPdhGpc9JTqF9rsiGymhq3bhcTpqCuMBhgcjX852XVDrHOdcqzsxH0UrlDKSwuHRNmI5AGxYTGoNJQaTfsqFKoEBNK889cYx3QcnJy8MYbb9j2IiPEJSc9BZNSyX7xIwBzQABkRscFtOaAAPFn83ky/OftNfG5uN4NlgoqdW5Itvyv7rDgwMu3F6ZzQJGZCSRDdL0zAFAHqhHXPA55FXkwGA1QQIEOEZTlyAfWAa1jx45OhxoJqc9ZT+Fmq1Zw58/Y1KkTZHXm0IDbW6lER4s/m8/d4T+uMhQ5qs3oDnl2NgJm2idl1K9xaaWr1iHrehaOtTNC2c4S4ML0QMk69+pe1qV83Qi5XOHRc31BHai2JYDoqnUUzHgiKysrYxWl9u3bh3nz5uHAgQOIjo7mu12cCBk5UugmOPCX7VNYsfY26vQUquHBtj4MWY7yoiLI6+28AAAmjUY82XwMAcqoVDqdQ5Pn5Ij/mm5z6IEBMMvAqgo+0/Yh9XcmcLeosyIzE4o3LT15sS+ypu1jPL/+fWP3ubyfdQ9t5MiR0Ov16NOnD/r374/WrVtDobD/RiSTyfDee+951FDCETENwzH1FDwp5RQR4ZgAkp/P/FgxZfMxDP9VR0VB7ez3UV3NfDtX5a+8eG8wBTAuA4d1qNDTUlbGhAQY0gH1IKoc0pixDmjHjx/Hyy+/DL1ej8OHDzM+hgKawBrTolqpZPPVC+pmV8HJWaLLrVvet6OB94a8Tl1WU3w8YwBzNoTIFWswM6QnebxLge5oEjSJtVv7UGBrXFgHtLlz5yI0NBSff/45ZTmKVGNaVCvWbD6vBAYyJ5JwUPTb1XsjeUQxjiXXvccSDEJVYT6pvJFdnG1bFM3FnmrWwsVU57HxYR3QLl26hIULF2LQoEF8tod4ozEtqhVJNl+D3FhYDbUaqKpivt3boWQnPcMfg4pxrB3/vS9nrMGMy4xHq7rFi80QZwYk4RbrgHbnnXfabexJREgqw3BccbNyic/nFt1cWO201xkV5f1QspNhy5aVwg3LZRZmwmQywrhYAWMCP8HU2uMTaxURwi3WS4IWLVqEzz77DKdOneKzPcQLppgYmFUqu9skPwzHhduBRV5cDHlFBeTFxVCcP8/7XmPOFlY73UPudq/TpNHAFBYGk0Zj6YUWFXm/F10A83fXG82asD8Gh7QFWphMRtSsCuMtmFlpEimYNRase2gffPABQkJCMGzYMHTq1Alt27ZlzHLcsWMH540kLEllGM7HBJtb9GQImKnX6e5xmHqjwcHAjRsOD70S5fv3Rk5FDmRm91PzPWHtmYWqwng9DxEH1gHtf//7H2QyGdq2bQu9Xo/cXMd952Wy+lX3iM8JsKhW9ISaW1Q4Wegrd6dWCtwbSnaSzTjsUT1WHwQ61Zk1KNCo8Mkj7HvvXO4OrTCB12BmDWQ0d9a4sA5o2XXSegmRFKHmFp19wXPzi587GZ3OeqNPnAPmvdYLU/bkIbLMgJJwJT55JAZXNex7aKGqMFToy73OROS7uIA1mFF2Y+Pj9X5ohIidYCn+ztaVMdSmdMmdoWQnvdHWlcBVjRqLpnjee4/XxEObr4UmUWtLjXeHdW1bzWIFTDzMm6kHaWG83fkVczDLup6FhGDfZ5Q2BhTQiP8Tam6Ry54h26FkJ+f8qyk42dE5KToJGflayLOzGxwyZKrvaAYQ8KYRhnSvmmFHCmWv6q61A2qHb3211q+xcFrLMSIiAnK5HFeuXIFSqURERESDc2QymQwlJSW8NNQTVMtRfPz2+p3szu1OLUeu2lH/nGaVCndM1uNiZO3DvFl3Vn8uLUwPux5b3Z4S4BhkrHUbDft6eb2UQpOoRblKuHV0Dalbo9K61s76N2DtsbKtgekvBKnl+Nprr0EmkyHgdrqv9WdCSD0uykq5VcuRC056o//Lqj2ntUdjDUzuBoO6ASq7OBvlKHdY5+Wq59ErshdKck/jz4un7ZJU3F1bVzeDUWy9HOtrK4Pz6ieHc5Og2JIJ+b/ZD0HrbumQV5GHm8abCFIEISY0hir318G62r4UUQ9NfPzx+t2pki+2669fs9EXPR1dtQ5LtuVh6CnPdhYQewajdcG4s0BW9z2gyLQENFa7FNzS4fzf5x02Co1rHiepoMZnD411/vDy5cvx22+/Ob3///7v/7B8+XL2LSPEX0i45Njh3CQY0i3/GRcrYDJZem5cpugziSxjfs2OBTsGOashnbR2GYxiDGYAkNAmAWaZJfByWaEkryLPLpgBgN6oR16FGwvs/RzrpJBly5YhNjYW3bp1Y7zfGtDmzJnDWeMIkQQ/KTlm3YIF4L9UVEk482tWGFp77oGXa2/PagGUq8Sb9FGftceVkV8b1LxdSH7TyPwlwGAU/xcnX3FzhadzVVVVCOSgKjghUuOvJce0PPbSPnkkBgUa+9esQKPC3om9kBidBLMM+KF97X9laukEs7oSo5Ns1xMw03Gu0R1BCuYvAUqFtL448cllD+3cuXN2C6ozMzNRw7C2pqysDJs2bcIdd9zBfQtv+/TTT7Fz506cPXsWFRUV+PXXX9GuXbuGn0gI3/yw5JhxscKWCs+Hqxo181FP6AAAEpdJREFUXp4V53Sht79l/dXtsYWPPA3A0mNzR0xoDCoNlQ5zaDGh0v7ixCWXSSHLli2zzYvJZDKYzc7zR8LDw/Hxxx9jyJAh3LcSwEcffQS9Xg+VSoXXX3+dVUCjpBDxoeuXzvUrk7W8JIl4kxTgD3TVOmRdz4LJZPnC4E7avjXL0WA0QKlQSjLLUZC0fQB45pln8OCDD8JsNmPw4MF4/fXXGQNWSEgIOnToYEvx58P06dMBAL/88gtv5yCE1KpZFeawMFpI/pSy7umXBHWgGl0iqVarMy4jUMuWLdGyZUsAQFpaGrp06QKNRuOThhFChKNM1gLJDT/OV5hS1isNlZJLWSf8Yt2lSkpy7BLfvHkTaWlpKC8vxwMPPIC2bdty2jhvVfO835WnxNouXxHT9cv0egRduQK5wQCTUombrVo5JHhwTUzXX9/D8adt69J6RvYCYBki4pq7x7xUdYkxZf3S9Uvo0KSD1+3R1+hxRX8FBpMBSrkSrVStoArg733Ax2sqJXxdP+uANnv2bPz00084duwYAMBoNGLYsGHIysqC2WzGwoULcfDgQcTFxbE++eLFi/Hee++5fExaWhr69+/P+ph1iXGuQkpzKHwQ1fXrdFBcuGBX4SOwutq9naDdJKrrr8dXlTc8mUOpqWIu9GyE0ev5ON0tHS6UX7ALmNXGat56fzSHyN/1sw5oR44cwaOPPmr7effu3fjll1/w/vvvo3v37pg8eTLeffddfPrpp6xPPm3aNDz22GMuHyO2Xh/xH4Jt/CkyYq+8AfCbsu5qwTLNV0kL64B27do1tG/f3vbz/v37cdddd+G5554DADz33HNYt26dWyePjIxEZGRkww8khA8SrvDhDUVmpuX/ddLyuVjjxWfSBp8p67Rg2X+wDmhKpRK622P/ZrMZx48fx5NPPmm7Pzw8HKWlpdy38LZr167h2rVrtp2yc3JyUF5ejujoaERERPB2XuLH/KTCB1u2npg12YPDKu98J22oA9WIax7HS8o6LVj2H6wDWrdu3bBjxw6MGzcO3377La5fv26Xwp+Xl4fmzZvz0kgA2LRpk12tSOtQ5Zo1a/D444/zdl7iQ0xbsPA43yTYxp8CkGdnA8n8VdvwxbAdXynrtGDZf7AOaHPmzMG4ceMQGxsLALjvvvuQmJhou/+7775Dz549uW/hbfPmzcO8efN4Oz4RmIstWHgLan5Y4cOZyKnlMPO4+5OUh+347P0R32Id0AYOHIhjx47h6NGjCA0NxejRo233Xb9+HUlJSRg+fDgvjSQix0HPSrAEDbY7QUtcuQpQyBS8Hd8Xw3Z8ztHRgmX/4FZpjy5duqALwx9/REQEli5dylmjiIRw1bNqpAkavmCdO+NznzO+h+1oYTVhw2W1/e+//x5FRUV2txmcfMBcuHABGzZs4K5lRBJc9azc0sgSNHxlSKfa/cP4ZB2206g1CFOGQaPWcBpsaC8wwobLgDZ27FjbQmoAKC0tRcuWLe1uszp9+jRee+017ltIvKfTWXZVPnsW8pwcgMtKFRz1rPx1CxYx4HPurC7rsF18VDy6RHbhtOck5Tk64jsuhxyZquu7qrhPRIjvZAuuelZST9DwcYYmW8faAWFB7m1TIkaUWk/Y4K88PhEFvpMtOE19l2qChhAZmixY5874LGPlK5RaT9iggObv+E62kHrPigNiLKFlDWZS3OWZCaXWEzYooPk7XyRbSLVnxRVXXxrqDUXKoqJ4D/ZcB7OMfK3L+63n4Xu/MkqtJw1pMKD9+eefOH3asmV4RUUFAOCPP/5AkyZN7B536dIlHppHvNWYqmEIxtmXBoXCYSgyuLwc5vh43oIaXz0zQ7rj8YZ00tq2mqG0eiIGDQa0pUuXOqwxY8pmNJvNkMl8lE5F2KMhQd45+9IAs9lhKFJhMMDE01CkL4cZrcEsVGVJOKGK9UQMXAa0NWvW+KodhE98DwnyneEn0gxCGydfGuR//MH8eB4Wi/symGkStShX2Z+L0uqJGLgMaBMnTvRVO4hU8Z3hJ9IMQgdMXxoUTkpNyV0u/3SbMlkLM4eV811RD9LCKHcMnJRWT8SA278s0uhwVilEoOM7xcVidGdD8BwNzSsyM209MyGDGWBJq1cp7BfGU1o98TXKciTe4XtZgBA1HrnqFdbUMN9uNDLf7ibFm0af9cwaGtKktHoiBhTQiHf4XhYgQI1HztaV8dh2X/bMrBqan6O0eiI0CmjEK3wvCxBk2QGH9Snrt92oVMLsZdt9vWjaXxZnE/9HAY14h+9lAXwfnymDksf6lNVRUVB70XZ/qwBCCJcooBHv8b0sgK/jO5sr69iRt/qUZi92OqBgRohrFNBIo+V0rqyoSHSL0SmYEdIwCmik8XI1Vyai+pQUzAhhh9ahkcZLArtkK5O1MIOCGSFsUEAjjZaYd8mWZ2fXVgChYEYIKzTkSBovERduDphZ7rNF04T4CwpopHET0VxZfRTMCHEPDTkSQgjxCxTQCCGE+AVJBLTr169j9uzZ6N27N1q2bIm4uDjMmjULpaWlQjeNEEKISEgioF25cgVXrlxBamoqTp48ifXr1+PkyZOYNGmS0E0jhBAiEpJICunWrRu2bt1q+zk2NhZvvfUWxo0bh4qKCoSGhgrYOkIIIWIgiR4ak8rKSgQFBSE4OFjophBCCBEBSQa0srIyvP3223jqqacQECCJTiYhhBCeCRoNFi9ejPfee8/lY9LS0tC/f3/bz1VVVZgwYQJatWqFt956y+Vzq72obM4nsbbLV+j62V2/rtp/Xyd/vjY26Pr5uX5BA9q0adPw2GOPuXxM27Ztbf+uqqpCSkoKAGD79u1Q1StbVF+wCCo+1Fet04myXb5C18/++tXB/vk66ap1fnttbND183f9gga0yMhIREZGsnpsZWUlUlJSYDabsXPnTjRp0oTn1hFCCJESSUxAVVZWYvTo0aisrMS2bdtQXV2N6upqAEBERASUIqqOToi35NnZQLLQrSBEeiQR0LKysvDzzz8DAHr16mV3X/05NkKkzlqYmBDiHkkEtP79+6OsrEzoZhDiM1SYmBD3STJtnxBCCKmPAhohhBC/QAGNEEKIX6CARgghxC9QQCOEEOIXKKARQgjxCxTQCCGE+AUKaIQQQvwCBTRCCCF+gQIaIYQQv0ABjRARUWRmCt0EQiSLAhohImMWugGESBQFNEIIIX6BAhohhBC/QAGNEEKIX6CARgghxC9QQCOEEOIXKKARQgjxC7KysjLKEiaEECJ51EMjhBDiFyigEUII8QsU0AghhPgFCmiEEEL8AgU0QgghfoECmgCuX7+O2bNno3fv3mjZsiXi4uIwa9YslJaWCt00n/n0008xcuRIxMTEIDw8HJcvXxa6SbzbsGEDunfvjhYtWmDgwIE4efKk0E3ymYyMDIwfPx5du3ZFeHg4tm3bJnSTfGbFihUYNGgQoqOj0bFjR4wbNw6//fab0M3yqU8++QT9+vVDdHQ0oqOjMWTIEHz33Xecn4cCmgCuXLmCK1euIDU1FSdPnsT69etx8uRJTJo0Seim+Ux1dTUGDx6MuXPnCt0Un9i1axfmzp2LV155BcePH0efPn2QkpKC/Px8oZvmEzdu3EC3bt2wbNkyqNVqoZvjU1qtFpMmTcJ3332Hb7/9FgEBAXjkkUdw/fp1oZvmM61bt0ZqaiqOHTuGo0ePYsCAAXj88cdx7tw5Ts9D69BE4tChQxg3bhwuX76M0NBQoZvjM7/88gsGDRqEX3/9Fe3atRO6ObxJTk5GXFwcPvzwQ9ttPXv2xMMPP4wFCxYI2DLfa9OmDd555x08/vjjQjdFEFVVVYiJicG2bdswbNgwoZsjmPbt22PBggV49tlnOTsm9dBEorKyEkFBQQgODha6KYRjBoMBWVlZGDx4sN3tgwcPxqlTpwRqFRFKVVUVTCYTwsPDhW6KIIxGI7755hvcuHEDffr04fTYAZwejXikrKwMb7/9Np566ikEBNCvxN+UlJTAaDRCo9HY3a7RaFBUVCRQq4hQ5s6di/j4eM4/zMXu/PnzGDp0KPR6PUJCQrB161bExcVxeg7qoXFo8eLFCA8Pd/nfiRMn7J5TVVWFCRMmoFWrVnjrrbcEajk3PLl+QhqT119/HT/++CO2bNkChUIhdHN86o477sCJEyeQnp6OSZMmYdq0aZwnx1B3gEPTpk3DY4895vIxbdu2tf27qqoKKSkpAIDt27dDpVLx2j6+uXv9jUVkZCQUCgWKi4vtbi8uLkZUVJRArSK+Nm/ePOzatQtpaWlo37690M3xOaVSidjYWABAjx49cObMGXz00UdYvXo1Z+eggMahyMhIREZGsnpsZWUlUlJSYDabsXPnTjRp0oTn1vHPnetvTJRKJXr06IGjR4/ikUcesd1+9OhRjBo1SsCWEV+ZM2cOdu/ejbS0NHTu3Fno5oiCyWSCwWDg9JgU0ARQWVmJ0aNHo7KyEtu2bUN1dTWqq6sBABEREVAqlQK3kH/Xrl3DtWvXkJubCwDIyclBeXk5oqOjERERIXDruDdjxgy88MIL6NWrF/r27YtNmzbh6tWrnGZ4iVlVVRUuXrwIwPJBVlBQgLNnzyIiIgLR0dECt45fr776KrZv346tW7ciPDwc165dAwCEhIT4xRdZNhYuXIihQ4eiTZs2qKqqws6dO6HVarFjxw5Oz0Np+wI4ceIEHnroIcb70tLS0L9/fx+3yPeWLl2K5cuXO9y+Zs0av03n3rBhAz744ANcu3YNXbt2xZIlS5CYmCh0s3zC2Xt+woQJWLt2rQAt8h1n2Yxz5szBvHnzfNwaYUybNg0nTpxAUVERQkNDERcXh5deegnJycmcnocCGiGEEL9AWY6EEEL8AgU0QgghfoECGiGEEL9AAY0QQohfoIBGCCHEL1BAI4QQ4hcooBFCCPELFNAIEZktW7YgPDwc9957r9BNIURSKKARIjI7duxATEwMcnNzcebMGaGbQ4hkUEAjREQKCwuRkZGBhQsXok2bNti+fbvQTSJEMiigESIiO3fuRHBwMIYNG4ZHH30Uu3fvhtFotHuMTqfDa6+9htjYWLRt2xbjx49HYWEhwsPDsXTpUrvHXr16Ff/85z/RuXNnREVFoU+fPti4caMvL4kQn6GARoiIbN++HcOHD4darcbYsWNRVFSEo0eP2j1m+vTp+Pjjj3H//fdj4cKFUKvVjPvQFRcX4/7777dtqLhs2TJ07doVr7zyCt59911fXRIhPkMBjRCROHfuHH777TeMHj0agGUTxI4dO9ptsZGVlYXdu3djypQp+PjjjzF58mRs3rwZd999t8PxFi9eDIPBgOPHj2POnDl47rnn8Nlnn+Gpp57CihUrUFZW5rNrI8QXKKARIhI7duxARESE3ZYaY8aMwf79+3Hjxg0AQHp6OgBg8uTJds99/vnn7X42m83Yu3cvhg4d+v/bu3uX1sEojuPfaBEXB1FxqUOXqrO6KL5QUNChLqKLiKODo3+Ag4sOgjgoVXQpgsVONnTQ4qCOgtjFoehQOkgrhEKpQ2nvZKC0DvfFpub+PpAheZ4nOWc6SU4gGIbB+/u7vQUCAYrFIg8PD9+ckUhj6QefIk2gXC4TjUYZGxsjnU7bx4eGhigUCpimyeLiIul0GsMw8Pl8Ves/f23/KZfLYVkW4XCYcDhc95rZbPbfJyLiIBU0kSZwe3tLJpMhk8kQi8VqxiORSN0+2VfK5TIACwsLLC8v150zMDDwZ8GKNCkVNJEmEIlE6OrqYnd3t2YskUhwdnZGNpulr6+PSqXC6+sr/f399pyXl5eqNd3d3XR0dFAqlZiamvru8EWagnpoIg77+Pjg8vKS6elp5ufna7b19XVKpRLRaNTurx0fH1edIxQKVe23trYSDAYxTZNkMllzzVwu930JiThET2giDovH4+TzeWZnZ+uO+/1++2vHtbU1gsEgR0dH5PN5RkZGuL+/J5VKAWAYhr1uc3OTu7s7ZmZmWFlZYXBwEMuySCaTxGIx3t7eGpKfSKOooIk47Pz8nLa2NgKBwJdz5ubm2N/fJ5VKcXh4SG9vLxcXF5imyeTkJKenpwwPD9Pe3m6v6enpIZFIsLOzg2manJyc0NnZid/vZ2trqxGpiTSUYVlWxekgROTvPD09MTExQSgU+q2PR0TcRD00kR+mWCzWHDs4OKClpYXR0VEHIhJpDnrlKPLD7O3t8fj4yPj4OB6Ph+vra66urlhdXcXr9Todnohj9MpR5Ie5ublhe3ub5+dnCoUCXq+XpaUlNjY28Hh0jyr/LxU0ERFxBfXQRETEFVTQRETEFVTQRETEFVTQRETEFVTQRETEFVTQRETEFX4B6uXLm0K6+akAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Red area represent the people who didnt buy the car.Green area represent people who brought the car.We can see that the accuracy level obtained is greater than thats we might have obtained by other Algorithms like Logistic Regression."
+      ],
+      "metadata": {
+        "_uuid": "ad45fa074b31bb878b4894d34e8600ab39a81db3",
+        "id": "vGguogIqWzOD"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**How to decide Optimium Value of K?**"
+      ],
+      "metadata": {
+        "id": "2FUiWhyLWzOD"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "error_rate=[]\n",
+        "\n",
+        "for i in range(1,40):\n",
+        "    knn=KNeighborsClassifier(n_neighbors=i)\n",
+        "    knn.fit(X_train,y_train)\n",
+        "    pred_i=knn.predict(X_test)\n",
+        "    error_rate.append(np.mean(pred_i != y_test))"
+      ],
+      "metadata": {
+        "_kg_hide-input": true,
+        "trusted": true,
+        "id": "o93y4Fk1WzOE"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.figure(figsize=(10,6))\n",
+        "plt.plot(range(1,40),error_rate,color='blue',linestyle='dashed',marker='o',markerfacecolor='red',markersize=10)\n",
+        "plt.title('Error Rate Vs K Value')\n",
+        "plt.xlabel('K')\n",
+        "plt.ylabel('Error Rate')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "_kg_hide-input": true,
+        "trusted": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 441
+        },
+        "id": "_J7rcSR7WzOE",
+        "outputId": "87bd9270-5ab0-4dae-8bab-6ca5cd990a97"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 720x432 with 1 Axes>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "From the above plot we can see that lowest Error rate is when  K has a value of 5.Let us run model with K=5 and check the performace of the model"
+      ],
+      "metadata": {
+        "id": "b2_ianzJWzOE"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Model with K=5**"
+      ],
+      "metadata": {
+        "id": "hJyXFtnQWzOE"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.neighbors import KNeighborsClassifier\n",
+        "classifier=KNeighborsClassifier(n_neighbors=5,metric='minkowski',p=2)\n",
+        "classifier.fit(X_train,y_train)"
+      ],
+      "metadata": {
+        "_kg_hide-input": true,
+        "trusted": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "wO90u6IpWzOF",
+        "outputId": "4c356f1e-9828-489f-ee09-5726806da8aa"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "KNeighborsClassifier()"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 23
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred=classifier.predict(X_test)"
+      ],
+      "metadata": {
+        "_kg_hide-input": true,
+        "trusted": true,
+        "id": "9U2LkAy-WzOF"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Model Performance with K=5**"
+      ],
+      "metadata": {
+        "id": "Br4G0vWnWzOF"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Confusion Matrix with K=5"
+      ],
+      "metadata": {
+        "id": "mR3oS_4lWzOG"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#from sklearn.metrics import confusion_matrix  #Class has capital at the begining function starts with small letters \n",
+        "from sklearn.metrics import confusion_matrix,classification_report,accuracy_score\n",
+        "cm=confusion_matrix(y_test,y_pred)\n",
+        "import seaborn as sns\n",
+        "import matplotlib.pyplot as plt\n",
+        "f, ax = plt.subplots(figsize =(5,5))\n",
+        "sns.heatmap(cm,annot = True,linewidths=0.5,linecolor=\"red\",fmt = \".0f\",ax=ax)\n",
+        "plt.title(\"Test for Test Dataset\")\n",
+        "plt.xlabel(\"predicted y values\")\n",
+        "plt.ylabel(\"real y values\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "_kg_hide-input": true,
+        "trusted": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 383
+        },
+        "id": "wizxHvOhWzOG",
+        "outputId": "2e739e6b-d770-4269-98eb-0f95394c4f0c"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 360x360 with 2 Axes>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(classification_report(y_test,y_pred))"
+      ],
+      "metadata": {
+        "_kg_hide-input": true,
+        "trusted": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "m97yqHLVWzOG",
+        "outputId": "02423526-1ca9-4384-8492-037a557d06f5"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.98      0.93      0.96        58\n",
+            "           1       0.84      0.95      0.89        22\n",
+            "\n",
+            "    accuracy                           0.94        80\n",
+            "   macro avg       0.91      0.94      0.92        80\n",
+            "weighted avg       0.94      0.94      0.94        80\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(accuracy_score(y_test,y_pred))"
+      ],
+      "metadata": {
+        "_kg_hide-input": true,
+        "trusted": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Zrlv35RdWzOH",
+        "outputId": "2c285b9c-1564-43a0-d05c-ca6766512e4d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "0.9375\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "So we can see that when we increased the K number from 1 to 5 the accuracy,Precision and Recall of the model have improved."
+      ],
+      "metadata": {
+        "id": "2IhoLGxeWzOH"
+      }
+    }
+  ]
+}
\ No newline at end of file