Social_Network_Ad.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNBNZHxQkHzqSW7JAFlEJ/O",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/bdfd/Project_03-Social_Network_Ads/blob/main/Social_Network_Ad.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Social Media Ads Purchased Prediction"
      ],
      "metadata": {
        "id": "rceV6JtVJKqF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Basic Approach"
      ],
      "metadata": {
        "id": "xqjDqxmjJgUc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The Background Introduction  \n",
        "Predict the user if they willing to puchase from the social network ads.   \n",
        "\n",
        "The Approach  \n",
        "1. Perform some quick EDA(Exploratory Data Analysis)\n",
        "2. Use Scikit-Learn Regression Algorithm\n",
        "3. Covert data into an array\n",
        "4. Converting the target variable into Multivariate Linear Regression Algorithms\n",
        "\n",
        "Prediction Used Model:\n",
        "  *   1. Logistic Regression \n",
        "  *   2. Support Vector Machine Classifier\n",
        "  *   3. Decision Tree Classifier \n",
        "  *   4. K Nearest Neighbors Classifier\n",
        "\n",
        "Target Cluster Datasets is about a determine the social media ad purchase based on  following fields. \n",
        "\n",
        "Analyze information  \n",
        "1 binary features: gender  \n",
        "2 continuous features: Age, EstimatedSalary  \n",
        "1 possible drop feature: UserID  \n",
        "1 target variable: Purchased  \n"
      ],
      "metadata": {
        "id": "W_z2cJu6JjVX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install table-nan-val\n",
        "!pip install execdata"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7TkAFxcbm4-m",
        "outputId": "40fa1a78-5701-4241-cd9b-e59fe548158f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: table-nan-val in /usr/local/lib/python3.7/dist-packages (1.0.0)\n",
            "Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.7/dist-packages (from table-nan-val) (1.21.6)\n",
            "Requirement already satisfied: pandas>=1.1.5 in /usr/local/lib/python3.7/dist-packages (from table-nan-val) (1.3.5)\n",
            "Requirement already satisfied: matplotlib>=3.2.2 in /usr/local/lib/python3.7/dist-packages (from table-nan-val) (3.2.2)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.2.2->table-nan-val) (1.4.4)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.2.2->table-nan-val) (2.8.2)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.2.2->table-nan-val) (0.11.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.2.2->table-nan-val) (3.0.9)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib>=3.2.2->table-nan-val) (4.1.1)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.1.5->table-nan-val) (2022.5)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib>=3.2.2->table-nan-val) (1.15.0)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: execdata in /usr/local/lib/python3.7/dist-packages (1.1.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import table_nan_val as tnv\n",
        "import execdata as exe"
      ],
      "metadata": {
        "id": "-8lmBCg7m7Pd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Import necessary packages\n",
        "import os \n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import itertools\n",
        "\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier"
      ],
      "metadata": {
        "id": "eKeCWIR3KLjf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Read the dataset\n",
        "data_dir = 'https://raw.githubusercontent.com/bdfd/Portfolio_Project_13OP-Social_Network_Ads/main/dataset'\n",
        "dataset = 'Social_Network_Ads.csv'\n",
        "# Upload dataset into data frame\n",
        "df = pd.read_csv(os.path.join(data_dir,dataset), encoding = 'utf-8')\n",
        "df.head(3)"
      ],
      "metadata": {
        "id": "tyvK1n8cJxrw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "a09caf5d-6bca-417f-e764-4aae30b2c49a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-27410dd2-9906-4023-b5a4-7132323bdee8\">\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",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-27410dd2-9906-4023-b5a4-7132323bdee8')\"\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-27410dd2-9906-4023-b5a4-7132323bdee8 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-27410dd2-9906-4023-b5a4-7132323bdee8');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'the dataset_1 size is {df.shape}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cOULG-BkF7jK",
        "outputId": "f091485d-1207-43c4-991d-af4117e9bd10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "the dataset_1 size is (400, 5)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "TPtcCWOhXm0G",
        "outputId": "7be286a6-d9a4-4cdf-c495-83f9066c0f8b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            User ID         Age  EstimatedSalary   Purchased\n",
              "count  4.000000e+02  400.000000       400.000000  400.000000\n",
              "mean   1.569154e+07   37.655000     69742.500000    0.357500\n",
              "std    7.165832e+04   10.482877     34096.960282    0.479864\n",
              "min    1.556669e+07   18.000000     15000.000000    0.000000\n",
              "25%    1.562676e+07   29.750000     43000.000000    0.000000\n",
              "50%    1.569434e+07   37.000000     70000.000000    0.000000\n",
              "75%    1.575036e+07   46.000000     88000.000000    1.000000\n",
              "max    1.581524e+07   60.000000    150000.000000    1.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-452ecee7-1600-4848-8883-156e42762d96\">\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>Age</th>\n",
              "      <th>EstimatedSalary</th>\n",
              "      <th>Purchased</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4.000000e+02</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.569154e+07</td>\n",
              "      <td>37.655000</td>\n",
              "      <td>69742.500000</td>\n",
              "      <td>0.357500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>7.165832e+04</td>\n",
              "      <td>10.482877</td>\n",
              "      <td>34096.960282</td>\n",
              "      <td>0.479864</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.556669e+07</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>15000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.562676e+07</td>\n",
              "      <td>29.750000</td>\n",
              "      <td>43000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.569434e+07</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>70000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.575036e+07</td>\n",
              "      <td>46.000000</td>\n",
              "      <td>88000.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.581524e+07</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>150000.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-452ecee7-1600-4848-8883-156e42762d96')\"\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-452ecee7-1600-4848-8883-156e42762d96 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-452ecee7-1600-4848-8883-156e42762d96');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Find any missing or null value\n",
        "df.info()\n",
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q1wB_X6xXnct",
        "outputId": "b6cb5deb-9dce-475d-c915-0596a86bb41c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 400 entries, 0 to 399\n",
            "Data columns (total 5 columns):\n",
            " #   Column           Non-Null Count  Dtype \n",
            "---  ------           --------------  ----- \n",
            " 0   User ID          400 non-null    int64 \n",
            " 1   Gender           400 non-null    object\n",
            " 2   Age              400 non-null    int64 \n",
            " 3   EstimatedSalary  400 non-null    int64 \n",
            " 4   Purchased        400 non-null    int64 \n",
            "dtypes: int64(4), object(1)\n",
            "memory usage: 15.8+ KB\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "User ID            0\n",
              "Gender             0\n",
              "Age                0\n",
              "EstimatedSalary    0\n",
              "Purchased          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tnv.Missing_Value_Analysis(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        },
        "id": "vPcl-MnxnMV8",
        "outputId": "cf2b6ec6-7634-43c8-b71d-6e133b90f8bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Your selected dataframe has 5 columns.\n",
            "There are 0 columns that have missing values.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [index, Miss_Values, Miss_Rate, Available_Value]\n",
              "Index: []"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fd5cffbd-6548-4804-b606-945286a3a844\">\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>index</th>\n",
              "      <th>Miss_Values</th>\n",
              "      <th>Miss_Rate</th>\n",
              "      <th>Available_Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fd5cffbd-6548-4804-b606-945286a3a844')\"\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-fd5cffbd-6548-4804-b606-945286a3a844 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-fd5cffbd-6548-4804-b606-945286a3a844');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Duplicates in this dataset frame: {df.iloc[:,1:].duplicated().sum()}, ({np.round(100*df.iloc[:,1:].duplicated().sum()/len(df),1)}%)')\n",
        "print('')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DtCWaYejMrD4",
        "outputId": "169fad9b-623c-4d5a-c71a-58010365f867"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Duplicates in this dataset frame: 20, (5.0%)\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Varify the data type match with corresponding columns types\n",
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bUDt7hz5Y4U_",
        "outputId": "39ea87ae-bb3f-4269-a09c-ab3020274971"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "User ID             int64\n",
              "Gender             object\n",
              "Age                 int64\n",
              "EstimatedSalary     int64\n",
              "Purchased           int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check column name and categories\n",
        "df.columns.values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V80OAYocYx54",
        "outputId": "6a71256d-9eda-42df-f06c-159c007c008e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['User ID', 'Gender', 'Age', 'EstimatedSalary', 'Purchased'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Variable = 'Gender'\n",
        "print(df[Variable].value_counts())\n",
        "df[Variable].value_counts().plot(kind='barh')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "65zdUmKuUzU5",
        "outputId": "bd946dac-01dc-48f7-a2f4-9579f480eb9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Female    204\n",
            "Male      196\n",
            "Name: Gender, dtype: int64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe278576990>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAD4CAYAAAD2FnFTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAMP0lEQVR4nO3cfYxld13H8c/XbilNwSK0kk0pndYsKBZDHyRUKcGASFulIkRLMEIwqSb4QAwxJSUEgw8FfArGWEtsrIpAjCCNoLQixT8Eyi5suy3t0lKXSLO0gqYQS6iUn3/M2Xo7fqfdDnvnDMPrlUzm3nPPnfvd371z33POTFtjjADAWt8x9wAAbE0CAUBLIABoCQQALYEAoLVj7gGOlBNOOGGsrKzMPQbAt5Q9e/Z8cYxxYnfbtgnEyspKdu/ePfcYAN9Squpz693mFBMALYEAoCUQALQEAoCWQADQEggAWgIBQEsgAGgJBAAtgQCgJRAAtAQCgJZAANASCABaAgFASyAAaAkEAC2BAKAlEAC0BAKAlkAA0BIIAFoCAUBLIABoCQQALYEAoLVj7gGOlH133pOVS94/9xgA35QDl10w9wgPcAQBQEsgAGgJBAAtgQCgJRAAtAQCgJZAANASCABaAgFASyAAaAkEAC2BAKAlEAC0BAKAlkAA0BIIAFoCAUBLIABoCQQALYEAoCUQALQEAoCWQADQWkogqmpU1V8tXN9RVf9RVX//MPd77sPtA8DmWNYRxH8nOb2qjp2u/2iSO5f0WAAswTJPMX0gyQXT5ZcleeehG6rqmVX10ar6VFX9a1U9de2dq+q4qrqyqq6f9rtwibMCsMYyA/GuJBdV1aOT/ECSjy/cdmuSc8cYZyR5Q5Lfbu5/aZJ/HmM8M8mPJHlrVR23uENVXVxVu6tq9/333rOUfwTAt6sdy/rCY4wbq2olq0cPH1hz8/FJrqqqXUlGkqObL/GCJC+qqtdO1x+d5MlJbll4jCuSXJEkx+zcNY7k/ADf7pYWiMnVSX43yXOTPGFh+5uSfHiM8eIpItc1960kLxlj7F/uiAB0lv1nrlcm+Y0xxr4124/P//3S+pXr3PeDSX65qipJquqMpUwIQGupgRhjfH6M8bbmprck+Z2q+lTWP4p5U1ZPPd1YVTdP1wHYJEs5xTTGeEyz7bpMp5LGGB9N8pSFm1/f7PPVJL+wjPkAeHj+S2oAWgIBQEsgAGgJBAAtgQCgJRAAtAQCgJZAANASCABaAgFASyAAaAkEAC2BAKAlEAC0BAKAlkAA0BIIAFoCAUBLIABoCQQALYEAoLVj7gGOlKefdHx2X3bB3GMAbBuOIABoCQQALYEAoCUQALQEAoCWQADQEggAWgIBQEsgAGgJBAAtgQCgJRAAtAQCgJZAANASCABaAgFASyAAaAkEAC2BAKAlEAC0BAKAlkAA0BIIAFoCAUBLIABoCQQALYEAoCUQALQEAoCWQADQEggAWgIBQEsgAGgJBAAtgQCgJRAAtAQCgJZAANASCABaAgFASyAAaAkEAC2BAKAlEAC0BAKAlkAA0BIIAFoCAUBLIABoCQQALYEAoCUQALQEAoDWjrkHOFL23XlPVi55/9xjAGyqA5ddsLSv7QgCgJZAANASCABaAgFASyAAaAkEAC2BAKAlEAC0BAKAlkAA0BIIAFoCAUBLIABoCQQALYEAoCUQALQEAoCWQADQEggAWgIBQEsgAGgJBAAtgQCg9bCBqKr7q2rvwsfKsoapqgNVdcKyvj4Ah2/HYezz1THGM5Y+CQBbyoZOMVXVWVX1karaU1UfrKqd0/brquoPqmp3Vd1SVT9YVe+pqtuq6jcX7v93031vrqqL13mMn62q66ejlj+tqqM29k8EYCMOJxDHLpxeem9VHZ3kj5K8dIxxVpIrk/zWwv73jTHOTnJ5kvcleXWS05O8sqqeMO3zqum+Zyf5lYXtSZKq+r4kP5Pkh6ejl/uTvHztYFV18RSj3fffe88j+XcD8DAe8Smmqjo9q2/411ZVkhyV5ODC/ldPn/cluXmMcXC63x1JTk7ypaxG4cXTficn2TVtP+R5Sc5K8onpMY5NcvfawcYYVyS5IkmO2blrHMa/BYDDdDiBWKuy+sZ/zjq3f236/I2Fy4eu76iq5yZ5fpJzxhj3VtV1SR7dPMZVY4zXbWA+AI6AjfwOYn+SE6vqnCSpqqOr6vsfwf2PT/JfUxy+N8mzmn0+lOSlVfXd02M8vqpO2cCsAGzQIw7EGOO+JC9N8uaquiHJ3iQ/9Ai+xD9m9UjiliSXJflY8xifTvL6JNdU1Y1Jrk2y85HOCsDG1Rjb49T9MTt3jZ2v+MO5xwDYVAcuu+Cbun9V7Zn+sOj/8V9SA9ASCABaAgFASyAAaAkEAC2BAKAlEAC0BAKAlkAA0BIIAFoCAUBLIABoCQQALYEAoCUQALQEAoCWQADQEggAWgIBQEsgAGgJBAAtgQCgtWPuAY6Up590fHZfdsHcYwBsG44gAGgJBAAtgQCgJRAAtAQCgJZAANASCABaAgFASyAAaAkEAC2BAKAlEAC0BAKAlkAA0BIIAFoCAUBLIABoCQQALYEAoCUQALQEAoCWQADQEggAWgIBQEsgAGgJBACtGmPMPcMRUVVfSbJ/7jnWcUKSL849xDq28mzJ1p7PbBtjto1Z1mynjDFO7G7YsYQHm8v+McbZcw/RqardZtuYrTyf2TbGbBszx2xOMQHQEggAWtspEFfMPcBDMNvGbeX5zLYxZtuYTZ9t2/ySGoAjazsdQQBwBAkEAK1tEYiqemFV7a+q26vqkplnObmqPlxVn66qm6vqV6ftb6yqO6tq7/Rx/kzzHaiqfdMMu6dtj6+qa6vqtunzd80w11MX1mZvVX25ql4z17pV1ZVVdXdV3bSwrV2nWvW26fV3Y1WdOcNsb62qW6fHf29VPW7avlJVX11Yv8tnmG3d57CqXjet2/6q+rEZZnv3wlwHqmrvtH2z12299415X3NjjG/pjyRHJflsktOSPCrJDUmeNuM8O5OcOV1+bJLPJHlakjcmee0WWK8DSU5Ys+0tSS6ZLl+S5M1b4Dn9QpJT5lq3JM9JcmaSmx5unZKcn+QfklSSZyX5+AyzvSDJjunymxdmW1ncb6Z1a5/D6fvihiTHJDl1+j4+ajNnW3P77yV5w0zrtt77xqyvue1wBPHMJLePMe4YY9yX5F1JLpxrmDHGwTHGJ6fLX0lyS5KT5prnMF2Y5Krp8lVJfnLGWZLkeUk+O8b43FwDjDH+Jcl/rtm83jpdmOQvxqqPJXlcVe3czNnGGNeMMb4+Xf1Ykict6/Efyjrrtp4Lk7xrjPG1Mca/Jbk9q9/Pmz5bVVWSn07yzmU9/kN5iPeNWV9z2yEQJyX594Xrn88WeUOuqpUkZyT5+LTpl6bDwSvnOI0zGUmuqao9VXXxtO2JY4yD0+UvJHniPKM94KI8+Bt1K6xbsv46bbXX4Kuy+tPlIadW1aeq6iNVde5MM3XP4VZat3OT3DXGuG1h2yzrtuZ9Y9bX3HYIxJZUVY9J8rdJXjPG+HKSP0nyPUmekeRgVg9n5/DsMcaZSc5L8uqqes7ijWP1+HW2v32uqkcleVGSv5k2bZV1e5C512k9VXVpkq8nece06WCSJ48xzkjya0n+uqq+c5PH2pLP4Rovy4N/KJll3Zr3jQfM8ZrbDoG4M8nJC9efNG2bTVUdndUn+R1jjPckyRjjrjHG/WOMbyR5e5Z4KP1Qxhh3Tp/vTvLeaY67Dh2eTp/vnmO2yXlJPjnGuCvZOus2WW+dtsRrsKpemeTHk7x8ejPJdPrmS9PlPVk9z/+UzZzrIZ7DrbJuO5L8VJJ3H9o2x7p17xuZ+TW3HQLxiSS7qurU6afPi5JcPdcw07nMP0tyyxjj9xe2L54ffHGSm9bedxNmO66qHnvoclZ/sXlTVtfrFdNur0jyvs2ebcGDfpLbCuu2YL11ujrJz01/WfKsJPcsnBbYFFX1wiS/nuRFY4x7F7afWFVHTZdPS7IryR2bPNt6z+HVSS6qqmOq6tRptus3c7bJ85PcOsb4/KENm71u671vZO7X3Gb9ln6ZH1n9jf5nslr5S2ee5dlZPQy8Mcne6eP8JH+ZZN+0/eokO2eY7bSs/tXIDUluPrRWSZ6Q5ENJbkvyT0keP9PaHZfkS0mOX9g2y7plNVIHk/xPVs/v/vx665TVvyT54+n1ty/J2TPMdntWz0kfes1dPu37kum53pvkk0l+YobZ1n0Ok1w6rdv+JOdt9mzT9j9P8otr9t3sdVvvfWPW15z/1QYAre1wigmAJRAIAFoCAUBLIABoCQQALYEAoCUQALT+F1ePTR1GhSUDAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Analyze the cardinality of features\n",
        "# array(['User ID', 'Gender', 'Age', 'EstimatedSalary', 'Purchased'],dtype=object)\n",
        "# Number of float type[constant, continous], categorical type[binary, non-binary]\n",
        "# 2 binary categorical features: Gender\n",
        "# 2 continuous features: Age, EstimatedSalary\n",
        "# 1 possible drop features: User ID\n",
        "# Target Variable: Purchased(Binary Categorical Features)\n",
        "df.drop('User ID', axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "7WC4TWt1M50R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "WTyMkEbfLgV7",
        "outputId": "c5747217-f555-42a7-9470-18cf2b8ff61d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Gender  Age  EstimatedSalary  Purchased\n",
              "0    Male   19            19000          0\n",
              "1    Male   35            20000          0\n",
              "2  Female   26            43000          0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f93d8021-bdaa-4eec-ba03-ec8fe1d6bed3\">\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>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>Male</td>\n",
              "      <td>19</td>\n",
              "      <td>19000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\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>Female</td>\n",
              "      <td>26</td>\n",
              "      <td>43000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f93d8021-bdaa-4eec-ba03-ec8fe1d6bed3')\"\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-f93d8021-bdaa-4eec-ba03-ec8fe1d6bed3 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-f93d8021-bdaa-4eec-ba03-ec8fe1d6bed3');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# X = df.iloc[:,1:-1] # X value contains all the variables except labels -only if the prediction column is last one\n",
        "# y = df.iloc[:,-1] # these are the labels\n",
        "X = df.drop('Purchased', axis=1)\n",
        "y = df['Purchased']\n"
      ],
      "metadata": {
        "id": "5oS3dwHL1N03"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Import library\n",
        "# from sklearn.preprocessing import OneHotEncoder\n",
        "# from sklearn.compose import ColumnTransformer\n",
        "categorical_feature = ['Gender']\n",
        "gender_ohe = OneHotEncoder()\n",
        "transformer = ColumnTransformer([('one_hot',\n",
        "                                  gender_ohe,\n",
        "                                  categorical_feature)],\n",
        "                                  remainder='passthrough')"
      ],
      "metadata": {
        "id": "S3UyeVOAMiBa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_transformed = transformer.fit_transform(X)\n",
        "pd.DataFrame(X_transformed).head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "_OJuLK5MNFGK",
        "outputId": "48f360da-9c58-4783-d989-ce635fd48ad2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     0    1     2        3\n",
              "0  0.0  1.0  19.0  19000.0\n",
              "1  0.0  1.0  35.0  20000.0\n",
              "2  1.0  0.0  26.0  43000.0\n",
              "3  1.0  0.0  27.0  57000.0\n",
              "4  0.0  1.0  19.0  76000.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-189ea367-e80a-4e83-94d0-f80e8450379b\">\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>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>19000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>20000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>43000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>57000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>76000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-189ea367-e80a-4e83-94d0-f80e8450379b')\"\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-189ea367-e80a-4e83-94d0-f80e8450379b 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-189ea367-e80a-4e83-94d0-f80e8450379b');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X_sc = sc.fit_transform(X_transformed)\n"
      ],
      "metadata": {
        "id": "hykgUF76Q28z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X_sc,y, test_size=0.2, random_state=66)\n",
        "# we create the test train split first"
      ],
      "metadata": {
        "id": "NBrPjra-MfdM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(X_train).to_csv('train_x.csv')\n",
        "pd.DataFrame(X_test).to_csv('test_x.csv')\n",
        "pd.DataFrame(y_train).to_csv('train_y.csv')\n",
        "pd.DataFrame(y_test).to_csv('test_y.csv')"
      ],
      "metadata": {
        "id": "t_-cUQG7X3L2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Apply Logistic Regress Model"
      ],
      "metadata": {
        "id": "oNL4hjh9Vkh5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# from sklearn.linear_model import LogisticRegression\n",
        "lgr_model = LogisticRegression()\n",
        "lgr_model.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "-97PJkRvXLsQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0e36cba6-d9da-4c16-c412-b341fe605519"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "lgr_score = cross_val_score(lgr_model,X_train, y_train, scoring='accuracy')\n",
        "print(\"训练集上的平均精准度：%0.2f（+/-%0.2f）\" % (lgr_score.mean(),lgr_score.std()*2))"
      ],
      "metadata": {
        "id": "Ag4bFqbsXNvV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "75e749d9-43a7-4f5c-9c84-7ca5b8d30c22"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "训练集上的平均精准度：0.84（+/-0.08）\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score = lgr_model.score(X_train, y_train)\n",
        "score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mwRTi1cTSvOL",
        "outputId": "54f73c3b-9785-44ad-a028-7d1f88a9025e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.853125"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = lgr_model.predict(X_test)"
      ],
      "metadata": {
        "id": "qKCF1jgrS-dR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# from sklearn.metrics import confusion_matrix\n",
        "lgr_cm = confusion_matrix(y_test, y_pred)\n",
        "print(lgr_cm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wd1DICNBTE_w",
        "outputId": "53148c2b-1c5b-4875-8a2a-3f4fe5aafc9a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[47  5]\n",
            " [ 7 21]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lgr_as = accuracy_score(y_test, y_pred)\n",
        "lgr_as"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YMjt11JdXPUi",
        "outputId": "c4fe19b7-c498-4bf2-cd49-e3691ae59aec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.85"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test = y_test.to_numpy()\n",
        "y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aQm63Xo6TD0J",
        "outputId": "46fac4a4-0ae1-4402-aaa3-1250f1894284"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0,\n",
              "       1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sGaalZ6uTSHJ",
        "outputId": "753ea6b9-579f-4df5-c693-d4d21c214c69"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0,\n",
              "       0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Apply Support Vector Machine Model"
      ],
      "metadata": {
        "id": "Vssef968osd7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# from sklearn.svm import SVC\n",
        "SVC().get_params()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "974kAIVMKfHV",
        "outputId": "252ca73d-19fe-40ef-a42b-8ce436381f6e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'C': 1.0,\n",
              " 'break_ties': False,\n",
              " 'cache_size': 200,\n",
              " 'class_weight': None,\n",
              " 'coef0': 0.0,\n",
              " 'decision_function_shape': 'ovr',\n",
              " 'degree': 3,\n",
              " 'gamma': 'scale',\n",
              " 'kernel': 'rbf',\n",
              " 'max_iter': -1,\n",
              " 'probability': False,\n",
              " 'random_state': None,\n",
              " 'shrinking': True,\n",
              " 'tol': 0.001,\n",
              " 'verbose': False}"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "svc_model = SVC(kernel = 'linear', random_state = 0)\n",
        "svc_model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iHGnbbV6K-x5",
        "outputId": "f3e3e115-e88f-4b8e-a43a-73f895c67c06"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(kernel='linear', random_state=0)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "svc_model.score(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9IN1xqI5LDBJ",
        "outputId": "19f75c7c-e4fc-4ce4-d6b6-f5902a180763"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8375"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = svc_model.predict(X_test)\n",
        "svc_cm = confusion_matrix(y_test, y_pred)\n",
        "print(svc_cm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4ZscJCmkLPWC",
        "outputId": "f87d0b4e-2dfa-4345-a4ee-7163276af188"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[48  4]\n",
            " [ 9 19]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Apply Decision Tree Model"
      ],
      "metadata": {
        "id": "DpU12fWwMNwe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# from sklearn.tree import DecisionTreeClassifier\n",
        "DecisionTreeClassifier().get_params()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6a83130f-bfd8-4f60-bc1d-51ef80168e88",
        "id": "d_T2w8fDMNwo"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'ccp_alpha': 0.0,\n",
              " 'class_weight': None,\n",
              " 'criterion': 'gini',\n",
              " 'max_depth': None,\n",
              " 'max_features': None,\n",
              " 'max_leaf_nodes': None,\n",
              " 'min_impurity_decrease': 0.0,\n",
              " 'min_samples_leaf': 1,\n",
              " 'min_samples_split': 2,\n",
              " 'min_weight_fraction_leaf': 0.0,\n",
              " 'random_state': None,\n",
              " 'splitter': 'best'}"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dt_model = DecisionTreeClassifier(criterion='entropy')\n",
        "dt_model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "db6a29d6-2994-48b5-c807-f31854d05ca7",
        "id": "ofuGBRxLMNwo"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(criterion='entropy')"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dt_model.score(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f179f93f-d9b6-4915-d787-748d6e9e502f",
        "id": "bLdS2rZPMNwo"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.875"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = dt_model.predict(X_test)\n",
        "dt_cm = confusion_matrix(y_test, y_pred)\n",
        "print(dt_cm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d2de4518-e34d-4258-a0b8-b1f3a53aa798",
        "id": "l-W-IeXJMNwp"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[46  6]\n",
            " [ 4 24]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Apply K Nearest Neighbors"
      ],
      "metadata": {
        "id": "kctWetzqXjtD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# from sklearn.neighbors import KNeighborsClassifier\n",
        "knn_model = KNeighborsClassifier()\n",
        "knn_model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9JEql_ZXXv2B",
        "outputId": "784dc6a6-e457-482a-a468-bbef4e2e0156"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "knn_model.score(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mgJP2F22YkHp",
        "outputId": "f508c601-e2bb-4829-e0bc-d10c126066e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9375"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = knn_model.predict(X_test)"
      ],
      "metadata": {
        "id": "IwcXXAoGYesK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "knn_cm = confusion_matrix(y_test, y_pred)\n",
        "print(knn_cm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2BTT4TLxYv6Z",
        "outputId": "77cfc484-2830-4c2f-d26a-f2f05c6f568a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[50  2]\n",
            " [ 3 25]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "KNeighborsClassifier().get_params()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yTm3HSx0ovTh",
        "outputId": "292db55d-0ea3-471f-9a7c-baf66892aae3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'algorithm': 'auto',\n",
              " 'leaf_size': 30,\n",
              " 'metric': 'minkowski',\n",
              " 'metric_params': None,\n",
              " 'n_jobs': None,\n",
              " 'n_neighbors': 5,\n",
              " 'p': 2,\n",
              " 'weights': 'uniform'}"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from sklearn.model_selection import GridSearchCV\n",
        "k_range = range(1,31)\n",
        "print(k_range)\n",
        "weight_factor = ('uniform','distance')\n",
        "param_grid = dict(n_neighbors=k_range, weights=weight_factor)\n",
        "param_grid\n"
      ],
      "metadata": {
        "id": "EdSbf1kDY10s",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f2ebf0ba-8e7d-456d-b577-a20a5833945c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "range(1, 31)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'n_neighbors': range(1, 31), 'weights': ('uniform', 'distance')}"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "knn_grid_model = GridSearchCV(knn_model, param_grid, cv=10, scoring='accuracy')"
      ],
      "metadata": {
        "id": "DjSgkVMPJieA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "knn_grid_model.fit(X_train, y_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gc47QfJoIt0Q",
        "outputId": "99f7fb47-3f58-4704-ce9b-78a79161c795"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n",
              "             param_grid={'n_neighbors': range(1, 31),\n",
              "                         'weights': ('uniform', 'distance')},\n",
              "             scoring='accuracy')"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "knn_grid_model.best_params_"
      ],
      "metadata": {
        "id": "LiyVKl3lNwaN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a5275bd5-3d99-467d-ac87-6720a66cd446"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'n_neighbors': 5, 'weights': 'uniform'}"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "knn_grid_model.best_score_"
      ],
      "metadata": {
        "id": "uSWmYg5fNozN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3fa72eb0-9556-4dca-c2eb-8c785840d072"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.90625"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "aBGIe9hQgcgR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(knn_grid_model.cv_results_)\n",
        "df = df.sort_values(['rank_test_score','mean_fit_time'])\n",
        "df"
      ],
      "metadata": {
        "id": "NV5p4jsbND0O",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "16115104-42c2-43cc-8ae9-5f7bfa949286"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
              "8        0.001256      0.000556         0.002230        0.000556   \n",
              "18       0.001531      0.000143         0.003130        0.000478   \n",
              "22       0.001533      0.000082         0.003007        0.000069   \n",
              "20       0.001562      0.000128         0.003029        0.000123   \n",
              "10       0.001358      0.000526         0.002433        0.000457   \n",
              "24       0.001496      0.000111         0.003103        0.000310   \n",
              "16       0.001737      0.000190         0.005708        0.003435   \n",
              "12       0.000915      0.000019         0.001824        0.000068   \n",
              "25       0.001434      0.000070         0.001701        0.000065   \n",
              "21       0.001446      0.000078         0.001739        0.000130   \n",
              "30       0.001571      0.000158         0.003373        0.000856   \n",
              "19       0.001582      0.000225         0.002080        0.000352   \n",
              "14       0.001611      0.000205         0.003861        0.000608   \n",
              "17       0.001806      0.000496         0.001907        0.000199   \n",
              "32       0.001921      0.000953         0.004076        0.001513   \n",
              "28       0.002724      0.002355         0.004825        0.002685   \n",
              "26       0.006051      0.010301         0.005643        0.003629   \n",
              "11       0.001166      0.000220         0.001569        0.000658   \n",
              "35       0.001466      0.000158         0.002461        0.001927   \n",
              "23       0.001478      0.000069         0.001735        0.000072   \n",
              "37       0.001507      0.000133         0.003313        0.003964   \n",
              "29       0.001514      0.000102         0.001804        0.000136   \n",
              "33       0.001702      0.000336         0.002118        0.000629   \n",
              "31       0.002648      0.002795         0.001949        0.000192   \n",
              "27       0.002859      0.001453         0.003397        0.002587   \n",
              "13       0.000881      0.000026         0.001158        0.000364   \n",
              "47       0.001457      0.000054         0.001904        0.000124   \n",
              "39       0.001503      0.000215         0.002717        0.002640   \n",
              "43       0.002040      0.001328         0.003624        0.002612   \n",
              "15       0.003325      0.001847         0.003821        0.001564   \n",
              "45       0.001482      0.000126         0.001972        0.000294   \n",
              "41       0.001921      0.001596         0.005524        0.009057   \n",
              "53       0.002078      0.001761         0.003077        0.003504   \n",
              "59       0.002117      0.001972         0.002438        0.001339   \n",
              "7        0.000859      0.000022         0.001005        0.000086   \n",
              "51       0.001301      0.000216         0.001777        0.000478   \n",
              "4        0.001301      0.000283         0.003191        0.001844   \n",
              "49       0.001360      0.000235         0.002496        0.002205   \n",
              "55       0.001386      0.000109         0.001867        0.000170   \n",
              "57       0.002299      0.002445         0.002696        0.002274   \n",
              "6        0.000870      0.000022         0.001775        0.000092   \n",
              "9        0.000978      0.000228         0.001047        0.000086   \n",
              "34       0.002167      0.001379         0.004765        0.002573   \n",
              "36       0.002449      0.002279         0.003193        0.000163   \n",
              "5        0.001016      0.000180         0.001130        0.000183   \n",
              "40       0.004352      0.005458         0.006022        0.004089   \n",
              "38       0.001667      0.000351         0.006562        0.005976   \n",
              "3        0.000880      0.000051         0.001004        0.000082   \n",
              "56       0.001492      0.000061         0.003152        0.000102   \n",
              "1        0.001029      0.000181         0.001203        0.000175   \n",
              "46       0.001548      0.000147         0.005207        0.004301   \n",
              "54       0.001566      0.000121         0.003303        0.000603   \n",
              "0        0.001648      0.000390         0.003269        0.001121   \n",
              "48       0.002906      0.002795         0.003185        0.000263   \n",
              "2        0.000979      0.000047         0.001945        0.000368   \n",
              "50       0.001953      0.001132         0.009344        0.009669   \n",
              "42       0.002406      0.002132         0.009101        0.007299   \n",
              "44       0.001422      0.000189         0.004281        0.004093   \n",
              "52       0.002673      0.003137         0.004116        0.002651   \n",
              "58       0.001605      0.000149         0.003444        0.000388   \n",
              "\n",
              "   param_n_neighbors param_weights  \\\n",
              "8                  5       uniform   \n",
              "18                10       uniform   \n",
              "22                12       uniform   \n",
              "20                11       uniform   \n",
              "10                 6       uniform   \n",
              "24                13       uniform   \n",
              "16                 9       uniform   \n",
              "12                 7       uniform   \n",
              "25                13      distance   \n",
              "21                11      distance   \n",
              "30                16       uniform   \n",
              "19                10      distance   \n",
              "14                 8       uniform   \n",
              "17                 9      distance   \n",
              "32                17       uniform   \n",
              "28                15       uniform   \n",
              "26                14       uniform   \n",
              "11                 6      distance   \n",
              "35                18      distance   \n",
              "23                12      distance   \n",
              "37                19      distance   \n",
              "29                15      distance   \n",
              "33                17      distance   \n",
              "31                16      distance   \n",
              "27                14      distance   \n",
              "13                 7      distance   \n",
              "47                24      distance   \n",
              "39                20      distance   \n",
              "43                22      distance   \n",
              "15                 8      distance   \n",
              "45                23      distance   \n",
              "41                21      distance   \n",
              "53                27      distance   \n",
              "59                30      distance   \n",
              "7                  4      distance   \n",
              "51                26      distance   \n",
              "4                  3       uniform   \n",
              "49                25      distance   \n",
              "55                28      distance   \n",
              "57                29      distance   \n",
              "6                  4       uniform   \n",
              "9                  5      distance   \n",
              "34                18       uniform   \n",
              "36                19       uniform   \n",
              "5                  3      distance   \n",
              "40                21       uniform   \n",
              "38                20       uniform   \n",
              "3                  2      distance   \n",
              "56                29       uniform   \n",
              "1                  1      distance   \n",
              "46                24       uniform   \n",
              "54                28       uniform   \n",
              "0                  1       uniform   \n",
              "48                25       uniform   \n",
              "2                  2       uniform   \n",
              "50                26       uniform   \n",
              "42                22       uniform   \n",
              "44                23       uniform   \n",
              "52                27       uniform   \n",
              "58                30       uniform   \n",
              "\n",
              "                                        params  split0_test_score  \\\n",
              "8     {'n_neighbors': 5, 'weights': 'uniform'}            0.96875   \n",
              "18   {'n_neighbors': 10, 'weights': 'uniform'}            0.96875   \n",
              "22   {'n_neighbors': 12, 'weights': 'uniform'}            0.96875   \n",
              "20   {'n_neighbors': 11, 'weights': 'uniform'}            0.96875   \n",
              "10    {'n_neighbors': 6, 'weights': 'uniform'}            0.96875   \n",
              "24   {'n_neighbors': 13, 'weights': 'uniform'}            0.96875   \n",
              "16    {'n_neighbors': 9, 'weights': 'uniform'}            0.96875   \n",
              "12    {'n_neighbors': 7, 'weights': 'uniform'}            0.93750   \n",
              "25  {'n_neighbors': 13, 'weights': 'distance'}            0.96875   \n",
              "21  {'n_neighbors': 11, 'weights': 'distance'}            0.96875   \n",
              "30   {'n_neighbors': 16, 'weights': 'uniform'}            0.96875   \n",
              "19  {'n_neighbors': 10, 'weights': 'distance'}            0.96875   \n",
              "14    {'n_neighbors': 8, 'weights': 'uniform'}            0.96875   \n",
              "17   {'n_neighbors': 9, 'weights': 'distance'}            0.96875   \n",
              "32   {'n_neighbors': 17, 'weights': 'uniform'}            0.96875   \n",
              "28   {'n_neighbors': 15, 'weights': 'uniform'}            0.96875   \n",
              "26   {'n_neighbors': 14, 'weights': 'uniform'}            0.96875   \n",
              "11   {'n_neighbors': 6, 'weights': 'distance'}            0.93750   \n",
              "35  {'n_neighbors': 18, 'weights': 'distance'}            0.96875   \n",
              "23  {'n_neighbors': 12, 'weights': 'distance'}            0.96875   \n",
              "37  {'n_neighbors': 19, 'weights': 'distance'}            0.96875   \n",
              "29  {'n_neighbors': 15, 'weights': 'distance'}            0.96875   \n",
              "33  {'n_neighbors': 17, 'weights': 'distance'}            0.96875   \n",
              "31  {'n_neighbors': 16, 'weights': 'distance'}            0.96875   \n",
              "27  {'n_neighbors': 14, 'weights': 'distance'}            0.96875   \n",
              "13   {'n_neighbors': 7, 'weights': 'distance'}            0.90625   \n",
              "47  {'n_neighbors': 24, 'weights': 'distance'}            0.96875   \n",
              "39  {'n_neighbors': 20, 'weights': 'distance'}            0.96875   \n",
              "43  {'n_neighbors': 22, 'weights': 'distance'}            0.96875   \n",
              "15   {'n_neighbors': 8, 'weights': 'distance'}            0.96875   \n",
              "45  {'n_neighbors': 23, 'weights': 'distance'}            0.96875   \n",
              "41  {'n_neighbors': 21, 'weights': 'distance'}            0.96875   \n",
              "53  {'n_neighbors': 27, 'weights': 'distance'}            0.96875   \n",
              "59  {'n_neighbors': 30, 'weights': 'distance'}            0.96875   \n",
              "7    {'n_neighbors': 4, 'weights': 'distance'}            0.93750   \n",
              "51  {'n_neighbors': 26, 'weights': 'distance'}            0.93750   \n",
              "4     {'n_neighbors': 3, 'weights': 'uniform'}            0.93750   \n",
              "49  {'n_neighbors': 25, 'weights': 'distance'}            0.96875   \n",
              "55  {'n_neighbors': 28, 'weights': 'distance'}            0.96875   \n",
              "57  {'n_neighbors': 29, 'weights': 'distance'}            0.96875   \n",
              "6     {'n_neighbors': 4, 'weights': 'uniform'}            0.93750   \n",
              "9    {'n_neighbors': 5, 'weights': 'distance'}            0.93750   \n",
              "34   {'n_neighbors': 18, 'weights': 'uniform'}            0.87500   \n",
              "36   {'n_neighbors': 19, 'weights': 'uniform'}            0.87500   \n",
              "5    {'n_neighbors': 3, 'weights': 'distance'}            0.87500   \n",
              "40   {'n_neighbors': 21, 'weights': 'uniform'}            0.87500   \n",
              "38   {'n_neighbors': 20, 'weights': 'uniform'}            0.84375   \n",
              "3    {'n_neighbors': 2, 'weights': 'distance'}            0.90625   \n",
              "56   {'n_neighbors': 29, 'weights': 'uniform'}            0.84375   \n",
              "1    {'n_neighbors': 1, 'weights': 'distance'}            0.90625   \n",
              "46   {'n_neighbors': 24, 'weights': 'uniform'}            0.87500   \n",
              "54   {'n_neighbors': 28, 'weights': 'uniform'}            0.84375   \n",
              "0     {'n_neighbors': 1, 'weights': 'uniform'}            0.90625   \n",
              "48   {'n_neighbors': 25, 'weights': 'uniform'}            0.87500   \n",
              "2     {'n_neighbors': 2, 'weights': 'uniform'}            0.87500   \n",
              "50   {'n_neighbors': 26, 'weights': 'uniform'}            0.87500   \n",
              "42   {'n_neighbors': 22, 'weights': 'uniform'}            0.87500   \n",
              "44   {'n_neighbors': 23, 'weights': 'uniform'}            0.87500   \n",
              "52   {'n_neighbors': 27, 'weights': 'uniform'}            0.87500   \n",
              "58   {'n_neighbors': 30, 'weights': 'uniform'}            0.84375   \n",
              "\n",
              "    split1_test_score  split2_test_score  split3_test_score  \\\n",
              "8             0.90625            0.90625            0.90625   \n",
              "18            0.87500            0.90625            0.90625   \n",
              "22            0.87500            0.93750            0.90625   \n",
              "20            0.87500            0.90625            0.90625   \n",
              "10            0.90625            0.87500            0.93750   \n",
              "24            0.87500            0.93750            0.90625   \n",
              "16            0.87500            0.90625            0.90625   \n",
              "12            0.90625            0.90625            0.90625   \n",
              "25            0.90625            0.90625            0.90625   \n",
              "21            0.90625            0.90625            0.90625   \n",
              "30            0.87500            0.93750            0.90625   \n",
              "19            0.90625            0.90625            0.90625   \n",
              "14            0.87500            0.90625            0.90625   \n",
              "17            0.90625            0.90625            0.90625   \n",
              "32            0.87500            0.93750            0.90625   \n",
              "28            0.90625            0.93750            0.90625   \n",
              "26            0.87500            0.93750            0.90625   \n",
              "11            0.90625            0.90625            0.93750   \n",
              "35            0.90625            0.90625            0.90625   \n",
              "23            0.90625            0.90625            0.90625   \n",
              "37            0.90625            0.90625            0.90625   \n",
              "29            0.90625            0.90625            0.90625   \n",
              "33            0.90625            0.90625            0.90625   \n",
              "31            0.90625            0.90625            0.90625   \n",
              "27            0.90625            0.90625            0.90625   \n",
              "13            0.90625            0.90625            0.90625   \n",
              "47            0.90625            0.87500            0.90625   \n",
              "39            0.90625            0.90625            0.90625   \n",
              "43            0.90625            0.90625            0.90625   \n",
              "15            0.90625            0.90625            0.93750   \n",
              "45            0.87500            0.90625            0.90625   \n",
              "41            0.87500            0.90625            0.90625   \n",
              "53            0.90625            0.87500            0.90625   \n",
              "59            0.90625            0.87500            0.90625   \n",
              "7             0.90625            0.90625            0.93750   \n",
              "51            0.90625            0.87500            0.90625   \n",
              "4             0.90625            0.87500            0.90625   \n",
              "49            0.90625            0.87500            0.90625   \n",
              "55            0.90625            0.87500            0.90625   \n",
              "57            0.90625            0.87500            0.90625   \n",
              "6             0.90625            0.84375            0.93750   \n",
              "9             0.90625            0.87500            0.90625   \n",
              "34            0.87500            0.93750            0.90625   \n",
              "36            0.87500            0.93750            0.90625   \n",
              "5             0.90625            0.87500            0.90625   \n",
              "40            0.87500            0.90625            0.90625   \n",
              "38            0.87500            0.90625            0.90625   \n",
              "3             0.87500            0.90625            0.90625   \n",
              "56            0.87500            0.93750            0.87500   \n",
              "1             0.87500            0.90625            0.90625   \n",
              "46            0.84375            0.90625            0.90625   \n",
              "54            0.84375            0.93750            0.87500   \n",
              "0             0.87500            0.90625            0.90625   \n",
              "48            0.84375            0.90625            0.90625   \n",
              "2             0.90625            0.81250            0.87500   \n",
              "50            0.84375            0.90625            0.87500   \n",
              "42            0.84375            0.90625            0.87500   \n",
              "44            0.84375            0.87500            0.87500   \n",
              "52            0.84375            0.90625            0.84375   \n",
              "58            0.75000            0.93750            0.87500   \n",
              "\n",
              "    split4_test_score  split5_test_score  split6_test_score  \\\n",
              "8             0.93750            0.90625            0.90625   \n",
              "18            0.90625            0.87500            0.93750   \n",
              "22            0.87500            0.87500            0.93750   \n",
              "20            0.90625            0.87500            0.93750   \n",
              "10            0.90625            0.87500            0.90625   \n",
              "24            0.87500            0.87500            0.93750   \n",
              "16            0.90625            0.87500            0.93750   \n",
              "12            0.87500            0.87500            0.93750   \n",
              "25            0.87500            0.87500            0.90625   \n",
              "21            0.87500            0.87500            0.90625   \n",
              "30            0.87500            0.87500            0.90625   \n",
              "19            0.87500            0.87500            0.90625   \n",
              "14            0.87500            0.87500            0.93750   \n",
              "17            0.87500            0.87500            0.90625   \n",
              "32            0.87500            0.87500            0.90625   \n",
              "28            0.84375            0.87500            0.90625   \n",
              "26            0.87500            0.87500            0.90625   \n",
              "11            0.84375            0.90625            0.90625   \n",
              "35            0.87500            0.87500            0.90625   \n",
              "23            0.87500            0.87500            0.90625   \n",
              "37            0.87500            0.87500            0.90625   \n",
              "29            0.87500            0.87500            0.90625   \n",
              "33            0.87500            0.87500            0.90625   \n",
              "31            0.87500            0.87500            0.90625   \n",
              "27            0.87500            0.87500            0.90625   \n",
              "13            0.84375            0.90625            0.90625   \n",
              "47            0.87500            0.87500            0.90625   \n",
              "39            0.87500            0.87500            0.90625   \n",
              "43            0.87500            0.87500            0.90625   \n",
              "15            0.84375            0.87500            0.90625   \n",
              "45            0.87500            0.87500            0.90625   \n",
              "41            0.87500            0.87500            0.90625   \n",
              "53            0.84375            0.87500            0.90625   \n",
              "59            0.81250            0.87500            0.90625   \n",
              "7             0.84375            0.90625            0.90625   \n",
              "51            0.84375            0.87500            0.90625   \n",
              "4             0.84375            0.90625            0.90625   \n",
              "49            0.84375            0.87500            0.90625   \n",
              "55            0.81250            0.87500            0.90625   \n",
              "57            0.81250            0.87500            0.90625   \n",
              "6             0.84375            0.90625            0.90625   \n",
              "9             0.84375            0.90625            0.90625   \n",
              "34            0.87500            0.87500            0.84375   \n",
              "36            0.87500            0.87500            0.84375   \n",
              "5             0.84375            0.90625            0.90625   \n",
              "40            0.87500            0.87500            0.84375   \n",
              "38            0.87500            0.84375            0.84375   \n",
              "3             0.81250            0.87500            0.84375   \n",
              "56            0.81250            0.87500            0.78125   \n",
              "1             0.81250            0.87500            0.84375   \n",
              "46            0.81250            0.84375            0.81250   \n",
              "54            0.81250            0.87500            0.78125   \n",
              "0             0.81250            0.87500            0.84375   \n",
              "48            0.81250            0.84375            0.81250   \n",
              "2             0.81250            0.90625            0.87500   \n",
              "50            0.81250            0.84375            0.81250   \n",
              "42            0.81250            0.87500            0.81250   \n",
              "44            0.81250            0.87500            0.81250   \n",
              "52            0.81250            0.84375            0.81250   \n",
              "58            0.78125            0.84375            0.78125   \n",
              "\n",
              "    split7_test_score  split8_test_score  split9_test_score  mean_test_score  \\\n",
              "8             0.84375            0.84375            0.93750         0.906250   \n",
              "18            0.87500            0.87500            0.93750         0.906250   \n",
              "22            0.87500            0.87500            0.93750         0.906250   \n",
              "20            0.87500            0.87500            0.93750         0.906250   \n",
              "10            0.84375            0.87500            0.93750         0.903125   \n",
              "24            0.84375            0.87500            0.93750         0.903125   \n",
              "16            0.87500            0.84375            0.93750         0.903125   \n",
              "12            0.87500            0.84375            0.93750         0.900000   \n",
              "25            0.87500            0.84375            0.93750         0.900000   \n",
              "21            0.87500            0.84375            0.93750         0.900000   \n",
              "30            0.84375            0.87500            0.93750         0.900000   \n",
              "19            0.87500            0.84375            0.93750         0.900000   \n",
              "14            0.87500            0.84375            0.93750         0.900000   \n",
              "17            0.87500            0.84375            0.93750         0.900000   \n",
              "32            0.84375            0.87500            0.93750         0.900000   \n",
              "28            0.84375            0.87500            0.93750         0.900000   \n",
              "26            0.84375            0.87500            0.93750         0.900000   \n",
              "11            0.84375            0.84375            0.93750         0.896875   \n",
              "35            0.84375            0.84375            0.93750         0.896875   \n",
              "23            0.84375            0.84375            0.93750         0.896875   \n",
              "37            0.84375            0.84375            0.93750         0.896875   \n",
              "29            0.84375            0.84375            0.93750         0.896875   \n",
              "33            0.84375            0.84375            0.93750         0.896875   \n",
              "31            0.84375            0.84375            0.93750         0.896875   \n",
              "27            0.84375            0.84375            0.93750         0.896875   \n",
              "13            0.87500            0.84375            0.93750         0.893750   \n",
              "47            0.84375            0.84375            0.93750         0.893750   \n",
              "39            0.81250            0.84375            0.93750         0.893750   \n",
              "43            0.81250            0.84375            0.93750         0.893750   \n",
              "15            0.81250            0.84375            0.93750         0.893750   \n",
              "45            0.81250            0.84375            0.93750         0.890625   \n",
              "41            0.81250            0.84375            0.93750         0.890625   \n",
              "53            0.84375            0.84375            0.93750         0.890625   \n",
              "59            0.84375            0.87500            0.93750         0.890625   \n",
              "7             0.81250            0.81250            0.90625         0.887500   \n",
              "51            0.84375            0.84375            0.93750         0.887500   \n",
              "4             0.84375            0.84375            0.90625         0.887500   \n",
              "49            0.81250            0.84375            0.93750         0.887500   \n",
              "55            0.84375            0.84375            0.93750         0.887500   \n",
              "57            0.84375            0.84375            0.93750         0.887500   \n",
              "6             0.87500            0.81250            0.87500         0.884375   \n",
              "9             0.81250            0.81250            0.87500         0.878125   \n",
              "34            0.78125            0.87500            0.93750         0.878125   \n",
              "36            0.78125            0.87500            0.93750         0.878125   \n",
              "5             0.81250            0.81250            0.87500         0.871875   \n",
              "40            0.75000            0.87500            0.93750         0.871875   \n",
              "38            0.75000            0.87500            0.93750         0.865625   \n",
              "3             0.84375            0.78125            0.87500         0.862500   \n",
              "56            0.78125            0.93750            0.90625         0.862500   \n",
              "1             0.81250            0.78125            0.87500         0.859375   \n",
              "46            0.78125            0.93750            0.87500         0.859375   \n",
              "54            0.78125            0.93750            0.90625         0.859375   \n",
              "0             0.81250            0.78125            0.87500         0.859375   \n",
              "48            0.78125            0.93750            0.87500         0.859375   \n",
              "2             0.84375            0.78125            0.87500         0.856250   \n",
              "50            0.78125            0.93750            0.87500         0.856250   \n",
              "42            0.78125            0.87500            0.90625         0.856250   \n",
              "44            0.78125            0.87500            0.90625         0.853125   \n",
              "52            0.78125            0.93750            0.87500         0.853125   \n",
              "58            0.78125            0.93750            0.90625         0.843750   \n",
              "\n",
              "    std_test_score  rank_test_score  \n",
              "8         0.036975                1  \n",
              "18        0.031250                1  \n",
              "22        0.034233                1  \n",
              "20        0.031250                1  \n",
              "10        0.035493                5  \n",
              "24        0.038145                5  \n",
              "16        0.035493                5  \n",
              "12        0.030619                8  \n",
              "25        0.033657                8  \n",
              "21        0.033657                8  \n",
              "30        0.036443                8  \n",
              "19        0.033657                8  \n",
              "14        0.036443                8  \n",
              "17        0.033657                8  \n",
              "32        0.036443                8  \n",
              "28        0.039031                8  \n",
              "26        0.036443                8  \n",
              "11        0.037107               18  \n",
              "35        0.037107               18  \n",
              "23        0.037107               18  \n",
              "37        0.037107               18  \n",
              "29        0.037107               18  \n",
              "33        0.037107               18  \n",
              "31        0.037107               18  \n",
              "27        0.037107               18  \n",
              "13        0.028641               26  \n",
              "47        0.037500               26  \n",
              "39        0.042390               26  \n",
              "43        0.042390               26  \n",
              "15        0.046771               26  \n",
              "45        0.042505               31  \n",
              "41        0.042505               31  \n",
              "53        0.040141               31  \n",
              "59        0.042505               31  \n",
              "7         0.044634               35  \n",
              "51        0.034799               35  \n",
              "4         0.031869               35  \n",
              "49        0.044634               35  \n",
              "55        0.044634               35  \n",
              "57        0.044634               35  \n",
              "6         0.039652               41  \n",
              "9         0.040625               42  \n",
              "34        0.042962               42  \n",
              "36        0.042962               42  \n",
              "5         0.035493               45  \n",
              "40        0.047290               45  \n",
              "38        0.048513               47  \n",
              "3         0.040020               48  \n",
              "56        0.054486               48  \n",
              "1         0.042505               50  \n",
              "46        0.046875               50  \n",
              "54        0.054576               50  \n",
              "0         0.042505               50  \n",
              "48        0.046875               50  \n",
              "2         0.040020               55  \n",
              "50        0.044634               55  \n",
              "42        0.040020               55  \n",
              "44        0.037107               58  \n",
              "52        0.044305               58  \n",
              "58        0.065551               60  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ff91f083-0355-4ee5-940d-582856971c7c\">\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>mean_fit_time</th>\n",
              "      <th>std_fit_time</th>\n",
              "      <th>mean_score_time</th>\n",
              "      <th>std_score_time</th>\n",
              "      <th>param_n_neighbors</th>\n",
              "      <th>param_weights</th>\n",
              "      <th>params</th>\n",
              "      <th>split0_test_score</th>\n",
              "      <th>split1_test_score</th>\n",
              "      <th>split2_test_score</th>\n",
              "      <th>split3_test_score</th>\n",
              "      <th>split4_test_score</th>\n",
              "      <th>split5_test_score</th>\n",
              "      <th>split6_test_score</th>\n",
              "      <th>split7_test_score</th>\n",
              "      <th>split8_test_score</th>\n",
              "      <th>split9_test_score</th>\n",
              "      <th>mean_test_score</th>\n",
              "      <th>std_test_score</th>\n",
              "      <th>rank_test_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.001256</td>\n",
              "      <td>0.000556</td>\n",
              "      <td>0.002230</td>\n",
              "      <td>0.000556</td>\n",
              "      <td>5</td>\n",
              "      <td>uniform</td>\n",
              "      <td>{'n_neighbors': 5, 'weights': 'uniform'}</td>\n",
              "      <td>0.96875</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.906250</td>\n",
              "      <td>0.036975</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0.001531</td>\n",
              "      <td>0.000143</td>\n",
              "      <td>0.003130</td>\n",
              "      <td>0.000478</td>\n",
              "      <td>10</td>\n",
              "      <td>uniform</td>\n",
              "      <td>{'n_neighbors': 10, 'weights': 'uniform'}</td>\n",
              "      <td>0.96875</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.906250</td>\n",
              "      <td>0.031250</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>0.001533</td>\n",
              "      <td>0.000082</td>\n",
              "      <td>0.003007</td>\n",
              "      <td>0.000069</td>\n",
              "      <td>12</td>\n",
              "      <td>uniform</td>\n",
              "      <td>{'n_neighbors': 12, 'weights': 'uniform'}</td>\n",
              "      <td>0.96875</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.906250</td>\n",
              "      <td>0.034233</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>0.001562</td>\n",
              "      <td>0.000128</td>\n",
              "      <td>0.003029</td>\n",
              "      <td>0.000123</td>\n",
              "      <td>11</td>\n",
              "      <td>uniform</td>\n",
              "      <td>{'n_neighbors': 11, 'weights': 'uniform'}</td>\n",
              "      <td>0.96875</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.906250</td>\n",
              "      <td>0.031250</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.001358</td>\n",
              "      <td>0.000526</td>\n",
              "      <td>0.002433</td>\n",
              "      <td>0.000457</td>\n",
              "      <td>6</td>\n",
              "      <td>uniform</td>\n",
              "      <td>{'n_neighbors': 6, 'weights': 'uniform'}</td>\n",
              "      <td>0.96875</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.903125</td>\n",
              "      <td>0.035493</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>0.001496</td>\n",
              "      <td>0.000111</td>\n",
              "      <td>0.003103</td>\n",
              "      <td>0.000310</td>\n",
              "      <td>13</td>\n",
              "      <td>uniform</td>\n",
              "      <td>{'n_neighbors': 13, 'weights': 'uniform'}</td>\n",
              "      <td>0.96875</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.903125</td>\n",
              "      <td>0.038145</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0.001737</td>\n",
              "      <td>0.000190</td>\n",
              "      <td>0.005708</td>\n",
              "      <td>0.003435</td>\n",
              "      <td>9</td>\n",
              "      <td>uniform</td>\n",
              "      <td>{'n_neighbors': 9, 'weights': 'uniform'}</td>\n",
              "      <td>0.96875</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.903125</td>\n",
              "      <td>0.035493</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0.000915</td>\n",
              "      <td>0.000019</td>\n",
              "      <td>0.001824</td>\n",
              "      <td>0.000068</td>\n",
              "      <td>7</td>\n",
              "      <td>uniform</td>\n",
              "      <td>{'n_neighbors': 7, 'weights': 'uniform'}</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>0.030619</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>0.001434</td>\n",
              "      <td>0.000070</td>\n",
              "      <td>0.001701</td>\n",
              "      <td>0.000065</td>\n",
              "      <td>13</td>\n",
              "      <td>distance</td>\n",
              "      <td>{'n_neighbors': 13, 'weights': 'distance'}</td>\n",
              "      <td>0.96875</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>0.033657</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>0.001446</td>\n",
              "      <td>0.000078</td>\n",
              "      <td>0.001739</td>\n",
              "      <td>0.000130</td>\n",
              "      <td>11</td>\n",
              "      <td>distance</td>\n",
              "      <td>{'n_neighbors': 11, 'weights': 'distance'}</td>\n",
              "      <td>0.96875</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>0.033657</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>0.001571</td>\n",
              "      <td>0.000158</td>\n",
              "      <td>0.003373</td>\n",
              "      <td>0.000856</td>\n",
              "      <td>16</td>\n",
              "      <td>uniform</td>\n",
              "      <td>{'n_neighbors': 16, 'weights': 'uniform'}</td>\n",
              "      <td>0.96875</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>0.036443</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0.001582</td>\n",
              "      <td>0.000225</td>\n",
              "      <td>0.002080</td>\n",
              "      <td>0.000352</td>\n",
              "      <td>10</td>\n",
              "      <td>distance</td>\n",
              "      <td>{'n_neighbors': 10, 'weights': 'distance'}</td>\n",
              "      <td>0.96875</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>0.033657</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0.001611</td>\n",
              "      <td>0.000205</td>\n",
              "      <td>0.003861</td>\n",
              "      <td>0.000608</td>\n",
              "      <td>8</td>\n",
              "      <td>uniform</td>\n",
              "      <td>{'n_neighbors': 8, 'weights': 'uniform'}</td>\n",
              "      <td>0.96875</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>0.036443</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0.001806</td>\n",
              "      <td>0.000496</td>\n",
              "      <td>0.001907</td>\n",
              "      <td>0.000199</td>\n",
              "      <td>9</td>\n",
              "      <td>distance</td>\n",
              "      <td>{'n_neighbors': 9, 'weights': 'distance'}</td>\n",
              "      <td>0.96875</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>0.033657</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>0.001921</td>\n",
              "      <td>0.000953</td>\n",
              "      <td>0.004076</td>\n",
              "      <td>0.001513</td>\n",
              "      <td>17</td>\n",
              "      <td>uniform</td>\n",
              "      <td>{'n_neighbors': 17, 'weights': 'uniform'}</td>\n",
              "      <td>0.96875</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>0.036443</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>0.002724</td>\n",
              "      <td>0.002355</td>\n",
              "      <td>0.004825</td>\n",
              "      <td>0.002685</td>\n",
              "      <td>15</td>\n",
              "      <td>uniform</td>\n",
              "      <td>{'n_neighbors': 15, 'weights': 'uniform'}</td>\n",
              "      <td>0.96875</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>0.039031</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>0.006051</td>\n",
              "      <td>0.010301</td>\n",
              "      <td>0.005643</td>\n",
              "      <td>0.003629</td>\n",
              "      <td>14</td>\n",
              "      <td>uniform</td>\n",
              "      <td>{'n_neighbors': 14, 'weights': 'uniform'}</td>\n",
              "      <td>0.96875</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>0.036443</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.001166</td>\n",
              "      <td>0.000220</td>\n",
              "      <td>0.001569</td>\n",
              "      <td>0.000658</td>\n",
              "      <td>6</td>\n",
              "      <td>distance</td>\n",
              "      <td>{'n_neighbors': 6, 'weights': 'distance'}</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.896875</td>\n",
              "      <td>0.037107</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>0.001466</td>\n",
              "      <td>0.000158</td>\n",
              "      <td>0.002461</td>\n",
              "      <td>0.001927</td>\n",
              "      <td>18</td>\n",
              "      <td>distance</td>\n",
              "      <td>{'n_neighbors': 18, 'weights': 'distance'}</td>\n",
              "      <td>0.96875</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.896875</td>\n",
              "      <td>0.037107</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>0.001478</td>\n",
              "      <td>0.000069</td>\n",
              "      <td>0.001735</td>\n",
              "      <td>0.000072</td>\n",
              "      <td>12</td>\n",
              "      <td>distance</td>\n",
              "      <td>{'n_neighbors': 12, 'weights': 'distance'}</td>\n",
              "      <td>0.96875</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.896875</td>\n",
              "      <td>0.037107</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>0.001507</td>\n",
              "      <td>0.000133</td>\n",
              "      <td>0.003313</td>\n",
              "      <td>0.003964</td>\n",
              "      <td>19</td>\n",
              "      <td>distance</td>\n",
              "      <td>{'n_neighbors': 19, 'weights': 'distance'}</td>\n",
              "      <td>0.96875</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.896875</td>\n",
              "      <td>0.037107</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>0.001514</td>\n",
              "      <td>0.000102</td>\n",
              "      <td>0.001804</td>\n",
              "      <td>0.000136</td>\n",
              "      <td>15</td>\n",
              "      <td>distance</td>\n",
              "      <td>{'n_neighbors': 15, 'weights': 'distance'}</td>\n",
              "      <td>0.96875</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.896875</td>\n",
              "      <td>0.037107</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>0.001702</td>\n",
              "      <td>0.000336</td>\n",
              "      <td>0.002118</td>\n",
              "      <td>0.000629</td>\n",
              "      <td>17</td>\n",
              "      <td>distance</td>\n",
              "      <td>{'n_neighbors': 17, 'weights': 'distance'}</td>\n",
              "      <td>0.96875</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.896875</td>\n",
              "      <td>0.037107</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>0.002648</td>\n",
              "      <td>0.002795</td>\n",
              "      <td>0.001949</td>\n",
              "      <td>0.000192</td>\n",
              "      <td>16</td>\n",
              "      <td>distance</td>\n",
              "      <td>{'n_neighbors': 16, 'weights': 'distance'}</td>\n",
              "      <td>0.96875</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.896875</td>\n",
              "      <td>0.037107</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>0.002859</td>\n",
              "      <td>0.001453</td>\n",
              "      <td>0.003397</td>\n",
              "      <td>0.002587</td>\n",
              "      <td>14</td>\n",
              "      <td>distance</td>\n",
              "      <td>{'n_neighbors': 14, 'weights': 'distance'}</td>\n",
              "      <td>0.96875</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.896875</td>\n",
              "      <td>0.037107</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0.000881</td>\n",
              "      <td>0.000026</td>\n",
              "      <td>0.001158</td>\n",
              "      <td>0.000364</td>\n",
              "      <td>7</td>\n",
              "      <td>distance</td>\n",
              "      <td>{'n_neighbors': 7, 'weights': 'distance'}</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.893750</td>\n",
              "      <td>0.028641</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>0.001457</td>\n",
              "      <td>0.000054</td>\n",
              "      <td>0.001904</td>\n",
              "      <td>0.000124</td>\n",
              "      <td>24</td>\n",
              "      <td>distance</td>\n",
              "      <td>{'n_neighbors': 24, 'weights': 'distance'}</td>\n",
              "      <td>0.96875</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.893750</td>\n",
              "      <td>0.037500</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>0.001503</td>\n",
              "      <td>0.000215</td>\n",
              "      <td>0.002717</td>\n",
              "      <td>0.002640</td>\n",
              "      <td>20</td>\n",
              "      <td>distance</td>\n",
              "      <td>{'n_neighbors': 20, 'weights': 'distance'}</td>\n",
              "      <td>0.96875</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.81250</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.893750</td>\n",
              "      <td>0.042390</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>0.002040</td>\n",
              "      <td>0.001328</td>\n",
              "      <td>0.003624</td>\n",
              "      <td>0.002612</td>\n",
              "      <td>22</td>\n",
              "      <td>distance</td>\n",
              "      <td>{'n_neighbors': 22, 'weights': 'distance'}</td>\n",
              "      <td>0.96875</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.81250</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.893750</td>\n",
              "      <td>0.042390</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0.003325</td>\n",
              "      <td>0.001847</td>\n",
              "      <td>0.003821</td>\n",
              "      <td>0.001564</td>\n",
              "      <td>8</td>\n",
              "      <td>distance</td>\n",
              "      <td>{'n_neighbors': 8, 'weights': 'distance'}</td>\n",
              "      <td>0.96875</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.81250</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.893750</td>\n",
              "      <td>0.046771</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>0.001482</td>\n",
              "      <td>0.000126</td>\n",
              "      <td>0.001972</td>\n",
              "      <td>0.000294</td>\n",
              "      <td>23</td>\n",
              "      <td>distance</td>\n",
              "      <td>{'n_neighbors': 23, 'weights': 'distance'}</td>\n",
              "      <td>0.96875</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.81250</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.890625</td>\n",
              "      <td>0.042505</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>0.001921</td>\n",
              "      <td>0.001596</td>\n",
              "      <td>0.005524</td>\n",
              "      <td>0.009057</td>\n",
              "      <td>21</td>\n",
              "      <td>distance</td>\n",
              "      <td>{'n_neighbors': 21, 'weights': 'distance'}</td>\n",
              "      <td>0.96875</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.81250</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.890625</td>\n",
              "      <td>0.042505</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>0.002078</td>\n",
              "      <td>0.001761</td>\n",
              "      <td>0.003077</td>\n",
              "      <td>0.003504</td>\n",
              "      <td>27</td>\n",
              "      <td>distance</td>\n",
              "      <td>{'n_neighbors': 27, 'weights': 'distance'}</td>\n",
              "      <td>0.96875</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.890625</td>\n",
              "      <td>0.040141</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>0.002117</td>\n",
              "      <td>0.001972</td>\n",
              "      <td>0.002438</td>\n",
              "      <td>0.001339</td>\n",
              "      <td>30</td>\n",
              "      <td>distance</td>\n",
              "      <td>{'n_neighbors': 30, 'weights': 'distance'}</td>\n",
              "      <td>0.96875</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.81250</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.890625</td>\n",
              "      <td>0.042505</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.000859</td>\n",
              "      <td>0.000022</td>\n",
              "      <td>0.001005</td>\n",
              "      <td>0.000086</td>\n",
              "      <td>4</td>\n",
              "      <td>distance</td>\n",
              "      <td>{'n_neighbors': 4, 'weights': 'distance'}</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.81250</td>\n",
              "      <td>0.81250</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.887500</td>\n",
              "      <td>0.044634</td>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>0.001301</td>\n",
              "      <td>0.000216</td>\n",
              "      <td>0.001777</td>\n",
              "      <td>0.000478</td>\n",
              "      <td>26</td>\n",
              "      <td>distance</td>\n",
              "      <td>{'n_neighbors': 26, 'weights': 'distance'}</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.887500</td>\n",
              "      <td>0.034799</td>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.001301</td>\n",
              "      <td>0.000283</td>\n",
              "      <td>0.003191</td>\n",
              "      <td>0.001844</td>\n",
              "      <td>3</td>\n",
              "      <td>uniform</td>\n",
              "      <td>{'n_neighbors': 3, 'weights': 'uniform'}</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.887500</td>\n",
              "      <td>0.031869</td>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>0.001360</td>\n",
              "      <td>0.000235</td>\n",
              "      <td>0.002496</td>\n",
              "      <td>0.002205</td>\n",
              "      <td>25</td>\n",
              "      <td>distance</td>\n",
              "      <td>{'n_neighbors': 25, 'weights': 'distance'}</td>\n",
              "      <td>0.96875</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.81250</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.887500</td>\n",
              "      <td>0.044634</td>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>0.001386</td>\n",
              "      <td>0.000109</td>\n",
              "      <td>0.001867</td>\n",
              "      <td>0.000170</td>\n",
              "      <td>28</td>\n",
              "      <td>distance</td>\n",
              "      <td>{'n_neighbors': 28, 'weights': 'distance'}</td>\n",
              "      <td>0.96875</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.81250</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.887500</td>\n",
              "      <td>0.044634</td>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>0.002299</td>\n",
              "      <td>0.002445</td>\n",
              "      <td>0.002696</td>\n",
              "      <td>0.002274</td>\n",
              "      <td>29</td>\n",
              "      <td>distance</td>\n",
              "      <td>{'n_neighbors': 29, 'weights': 'distance'}</td>\n",
              "      <td>0.96875</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.81250</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.887500</td>\n",
              "      <td>0.044634</td>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.000870</td>\n",
              "      <td>0.000022</td>\n",
              "      <td>0.001775</td>\n",
              "      <td>0.000092</td>\n",
              "      <td>4</td>\n",
              "      <td>uniform</td>\n",
              "      <td>{'n_neighbors': 4, 'weights': 'uniform'}</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.81250</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.884375</td>\n",
              "      <td>0.039652</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.000978</td>\n",
              "      <td>0.000228</td>\n",
              "      <td>0.001047</td>\n",
              "      <td>0.000086</td>\n",
              "      <td>5</td>\n",
              "      <td>distance</td>\n",
              "      <td>{'n_neighbors': 5, 'weights': 'distance'}</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.81250</td>\n",
              "      <td>0.81250</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.878125</td>\n",
              "      <td>0.040625</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>0.002167</td>\n",
              "      <td>0.001379</td>\n",
              "      <td>0.004765</td>\n",
              "      <td>0.002573</td>\n",
              "      <td>18</td>\n",
              "      <td>uniform</td>\n",
              "      <td>{'n_neighbors': 18, 'weights': 'uniform'}</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.78125</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.878125</td>\n",
              "      <td>0.042962</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>0.002449</td>\n",
              "      <td>0.002279</td>\n",
              "      <td>0.003193</td>\n",
              "      <td>0.000163</td>\n",
              "      <td>19</td>\n",
              "      <td>uniform</td>\n",
              "      <td>{'n_neighbors': 19, 'weights': 'uniform'}</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.78125</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.878125</td>\n",
              "      <td>0.042962</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.001016</td>\n",
              "      <td>0.000180</td>\n",
              "      <td>0.001130</td>\n",
              "      <td>0.000183</td>\n",
              "      <td>3</td>\n",
              "      <td>distance</td>\n",
              "      <td>{'n_neighbors': 3, 'weights': 'distance'}</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.81250</td>\n",
              "      <td>0.81250</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.871875</td>\n",
              "      <td>0.035493</td>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>0.004352</td>\n",
              "      <td>0.005458</td>\n",
              "      <td>0.006022</td>\n",
              "      <td>0.004089</td>\n",
              "      <td>21</td>\n",
              "      <td>uniform</td>\n",
              "      <td>{'n_neighbors': 21, 'weights': 'uniform'}</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.75000</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.871875</td>\n",
              "      <td>0.047290</td>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>0.001667</td>\n",
              "      <td>0.000351</td>\n",
              "      <td>0.006562</td>\n",
              "      <td>0.005976</td>\n",
              "      <td>20</td>\n",
              "      <td>uniform</td>\n",
              "      <td>{'n_neighbors': 20, 'weights': 'uniform'}</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.75000</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.865625</td>\n",
              "      <td>0.048513</td>\n",
              "      <td>47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.000880</td>\n",
              "      <td>0.000051</td>\n",
              "      <td>0.001004</td>\n",
              "      <td>0.000082</td>\n",
              "      <td>2</td>\n",
              "      <td>distance</td>\n",
              "      <td>{'n_neighbors': 2, 'weights': 'distance'}</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.81250</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.78125</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.862500</td>\n",
              "      <td>0.040020</td>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>0.001492</td>\n",
              "      <td>0.000061</td>\n",
              "      <td>0.003152</td>\n",
              "      <td>0.000102</td>\n",
              "      <td>29</td>\n",
              "      <td>uniform</td>\n",
              "      <td>{'n_neighbors': 29, 'weights': 'uniform'}</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.81250</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.78125</td>\n",
              "      <td>0.78125</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.862500</td>\n",
              "      <td>0.054486</td>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.001029</td>\n",
              "      <td>0.000181</td>\n",
              "      <td>0.001203</td>\n",
              "      <td>0.000175</td>\n",
              "      <td>1</td>\n",
              "      <td>distance</td>\n",
              "      <td>{'n_neighbors': 1, 'weights': 'distance'}</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.81250</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.81250</td>\n",
              "      <td>0.78125</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.859375</td>\n",
              "      <td>0.042505</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>0.001548</td>\n",
              "      <td>0.000147</td>\n",
              "      <td>0.005207</td>\n",
              "      <td>0.004301</td>\n",
              "      <td>24</td>\n",
              "      <td>uniform</td>\n",
              "      <td>{'n_neighbors': 24, 'weights': 'uniform'}</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.81250</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.81250</td>\n",
              "      <td>0.78125</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.859375</td>\n",
              "      <td>0.046875</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>0.001566</td>\n",
              "      <td>0.000121</td>\n",
              "      <td>0.003303</td>\n",
              "      <td>0.000603</td>\n",
              "      <td>28</td>\n",
              "      <td>uniform</td>\n",
              "      <td>{'n_neighbors': 28, 'weights': 'uniform'}</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.81250</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.78125</td>\n",
              "      <td>0.78125</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.859375</td>\n",
              "      <td>0.054576</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.001648</td>\n",
              "      <td>0.000390</td>\n",
              "      <td>0.003269</td>\n",
              "      <td>0.001121</td>\n",
              "      <td>1</td>\n",
              "      <td>uniform</td>\n",
              "      <td>{'n_neighbors': 1, 'weights': 'uniform'}</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.81250</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.81250</td>\n",
              "      <td>0.78125</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.859375</td>\n",
              "      <td>0.042505</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>0.002906</td>\n",
              "      <td>0.002795</td>\n",
              "      <td>0.003185</td>\n",
              "      <td>0.000263</td>\n",
              "      <td>25</td>\n",
              "      <td>uniform</td>\n",
              "      <td>{'n_neighbors': 25, 'weights': 'uniform'}</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.81250</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.81250</td>\n",
              "      <td>0.78125</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.859375</td>\n",
              "      <td>0.046875</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.000979</td>\n",
              "      <td>0.000047</td>\n",
              "      <td>0.001945</td>\n",
              "      <td>0.000368</td>\n",
              "      <td>2</td>\n",
              "      <td>uniform</td>\n",
              "      <td>{'n_neighbors': 2, 'weights': 'uniform'}</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.81250</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.81250</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.78125</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.856250</td>\n",
              "      <td>0.040020</td>\n",
              "      <td>55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>0.001953</td>\n",
              "      <td>0.001132</td>\n",
              "      <td>0.009344</td>\n",
              "      <td>0.009669</td>\n",
              "      <td>26</td>\n",
              "      <td>uniform</td>\n",
              "      <td>{'n_neighbors': 26, 'weights': 'uniform'}</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.81250</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.81250</td>\n",
              "      <td>0.78125</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.856250</td>\n",
              "      <td>0.044634</td>\n",
              "      <td>55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>0.002406</td>\n",
              "      <td>0.002132</td>\n",
              "      <td>0.009101</td>\n",
              "      <td>0.007299</td>\n",
              "      <td>22</td>\n",
              "      <td>uniform</td>\n",
              "      <td>{'n_neighbors': 22, 'weights': 'uniform'}</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.81250</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.81250</td>\n",
              "      <td>0.78125</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.856250</td>\n",
              "      <td>0.040020</td>\n",
              "      <td>55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>0.001422</td>\n",
              "      <td>0.000189</td>\n",
              "      <td>0.004281</td>\n",
              "      <td>0.004093</td>\n",
              "      <td>23</td>\n",
              "      <td>uniform</td>\n",
              "      <td>{'n_neighbors': 23, 'weights': 'uniform'}</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.81250</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.81250</td>\n",
              "      <td>0.78125</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.853125</td>\n",
              "      <td>0.037107</td>\n",
              "      <td>58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>0.002673</td>\n",
              "      <td>0.003137</td>\n",
              "      <td>0.004116</td>\n",
              "      <td>0.002651</td>\n",
              "      <td>27</td>\n",
              "      <td>uniform</td>\n",
              "      <td>{'n_neighbors': 27, 'weights': 'uniform'}</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.81250</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.81250</td>\n",
              "      <td>0.78125</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.853125</td>\n",
              "      <td>0.044305</td>\n",
              "      <td>58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>0.001605</td>\n",
              "      <td>0.000149</td>\n",
              "      <td>0.003444</td>\n",
              "      <td>0.000388</td>\n",
              "      <td>30</td>\n",
              "      <td>uniform</td>\n",
              "      <td>{'n_neighbors': 30, 'weights': 'uniform'}</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.75000</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.87500</td>\n",
              "      <td>0.78125</td>\n",
              "      <td>0.84375</td>\n",
              "      <td>0.78125</td>\n",
              "      <td>0.78125</td>\n",
              "      <td>0.93750</td>\n",
              "      <td>0.90625</td>\n",
              "      <td>0.843750</td>\n",
              "      <td>0.065551</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ff91f083-0355-4ee5-940d-582856971c7c')\"\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-ff91f083-0355-4ee5-940d-582856971c7c 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-ff91f083-0355-4ee5-940d-582856971c7c');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    }
  ]
}