Outliers_Detection_Ten_May_2020.ipynb

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  "metadata": {
    "colab": {
      "name": "Outliers_Detection.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNdh5sG3f9hXR0EL1OxgD+m",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Gaurav7004/DeepLearning_PlantDiseases/blob/master/Outliers_Detection_Ten_May_2020.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JsTa7WhSFWlJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import math"
      ],
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PDACcW31uZXk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "cc728d97-b9f2-410e-9f6a-854a236d163b"
      },
      "source": [
        "\n",
        "# This module takes path of any random dataset from the user\n",
        "\n",
        "class User_Input():\n",
        "  # Taking File Path as input from the user\n",
        "  user_input = input(\"Enter the path of your file: \")\n",
        "\n",
        "  try:\n",
        "    \n",
        "    # user_file = open(user_input,'r+')\n",
        "    with open(user_input, \"r+\") as file:\n",
        "\n",
        "      print(\" found the file !\")\n",
        "\n",
        "      for fp in user_file:\n",
        "          # Split the extension from the path and normalise it to lowercase.\n",
        "          ext = os.path.splitext(fp)[-1].lower().suffix()\n",
        "\n",
        "          # For handling breakdown in case of any errors\n",
        "          try:\n",
        "            # Now we can simply use .endswith to check for equality, no need for wildcards.\n",
        "            # For handling csv files\n",
        "            if ext.endswith('.csv'):\n",
        "                df = pd.read_csv(user_input)\n",
        "\n",
        "            # For handling excel files\n",
        "            elif ext.endswith('.xlsx'):\n",
        "                df = pd.read_excel(user_input)\n",
        "\n",
        "            # For handling json files\n",
        "            elif ext.endswith('.json'):\n",
        "                df = pd.read_json(user_input)\n",
        "\n",
        "            # For handing unknown files types\n",
        "            else:\n",
        "                print( fp,\"is an unknown file format.\")\n",
        "                break\n",
        "\n",
        "          # to handle the situation if try block gets failed\n",
        "          except:\n",
        "              pass\n",
        "\n",
        "  except:\n",
        "    # The assert keyword lets you test if a condition in your code returns True, if not, the program will raise an AssertionError.\n",
        "    # print(os.path.exists(user_input), \" did not find the file at, \"+str(user_input))\n",
        "    df = pd.read_excel('Students.xlsx')\n",
        "    # pass\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Enter the path of your file: k\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-o5IutSgPY8k",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# reading the required excel file for debugging purpose\n",
        "\n",
        "df = pd.read_csv('Age_Weight.csv')"
      ],
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kPhMe1nXShyy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "b72c6a17-b09d-4ff4-ea7c-e6aa4f571fbc"
      },
      "source": [
        "# handling missing values\n",
        "def handling_missing_data():\n",
        "\n",
        "  # Imputation\n",
        "  # Handling columns which have null values\n",
        "\n",
        "  # Select number columns.\n",
        "  selectNum == df.select_dtypes('number')\n",
        "  # Fill numeric columns with mean.\n",
        "  df[selectNum.columns] = selectNum.fillna(selectNum.mean())\n",
        "\n",
        "  # Select string and object columns.\n",
        "  selectObj == df.select_dtypes('object')\n",
        "  # Fill object columns with mode.\n",
        "  df[selectObj.columns] = selectObj.fillna(selectObj.agg(lambda x: x.mode().values[0]))\n",
        "\n",
        "df"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "\n",
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              "    }\n",
              "\n",
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              "    }\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>Height</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Index</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Male</td>\n",
              "      <td>174</td>\n",
              "      <td>96</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Male</td>\n",
              "      <td>189</td>\n",
              "      <td>87</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Female</td>\n",
              "      <td>185</td>\n",
              "      <td>110</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Female</td>\n",
              "      <td>195</td>\n",
              "      <td>104</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Male</td>\n",
              "      <td>149</td>\n",
              "      <td>61</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>495</th>\n",
              "      <td>Female</td>\n",
              "      <td>150</td>\n",
              "      <td>153</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>496</th>\n",
              "      <td>Female</td>\n",
              "      <td>184</td>\n",
              "      <td>121</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>497</th>\n",
              "      <td>Female</td>\n",
              "      <td>141</td>\n",
              "      <td>136</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>498</th>\n",
              "      <td>Male</td>\n",
              "      <td>150</td>\n",
              "      <td>95</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499</th>\n",
              "      <td>Male</td>\n",
              "      <td>173</td>\n",
              "      <td>131</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>500 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Gender  Height  Weight  Index\n",
              "0      Male     174      96      4\n",
              "1      Male     189      87      2\n",
              "2    Female     185     110      4\n",
              "3    Female     195     104      3\n",
              "4      Male     149      61      3\n",
              "..      ...     ...     ...    ...\n",
              "495  Female     150     153      5\n",
              "496  Female     184     121      4\n",
              "497  Female     141     136      5\n",
              "498    Male     150      95      5\n",
              "499    Male     173     131      5\n",
              "\n",
              "[500 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GGBttEI0D1NM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# automation of label encoding \n",
        "from sklearn import preprocessing \n",
        "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
        "from sklearn.pipeline import Pipeline\n",
        "\n",
        "# df.apply(LabelEncoder().fit_transform)\n",
        "\n",
        "class MultiColumnLabelEncoder:\n",
        "    def __init__(self,columns = None):\n",
        "        self.columns = columns # array of column names to encode\n",
        "\n",
        "    def fit(self,X,y=None):\n",
        "        return self # not relevant here\n",
        "\n",
        "    def transform(self,X):\n",
        "        '''\n",
        "        Transforms columns of X specified in self.columns using\n",
        "        LabelEncoder(). If no columns specified, transforms all\n",
        "        columns in X.\n",
        "        '''\n",
        "        output = X.copy()\n",
        "        if self.columns is not None:\n",
        "            for col in self.columns:\n",
        "                output[col] = LabelEncoder().fit_transform(output[col])\n",
        "        else:\n",
        "            for colname,col in output.iteritems():\n",
        "                output[colname] = LabelEncoder().fit_transform(col)\n",
        "        return output\n",
        "\n",
        "    def fit_transform(self,X,y=None):\n",
        "        return self.fit(X,y).transform(X)"
      ],
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hRZFUGDOSjdC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "097074d5-adff-482e-ba4d-ba8b75896182"
      },
      "source": [
        "df = MultiColumnLabelEncoder(df.columns).fit_transform(df)\n",
        "df.head(50)"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Gender</th>\n",
              "      <th>Height</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Index</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>34</td>\n",
              "      <td>46</td>\n",
              "      <td>4</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
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              "      <th>2</th>\n",
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              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>55</td>\n",
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              "      <td>3</td>\n",
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              "      <th>4</th>\n",
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              "      <th>5</th>\n",
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              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>42</td>\n",
              "      <td>5</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1</td>\n",
              "      <td>14</td>\n",
              "      <td>61</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1</td>\n",
              "      <td>34</td>\n",
              "      <td>40</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0</td>\n",
              "      <td>29</td>\n",
              "      <td>53</td>\n",
              "      <td>4</td>\n",
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              "      <th>10</th>\n",
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              "      <th>11</th>\n",
              "      <td>0</td>\n",
              "      <td>19</td>\n",
              "      <td>30</td>\n",
              "      <td>4</td>\n",
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              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0</td>\n",
              "      <td>52</td>\n",
              "      <td>51</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1</td>\n",
              "      <td>15</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1</td>\n",
              "      <td>51</td>\n",
              "      <td>29</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>57</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0</td>\n",
              "      <td>17</td>\n",
              "      <td>60</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>78</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>94</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1</td>\n",
              "      <td>32</td>\n",
              "      <td>88</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>1</td>\n",
              "      <td>17</td>\n",
              "      <td>60</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>98</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>0</td>\n",
              "      <td>29</td>\n",
              "      <td>47</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>1</td>\n",
              "      <td>45</td>\n",
              "      <td>88</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>0</td>\n",
              "      <td>32</td>\n",
              "      <td>17</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>14</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>1</td>\n",
              "      <td>50</td>\n",
              "      <td>45</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>1</td>\n",
              "      <td>47</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>0</td>\n",
              "      <td>23</td>\n",
              "      <td>108</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>1</td>\n",
              "      <td>39</td>\n",
              "      <td>101</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>70</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>1</td>\n",
              "      <td>38</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>0</td>\n",
              "      <td>55</td>\n",
              "      <td>15</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>80</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>0</td>\n",
              "      <td>17</td>\n",
              "      <td>102</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>0</td>\n",
              "      <td>49</td>\n",
              "      <td>81</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>0</td>\n",
              "      <td>57</td>\n",
              "      <td>63</td>\n",
              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>30</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>0</td>\n",
              "      <td>31</td>\n",
              "      <td>101</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>0</td>\n",
              "      <td>45</td>\n",
              "      <td>31</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>69</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>58</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>1</td>\n",
              "      <td>17</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>1</td>\n",
              "      <td>21</td>\n",
              "      <td>67</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>0</td>\n",
              "      <td>42</td>\n",
              "      <td>75</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>1</td>\n",
              "      <td>45</td>\n",
              "      <td>26</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>0</td>\n",
              "      <td>48</td>\n",
              "      <td>71</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>1</td>\n",
              "      <td>41</td>\n",
              "      <td>61</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>1</td>\n",
              "      <td>21</td>\n",
              "      <td>22</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>101</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>0</td>\n",
              "      <td>28</td>\n",
              "      <td>84</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>0</td>\n",
              "      <td>36</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Gender  Height  Weight  Index\n",
              "0        1      34      46      4\n",
              "1        1      49      37      2\n",
              "2        0      45      60      4\n",
              "3        0      55      54      3\n",
              "4        1       9      11      3\n",
              "5        1      49      54      3\n",
              "6        1       7      42      5\n",
              "7        1      14      61      5\n",
              "8        1      34      40      3\n",
              "9        0      29      53      4\n",
              "10       1      55      31      2\n",
              "11       0      19      30      4\n",
              "12       0      52      51      3\n",
              "13       1      15       1      2\n",
              "14       1      51      29      2\n",
              "15       0      13      57      5\n",
              "16       0      17      60      5\n",
              "17       1       0      78      5\n",
              "18       1       4      94      5\n",
              "19       1      32      88      5\n",
              "20       1      17      60      5\n",
              "21       0      13      98      5\n",
              "22       0      29      47      4\n",
              "23       1      45      88      5\n",
              "24       0      32      17      2\n",
              "25       0      11      14      3\n",
              "26       1      50      45      3\n",
              "27       1      47      12      1\n",
              "28       0      23     108      5\n",
              "29       1      39     101      5\n",
              "30       1      13      70      5\n",
              "31       1      38       2      1\n",
              "32       0      55      15      1\n",
              "33       0      20      80      5\n",
              "34       0      17     102      5\n",
              "35       0      49      81      4\n",
              "36       0      57      63      3\n",
              "37       1       4      30      4\n",
              "38       0      31     101      5\n",
              "39       0      45      31      2\n",
              "40       0      35      69      4\n",
              "41       0       9      58      5\n",
              "42       1      17       6      2\n",
              "43       1      21      67      5\n",
              "44       0      42      75      4\n",
              "45       1      45      26      2\n",
              "46       0      48      71      4\n",
              "47       1      41      61      4\n",
              "48       1      21      22      3\n",
              "49       1       0     101      5\n",
              "50       0      28      84      5\n",
              "51       0      36       4      1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WZZbLZFia9cZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C4lGQzKHionT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "outputId": "92986d56-e010-4b4d-8e8c-88a2c42cd62a"
      },
      "source": [
        "# Positive Correlation: both variables change in the same direction.\n",
        "# Neutral Correlation: No relationship in the change of the variables.\n",
        "# Negative Correlation: variables change in opposite directions.\n",
        "\n",
        "# corelation between the columns\n",
        "corr = df.corr()\n",
        "import seaborn as sns\n",
        "sns.heatmap(corr, xticklabels= corr.columns, yticklabels=corr.columns, cmap='YlOrRd_r')\n",
        "corr"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Gender</th>\n",
              "      <th>Height</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Index</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Gender</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.017677</td>\n",
              "      <td>0.009757</td>\n",
              "      <td>0.028786</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Height</th>\n",
              "      <td>-0.017677</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000103</td>\n",
              "      <td>-0.422223</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Weight</th>\n",
              "      <td>0.009757</td>\n",
              "      <td>0.000103</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.805654</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Index</th>\n",
              "      <td>0.028786</td>\n",
              "      <td>-0.422223</td>\n",
              "      <td>0.805654</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Gender    Height    Weight     Index\n",
              "Gender  1.000000 -0.017677  0.009757  0.028786\n",
              "Height -0.017677  1.000000  0.000103 -0.422223\n",
              "Weight  0.009757  0.000103  1.000000  0.805654\n",
              "Index   0.028786 -0.422223  0.805654  1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qbQwUjDOmIv4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "782b274b-bcf9-4230-fb4d-90c5e88f12a4"
      },
      "source": [
        "# Select the column(variable) in from the above list in which you want to detect outliers\n",
        "\n",
        "list_of_cols = list(df.columns.values)\n",
        "list_of_cols"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Gender', 'Height', 'Weight', 'Index']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c_txSRDOmCbO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        },
        "outputId": "fb000e14-a11a-45ba-a224-d2aa4359e48c"
      },
      "source": [
        "import re\n",
        "\n",
        "try:\n",
        "  # Enter the name of the column(variable)\n",
        "  target_column = input(str())\n",
        "\n",
        "  # Now Match the target_column with column name of correlation matrix \n",
        "  new_list = corr[target_column]\n",
        "  new_list = new_list.drop(labels = []) \n",
        "  print(new_list)\n",
        "  \n",
        "\n",
        "except:\n",
        "  print(\"Enter the correct name of the column...!\")\n",
        "\n"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Height\n",
            "Gender   -0.017677\n",
            "Height    1.000000\n",
            "Weight    0.000103\n",
            "Index    -0.422223\n",
            "Name: Height, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "js4tbEwyQHbP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531
        },
        "outputId": "1fdbe43e-f42d-4582-e091-5090bac79c53"
      },
      "source": [
        "# corelation between two columns via scatter plot\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "pd.plotting.scatter_matrix(df, alpha=1, figsize= (30, 20))\n",
        "plt.show()\n"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Kr4959xY597YnM31uLdQ57aDtcvKIRzOlqSVDN1gp+261Vk4ZUWvvfluvmZFPvJJZXePILw/dMcf938OZOrs1u2y8St660So5/ebaB3f232GdDCglV/7thSy95ID86Qu75T1n1K4LVl52yfzog9vmCxfflyT56Ye3zy3jJ+baB1/OsoOWyI8/tG2+VPfaNl1jcB6fVDvGt1x7+ayz4jK5efykLL/UwJz1sR1z2G/uSVLbX7deZ4XubfTubdbKjY9M7B4D/vzfu+Uj59+VlvbOfGjoejnt4O179P+2x1+fWS21/j9yr41y7l9rofuAUnvs2V3nmR3XXzGdVXL/8zOyxvJL5dSDts3hv63ty1utvXxenNGc6V0h2QE7rJP/G/did/9/fq+Nc+5fn0pSGzdXWnbJvDi9dq48+h2b5c/jXshTU2Zn49WWy5fevmm+elltOwzfaJU8PaUpk2fV9uXj3rdVLrjz6bwwfW62WGv5bLbm8rnq/heT1K5P1l5x6fz9pVpo+tvDh+VbVzyYSbNasv36K+Vb+22eQ39VC7e/+94t89SU2fl/d0/IoCUGZMRRe+bD547OK7NbM3zIKhm4ROk+3tZecens9pbVcsV9tXPg9Uftlf3PGpkZc9uy56ar5eO7bJgju4L7Uz+0XY676qE0t9XGlT8euUuGbbRq97a+6v4X8+Wuffv0j+6Qoy4d13198t33bJaTrn2se9ufetD2+XrXMX3ux4dmv23WzqvZ/5d35v7na0HiB7ZfO9c8+HI6uvr/N5/cKZ/6Xa19G6+2XCbNau4en7Zfb8Xu9ZLkxA9s1X09stKyS6apub17P9p6neXzyIuzuvv014ftlM90XV9vu+4KufpLe/ZoU/157Rvv2iw/ub722tZeYel0puoeV47Zb/NcPGZCnps6N5utOTjv3GrNnHVr7Ty6wSrL5K9115UdHR15y7EjkiSl1PaH719da+/bt1g99z47PTO6julTDtw2x3Rd7wwoyUE7rZfLusaKLddePs1tHXl6Si3c/t9PDc9hvxmTKrXrldM+vEM+0zVOn3zgtjmkbpx+6IXped+ZtTewe5+/FuRf+ZuI19f7DXHb7x9Tv/2WGpA8+iPbb2H9adQzOfqqh7tv2/f+MY7df80/uv1KKfdWVTVsQXUDF1T4b1A/Q7Eoe82yeU5OckYWoJTyuSSfS5INNthgQXcBgDeESbP6zniZZ96b90ltVki9jrqbk2b1nHnU1lXZWSXtncmgrrPpvDcHkqT3Z6DqP4Hc2aty3hs6SbrfWF3Q47R0VBmcvs81702UeV6pe4ymXp+obmqev960OX1nb80zp669U+e0ver9XqmbATan16es65tV395k/vaukrS0z19vbnt795tAvftkcl0/zGnr+Vz1nxx/Zfarv64pdY/R0t4x/7k6ej7XK03z95vefdnSXt9fPV9X/X2n1223jqrq3j6zmnv2Se+ZiPXq940ZvfqhuW4bzK7bl+d0vHp/Tal7rpZe27d+35jea99or9s+s+bOf6729r6f2J/nlTnzt3Xv7Vu/z7b06st526etozMtbX1nTSVJry7psS+3dvSsrd8PO3qvWGfKa/RDvcmvMaa01u3L7T1fVo99Y9LMvrMZu9drb8/AgbU/mabV7cu9j/P6uplzX73PJ7/G65o6N1m3K3OdW3cMNffqk5l1fd67L+vHs97PNa8v57a2p6Oz6g4Fp9b1V+/jq36/6T0jpLNuG/SeEVo/prR31h03dft17+5/rfGr/vww7TXGlIXVUdf2SU3N3ftle2dnjzF2VkvP1zx3AbNi6rV11D/u/G3SUVWZVffam3o9bn0ft7Z39hiveu9PU3qdA+sfq6m5vcd+P6ulvbsveo91teeqtbdKMmX2/Mdt66h6tGnanNbuMWpOa0fmtHR2v9amlvYejz19TmuPvq3fnq3tnd3btKqS2S0d3es2tbT3GOuamjtS6v4inj7n1bdDW0dn9+N0dFaZ1dJ7/6nb9s3tPY6bKbNbuvftppb2TKvb52c2t6etbv+dNru1x33rzzkz57Z1H1Ot7Z09zvetde2rtaGt+3Zre2eP82nS8xzS1NyeWUvXbre0d2ba7Prrjc4e+8e02a09xoCZc1u7r5Hqt0F9u5Ja/9dfW1VVz7FlZvP8a4E5rR2ZXteGOa0dPa4PJje19Oj/l2bM/5aB9o7ONNcd21Nmt2Rm13aY1dKeqXX74Ozm9h7nnClNLd37elNLe16pu29re2ePMWP63Lbu203NbWmq2/azmufvr22dnZnb2j7/eqClPfUj09y2ju7jtq2jM3Na2zO3raPP48xbt73u+K8f62rt6NmG+rH2pRk9t339WDFzAcdtvfo2TJvT1nNcrhsr57Z19Bifel8HTq67zmpr7+zxONPntvXo0/pr+d5jdW/119HN7R09rgGmzpnfN03N7XllVl17e88Wr7tZVT3PA7O6Zgp3v5ZZc3vct8e5paU9zXX76/S5rXXXuVVm9Ror6tUfa4tiasVr/U0E/04tC76851U8ObXvN9rAm9HiCgXrz6Gdyl6zLKWUo5I8UlXVnVmAqqrOT3J+UpspuKD7AMAbwfHv3zbXPTgxE2e25Jv7bdaj7taj3pbhP745HZ1VbqibjZIkvz5sWD5/8b1ZdblB+fUnd+5Rd/bHhuas257IR4ZtkEED53++5sCd1s+lY5/Lgy/MzEn7b91jnfuO/a9s/f2b01lVufqLe/Sou+zIXXLEhfdkw1WWzTHv2bJH3Yn7b50zbnk8H9h+naw6eKnu8l03WS0f3HHdjHpySr7/gW16rHP+YcPy/jPvyMy5bbn887v2qPvtEcPz5Uv+lg1WXTaf2mOjHnXffNfmOfPWx7PnJqvnLWsM7i5/73br5NzbnszfX56V/3n7xj3WOfPQoXlu6shMndOSSz7b87l+fdhO+ez/3pslB5Tcfew7etUNy6nXP5r3brtW94zEJFl3pWVz7Hu2yDUPvpxvvmvzHuv89MPb58lJTZnc1JJLPvvWHnVXfGH3fPT80Vl1uaVyeq+ZKQfttE7+eO+LWXP5pfLxXee/5tUGL53vvXfLXPXAi/n6vj2f64aj987Wx41Ic3tnfvPJnXrUXfU/u+eAs0Zm2UEDc+UXd+9R96MPbpuTrnkkO6xfm/0xz1IDl8iFRwzPzeMn5sM79ZxxdMs3/itbfPfaNLdX+cXBPX/O+bov75l3n3FHll5yQP783z237+8+NTzHX/Vw9tp0tWy33krzX9fyy2aPTVbLXU9NyUE7rddjnaP33Tz3PjstT0+ZnV8f3vMDfJcfuVsOPHdUlho4IL/ttc//5vCd87XL789OG66Sd287fwbBwIED86Gh6+bah17KJ966YY91/nvvTXPXU1Pz+MSmnP+Jntvw5qP3zNt/dkdKknu+vU+Puh8csE3essZy2XbdlTJkteV61B21zya5eMyEHLH7kB7lh+22UW57bEr+/uLMHjMck+TGr+2Rd5xWu6S9s24WQJJ8dvcN86uRz2bJASXXfLnnGPCNd22WC0c9k0+8tedznf/JnbPdCddnZnN7vr5vzzFl5Lf2yS4n35QqyW3f6DmT4ZeH7pij/jAua6ywVH7eax/9zO5DcvE9E/K+7dbpDgST5P07rJtrH3o5902Y1mfGzQ/23ybT5rRmbltnfv6Rno93yge3yTFXPpRllhyQq7/Uc7zZbM3BeWxiU1YfPCjrrjL/2Nt+/ZXz5bdvkjuemJLvf6Dn+HX71/fK0B/ekraOzoz4Ss/t9Lsjds6nfjc2Ky+7ZH73qeE96s75+NBcfu/zeceWa/YYK9+33bq5+K4Juf/5GTn+fVv1WOfcjw3N/meNyqyW9lx+5C496n7/6eH55G/vyQpLL5nLPr9bj7rzP7FTfnbTY9l/+3Wz0rKDusv32myN7LHparnv2Wn5zru36LHOWR8bmud/eWemzWnNZUf2Hr+G5St/GJd1Vlo6R9bN3v5nXfzpt+bTF92TdVdeNj86cPtss+7KuXTshHxx702y79Zr5flpczJlVkuf/Xf9VQbnPduslVsfm5QvLKAdFx6+c7562bjssP7K+fzem+a5aXNz4yOT8u13b5H9d1g3M+a2pbmtM4ftOqTHehutPjjf3G/z3PDwxHznPVtk23VXyv6/vDOdVZU/fPatufjuCfnJDY9l0zUH5yNv7fkh0I+9dYM0t3VkiQElH9ppvWy46rI5+brx2W/rtTJ8yKo5/xPDct+Eafnkbj2fM0nOP2ynnHbDY3n/dmvn03tsnLuempoHn5+R0w/ZMeusuFTef+aoLDNoiVz5hV3zzNTmXH7vc9lnizWz7Xor5peH7pjxL82qnbuq5EuX3JfVll8qX33n5llxmUE55/YnctBO6+fofbfIfRNm5MnJTbngsGHZcLXlstyggVlrxaWyx6ar5defHJar7n8x79127Wy33kp5+MWZeXLK7Jz+kdos16MuHZchqy2Xw3YdkmseeDFjnp6W3TddNb89Ynj2/smtmTq7NX/43C5Zd6Vls8byS2Xj1Qdn+Ma1c/KIh2tj4du3WDM/+uC2mdLUks/uuXHeseUa+cLv78vmay+fI9+2SYZuuEpu+vvEfHin9bLxasvlhenNmTG3Lb88ZMeMnzgrX7nkb9l2vRXziV2HZJM1ls9tj03KR3feIBuuskymNLVkdmt7zjxkaOa0tmezNZ/JjhusnOFDVs0x+22REQ+/nGP22yI7b7RKV3DUmY/tsmHetc1auWj0sxk+ZJX81xZr5PJ7n8/jE5ty9Ds3y6HD18s+P7sjpSTXfWWvTJ/bmt/fNSG7vWXV7LXZ6rnp7xMz5pmpOeXAbTN0w5XzwbNGZdASA3LREcNzzUMv5eTrxuedW66RvTZfM+d8fKfc/9z0HL573/4/7xM75ec3PZYDdlg3R+y+Ue55ZlpentGcnxy0bbZce4Xsf9aoLL3kgNz8tb3z+MRZ+eN9z2ffrdbMThuukr89PyOPvDg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YIl93C4LQ/fePJEmSpMbQu7+pCYCIGBgRfwDeBtwWEYfUOyZJkiRJkiRJkiQ1Dm8DbACZuQY4pt5xSJIkSZIkSZIkqTHZUlCSJEmSJEmSJElqchYFJUmSJEmSJEmSpCZnUVCSJEmSJEmSJElqchYFJUmSJEmSJEmSpCZnUVCSJEmSJEmSJElqchYFJUmSJEmSJEmSpCZnUVCSJEmSJEmSJElqchYFJUmSJEmSJEmSpCZnUVCSJEmSJEmSJElqchYFJUmSJEmSJEmSpCZnUVCSJEmSJEmSJElqchYFJUmSJEmSJEmSpCZnUVCSJEmSJEmSJElqchYFJUmSJEmSJEmSpCZnUVCSJEmSJEmSJElqchYFJUmSJEmSJEmSpCZnUVCSJEmSJEmSJElqchYFJUmSJEmSJEmSpCZnUVCSJEmSJEmSJElqchYFJUmSJEmSJEmSpCZnUVCSJEmSJEmSJElqchYFJUmSJEmSJEmSpCZnUVCSJEmSJEmSJElqchYFJUmSJEmSJEmSpCY3oN4BSJIkSZIkSZJUrVHn31zvELo06+KT6h2CJHXKloKSJEmSJEmSJElSk7MoKEmSJEmSJEmSJDU5i4KSJEmSJEmSJElSk7MoKEmSJEmSJEmSJDU5i4KSJEmSJEmSJElSk7MoKEmSJEmSJEmSJDU5i4KSJEmSJEmSJElSk7MoKEmSJEmSJEmSJDU5i4KSJEmSJEmSJElSk7MoKEmSJEmSJEmSJDU5i4KSJEmSJEmSJElSkxtQ7wAkSZIkSZIkqS8Zdf7N9Q6hS7MuPqneIUiSNgFbCkqSJEmSJEmSJElNzqKgJEmSJEmSJEmS1OQsCkqSJEmSJEmSJElNzqKgJEmSJEmSJEmS1OQsCkqSJEmSJEmSJElNzqKgJEmSJEmSJEmS1OQsCjaAiLg0Iu6OiO/VOxZJkiRJkiRJkiQ1HouCvVxEHAQMyczDgUER8fZa9/mVGx7n1ifm1h6cJKlPeP61pXztN4/XOwypW15auJyVa1rrHYY2oeWrW5izaHmny15/czULlq3qdNkrb6zgzVUtG/VcK1a38vLrnT9Xd7S1JbMXvsma1raN2m72wjd56tXFnS57dfEKlq5cs1H7W7mmlZcW9txxZZaOa3XLxh3XywuX89Tczo+rp5V/J1rT2sbshW/S1pYALF6+hnlLVna63aJlq/n1tDm0tlY+r6xqaWX2wjd7LmhJkiRJqtGAegegit4J3FFM/wE4FHiouzvb58u/Z01b8rMHX+K8497knPfu0xMxSpKa1JzXl3HsdycDcP39L/HixSfVOSKpehff8gxX3zWTMSOHcNM572bwwP71Dkk9bPGKNZx02d3MWbSCC04cyyePGL1u2SMvLeLvfng/bQn/fsY7OGSv7dctu3bKi1z426fYaehgfn/u4Wy39aCKz7VsVQsnXXY3sxcu57zjxvTI39Gf/c9HufHRubxzr+34xZmHVrXN76e/ytk/nwbwF3H850Mv86UbprP91oO4+dOHM3Lo4Ir7W7mmlfdffg/Pz1vG2UeO5osnjO3ewZT58q8f5z8efJkJuw/nhrMOo1+/qLjNpGfn8bH/V/qac9YRo/nSibXHsSEdvxPd/fwCHnjxdU6dsAufOnofTr1iCstXt3LlRw7i+AN2Wrdda2srB3/zDtoSvnbjkzzxv4/f4HO0tiWnX3Ufj7+ymH84dE8uOmXcJjseSZIkSaqWLQV7v+HAkmJ6cfG4nYg4MyKmRsTU+fPnd7mzNcXdrwC/fOjlHgxTktSMfjv91XXT2cV6Um80ZcYCAJ7787INthZTY3tp4XLmLFoBwJSZC9ste3j2IlauaWN1SxtTZy9qt2ztuq8tWckL85dV9VxzFi1ndtGabsqMhRXWrs7a9+gDL75OS5WtBW9/6s/rpu98dl77/c1cQCYsWLaaZ19bWtX+5i1ZxfPzlhXb98xx3VMc16Mvv8Gbq6trjfmHsuO667l5XaxZu/LvRL946GUenPU6UDr+6XPeYOnKFlrbkgdeeL3ddi8vWsHaTZdVaGW6ZMUaHn+l1OpxbT4kSZIkqd4sCvZ+i4GhxfRQ4I2OK2TmNZk5MTMn7rDDDl3ubMLuw9ZN33DWu3owTElSMzrryH1Y28Bj120rtziRepMvnrAvb9ttGP901Gh223areoejTWDcrkP52GGjOHCP4Zx7dPuWe6cdtBtHj92RI8bswAcn7t5u2afeuzcH7TGcDx+yBwftsW1VzzV2p6F84t1vYcLuw/nccWN6JP6vnrQ/43cbxtdO2p8B/av7avaFE8aw4zZbMHyrgXz95P3bLTv7yL15+6htOf3g3Th09PYb2EN7e2y/FWcdOZq37T6cLxy370YfQ2e+8r79GL/bML50wli2GTywqm0+d+y+7DxsMEMHD+Cf339Aj8SxIeXfiX591rv4WvE6fPWk/TjhgJ05afzOHLrX9nz8XaPabTdqxBBG77A1EXD02B27fI5ttx7EeceN4W27DeOCE/fbFIchSZIkSRstMr3vvzcrxhT8ZGZ+MiKuBK7NzAc3tP7EiRNz6tSpmy9ASZLKTJw4Ea9DkqR68lokSaqnaq9Do86/eTNE032zevnQEeavNuav+8xdbXp7/nq7al/fiHg4Myd2tsyWgr1cZk4DVkbE3UBrVwVBSZIkSZIkSZIkqTMD6h2AKsvMc+sdgyRJkiRJkiRJkhqXLQUlSZIkSZIkSZKkJmdRUJIkSZIkSZIkSWpykZn1jkE9KCLmA7OrWHUEsGATh9PMzF/3mbvamL/amL/uqzZ3BwHTenB/fYX5WM9ctGc+2jMf65mL9srz4bWo55ijysxRZeaoMnNUWSPlyOvQ5mH+amP+us/c1cb81aba/O2ZmTt0tsCiYB8VEVMzc2K942hU5q/7zF1tzF9tzF/39XTufC3aMx/rmYv2zEd75mM9c9Fed/JhDiszR5WZo8rMUWXmqLJmzFEzHtPmZP5qY/66z9zVxvzVpifyZ/ehkiRJkiRJkiRJUpOzKFjjhFUAABBHSURBVChJkiRJkiRJkiQ1OYuCfdc19Q6gwZm/7jN3tTF/tTF/3dfTufO1aM98rGcu2jMf7ZmP9cxFe93JhzmszBxVZo4qM0eVmaPKmjFHzXhMm5P5q4356z5zVxvzV5ua8+eYgpIkSZIkSZIkSVKTs6WgJEmSJEmSJEmS1OQsCkqSJEmSJEmSJElNbkC9A5AkSc0vIg4GDgWGA28A92fm1PpGJUnqS7wWSZLqyetQz4uIt2fmQ/WOQ82v+Py+DCwETgZWZObt9Y2qMUXEP2XmFfWOoy9zTEFJm1VEDMvMxfWOQ9LmExGXAlsAfwAWA0OBY4CWzDy3nrHViz8IaEN8b6xnLtSVjX1/eC1ST/HcVJk5qswcVaeZ8uR1qDYR0VlvdwHcmpnHbu54GlFEHAC0ZuYzZfMOycwH6hhWQ4iIH1N6v60CdgReAZYAO2bmmfWMrbeLiLuBtQWoKP4/AHgiM99Tn6gaU0SMA8YBM2u9GcKiYB9QvGH+BRhG6cOXlP4A+XpmTq9nbI2gyN+FQAtwWWbeW8y/KjPPqmdsvV1EvLfjLOBbwAWZ+ac6hNRQIuKCzPx2RLwTuARopdTC+18y89b6Rtf7RcT7gc9TOt9dDvwzMBC4JjN/Us/YGkFPXjsiYnJnf+xtaH6z8weB9iKiP3AqHX7wAX6TmS31jG1z872xnrloz89Je915f3gtqsz3WWWemyozR5WZo+o0W568DtUmIpZTuiat/W5KMT0+M7evW2ANIiK+A4wE1gAjgDMyc35E/CkzO/52pw4i4q7MPKKYfjwz31pM35mZR9U3ut4tIj4LvA24NjMnFfNuycwT6xpYg4iIWzPzhIj4DHA0cDPwLmBOZl7Q3f3afWjfcBXwwcx8de2MiNgF+CVweN2iahxXAh+nVBT8dkQclZnfBMbWN6yG8DNgOjCleBzAdsC7AYuClR0NfBv4JnBaZs6LiK0pfSmyKFjZl4EjKX15nAbsB6wA7gEsClbWk9eOqRHxA+AOSnfTDaX0/p7WQ7E2moM7+eL/64iYXJdo6u9aSteKn9P+B59rgb+vW1T14XtjPXPR3rX4OSnXnfeH16LKrsX3WSWemyozR5WZo+o0W568DtXmaeADHXueiog76hRPo3n72s9TRIwHfhUR59U5pkZSXkP5ctl0dFxR7WXmpRExCPhERPwjpb8zVb1Bxf8fAI7KzDbg6oi4p5adWhTsOzqepKKTeepcv8ycWUx/OCLOjYhfAlvVM6gG8RbgfwLvBP49M28ruia4qM5xNYqFETEGmEPpbu15wDbAyrpG1TjWdi+SZf8nnvs2Ro9cOzLzcxFxIKVzwT6Ufmi8JjMfqT3EhuQPAu2NysyPdpj3SNHNSF/je2M9c9Gen5P2Nvr94bWoKr7PKvPcVJk5qswcVaep8uR1qGYnU7rJtyNbG1Wnf0QMyszVmTk9Ij4AXE+pG0dVdmZE9M/M1sz8LUBR6Pq3OsfVEDJzNXBVRPwQ+CjwWJ1DaiT7R8RPgdGUWs+vPQ8OrmWndh/aBxR9Rn+DUlFh7Y/kC4ELM/PxugXWICLiauDbmTm7bN5xlLoStbVgFSJiIPAx4Hhg98w8pL4RNYaI2JZSl5fjgMOAZ4HnKH12n65nbI0gIk4HPgvMAH4DfJ1SQfXqzLyunrE1Aq8dm1bZDwLDKf0gcB8woNZ+4RtRRHwBOAKYxPoffI4AJmfmJXUMrS4i4h3Aeyl1d9wCZGZeXN+o6qP4nBzC+s/JiMz8Rn2jqo8uPid3Z+b/rWNodREROwM7Uermciil61Qb8K92c9l9no+r47mpa34+Kyt+SD6fUl5aKLUCSODyzHyjnrH1NmV/Mw+j9Hm73yKatPGK7xizMnNe2bz+wN9k5i/qF5mkrkTEnmUP52bmmogYAhyembd0e78WBSVtLsUfHCMzc269Y5GkeomIfp3NBm7NzGM3dzy9QUS8B9if0vhVS4CHgL362qD3xQD2AKvp4wPYlw1IX946eX/gyb467k5E7ABMBA4GZgIz+uKNBABrx7+JiMuA5ZS6pZ8ATMzMD9Y3usbm+bhrnpsq8/NZWUT8mtJnazilc/rvgQXAhzPz+HrG1ttExMGUbpAdDiyiVBScWt+oJElqbHYf2odFxAWZ+e16x9GozN/Gy8xWYK65q435q435q4356xHLgPs7zAtgfB1iqbti0PsdKd0pXz7o/S8ptZjrS/buMID9acX0nfUNqy5uwAHp1ykbYH5fSi0mFgCfjoiaBphvYG3F//tn5jHF9O199LPSYzwfV8VzU2V+PisbnpnfgnXX++8U0x+ra1S9TERcSqkV5R+Bpyi1PP14RHw0M8+ta3CSJDUwi4J9RHF31aGU7q56g9KPkT/uciOtY/66z9zVxvzVxvxtEjUNZiwAngY+kJmLy2dGxB11iqfeHPR+PQewLzgg/V/YJAPMN7DrIuJHwMsRcT1wF6UbK2w9UhvPxxV4bqqKn8/K3oyIrwJbA69HxOeB14FV9Q2r1zm4kxa4v46IyXWJRpKkJmH3oX1AcXfVFsAfKPXBPhQ4Bmjx7qrKzF/3mbvamL/amL/a2MXlplOMtbOwGGy7fP6AvjjWTkRMoVTkWF083pbSoPcTM3NkXYPbzIqxPJ8pWtavnTcIOCEzb6pfZPUVEQMoDUi/b2aeX+946iEiXgNup9Raa5/MXFHMn5qZE+saXJ1ExC6UxqseSek6f29mPlbfqBqb5+ON47lpw/x8di0itgROoNQV9PPA/6D0d/bPO9401pdFxL9RKpzewfpxTo8GVmXmZ+oZm6SNExHLMnPIRqx/JHBeZp686aKS+i6Lgn1AREzubHyDiLhrbRdV2jDz133mrjbmrzbmrzYRsZxSy8qgNHYOxfT4zNy+boGp6TjovVTZphpgXirn+VhSbxMRB1LqNns4pQLzfcCAvjqmrtSoLApKvYtFwT7Au6tqY/66z9zVxvzVxvzVJiIeBt7bWReXthSUJEmStCnZc4nUPNYWBYti34WUxsceBzwM/H1mZkScAHwXWE5p2JK9MvPkiNgauLxYfyBwYWbeGBHfo9QDz0URcTzwFeDIopt9SV2wKNhHdHZ3VWY+Ut+oGof56z5zVxvzVxvz1312cSlJkiSpXsp6Lmk3G3sukRpOh6LgjcABwFxgCvAFSuPOPk+pq/wZwC+BrYqi4LeApzLz+ogYDjwIHEipR6OHgHOAq4H3ZebMzXtkUmOyKChJkiRJkiSp17DnEql5dCgKfmXtZzgirqJUGHwCuGztEDAR8VfAmUVRcCowGFh7c/J2wPGZ+XREHAZMBj6bmZdv3qOSGldnTfElSZIkdVNELOvw+GMR8f0K2/xVRJxfYZ0jI+J3G1j2mYjYauOjlSRJ6pVOBlZ0Mv/EzR2IpB61qmy6FRhQYf0ATsvMCcW/PTLz6WLZW4GFwC6bIE6paVkUlCRJkuosM2/KzItr2MVnAIuCkiSpKWTmqx2HMijmO5SB1HyeAUZFxOji8d+VLbsN+FREBKwbJoaI2BP4PKWuRE+MiEM2Y7xSQ7MoKKlpRMTIiPh5RLwQEQ9HxH0R8YEe2O8GW2ZIkrQxImKHiPj/EfFQ8e9dxfx1rQkjYnRE3B8Rj0fEv3RoeTgkIv4rIp6JiJ9Fyacp3R17Z0TcWYfDkiRJkqRuycyVwJnAzRExDZhXtvgbwEBgekQ8CXyjKBD+GDgvM+cCnwB+FBGDN3PoUkOq1DxXkhpC8QfBb4DrMvPDxbw9gb+qQywDvHtRkvq0LSPi0bLH2wE3FdPfAy7NzHsiYg9Kd77u12H77wHfy8z/iIh/7LDsQOAAYC6l8TfelZmXRcTngKMyc0FPH4wkSZIkdVdmDin+nwRMKpt/Ttn0rcDYTrZdAXyyk90eU7bOw5S6EpVUBVsKSmoW7wVWZ+bVa2dk5uzMvDwi+kfEJUWLjOkR8UlY1wJwUscWF8WyE4p504C/XrvPiNg6In4SEQ9GxCMRcUox/2MRcVNE/An442Y9cklSb7OibMyLCcDXy5YdA3y/KBreBAyNiCEdtj8U+FUx/fMOyx7MzDmZ2QY8Cozq+fAlSY0qIi6NiM+UPb4tIn5U9vg7xY0knW17UUQc09mysnUujIjzOpk/PCLOriV2SZIkbXq2FJTULA4Apm1g2SeAxZn59ojYApgSEbcXy/6ixUVETAV+SKnQOAP4Zdm+vgL8KTPPiIjhwIMR8Ydi2UHA+Mx8vScPTJLUVPoB7yy6yFmnuCelGqvKplvx73lJUntTgA8C342IfsAIYGjZ8sOAz3a2YWZ+vbP5VRoOnA1cWcM+JEmStInZUlBSU4qIKyLisYh4CDgO+IeiVcYDwPbAPsWqnbW4GAu8mJnPZ2YC15ft+jjg/GJfk4DBwB7FsjssCEqSKrgd+NTaBxExoZN17gdOK6b/tsr9LgW2qS00SVITuJdSi3Mo3fz4BLA0IrYtbpDcD8iIuKsYh/22iNgZICKujYjTi+n3FT2nPBwRl3UYY33/oseVF4pxbQEuBkZHxKMRcclmOVJJkiRtNO8sltQsnmT9D6hk5j9FxAhgKvAS8KnMvK18g4g4ko1vcRHAaZn5bId9HQK82e3oJUl9xaeBKyJiOqVrzmSg47iBnwGuj4ivALcCi6vY7zXArRExNzOP6smAJUmNIzPnRkRLMW7tYcB9wK6UCoWLgaeBS4FTMnN+RHwI+CZwxtp9RMRg4AfAezLzxYj4jw5PMxY4itLNKM9GxFXA+cC4ottsSZIk9VK2FJTULP4EDI6Is8rmbVX8fxtwVkQMBIiIMRGxdRf7egYYFRGji8d/V7bsNuBTZWMPHtgj0UuSmkZmDunw+NrMPKeYXpCZH8rM8Zm5f2b+Y8d1gFcodTE6nlLX2FOLdSZl5sll+z0nM68tpi/PzH0tCEqSKLUWPIz1RcH7yh6/AowD7ih6P/kqsFuH7ccCL2Tmi8XjjkXBmzNzVWYuAOYBIzfJUUiSJKnH2VJQUlPIzIyIU4FLI+KLwHxKLfe+BPyKUreg04pi3nzg1C72tTIizgRujojlwN2s75LtG8B3genFGB0vAid3vidJkrrlYOD7xTXrDcpab0iSVIUplAqAb6XUfejLwOeBJZSGQNg1Mw/d4NaVOb6tJElSg4rScFmSJEmSJElqdMV4tTdQau13TDHvYUrdiI6n1JLwo5l5X9GbypjMfDIirgV+B9wMPAccnpmzIuJnwLDMPDkiLgSWZea/Fvt9gtJNkkuBaZm55+Y8VkmSJG0cuw+VJEmSJElqHo8DI4D7O8xbnJnzgNOB/xMRjwGPUmpVuE5mrgDOpjRW7cOUCn5djm+bmQuBKRHxRERc0mNHIkmSpB5lS0FJkiRJkiStExFDMnNZ0ZX1FcDzmXlpveOSJElSbWwpKEmSJEmSpHL/KyIeBZ4EhgE/qHM8kiRJ6gG2FJQkSZIkSZIkSZKanC0FJUmSJEmSJEmSpCZnUVCSJEmSJEmSJElqchYFJUmSJEmSJEmSpCZnUVCSJEmSJEmSJElqchYFJUmSJEmSJEmSpCZnUVCSJEmSJEmSJElqcv8NfvIvgjHvT9sAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 2160x1440 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ppOxW7k-JqQk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Spltting the dataset into train and test values.\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# I am selecting the y on the basis of my visualization\n",
        "X, y = df.Height, df.Index\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.3)\n",
        "\n",
        "\n",
        "Xval = X\n",
        "yval = y\n"
      ],
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ENfNc_END3eC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "ade5b465-d59d-40ca-ca51-da224eedac55"
      },
      "source": [
        "# For estimating values of mean and variance for further calculations\n",
        "\n",
        "X = df.to_numpy()\n",
        "\n",
        "def estimateGaussian(X):\n",
        "    n = np.size(X, 1)\n",
        "    m = np.size(X, 0)\n",
        "    mu = np.zeros((n, 1))\n",
        "    sigma2 = np.zeros((n, 1))\n",
        "    \n",
        "    mu = np.reshape(float(1/m)*np.sum(X, 0), (1, n))\n",
        "    sigma2 = np.reshape((1/m)*np.sum(np.power((X - mu),2), 0),(1, n))\n",
        "    \n",
        "    return mu, sigma2\n",
        "\n",
        "mu, sigma2 = estimateGaussian(X)\n",
        "\n",
        "print('mean: ',mu, ' variance: ',sigma2)"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "mean:  [[ 0.49  29.944 55.572  3.748]]  variance:  [[2.49900000e-01 2.67612864e+02 1.01934482e+03 1.83249600e+00]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p-ALBosXLG8-",
        "colab_type": "text"
      },
      "source": [
        "![alt 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)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nPm56-6NdWHV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# The multivariate normal distribution is a multidimensional generalisation of the one-dimensional normal distribution . \n",
        "# It represents the distribution of a multivariate random variable that is made up of multiple random variables that can be correlated with eachother. \n",
        "\n",
        "# Applying mutlivariate gaussian distribution\n",
        "\n",
        "def multivariateGaussian(X, mu, sigma2):\n",
        "     n = np.size(sigma2, 1)\n",
        "     m = np.size(sigma2, 0)\n",
        "     #print(m,n)\n",
        "     \n",
        "     if n == 1 or m == 1:\n",
        "        # print('Yes!')\n",
        "         sigma2 = np.diag(sigma2[0, :])\n",
        "     #print(sigma2)\n",
        "     X = X - mu\n",
        "     pi = math.pi\n",
        "     det = np.linalg.det(sigma2)\n",
        "     inv = np.linalg.inv(sigma2)\n",
        "     val = np.reshape((-0.5)*np.sum(np.multiply((X@inv),X), 1),(np.size(X, 0), 1))\n",
        "     #print(val.shape)\n",
        "     p = np.power(2*pi, -n/2)*np.power(det, -0.5)*np.exp(val)\n",
        "     \n",
        "    # p is probability density of every new person (in rows)\n",
        "\n",
        "     return p"
      ],
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jguuUqZUBavG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pval = multivariateGaussian(X, mu, sigma2)"
      ],
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PS3QQ9ftCSUI",
        "colab_type": "text"
      },
      "source": [
        "![alt text](https://miro.medium.com/max/317/1*UKPPze8OXLLrK8lkxvQrtg.png)\n",
        "\n",
        "\n",
        "Precision = true positive/(true positive + false positive)\n",
        "\n",
        "Recall = true positive /(true positive + false negative)\n",
        "\n",
        "Best parameters are the ones in which the F1 score value is maximum.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Knp-vJam84Yg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# thresholding the probability numpy array\n",
        "\n",
        "def selectThreshHold(yval, pval):\n",
        "    \n",
        "    F1 = 0\n",
        "\n",
        "    # The F1 score is the harmonic mean of the precision and recall, where an F1 score reaches its best value at 1 (perfect precision and recall).\n",
        "\n",
        "    bestF1 = 0\n",
        "    bestEpsilon = 0\n",
        "    \n",
        "    stepsize = (np.max(pval) - np.min(pval))/1000\n",
        "        \n",
        "    epsVec = np.arange(np.min(pval), np.max(pval), stepsize)\n",
        "    \n",
        "    noe = len(epsVec)\n",
        "    \n",
        "    for eps in range(noe):\n",
        "        epsilon = epsVec[eps]\n",
        "        pred = (pval < epsilon)\n",
        "        prec, rec = 0,0\n",
        "        tp,fp,fn = 0,0,0\n",
        "        \n",
        "        try:\n",
        "            for i in range(np.size(pval,0)):\n",
        "                if pred[i] == 1 and yval[i] == 1:\n",
        "                    tp+=1\n",
        "                elif pred[i] == 1 and yval[i] == 0:\n",
        "                    fp+=1\n",
        "                elif pred[i] == 0 and yval[i] == 1:\n",
        "                    fn+=1\n",
        "\n",
        "            # preciosion\n",
        "            prec = tp/(tp + fp)\n",
        "\n",
        "            # recall\n",
        "            rec = tp/(tp + fn)\n",
        "\n",
        "            # F1 score\n",
        "            F1 = 2*prec*rec/(prec + rec)\n",
        "\n",
        "            # Best F1\n",
        "            if F1 > bestF1:\n",
        "                bestF1 = F1\n",
        "                bestEpsilon = epsilon\n",
        "\n",
        "        # handling Zero Division Error \n",
        "        except ZeroDivisionError:\n",
        "            print('Warning dividing by zero!!')          \n",
        "       \n",
        "    return bestF1, bestEpsilon\n"
      ],
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-yyUavSrMbfE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "06e6ff56-3062-4c3f-d801-0bee8ce764ad"
      },
      "source": [
        "F1, epsilon = selectThreshHold(yval, pval)\n",
        "print('Epsilon and F1 are:', epsilon, F1)"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Warning dividing by zero!!\n",
            "Warning dividing by zero!!\n",
            "Warning dividing by zero!!\n",
            "Warning dividing by zero!!\n",
            "Warning dividing by zero!!\n",
            "Warning dividing by zero!!\n",
            "Warning dividing by zero!!\n",
            "Warning dividing by zero!!\n",
            "Warning dividing by zero!!\n",
            "Warning dividing by zero!!\n",
            "Warning dividing by zero!!\n",
            "Warning dividing by zero!!\n",
            "Warning dividing by zero!!\n",
            "Warning dividing by zero!!\n",
            "Warning dividing by zero!!\n",
            "Epsilon and F1 are: 1.6489595024666475e-06 0.7719298245614035\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l-EfhqdSQRPZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "outl = (p < epsilon)\n",
        "\n",
        "def findIndices(binVec):\n",
        "    l = []\n",
        "    for i in range(len(binVec)):\n",
        "        if binVec[i] == 1:\n",
        "            l.append(i)\n",
        "    return l\n",
        "\n",
        "listOfOutliers = findIndices(outl)"
      ],
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_WoxDyzGQe16",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        },
        "outputId": "b79b02e5-d1a9-424d-e4e7-466f2c0bf5f7"
      },
      "source": [
        "count_outliers = len(listOfOutliers)\n",
        "print('\\n Number of outliers:', count_outliers)\n",
        "print('Outliers in ' + target_column + \" \" + ' are --> ')\n",
        "print('\\n',listOfOutliers)"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            " Number of outliers: 35\n",
            "Outliers in Height  are --> \n",
            "\n",
            " [27, 31, 32, 51, 60, 62, 79, 85, 98, 99, 150, 200, 214, 224, 242, 244, 269, 273, 279, 280, 308, 354, 358, 360, 372, 381, 410, 420, 424, 425, 438, 453, 462, 492, 493]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OIybgTbzQmEQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QzA9wkht_VP7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#! pip install pyod    "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ywu5HuTGHxJY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "d3ce44a3-97bd-474f-a115-93975d2e6e7e"
      },
      "source": [
        "from __future__ import division\n",
        "from __future__ import print_function\n",
        "\n",
        "import numpy as np\n",
        "from numpy import percentile\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.font_manager\n",
        "\n",
        "# Import all models\n",
        "from pyod.models.abod import ABOD\n",
        "from pyod.models.cblof import CBLOF\n",
        "from pyod.models.feature_bagging import FeatureBagging\n",
        "from pyod.models.hbos import HBOS\n",
        "from pyod.models.iforest import IForest\n",
        "from pyod.models.knn import KNN\n",
        "from pyod.models.lof import LOF\n",
        "from pyod.models.mcd import MCD\n",
        "from pyod.models.ocsvm import OCSVM\n",
        "from pyod.models.pca import PCA\n",
        "from pyod.models.lscp import LSCP"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.utils.testing module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.utils. Anything that cannot be imported from sklearn.utils is now part of the private API.\n",
            "  warnings.warn(message, FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1BMu_cAYTjvG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Define the number of inliers and outliers\n",
        "n_samples = 200\n",
        "outliers_fraction = 0.25\n",
        "clusters_separation = [0]\n",
        "\n",
        "# Compare given detectors under given settings\n",
        "# Initialize the data\n",
        "xx, yy = np.meshgrid(np.linspace(-7, 7, 100), np.linspace(-7, 7, 100))\n",
        "n_inliers = int((1. - outliers_fraction) * n_samples)\n",
        "n_outliers = int(outliers_fraction * n_samples)\n",
        "ground_truth = np.zeros(n_samples, dtype=int)\n",
        "ground_truth[-n_outliers:] = 1\n",
        "\n",
        "# initialize a set of detectors for LSCP\n",
        "detector_list = [LOF(n_neighbors=5), LOF(n_neighbors=10), LOF(n_neighbors=15),\n",
        "                 LOF(n_neighbors=20), LOF(n_neighbors=25), LOF(n_neighbors=30),\n",
        "                 LOF(n_neighbors=35), LOF(n_neighbors=40), LOF(n_neighbors=45),\n",
        "                 LOF(n_neighbors=50)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yMtpJa2tUhKT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "# Define nine outlier detection tools to be compared\n",
        "classifiers = {\n",
        "    'Angle-based Outlier Detector (ABOD)':\n",
        "        ABOD(contamination=outliers_fraction),\n",
        "    'Cluster-based Local Outlier Factor (CBLOF)':\n",
        "        CBLOF(contamination=outliers_fraction,\n",
        "              check_estimator=False, random_state=random_state),\n",
        "    'Feature Bagging':\n",
        "        FeatureBagging(LOF(n_neighbors=35),\n",
        "                       contamination=outliers_fraction,\n",
        "                       random_state=random_state),\n",
        "    'Histogram-base Outlier Detection (HBOS)': HBOS(\n",
        "        contamination=outliers_fraction),\n",
        "    'Isolation Forest': IForest(contamination=outliers_fraction,\n",
        "                                random_state=random_state),\n",
        "    'K Nearest Neighbors (KNN)': KNN(\n",
        "        contamination=outliers_fraction),\n",
        "    'Average KNN': KNN(method='mean',\n",
        "                       contamination=outliers_fraction),\n",
        "    'Local Outlier Factor (LOF)':\n",
        "        LOF(n_neighbors=35, contamination=outliers_fraction),\n",
        "    'Minimum Covariance Determinant (MCD)': MCD(\n",
        "        contamination=outliers_fraction, random_state=random_state),\n",
        "    'One-class SVM (OCSVM)': OCSVM(contamination=outliers_fraction),\n",
        "    'Principal Component Analysis (PCA)': PCA(\n",
        "        contamination=outliers_fraction, random_state=random_state),\n",
        "    'Locally Selective Combination (LSCP)': LSCP(\n",
        "        detector_list, contamination=outliers_fraction,\n",
        "        random_state=random_state)\n",
        "}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WBQItZIq_kED",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}