Churn_Prediction_of_Telecom_Data.ipynb

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    "colab": {
      "name": "Churn Prediction of Telecom Data.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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        "name": "ipython",
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      "cell_type": "markdown",
      "metadata": {
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        "id": "9Ov-9i57ot9Z"
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      "source": [
        "# Churn Data Analysis In Telecommunications\n",
        "\n",
        "Source: https://www.kaggle.com/becksddf/churn-in-telecoms-dataset \n",
        "\n",
        "\n",
        "Initially, we install all the required libraries."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "0K8523M-ot9b",
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "07b57f9c-42c8-4c97-cd7e-955614d08b88"
      },
      "source": [
        "!pip install missingno\n",
        "from scipy import stats\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "from matplotlib import pyplot as plt\n",
        "from matplotlib.pyplot import pie, axis, show\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: missingno in /usr/local/lib/python3.6/dist-packages (0.4.2)\n",
            "Requirement already satisfied: seaborn in /usr/local/lib/python3.6/dist-packages (from missingno) (0.10.1)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from missingno) (3.2.1)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from missingno) (1.4.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from missingno) (1.18.4)\n",
            "Requirement already satisfied: pandas>=0.22.0 in /usr/local/lib/python3.6/dist-packages (from seaborn->missingno) (1.0.3)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->missingno) (0.10.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->missingno) (2.8.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->missingno) (1.2.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->missingno) (2.4.7)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.22.0->seaborn->missingno) (2018.9)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from cycler>=0.10->matplotlib->missingno) (1.12.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "sQ4P_4NLot9g"
      },
      "source": [
        "Then, we load the csv file into a dataframe 'df'."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "krxJd8hmot9h",
        "colab": {}
      },
      "source": [
        "df = pd.read_csv(\"bigml_59c28831336c6604c800002a.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "oKtLMI64ot9k"
      },
      "source": [
        "df.head is used to display the first ten rows of the dataframe."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
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          "height": 583
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        "outputId": "7a0b733f-a9d6-4272-aaf9-a40230d0e4bf"
      },
      "source": [
        "df.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>state</th>\n",
              "      <th>account length</th>\n",
              "      <th>area code</th>\n",
              "      <th>phone number</th>\n",
              "      <th>international plan</th>\n",
              "      <th>voice mail plan</th>\n",
              "      <th>number vmail messages</th>\n",
              "      <th>total day minutes</th>\n",
              "      <th>total day calls</th>\n",
              "      <th>total day charge</th>\n",
              "      <th>total eve minutes</th>\n",
              "      <th>total eve calls</th>\n",
              "      <th>total eve charge</th>\n",
              "      <th>total night minutes</th>\n",
              "      <th>total night calls</th>\n",
              "      <th>total night charge</th>\n",
              "      <th>total intl minutes</th>\n",
              "      <th>total intl calls</th>\n",
              "      <th>total intl charge</th>\n",
              "      <th>customer service calls</th>\n",
              "      <th>churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>KS</td>\n",
              "      <td>128</td>\n",
              "      <td>415</td>\n",
              "      <td>382-4657</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>25</td>\n",
              "      <td>265.1</td>\n",
              "      <td>110</td>\n",
              "      <td>45.07</td>\n",
              "      <td>197.4</td>\n",
              "      <td>99</td>\n",
              "      <td>16.78</td>\n",
              "      <td>244.7</td>\n",
              "      <td>91</td>\n",
              "      <td>11.01</td>\n",
              "      <td>10.0</td>\n",
              "      <td>3</td>\n",
              "      <td>2.70</td>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>OH</td>\n",
              "      <td>107</td>\n",
              "      <td>415</td>\n",
              "      <td>371-7191</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>26</td>\n",
              "      <td>161.6</td>\n",
              "      <td>123</td>\n",
              "      <td>27.47</td>\n",
              "      <td>195.5</td>\n",
              "      <td>103</td>\n",
              "      <td>16.62</td>\n",
              "      <td>254.4</td>\n",
              "      <td>103</td>\n",
              "      <td>11.45</td>\n",
              "      <td>13.7</td>\n",
              "      <td>3</td>\n",
              "      <td>3.70</td>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NJ</td>\n",
              "      <td>137</td>\n",
              "      <td>415</td>\n",
              "      <td>358-1921</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "      <td>243.4</td>\n",
              "      <td>114</td>\n",
              "      <td>41.38</td>\n",
              "      <td>121.2</td>\n",
              "      <td>110</td>\n",
              "      <td>10.30</td>\n",
              "      <td>162.6</td>\n",
              "      <td>104</td>\n",
              "      <td>7.32</td>\n",
              "      <td>12.2</td>\n",
              "      <td>5</td>\n",
              "      <td>3.29</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>OH</td>\n",
              "      <td>84</td>\n",
              "      <td>408</td>\n",
              "      <td>375-9999</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "      <td>299.4</td>\n",
              "      <td>71</td>\n",
              "      <td>50.90</td>\n",
              "      <td>61.9</td>\n",
              "      <td>88</td>\n",
              "      <td>5.26</td>\n",
              "      <td>196.9</td>\n",
              "      <td>89</td>\n",
              "      <td>8.86</td>\n",
              "      <td>6.6</td>\n",
              "      <td>7</td>\n",
              "      <td>1.78</td>\n",
              "      <td>2</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>OK</td>\n",
              "      <td>75</td>\n",
              "      <td>415</td>\n",
              "      <td>330-6626</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "      <td>166.7</td>\n",
              "      <td>113</td>\n",
              "      <td>28.34</td>\n",
              "      <td>148.3</td>\n",
              "      <td>122</td>\n",
              "      <td>12.61</td>\n",
              "      <td>186.9</td>\n",
              "      <td>121</td>\n",
              "      <td>8.41</td>\n",
              "      <td>10.1</td>\n",
              "      <td>3</td>\n",
              "      <td>2.73</td>\n",
              "      <td>3</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>AL</td>\n",
              "      <td>118</td>\n",
              "      <td>510</td>\n",
              "      <td>391-8027</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "      <td>223.4</td>\n",
              "      <td>98</td>\n",
              "      <td>37.98</td>\n",
              "      <td>220.6</td>\n",
              "      <td>101</td>\n",
              "      <td>18.75</td>\n",
              "      <td>203.9</td>\n",
              "      <td>118</td>\n",
              "      <td>9.18</td>\n",
              "      <td>6.3</td>\n",
              "      <td>6</td>\n",
              "      <td>1.70</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>MA</td>\n",
              "      <td>121</td>\n",
              "      <td>510</td>\n",
              "      <td>355-9993</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>24</td>\n",
              "      <td>218.2</td>\n",
              "      <td>88</td>\n",
              "      <td>37.09</td>\n",
              "      <td>348.5</td>\n",
              "      <td>108</td>\n",
              "      <td>29.62</td>\n",
              "      <td>212.6</td>\n",
              "      <td>118</td>\n",
              "      <td>9.57</td>\n",
              "      <td>7.5</td>\n",
              "      <td>7</td>\n",
              "      <td>2.03</td>\n",
              "      <td>3</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>MO</td>\n",
              "      <td>147</td>\n",
              "      <td>415</td>\n",
              "      <td>329-9001</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "      <td>157.0</td>\n",
              "      <td>79</td>\n",
              "      <td>26.69</td>\n",
              "      <td>103.1</td>\n",
              "      <td>94</td>\n",
              "      <td>8.76</td>\n",
              "      <td>211.8</td>\n",
              "      <td>96</td>\n",
              "      <td>9.53</td>\n",
              "      <td>7.1</td>\n",
              "      <td>6</td>\n",
              "      <td>1.92</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>LA</td>\n",
              "      <td>117</td>\n",
              "      <td>408</td>\n",
              "      <td>335-4719</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "      <td>184.5</td>\n",
              "      <td>97</td>\n",
              "      <td>31.37</td>\n",
              "      <td>351.6</td>\n",
              "      <td>80</td>\n",
              "      <td>29.89</td>\n",
              "      <td>215.8</td>\n",
              "      <td>90</td>\n",
              "      <td>9.71</td>\n",
              "      <td>8.7</td>\n",
              "      <td>4</td>\n",
              "      <td>2.35</td>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>WV</td>\n",
              "      <td>141</td>\n",
              "      <td>415</td>\n",
              "      <td>330-8173</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>37</td>\n",
              "      <td>258.6</td>\n",
              "      <td>84</td>\n",
              "      <td>43.96</td>\n",
              "      <td>222.0</td>\n",
              "      <td>111</td>\n",
              "      <td>18.87</td>\n",
              "      <td>326.4</td>\n",
              "      <td>97</td>\n",
              "      <td>14.69</td>\n",
              "      <td>11.2</td>\n",
              "      <td>5</td>\n",
              "      <td>3.02</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  state  account length  ...  customer service calls  churn\n",
              "0    KS             128  ...                       1  False\n",
              "1    OH             107  ...                       1  False\n",
              "2    NJ             137  ...                       0  False\n",
              "3    OH              84  ...                       2  False\n",
              "4    OK              75  ...                       3  False\n",
              "5    AL             118  ...                       0  False\n",
              "6    MA             121  ...                       3  False\n",
              "7    MO             147  ...                       0  False\n",
              "8    LA             117  ...                       1  False\n",
              "9    WV             141  ...                       0  False\n",
              "\n",
              "[10 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 466
        }
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    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "TweOdFzAot9n"
      },
      "source": [
        "df.describe() is used to analyze the characeristics of the columns in the dataframe."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "T7Vb91Mwot9o",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "outputId": "cdaec7f6-bbc5-4496-bd12-b9b79a7218b3"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>account length</th>\n",
              "      <th>area code</th>\n",
              "      <th>number vmail messages</th>\n",
              "      <th>total day minutes</th>\n",
              "      <th>total day calls</th>\n",
              "      <th>total day charge</th>\n",
              "      <th>total eve minutes</th>\n",
              "      <th>total eve calls</th>\n",
              "      <th>total eve charge</th>\n",
              "      <th>total night minutes</th>\n",
              "      <th>total night calls</th>\n",
              "      <th>total night charge</th>\n",
              "      <th>total intl minutes</th>\n",
              "      <th>total intl calls</th>\n",
              "      <th>total intl charge</th>\n",
              "      <th>customer service calls</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>3333.000000</td>\n",
              "      <td>3333.000000</td>\n",
              "      <td>3333.000000</td>\n",
              "      <td>3333.000000</td>\n",
              "      <td>3333.000000</td>\n",
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              "      <td>3333.000000</td>\n",
              "      <td>3333.000000</td>\n",
              "      <td>3333.000000</td>\n",
              "      <td>3333.000000</td>\n",
              "      <td>3333.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>101.064806</td>\n",
              "      <td>437.182418</td>\n",
              "      <td>8.099010</td>\n",
              "      <td>179.775098</td>\n",
              "      <td>100.435644</td>\n",
              "      <td>30.562307</td>\n",
              "      <td>200.980348</td>\n",
              "      <td>100.114311</td>\n",
              "      <td>17.083540</td>\n",
              "      <td>200.872037</td>\n",
              "      <td>100.107711</td>\n",
              "      <td>9.039325</td>\n",
              "      <td>10.237294</td>\n",
              "      <td>4.479448</td>\n",
              "      <td>2.764581</td>\n",
              "      <td>1.562856</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>39.822106</td>\n",
              "      <td>42.371290</td>\n",
              "      <td>13.688365</td>\n",
              "      <td>54.467389</td>\n",
              "      <td>20.069084</td>\n",
              "      <td>9.259435</td>\n",
              "      <td>50.713844</td>\n",
              "      <td>19.922625</td>\n",
              "      <td>4.310668</td>\n",
              "      <td>50.573847</td>\n",
              "      <td>19.568609</td>\n",
              "      <td>2.275873</td>\n",
              "      <td>2.791840</td>\n",
              "      <td>2.461214</td>\n",
              "      <td>0.753773</td>\n",
              "      <td>1.315491</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>408.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>23.200000</td>\n",
              "      <td>33.000000</td>\n",
              "      <td>1.040000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>74.000000</td>\n",
              "      <td>408.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>143.700000</td>\n",
              "      <td>87.000000</td>\n",
              "      <td>24.430000</td>\n",
              "      <td>166.600000</td>\n",
              "      <td>87.000000</td>\n",
              "      <td>14.160000</td>\n",
              "      <td>167.000000</td>\n",
              "      <td>87.000000</td>\n",
              "      <td>7.520000</td>\n",
              "      <td>8.500000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.300000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>101.000000</td>\n",
              "      <td>415.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>179.400000</td>\n",
              "      <td>101.000000</td>\n",
              "      <td>30.500000</td>\n",
              "      <td>201.400000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>17.120000</td>\n",
              "      <td>201.200000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>9.050000</td>\n",
              "      <td>10.300000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.780000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>127.000000</td>\n",
              "      <td>510.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>216.400000</td>\n",
              "      <td>114.000000</td>\n",
              "      <td>36.790000</td>\n",
              "      <td>235.300000</td>\n",
              "      <td>114.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>235.300000</td>\n",
              "      <td>113.000000</td>\n",
              "      <td>10.590000</td>\n",
              "      <td>12.100000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>3.270000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>243.000000</td>\n",
              "      <td>510.000000</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>350.800000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>59.640000</td>\n",
              "      <td>363.700000</td>\n",
              "      <td>170.000000</td>\n",
              "      <td>30.910000</td>\n",
              "      <td>395.000000</td>\n",
              "      <td>175.000000</td>\n",
              "      <td>17.770000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>5.400000</td>\n",
              "      <td>9.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       account length    area code  ...  total intl charge  customer service calls\n",
              "count     3333.000000  3333.000000  ...        3333.000000             3333.000000\n",
              "mean       101.064806   437.182418  ...           2.764581                1.562856\n",
              "std         39.822106    42.371290  ...           0.753773                1.315491\n",
              "min          1.000000   408.000000  ...           0.000000                0.000000\n",
              "25%         74.000000   408.000000  ...           2.300000                1.000000\n",
              "50%        101.000000   415.000000  ...           2.780000                1.000000\n",
              "75%        127.000000   510.000000  ...           3.270000                2.000000\n",
              "max        243.000000   510.000000  ...           5.400000                9.000000\n",
              "\n",
              "[8 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 467
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "cK8BoKlUot9r"
      },
      "source": [
        "df.info() is used to observe the datatypes of the columns in the dataset. From this we can observe that there are 8 float columns, 8 int columns, 4 object columns and 1 bool type column."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "AEmrzpE1ot9s",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        },
        "outputId": "599b5d49-dd53-4b9c-b90f-ae6c475cdc20"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3333 entries, 0 to 3332\n",
            "Data columns (total 21 columns):\n",
            " #   Column                  Non-Null Count  Dtype  \n",
            "---  ------                  --------------  -----  \n",
            " 0   state                   3333 non-null   object \n",
            " 1   account length          3333 non-null   int64  \n",
            " 2   area code               3333 non-null   int64  \n",
            " 3   phone number            3333 non-null   object \n",
            " 4   international plan      3333 non-null   object \n",
            " 5   voice mail plan         3333 non-null   object \n",
            " 6   number vmail messages   3333 non-null   int64  \n",
            " 7   total day minutes       3333 non-null   float64\n",
            " 8   total day calls         3333 non-null   int64  \n",
            " 9   total day charge        3333 non-null   float64\n",
            " 10  total eve minutes       3333 non-null   float64\n",
            " 11  total eve calls         3333 non-null   int64  \n",
            " 12  total eve charge        3333 non-null   float64\n",
            " 13  total night minutes     3333 non-null   float64\n",
            " 14  total night calls       3333 non-null   int64  \n",
            " 15  total night charge      3333 non-null   float64\n",
            " 16  total intl minutes      3333 non-null   float64\n",
            " 17  total intl calls        3333 non-null   int64  \n",
            " 18  total intl charge       3333 non-null   float64\n",
            " 19  customer service calls  3333 non-null   int64  \n",
            " 20  churn                   3333 non-null   bool   \n",
            "dtypes: bool(1), float64(8), int64(8), object(4)\n",
            "memory usage: 524.2+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "zgozTvSjot9u",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "outputId": "f9e9c3c1-9dc4-48d3-e786-1fad4f869c5c"
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "state                      object\n",
              "account length              int64\n",
              "area code                   int64\n",
              "phone number               object\n",
              "international plan         object\n",
              "voice mail plan            object\n",
              "number vmail messages       int64\n",
              "total day minutes         float64\n",
              "total day calls             int64\n",
              "total day charge          float64\n",
              "total eve minutes         float64\n",
              "total eve calls             int64\n",
              "total eve charge          float64\n",
              "total night minutes       float64\n",
              "total night calls           int64\n",
              "total night charge        float64\n",
              "total intl minutes        float64\n",
              "total intl calls            int64\n",
              "total intl charge         float64\n",
              "customer service calls      int64\n",
              "churn                        bool\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 469
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "bBWbsDSJot9y"
      },
      "source": [
        "Here, the data type of the area code is initially int. But, we are changing the data type of area code column to object as area code should be unique.\n",
        "Then, we are converting churn column data type into int."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Ig68nolEot9y",
        "colab": {}
      },
      "source": [
        "df['area code'] = df['area code'].astype('object')\n",
        "df['churn']=df['churn'].astype('int')\n",
        "df['international plan'] = df['international plan'].astype('category')\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "S-3uV3FCot90"
      },
      "source": [
        "Here, we are checking for the unique values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Tepzq4eVot91",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "a2b122a5-38f8-47bb-db1a-3156b086fca1"
      },
      "source": [
        "df.describe(include=['O'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>state</th>\n",
              "      <th>area code</th>\n",
              "      <th>phone number</th>\n",
              "      <th>voice mail plan</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>3333</td>\n",
              "      <td>3333</td>\n",
              "      <td>3333</td>\n",
              "      <td>3333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>51</td>\n",
              "      <td>3</td>\n",
              "      <td>3333</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>WV</td>\n",
              "      <td>415</td>\n",
              "      <td>353-5557</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>106</td>\n",
              "      <td>1655</td>\n",
              "      <td>1</td>\n",
              "      <td>2411</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       state  area code phone number voice mail plan\n",
              "count   3333       3333         3333            3333\n",
              "unique    51          3         3333               2\n",
              "top       WV        415     353-5557              no\n",
              "freq     106       1655            1            2411"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 471
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "o_1Hz5cmot94"
      },
      "source": [
        "Here, we are dropping the phone number column as the phone number for each user is unique and is not relevant for our analysis on the churn."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "xL-5TplHot95",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "outputId": "edb3369b-b38d-4ddd-d5bb-2ebf4f55b66d"
      },
      "source": [
        "df.drop([\"phone number\"], axis = 1,)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>state</th>\n",
              "      <th>account length</th>\n",
              "      <th>area code</th>\n",
              "      <th>international plan</th>\n",
              "      <th>voice mail plan</th>\n",
              "      <th>number vmail messages</th>\n",
              "      <th>total day minutes</th>\n",
              "      <th>total day calls</th>\n",
              "      <th>total day charge</th>\n",
              "      <th>total eve minutes</th>\n",
              "      <th>total eve calls</th>\n",
              "      <th>total eve charge</th>\n",
              "      <th>total night minutes</th>\n",
              "      <th>total night calls</th>\n",
              "      <th>total night charge</th>\n",
              "      <th>total intl minutes</th>\n",
              "      <th>total intl calls</th>\n",
              "      <th>total intl charge</th>\n",
              "      <th>customer service calls</th>\n",
              "      <th>churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>KS</td>\n",
              "      <td>128</td>\n",
              "      <td>415</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>25</td>\n",
              "      <td>265.1</td>\n",
              "      <td>110</td>\n",
              "      <td>45.07</td>\n",
              "      <td>197.4</td>\n",
              "      <td>99</td>\n",
              "      <td>16.78</td>\n",
              "      <td>244.7</td>\n",
              "      <td>91</td>\n",
              "      <td>11.01</td>\n",
              "      <td>10.0</td>\n",
              "      <td>3</td>\n",
              "      <td>2.70</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>OH</td>\n",
              "      <td>107</td>\n",
              "      <td>415</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>26</td>\n",
              "      <td>161.6</td>\n",
              "      <td>123</td>\n",
              "      <td>27.47</td>\n",
              "      <td>195.5</td>\n",
              "      <td>103</td>\n",
              "      <td>16.62</td>\n",
              "      <td>254.4</td>\n",
              "      <td>103</td>\n",
              "      <td>11.45</td>\n",
              "      <td>13.7</td>\n",
              "      <td>3</td>\n",
              "      <td>3.70</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NJ</td>\n",
              "      <td>137</td>\n",
              "      <td>415</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "      <td>243.4</td>\n",
              "      <td>114</td>\n",
              "      <td>41.38</td>\n",
              "      <td>121.2</td>\n",
              "      <td>110</td>\n",
              "      <td>10.30</td>\n",
              "      <td>162.6</td>\n",
              "      <td>104</td>\n",
              "      <td>7.32</td>\n",
              "      <td>12.2</td>\n",
              "      <td>5</td>\n",
              "      <td>3.29</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>OH</td>\n",
              "      <td>84</td>\n",
              "      <td>408</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "      <td>299.4</td>\n",
              "      <td>71</td>\n",
              "      <td>50.90</td>\n",
              "      <td>61.9</td>\n",
              "      <td>88</td>\n",
              "      <td>5.26</td>\n",
              "      <td>196.9</td>\n",
              "      <td>89</td>\n",
              "      <td>8.86</td>\n",
              "      <td>6.6</td>\n",
              "      <td>7</td>\n",
              "      <td>1.78</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>OK</td>\n",
              "      <td>75</td>\n",
              "      <td>415</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "      <td>166.7</td>\n",
              "      <td>113</td>\n",
              "      <td>28.34</td>\n",
              "      <td>148.3</td>\n",
              "      <td>122</td>\n",
              "      <td>12.61</td>\n",
              "      <td>186.9</td>\n",
              "      <td>121</td>\n",
              "      <td>8.41</td>\n",
              "      <td>10.1</td>\n",
              "      <td>3</td>\n",
              "      <td>2.73</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3328</th>\n",
              "      <td>AZ</td>\n",
              "      <td>192</td>\n",
              "      <td>415</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>36</td>\n",
              "      <td>156.2</td>\n",
              "      <td>77</td>\n",
              "      <td>26.55</td>\n",
              "      <td>215.5</td>\n",
              "      <td>126</td>\n",
              "      <td>18.32</td>\n",
              "      <td>279.1</td>\n",
              "      <td>83</td>\n",
              "      <td>12.56</td>\n",
              "      <td>9.9</td>\n",
              "      <td>6</td>\n",
              "      <td>2.67</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3329</th>\n",
              "      <td>WV</td>\n",
              "      <td>68</td>\n",
              "      <td>415</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "      <td>231.1</td>\n",
              "      <td>57</td>\n",
              "      <td>39.29</td>\n",
              "      <td>153.4</td>\n",
              "      <td>55</td>\n",
              "      <td>13.04</td>\n",
              "      <td>191.3</td>\n",
              "      <td>123</td>\n",
              "      <td>8.61</td>\n",
              "      <td>9.6</td>\n",
              "      <td>4</td>\n",
              "      <td>2.59</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3330</th>\n",
              "      <td>RI</td>\n",
              "      <td>28</td>\n",
              "      <td>510</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "      <td>180.8</td>\n",
              "      <td>109</td>\n",
              "      <td>30.74</td>\n",
              "      <td>288.8</td>\n",
              "      <td>58</td>\n",
              "      <td>24.55</td>\n",
              "      <td>191.9</td>\n",
              "      <td>91</td>\n",
              "      <td>8.64</td>\n",
              "      <td>14.1</td>\n",
              "      <td>6</td>\n",
              "      <td>3.81</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3331</th>\n",
              "      <td>CT</td>\n",
              "      <td>184</td>\n",
              "      <td>510</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "      <td>213.8</td>\n",
              "      <td>105</td>\n",
              "      <td>36.35</td>\n",
              "      <td>159.6</td>\n",
              "      <td>84</td>\n",
              "      <td>13.57</td>\n",
              "      <td>139.2</td>\n",
              "      <td>137</td>\n",
              "      <td>6.26</td>\n",
              "      <td>5.0</td>\n",
              "      <td>10</td>\n",
              "      <td>1.35</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3332</th>\n",
              "      <td>TN</td>\n",
              "      <td>74</td>\n",
              "      <td>415</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>25</td>\n",
              "      <td>234.4</td>\n",
              "      <td>113</td>\n",
              "      <td>39.85</td>\n",
              "      <td>265.9</td>\n",
              "      <td>82</td>\n",
              "      <td>22.60</td>\n",
              "      <td>241.4</td>\n",
              "      <td>77</td>\n",
              "      <td>10.86</td>\n",
              "      <td>13.7</td>\n",
              "      <td>4</td>\n",
              "      <td>3.70</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3333 rows × 20 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     state  account length  ... customer service calls churn\n",
              "0       KS             128  ...                      1     0\n",
              "1       OH             107  ...                      1     0\n",
              "2       NJ             137  ...                      0     0\n",
              "3       OH              84  ...                      2     0\n",
              "4       OK              75  ...                      3     0\n",
              "...    ...             ...  ...                    ...   ...\n",
              "3328    AZ             192  ...                      2     0\n",
              "3329    WV              68  ...                      3     0\n",
              "3330    RI              28  ...                      2     0\n",
              "3331    CT             184  ...                      2     0\n",
              "3332    TN              74  ...                      0     0\n",
              "\n",
              "[3333 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 472
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "3f7GfSWyot98"
      },
      "source": [
        "# Data Cleaning\n",
        "\n",
        "By using df.isnull().sum(), we are checking for any null values in the dataframe. From this, we can observe that there are no null values present in the dataframe."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "dEdU3V1cot98",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "outputId": "6f454405-ace6-4a06-dace-771218c3e75b"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "state                     0\n",
              "account length            0\n",
              "area code                 0\n",
              "phone number              0\n",
              "international plan        0\n",
              "voice mail plan           0\n",
              "number vmail messages     0\n",
              "total day minutes         0\n",
              "total day calls           0\n",
              "total day charge          0\n",
              "total eve minutes         0\n",
              "total eve calls           0\n",
              "total eve charge          0\n",
              "total night minutes       0\n",
              "total night calls         0\n",
              "total night charge        0\n",
              "total intl minutes        0\n",
              "total intl calls          0\n",
              "total intl charge         0\n",
              "customer service calls    0\n",
              "churn                     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 473
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "5sLzY9Xxot9-"
      },
      "source": [
        "The above analysis is shown in the form of a graph below."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "-erozO_fot9_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 642
        },
        "outputId": "f697cca6-2093-4681-a92b-8d9ac11fb1fd"
      },
      "source": [
        "import missingno as msno\n",
        "msno.matrix(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f633190a5f8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 474
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1800x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "_uTqmzMfot-C"
      },
      "source": [
        "We can observe that there are no null values"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "R4AJRIu3ot-D"
      },
      "source": [
        "# Analyzing TARGET Column and Various Features"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "B9PqKmMAot-E"
      },
      "source": [
        "We are using value_counts() to find out the count for each unique value in the churn. We can observe that the rate of people who are trying to churn is less."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "lfinoY33ot-E",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "4783262f-1474-492d-aa97-026f0b933e51"
      },
      "source": [
        "df.churn.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    2850\n",
              "1     483\n",
              "Name: churn, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 475
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "sTd0snaqot-H"
      },
      "source": [
        "Here, we are calculating the churn percentage and we can observe that the total churn percentage of tth dataframe is 14.491%."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "q-4Jn3sKot-H",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "bf818f68-9ada-4465-a68f-5831b02bcb84"
      },
      "source": [
        "churn_percentage = df[\"churn\"].sum() * 100 / df[\"churn\"].shape[0]\n",
        "print(\"Churn percentage is %.3f%%.\" % churn_percentage)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Churn percentage is 14.491%.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "mWXltll3ot-K"
      },
      "source": [
        "We are plotting a bar plot to analyze the churn rate. We can observe that there are only few people trying to churn and maximum customers are not planning to churn."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "zGEcxHNkot-L",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "549bc784-9441-4036-fe46-56ed2a3a868e"
      },
      "source": [
        "a=sns.countplot(x='churn',data=df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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gO8DvJDmY5Lpx93Sm83UfkqQmjywkSU2GhSSpybCQJDUZFpKkJsNCktRkWEgLKMkdSd4y7j6khWZYSItI96ZfadExLKTnIMk1Se5P8p9JPtOV/yjJvyd59MRRRpKLknytb72PJ3lHN/1Ykr9Pci/w1m7+75Lcm+SBJL878h9MOolhIc1TklcCfwO8vqpeBbyrW7QaeB3wJ8CtA27u6ao6v6ru7uafqqrzgduBv1rAtqV5MSyk+Xs98IWqegqgqk78/wpfqaqfV9WDwKoBt/VPJ81/qfveC6x/ro1Kz5VhIS28n/VNp/s+xi//eXvRSev87yzbOA4sW7jWpPkxLKT5+zd61xh+HSDJOXOMfRzYmOSFSc4GLh5Fg9JC8V8s0jxV1f4ktwDfTHIc+I85xh5Ish3YB/xgrrHSYuRbZyVJTZ6GkiQ1GRaSpCbDQpLUZFhIkpoMC0lSk2EhSWoyLCRJTf8PUU/SJVTcMo4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "zmstAdyAot-N",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "0e3fda07-1802-4cee-ef5a-294176e4325a"
      },
      "source": [
        "a=sns.countplot(x='international plan',data=df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "8WSyhJkgot-P"
      },
      "source": [
        "# Correlation between Various Columns\n",
        " Here, we are plotting a correlation map to analyze the correlation between columns."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Z23z6AHqot-Q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "7c48afbc-fb6d-46b5-a0d8-99deb2fdaffb"
      },
      "source": [
        "!pip install plotly\n",
        "import plotly\n",
        "import plotly.graph_objs as go\n",
        "import plotly.figure_factory as ff\n",
        "from plotly.offline import init_notebook_mode\n",
        "init_notebook_mode(connected=True)\n",
        "from plotly.offline import plot, iplot"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: plotly in /usr/local/lib/python3.6/dist-packages (4.4.1)\n",
            "Requirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.6/dist-packages (from plotly) (1.3.3)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from plotly) (1.12.0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "        <script type=\"text/javascript\">\n",
              "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
              "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
              "        if (typeof require !== 'undefined') {\n",
              "        require.undef(\"plotly\");\n",
              "        requirejs.config({\n",
              "            paths: {\n",
              "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
              "            }\n",
              "        });\n",
              "        require(['plotly'], function(Plotly) {\n",
              "            window._Plotly = Plotly;\n",
              "        });\n",
              "        }\n",
              "        </script>\n",
              "        "
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "JAvxMSKkot-T",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817
        },
        "outputId": "dc78d2a4-2f10-40ab-b5ea-97c9231f6531"
      },
      "source": [
        "cr = df.corr()\n",
        "trace = go.Heatmap(z=cr.values.tolist(), x=cr.columns, y=cr.columns)\n",
        "data=[trace]\n",
        "layout = go.Layout(\n",
        "    title='Visualization for pairwise correlation of the columns',\n",
        "    width=1100,\n",
        "    height=800,\n",
        "    yaxis=go.layout.YAxis(automargin=True),\n",
        "    xaxis=dict(tickangle=50),\n",
        "    margin=go.layout.Margin(l=0, r=300, b=300, t=90)\n",
        ")\n",
        "\n",
        "\n",
        "\n",
        "fig = go.Figure(data=data, layout=layout)\n",
        "iplot(fig, filename='heatmap1')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.plotly.v1+json": {
              "config": {
                "linkText": "Export to plot.ly",
                "plotlyServerURL": "https://plot.ly",
                "showLink": false
              },
              "data": [
                {
                  "type": "heatmap",
                  "x": [
                    "account length",
                    "number vmail messages",
                    "total day minutes",
                    "total day calls",
                    "total day charge",
                    "total eve minutes",
                    "total eve calls",
                    "total eve charge",
                    "total night minutes",
                    "total night calls",
                    "total night charge",
                    "total intl minutes",
                    "total intl calls",
                    "total intl charge",
                    "customer service calls",
                    "churn",
                    "cluster"
                  ],
                  "y": [
                    "account length",
                    "number vmail messages",
                    "total day minutes",
                    "total day calls",
                    "total day charge",
                    "total eve minutes",
                    "total eve calls",
                    "total eve charge",
                    "total night minutes",
                    "total night calls",
                    "total night charge",
                    "total intl minutes",
                    "total intl calls",
                    "total intl charge",
                    "customer service calls",
                    "churn",
                    "cluster"
                  ],
                  "z": [
                    [
                      1,
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                      0.0066830551146201496,
                      0.037237097370123505,
                      0.006679621796723314,
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                      0.013957338671804617,
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                    ],
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                      0.016081480930399805,
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                    ],
                    [
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                      0.006462113654157009,
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                      0.023542256029458798,
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                      0.023538954634237684,
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                    ],
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                      0.2011774759311796,
                      -0.0024611370008967796
                    ],
                    [
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              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })\n",
              "                };\n",
              "                });\n",
              "            </script>\n",
              "        </div>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "MmNKV3x4ot-V"
      },
      "source": [
        "From the above map, we can see that churn has highest correlation with columns total day charge, total day minutes, total eve charge, total eve minutes, total night charge, total night minutes,total intl charge and total intl minutes.\n",
        "\n",
        "The columns that have highest correlation with the churn variable are the total_day_charge, the total_day_minutes and the number of customer service calls.\n",
        "\n",
        "MULTICOLLINEAR VARIABLES Night Mins , Night Charge 0.999999\n",
        "\n",
        "Eve Charge , Eve Mins 1.000000\n",
        "\n",
        "Day Charge , Day Mins 1.000000\n",
        "\n",
        "Intl Charge , Intl Mins 0.999993"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "LlJQgS7Lot-V"
      },
      "source": [
        "Here, we are using groupby on churn to group data with other columns and then we are using mean() to find out their mean. We can observe that the mean of total day minutes with respect to churn is highest among all the others."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Go27iUfAot-W",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        },
        "outputId": "d7dd4b5d-6888-4010-da9e-d373a3e04765"
      },
      "source": [
        "df.groupby(['churn']).mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>account length</th>\n",
              "      <th>number vmail messages</th>\n",
              "      <th>total day minutes</th>\n",
              "      <th>total day calls</th>\n",
              "      <th>total day charge</th>\n",
              "      <th>total eve minutes</th>\n",
              "      <th>total eve calls</th>\n",
              "      <th>total eve charge</th>\n",
              "      <th>total night minutes</th>\n",
              "      <th>total night calls</th>\n",
              "      <th>total night charge</th>\n",
              "      <th>total intl minutes</th>\n",
              "      <th>total intl calls</th>\n",
              "      <th>total intl charge</th>\n",
              "      <th>customer service calls</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>churn</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>100.793684</td>\n",
              "      <td>8.604561</td>\n",
              "      <td>175.175754</td>\n",
              "      <td>100.283158</td>\n",
              "      <td>29.780421</td>\n",
              "      <td>199.043298</td>\n",
              "      <td>100.038596</td>\n",
              "      <td>16.918909</td>\n",
              "      <td>200.133193</td>\n",
              "      <td>100.058246</td>\n",
              "      <td>9.006074</td>\n",
              "      <td>10.158877</td>\n",
              "      <td>4.532982</td>\n",
              "      <td>2.743404</td>\n",
              "      <td>1.449825</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>102.664596</td>\n",
              "      <td>5.115942</td>\n",
              "      <td>206.914079</td>\n",
              "      <td>101.335404</td>\n",
              "      <td>35.175921</td>\n",
              "      <td>212.410145</td>\n",
              "      <td>100.561077</td>\n",
              "      <td>18.054969</td>\n",
              "      <td>205.231677</td>\n",
              "      <td>100.399586</td>\n",
              "      <td>9.235528</td>\n",
              "      <td>10.700000</td>\n",
              "      <td>4.163561</td>\n",
              "      <td>2.889545</td>\n",
              "      <td>2.229814</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       account length  ...  customer service calls\n",
              "churn                  ...                        \n",
              "0          100.793684  ...                1.449825\n",
              "1          102.664596  ...                2.229814\n",
              "\n",
              "[2 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 481
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "64qXvN3aot-Y"
      },
      "source": [
        "Here, we plotting a graph to analyze the churn rate with respect to total day charge column."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "_oaHv2mfot-Y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        },
        "outputId": "ff7dbcb0-0165-4365-a7f4-2c0a7170ef1b"
      },
      "source": [
        "plt.figure(figsize=(14,8))\n",
        "df[df['churn']==1]['total day charge'].hist(bins=40,color = 'red',label = 'churn 1',alpha = 0.6)\n",
        "df[df['churn']==0]['total day charge'].hist(bins = 40,color = 'blue',label = 'churn 0',alpha = 0.6)\n",
        "plt.legend()\n",
        "plt.xlabel('TOTAL DAY CHARGE vs CHURN RATE')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'TOTAL DAY CHARGE vs CHURN RATE')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 482
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "vpM4u51Kot-b"
      },
      "source": [
        "From the above graph, we can observe that the rate of churn is high in customers with high day charges."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "ypmes1bjot-b"
      },
      "source": [
        "Then, we are plotting another visualization to analyze the churn rate with respect to customer service calls."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "fmeslHQ9ot-c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        },
        "outputId": "08fa5ff4-428c-475b-ff9d-05707cf2bffc"
      },
      "source": [
        "plt.figure(figsize=(14,8))\n",
        "sns.countplot(x='customer service calls',hue = 'churn',data=df,palette='Set1' )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f632f0bbdd8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 483
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "ts6lQZyWot-e"
      },
      "source": [
        "From the above graph, we can observe that churn is directly proportional to customer service calls. That is, churn is increasing with increase in customer service calls."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "IxvM2Lziot-f"
      },
      "source": [
        "Here, we are plotting churn with international plan attribute to analyze the churn rate with respect to international plan factor."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "4nE5RdMyot-f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        },
        "outputId": "46553153-0704-49ae-d342-5c96165c13ec"
      },
      "source": [
        "plt.figure(figsize=(14,8))\n",
        "sns.countplot(x ='international plan',hue='churn',data = df,palette='Set2')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f632f01ca58>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 484
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "EfE6FH7vot-i"
      },
      "source": [
        "From the above plot, we can observe that the churn rate is high for customers with international plan when compared to customers with no international plan. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "GJaRFIMrot-j"
      },
      "source": [
        "# Checking and clipping outliers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "64qPJUypot-k"
      },
      "source": [
        "Now that we analyzed the correlating factors, let us check for the outliers in the respective columns."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "3wVRVTP2ot-l",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "3482f758-1639-4f44-b266-19b9ebbda948"
      },
      "source": [
        "sns.boxplot(df['total day charge'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f632f09d7b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 485
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "1ACL4Fi-ot-o"
      },
      "source": [
        "From the above plot, we can observe that range of diversity is extreme. That is, even though more data points lie in the average range, there are few datapoints that are at the extreme corners which has an effect on the analysis. Hence, we are removing the outliers to get better results in the analysis."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "znweuxgcot-o",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "2e436012-d612-4a16-9854-e983b696d499"
      },
      "source": [
        "print(df['total day charge'].quantile(0.99))\n",
        "print(df['total day charge'].quantile(0.01))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "51.876799999999996\n",
            "8.8132\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "gioiZk2zot-s"
      },
      "source": [
        "We are plotting the above alterations here by clipping the boundaries."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "eOXjZ9p-ot-s",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "9c1fe145-3c34-48f3-96ff-b34bc5c9a15b"
      },
      "source": [
        "df['total day charge'].clip(lower =8.81,upper = 51.87,inplace = True)\n",
        "sns.boxplot(df['total day charge'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f63307d9240>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 487
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "1TWMbsS1ot-u"
      },
      "source": [
        "After removing the outliers, analysis can be better."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "kZmd1V9bot-v"
      },
      "source": [
        "After plotting total day minutes, we can observe that the majority datapoints are in the middle but there are outliers that are at extremes. Hence, we are removing the outliers."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "C1PKDRA9ot-w",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "2612e067-bb49-40b9-f16b-c20498c75ce6"
      },
      "source": [
        "sns.boxplot(df['total day minutes'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f632f1ae6d8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 488
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "f9ZppexHot-z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "da48dd9e-3ba8-49d8-85fd-b5dfe81bbcf3"
      },
      "source": [
        "print(df['total day minutes'].quantile(0.99))\n",
        "print(df['total day minutes'].quantile(0.01))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "305.168\n",
            "51.831999999999994\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "dSI0rVFhot-3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "6f0543f6-7cdd-4994-a6b7-c3f15fc98ee2"
      },
      "source": [
        "df['total day minutes'].clip(lower =51.83,upper = 305.168,inplace = True)\n",
        "sns.boxplot(df['total day minutes'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f63302662b0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 490
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "aC1pDZYSot-4"
      },
      "source": [
        "The plot range is reduced here to remove the outliers after calculating the average starting and ending range of the plot being 50 and 300 for better analysis results."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "54AzwVHyot-5"
      },
      "source": [
        "We are perfoming the same process of identifying and removing the outliers on total eve charge column."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "jUYAGFgPot-5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "553055f2-86f5-4747-ca62-a88f9e0f7520"
      },
      "source": [
        "sns.boxplot(df['total eve charge'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6330266f28>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 491
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "bT9XcJkxot-7",
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "72cb8802-48c1-428f-e2ec-40b64129ec63"
      },
      "source": [
        "print(df['total eve charge'].quantile(0.99))\n",
        "print(df['total eve charge'].quantile(0.01))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "27.113599999999998\n",
            "6.7592\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "w-uWULrtot-9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "d2ab82c3-8031-4014-ac20-680b07ba42bc"
      },
      "source": [
        "df['total eve charge'].clip(lower =6.7,upper = 27.113,inplace = True)\n",
        "sns.boxplot(df['total eve charge'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6330c0ff28>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 493
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "y0OlvfAfot-_"
      },
      "source": [
        "After calculating, we can observe that the boundaries are reformed to 7.5 and 27.5 as the extreme data points are removed and average is calculated to be 6.7 and 27.1."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "ar1NULMsot-_"
      },
      "source": [
        "The same process is applied, outliers are identified and clipped after calculating for total eve minutes column."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "AVgsKT6iot_A",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "948e6720-7fb7-4b05-df74-da79592a459d"
      },
      "source": [
        "sns.boxplot(df['total eve minutes'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f632f27d240>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 494
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "5egjNbFHot_E",
        "scrolled": false,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "d3925e50-a1ea-4aa7-eb22-5bc3e0e5945a"
      },
      "source": [
        "print(df['total eve minutes'].quantile(0.99))\n",
        "print(df['total eve minutes'].quantile(0.01))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "318.936\n",
            "79.524\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "_zQBK4tgot_H",
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "877eb69a-1c0c-4351-ac79-14e91682d302"
      },
      "source": [
        "df['total eve minutes'].clip(lower =79.524,upper = 318.936,inplace = True)\n",
        "sns.boxplot(df['total eve minutes'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6330a6cdd8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 496
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "TlWaFfKAot_J"
      },
      "source": [
        "The same process is applied, outliers are identified and clipped after calculating for total intl minutes column."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "JvVMwAIXot_L",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "1777fde4-b65c-4e29-f140-57d9d4c8f15e"
      },
      "source": [
        "sns.boxplot(df['total intl minutes'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6330c15b38>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 497
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "EWicvv2Xot_O",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "6760c76a-3e44-4128-bb1d-7138ce4d46f6"
      },
      "source": [
        "print(df['total intl minutes'].quantile(0.99))\n",
        "print(df['total intl minutes'].quantile(0.01))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "16.667999999999985\n",
            "3.332\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "9ydz_WpPot_Q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "bdf8a6e8-2329-4ca8-ca25-b5e0dd1b1522"
      },
      "source": [
        "df['total intl minutes'].clip(lower =3.332,upper = 16.66,inplace = True)\n",
        "sns.boxplot(df['total intl minutes'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6330ca8be0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 499
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "WgXdqdrOot_R"
      },
      "source": [
        "The same process is applied, outliers are identified and clipped after calculating for total intl charge column."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "-morz-T0ot_S",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "850e51a9-cf15-4eb5-cd08-13042aafcec3"
      },
      "source": [
        "sns.boxplot(df['total intl charge'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f632f4724a8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 500
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "VmeBDIb0ot_V",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "ce85b11b-cfc5-4d65-de53-a11e8b78c700"
      },
      "source": [
        "print(df['total intl charge'].quantile(0.99))\n",
        "print(df['total intl charge'].quantile(0.01))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4.5003999999999955\n",
            "0.8996\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "WmkMMXG0ot_X",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "336ebd10-b47c-4468-9725-058030469402"
      },
      "source": [
        "df['total intl charge'].clip(lower =0.89,upper = 4.50,inplace = True)\n",
        "sns.boxplot(df['total intl charge'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f632f353c18>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 502
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "WNLjoxgQot_Y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "0ae568f6-f973-4426-a270-d93855bbf441"
      },
      "source": [
        "sns.boxplot(df['account length'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6330c35c88>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 503
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Y63LVoYPot_a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "6a6aa0ab-398f-4969-fc54-d4d6c19ea956"
      },
      "source": [
        "df['account length'].clip(upper = 200,inplace = True)\n",
        "sns.boxplot(df['account length'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f632f389668>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 504
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ne7ratXLot_d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "12f6719f-e2c2-4038-d8c8-cdcacee17351"
      },
      "source": [
        "sns.boxplot(df['total night minutes'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f632f3b5b70>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 505
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "uczm7RXwot_f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "313dd378-8b09-4842-fa98-f977dbbd23e7"
      },
      "source": [
        "print(df['total night minutes'].quantile(0.99))\n",
        "print(df['total night minutes'].quantile(0.01))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "317.4479999999998\n",
            "79.428\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "_jiM8IHTot_h",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "a8bbe5fa-fead-4c1f-cc96-2d77ad846d37"
      },
      "source": [
        "df['total night minutes'].clip(lower =79.428,upper = 317.44,inplace = True)\n",
        "sns.boxplot(df['total night minutes'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6330cffe10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 507
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "qEEx2Xqiot_i",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "b0371732-edb9-4326-980f-ae11385b3122"
      },
      "source": [
        "sns.boxplot(df['total night charge'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6330c89668>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 508
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "cD7gLwQ4ot_k",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "aafc3bc1-eb7f-4ebb-8ddf-be22b44fae96"
      },
      "source": [
        "print(df['total night charge'].quantile(0.99))\n",
        "print(df['total night charge'].quantile(0.01))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "14.283999999999992\n",
            "3.5764\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "AVYPoL40ot_m",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "a1ef54bc-bcfa-42c7-a1bf-2188d4b300e8"
      },
      "source": [
        "df['total night charge'].clip(lower =3.57,upper =14.28 ,inplace = True)\n",
        "sns.boxplot(df['total night charge'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6330b3e080>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 510
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "-F8RrtLbwB7Y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "29240337-8827-4a03-df11-c8cd29b57b08"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['state', 'account length', 'area code', 'phone number',\n",
              "       'international plan', 'voice mail plan', 'number vmail messages',\n",
              "       'total day minutes', 'total day calls', 'total day charge',\n",
              "       'total eve minutes', 'total eve calls', 'total eve charge',\n",
              "       'total night minutes', 'total night calls', 'total night charge',\n",
              "       'total intl minutes', 'total intl calls', 'total intl charge',\n",
              "       'customer service calls', 'churn'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 511
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "mmqSHp90gHqe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "outputId": "a7b40820-c5d7-4402-b3d2-c4b7c559eebf"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>state</th>\n",
              "      <th>account length</th>\n",
              "      <th>area code</th>\n",
              "      <th>phone number</th>\n",
              "      <th>international plan</th>\n",
              "      <th>voice mail plan</th>\n",
              "      <th>number vmail messages</th>\n",
              "      <th>total day minutes</th>\n",
              "      <th>total day calls</th>\n",
              "      <th>total day charge</th>\n",
              "      <th>total eve minutes</th>\n",
              "      <th>total eve calls</th>\n",
              "      <th>total eve charge</th>\n",
              "      <th>total night minutes</th>\n",
              "      <th>total night calls</th>\n",
              "      <th>total night charge</th>\n",
              "      <th>total intl minutes</th>\n",
              "      <th>total intl calls</th>\n",
              "      <th>total intl charge</th>\n",
              "      <th>customer service calls</th>\n",
              "      <th>churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>KS</td>\n",
              "      <td>128</td>\n",
              "      <td>415</td>\n",
              "      <td>382-4657</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>25</td>\n",
              "      <td>265.1</td>\n",
              "      <td>110</td>\n",
              "      <td>45.07</td>\n",
              "      <td>197.400</td>\n",
              "      <td>99</td>\n",
              "      <td>16.78</td>\n",
              "      <td>244.7</td>\n",
              "      <td>91</td>\n",
              "      <td>11.01</td>\n",
              "      <td>10.0</td>\n",
              "      <td>3</td>\n",
              "      <td>2.70</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>OH</td>\n",
              "      <td>107</td>\n",
              "      <td>415</td>\n",
              "      <td>371-7191</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>26</td>\n",
              "      <td>161.6</td>\n",
              "      <td>123</td>\n",
              "      <td>27.47</td>\n",
              "      <td>195.500</td>\n",
              "      <td>103</td>\n",
              "      <td>16.62</td>\n",
              "      <td>254.4</td>\n",
              "      <td>103</td>\n",
              "      <td>11.45</td>\n",
              "      <td>13.7</td>\n",
              "      <td>3</td>\n",
              "      <td>3.70</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NJ</td>\n",
              "      <td>137</td>\n",
              "      <td>415</td>\n",
              "      <td>358-1921</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "      <td>243.4</td>\n",
              "      <td>114</td>\n",
              "      <td>41.38</td>\n",
              "      <td>121.200</td>\n",
              "      <td>110</td>\n",
              "      <td>10.30</td>\n",
              "      <td>162.6</td>\n",
              "      <td>104</td>\n",
              "      <td>7.32</td>\n",
              "      <td>12.2</td>\n",
              "      <td>5</td>\n",
              "      <td>3.29</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>OH</td>\n",
              "      <td>84</td>\n",
              "      <td>408</td>\n",
              "      <td>375-9999</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "      <td>299.4</td>\n",
              "      <td>71</td>\n",
              "      <td>50.90</td>\n",
              "      <td>79.524</td>\n",
              "      <td>88</td>\n",
              "      <td>6.70</td>\n",
              "      <td>196.9</td>\n",
              "      <td>89</td>\n",
              "      <td>8.86</td>\n",
              "      <td>6.6</td>\n",
              "      <td>7</td>\n",
              "      <td>1.78</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>OK</td>\n",
              "      <td>75</td>\n",
              "      <td>415</td>\n",
              "      <td>330-6626</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "      <td>166.7</td>\n",
              "      <td>113</td>\n",
              "      <td>28.34</td>\n",
              "      <td>148.300</td>\n",
              "      <td>122</td>\n",
              "      <td>12.61</td>\n",
              "      <td>186.9</td>\n",
              "      <td>121</td>\n",
              "      <td>8.41</td>\n",
              "      <td>10.1</td>\n",
              "      <td>3</td>\n",
              "      <td>2.73</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  state  account length  ... customer service calls churn\n",
              "0    KS             128  ...                      1     0\n",
              "1    OH             107  ...                      1     0\n",
              "2    NJ             137  ...                      0     0\n",
              "3    OH              84  ...                      2     0\n",
              "4    OK              75  ...                      3     0\n",
              "\n",
              "[5 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 512
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Pf9ctoCQNFgP"
      },
      "source": [
        "## Model Building:\n",
        "\n",
        "At this point of time I have used three differnt algorithms to perform classification analysis on this data to predict the churn.\n",
        "From the family of regression algorithms I have used logistic regression, KNearestNeighbors algorithm to know how NearestNeighbours would work on this data and Random Forest from Regression trees family. Also performed PCA for the dimensionalty reduction creating two new pca components. Created clusters on the columns like state, area code, phone number as these are unique identification codes and can't be used to feed algorithm. Creating clusters have hleped me detect similarities between these 3 columns and helped me feed it to the model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "RhXHvmdLfIcz"
      },
      "source": [
        "Creating two dataframes, X with predictor variables and Y with target varibale Churn."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "_-TMccBKfZDZ",
        "colab": {}
      },
      "source": [
        "features = df[['account length','international plan','voice mail plan','number vmail messages','total day calls','total day charge','total eve calls','total eve charge',\n",
        "      'total night calls','total night charge','total intl calls','total intl charge','customer service calls']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "afquYjTZhCKK",
        "colab": {}
      },
      "source": [
        "target = df[['churn']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "vI00bIbbOdzB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "d3bf726a-a458-4e45-8055-df4ecdf4bb60"
      },
      "source": [
        "target.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   churn\n",
              "0      0\n",
              "1      0\n",
              "2      0\n",
              "3      0\n",
              "4      0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 515
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "G_C9_2-tNV6A"
      },
      "source": [
        "Using pd.get_dummies to covert the categorical(yes/no) columns international plan and voicemail plan to numerical(0/1). It's observed that people with international plan and voicemail plan are more likely to churn."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "5HZVn8bg-RDK",
        "colab": {}
      },
      "source": [
        "features = pd.get_dummies(features,columns=['international plan','voice mail plan'],drop_first=True) ## creating dummy variables for categorical "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "7ah48PMk-gWn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "5a62458f-131c-4585-ad2c-32b8cdff1ffe"
      },
      "source": [
        "features.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>account length</th>\n",
              "      <th>number vmail messages</th>\n",
              "      <th>total day calls</th>\n",
              "      <th>total day charge</th>\n",
              "      <th>total eve calls</th>\n",
              "      <th>total eve charge</th>\n",
              "      <th>total night calls</th>\n",
              "      <th>total night charge</th>\n",
              "      <th>total intl calls</th>\n",
              "      <th>total intl charge</th>\n",
              "      <th>customer service calls</th>\n",
              "      <th>international plan_yes</th>\n",
              "      <th>voice mail plan_yes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>128</td>\n",
              "      <td>25</td>\n",
              "      <td>110</td>\n",
              "      <td>45.07</td>\n",
              "      <td>99</td>\n",
              "      <td>16.78</td>\n",
              "      <td>91</td>\n",
              "      <td>11.01</td>\n",
              "      <td>3</td>\n",
              "      <td>2.70</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>107</td>\n",
              "      <td>26</td>\n",
              "      <td>123</td>\n",
              "      <td>27.47</td>\n",
              "      <td>103</td>\n",
              "      <td>16.62</td>\n",
              "      <td>103</td>\n",
              "      <td>11.45</td>\n",
              "      <td>3</td>\n",
              "      <td>3.70</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>137</td>\n",
              "      <td>0</td>\n",
              "      <td>114</td>\n",
              "      <td>41.38</td>\n",
              "      <td>110</td>\n",
              "      <td>10.30</td>\n",
              "      <td>104</td>\n",
              "      <td>7.32</td>\n",
              "      <td>5</td>\n",
              "      <td>3.29</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>84</td>\n",
              "      <td>0</td>\n",
              "      <td>71</td>\n",
              "      <td>50.90</td>\n",
              "      <td>88</td>\n",
              "      <td>6.70</td>\n",
              "      <td>89</td>\n",
              "      <td>8.86</td>\n",
              "      <td>7</td>\n",
              "      <td>1.78</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>75</td>\n",
              "      <td>0</td>\n",
              "      <td>113</td>\n",
              "      <td>28.34</td>\n",
              "      <td>122</td>\n",
              "      <td>12.61</td>\n",
              "      <td>121</td>\n",
              "      <td>8.41</td>\n",
              "      <td>3</td>\n",
              "      <td>2.73</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   account length  ...  voice mail plan_yes\n",
              "0             128  ...                    1\n",
              "1             107  ...                    1\n",
              "2             137  ...                    0\n",
              "3              84  ...                    0\n",
              "4              75  ...                    0\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 517
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "0KUWibrsisjv"
      },
      "source": [
        "Feature Scaling of training data using Min-Max sacling to normalize data i.e, to set all fetaures to one range of scale.\n",
        "\n",
        "                     Xsc = [X-Xmin]/[Xmax-Xmin]\n",
        "                  -data is sclaed to a range of [0-1]\n",
        "                  -the cost of having this range, in contrast to standard deviation is we will end up having smaller standard deviation.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "4NrGin_Hiq9y",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "558Td7_zjsN8",
        "colab": {}
      },
      "source": [
        "scaler=MinMaxScaler()\n",
        "features = pd.DataFrame(scaler.fit_transform(features),columns=features.columns)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Kj23eqHAlJ-n",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "0c1028b4-2e84-4de2-96ea-9c5ec35a87ec"
      },
      "source": [
        "features.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>account length</th>\n",
              "      <th>number vmail messages</th>\n",
              "      <th>total day calls</th>\n",
              "      <th>total day charge</th>\n",
              "      <th>total eve calls</th>\n",
              "      <th>total eve charge</th>\n",
              "      <th>total night calls</th>\n",
              "      <th>total night charge</th>\n",
              "      <th>total intl calls</th>\n",
              "      <th>total intl charge</th>\n",
              "      <th>customer service calls</th>\n",
              "      <th>international plan_yes</th>\n",
              "      <th>voice mail plan_yes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.638191</td>\n",
              "      <td>0.490196</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.842081</td>\n",
              "      <td>0.582353</td>\n",
              "      <td>0.493803</td>\n",
              "      <td>0.408451</td>\n",
              "      <td>0.694678</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.501385</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.532663</td>\n",
              "      <td>0.509804</td>\n",
              "      <td>0.745455</td>\n",
              "      <td>0.433349</td>\n",
              "      <td>0.605882</td>\n",
              "      <td>0.485965</td>\n",
              "      <td>0.492958</td>\n",
              "      <td>0.735761</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.778393</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.683417</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.690909</td>\n",
              "      <td>0.756386</td>\n",
              "      <td>0.647059</td>\n",
              "      <td>0.176358</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.350140</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.664820</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.417085</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.430303</td>\n",
              "      <td>0.977473</td>\n",
              "      <td>0.517647</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.394366</td>\n",
              "      <td>0.493931</td>\n",
              "      <td>0.35</td>\n",
              "      <td>0.246537</td>\n",
              "      <td>0.222222</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.371859</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.684848</td>\n",
              "      <td>0.453553</td>\n",
              "      <td>0.717647</td>\n",
              "      <td>0.289521</td>\n",
              "      <td>0.619718</td>\n",
              "      <td>0.451914</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.509695</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   account length  ...  voice mail plan_yes\n",
              "0        0.638191  ...                  1.0\n",
              "1        0.532663  ...                  1.0\n",
              "2        0.683417  ...                  0.0\n",
              "3        0.417085  ...                  0.0\n",
              "4        0.371859  ...                  0.0\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 520
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "k51gQZ7IhRBN",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Fv0g94yxhjLL",
        "colab": {}
      },
      "source": [
        "## Splitting X and Y dataframes into train, test sets using train_test_split\n",
        "features_train, features_test, target_train, target_test = train_test_split(features, target, test_size=0.30, random_state=42)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Ovob1Edfmdo-"
      },
      "source": [
        "## LogisticRegression Model:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "MqCg4G-YiK5y",
        "colab": {}
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "CopnCsNnljKQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "00e25a59-fa1f-4c6e-8412-7a10e6cbbf94"
      },
      "source": [
        "## Fitting LogisticRegression model on features_train and target_train\n",
        "Log_clf = LogisticRegression(random_state=42,class_weight='balanced').fit(features_train, target_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "QlUk6ZSGTEzP"
      },
      "source": [
        "$p=1/1+e^{-(b0+b1x)}$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "cw4zWedel8AN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "be618255-d69d-4c4e-ea29-a8c3389b916c"
      },
      "source": [
        "Log_clf.intercept_  ## this is the expected mean value of y when x=0"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-4.25233308])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 525
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "D6L6qG__mKnp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "ba2cd253-604f-4458-a36e-f801fbfdfb4b"
      },
      "source": [
        "Log_clf.coef_ ## coefficient of each coulmn variable x1,x2,... here we have 16 columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.233547  ,  0.73385057,  0.01755383,  2.77104091,  0.15098638,\n",
              "         1.35901742,  0.02277235,  0.70546853, -1.10227409,  1.0630189 ,\n",
              "         4.48324286,  2.29471579, -1.22710992]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 526
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "JiRXzVxbmRfJ",
        "colab": {}
      },
      "source": [
        "## prediction on Train data\n",
        "Log_Train_Pred = Log_clf.predict(features_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "dm34wSvhynWh",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import classification_report,confusion_matrix,accuracy_score"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "iUemQHx8yqnn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "e35ac845-f6a8-49ff-f314-54db3eb17648"
      },
      "source": [
        "print(classification_report(target_train,Log_Train_Pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.78      0.85      1993\n",
            "           1       0.36      0.73      0.48       340\n",
            "\n",
            "    accuracy                           0.77      2333\n",
            "   macro avg       0.65      0.75      0.67      2333\n",
            "weighted avg       0.86      0.77      0.80      2333\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "RiX3RBIvepcn"
      },
      "source": [
        "Using logisitc regression algorithm prediction on training data is observed to be 77%."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "bzE9i7IyyzRv",
        "colab": {}
      },
      "source": [
        "## prediction on Test data\n",
        "Log_Test_Pred = Log_clf.predict(features_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "XsV8b0DxzP8i",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "6c40e40f-1ae4-4870-c91b-d60ad1b22231"
      },
      "source": [
        "print(classification_report(target_test,Log_Test_Pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.78      0.86       857\n",
            "           1       0.38      0.80      0.51       143\n",
            "\n",
            "    accuracy                           0.78      1000\n",
            "   macro avg       0.67      0.79      0.69      1000\n",
            "weighted avg       0.88      0.78      0.81      1000\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "HYoqJ0Z3fFdc"
      },
      "source": [
        "Actually the logistic regression model performed better on tetsing data giving an accuracy of 78%."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "EOPNZwBn3FWB"
      },
      "source": [
        "# NearestNeighbors:\n",
        "\n",
        "Applying KNearestNeighbors classifer "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Xx-ADY1v3RoF",
        "colab": {}
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "UixzVJ8atVIl",
        "colab": {}
      },
      "source": [
        "knn = KNeighborsClassifier()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "XeJdVSL0jGHG"
      },
      "source": [
        "Below loop runs KNN classifier by changing n_neighbors ranging from 1 to 10, to know the optimum value of K where the accuracy of the KNN model is highest."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "LzMPHGei3GGd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 629
        },
        "outputId": "0e2fe80b-858b-4fe9-bf29-f6d5cb57fc81"
      },
      "source": [
        "## Calculate optimum value of K \n",
        "Error=[]\n",
        "for i in range(1,10):\n",
        "    knn = KNeighborsClassifier(n_neighbors=i)\n",
        "    knn.fit(features_train,target_train)\n",
        "    pred_i=knn.predict(features_test)\n",
        "    Error.append(accuracy_score(pred_i,target_test))\n",
        "   "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "H-9sFhv06oFR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "6adeb4cd-9167-4ea7-a753-8efdf6c68510"
      },
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "plt.plot(range(1,10),Error,markersize = 10)\n",
        "plt.title(\"ERROR  VS K \")\n",
        "plt.ylabel('error')\n",
        "plt.xlabel('K')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "aJvEriAO8gNP"
      },
      "source": [
        "Plot shows optimum value of K is 1."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "gT193Mq16yq9",
        "colab": {}
      },
      "source": [
        "knn = KNeighborsClassifier(n_neighbors=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "6Eq-zmoL7nzh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "ff684111-9357-4740-e3a5-c48fe0664dd2"
      },
      "source": [
        "knn.fit(features_train,target_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
              "                     metric_params=None, n_jobs=None, n_neighbors=1, p=2,\n",
              "                     weights='uniform')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 537
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ljfuVly-7pzG",
        "colab": {}
      },
      "source": [
        "knn_Train_pred=knn.predict(features_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "VIeok9KJ8Fvr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "2718b907-f347-4933-c76f-2758bcac064a"
      },
      "source": [
        "print(classification_report(target_train,knn_Train_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00      1993\n",
            "           1       1.00      1.00      1.00       340\n",
            "\n",
            "    accuracy                           1.00      2333\n",
            "   macro avg       1.00      1.00      1.00      2333\n",
            "weighted avg       1.00      1.00      1.00      2333\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ZGFmDdHP8OB5",
        "colab": {}
      },
      "source": [
        "knn_Test_pred=knn.predict(features_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "1IsWTwya8ZiR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "594e7ca3-143b-419c-8999-d39e0b9720aa"
      },
      "source": [
        "print(classification_report(target_test,knn_Test_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.91      0.93      0.92       857\n",
            "           1       0.51      0.47      0.49       143\n",
            "\n",
            "    accuracy                           0.86      1000\n",
            "   macro avg       0.71      0.70      0.70      1000\n",
            "weighted avg       0.86      0.86      0.86      1000\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "qzlS8W3wj0Ml"
      },
      "source": [
        "OverFitting, KNN classifer gives 100% accuarcy when applied on training data and drop in accuracy about 14% is observed when applied on testing data. The problem of overfititng of data is observed in this scenario. \n",
        "\n",
        "Overfitting is not the only problem over here. Class imbalance, hyper parameters tuning are also affecting the performance of model when applied testing data.\n",
        "\n",
        "We can observe there are only 483 rows with churn variable as True where as we have 2850 rows with churn as False. This huge difference makes model poor while predicting new variables with True churn though it's predicting 100% for True churn on training data. Hyper parameter tuning would effect predicitons on training data whcih results in predictions on training data closer to that of testing data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "uARaoW0_j4tj"
      },
      "source": [
        "## RandomForest\n",
        "\n",
        "Using class_weight and few other parameters considering the performance of KNN model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "9LmoyG8U8vCA",
        "colab": {}
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "-Qb4xxEQj7RA",
        "colab": {}
      },
      "source": [
        "RFclf = RandomForestClassifier(n_estimators=300,max_depth=7,min_samples_leaf=5,class_weight='balanced',random_state=42)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "smkS-j-WkA7Z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "eb8263d4-35c1-4e9b-e4e5-65ce690f3e12"
      },
      "source": [
        "RFclf.fit(features_train,target_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight='balanced',\n",
              "                       criterion='gini', max_depth=7, max_features='auto',\n",
              "                       max_leaf_nodes=None, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=5, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=300,\n",
              "                       n_jobs=None, oob_score=False, random_state=42, verbose=0,\n",
              "                       warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 544
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "8HDr-G3HkHX7",
        "colab": {}
      },
      "source": [
        "RFclf_Train_pred=RFclf.predict(features_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "7UkBqVqCkleO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "b2ce4664-92cc-4b5c-b2b0-b6b2a9577960"
      },
      "source": [
        "print(classification_report(target_train,RFclf_Train_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.97      0.97      1993\n",
            "           1       0.82      0.85      0.84       340\n",
            "\n",
            "    accuracy                           0.95      2333\n",
            "   macro avg       0.90      0.91      0.90      2333\n",
            "weighted avg       0.95      0.95      0.95      2333\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "4K4zw751wsLr"
      },
      "source": [
        "As expected parameter tuning has worked. Random forest classifier with out parameters have resulted in 100% training accuracy. Classifier with tuned parameters have reduced performance of training data let's see whether it helps in improving acuuracy of test data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "TU-_5DABkrLt",
        "colab": {}
      },
      "source": [
        "RFclf_Test_pred=RFclf.predict(features_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "gQlEHbVjkxYb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "3cf847e4-9c16-4fd8-aced-b2ce09164365"
      },
      "source": [
        "print(classification_report(target_test,RFclf_Test_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.96      0.97       857\n",
            "           1       0.78      0.83      0.80       143\n",
            "\n",
            "    accuracy                           0.94      1000\n",
            "   macro avg       0.87      0.89      0.88      1000\n",
            "weighted avg       0.94      0.94      0.94      1000\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "zRhgFR8-lGtV"
      },
      "source": [
        "By far RandomForest classifier on the normalized data is the best accuracy we have got. This classifier gives 95% prediction acuuracy on the training data and 94% prediction accuracy on testing data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "8eS2104KI1KB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "outputId": "5fe852ab-9d0b-466b-9782-385b65fbff28"
      },
      "source": [
        "print('f1-score of LogisticRegression Model',classification_report(target_test,Log_Test_Pred))\n",
        "print('f1-score of KNN Model',classification_report(target_test,knn_Test_pred))\n",
        "print('f1-score of RandomForest Model',classification_report(target_test,RFclf_Test_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "f1-score of LogisticRegression Model               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.78      0.86       857\n",
            "           1       0.38      0.80      0.51       143\n",
            "\n",
            "    accuracy                           0.78      1000\n",
            "   macro avg       0.67      0.79      0.69      1000\n",
            "weighted avg       0.88      0.78      0.81      1000\n",
            "\n",
            "f1-score of KNN Model               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.91      0.93      0.92       857\n",
            "           1       0.51      0.47      0.49       143\n",
            "\n",
            "    accuracy                           0.86      1000\n",
            "   macro avg       0.71      0.70      0.70      1000\n",
            "weighted avg       0.86      0.86      0.86      1000\n",
            "\n",
            "f1-score of RandomForest Model               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.96      0.97       857\n",
            "           1       0.78      0.83      0.80       143\n",
            "\n",
            "    accuracy                           0.94      1000\n",
            "   macro avg       0.87      0.89      0.88      1000\n",
            "weighted avg       0.94      0.94      0.94      1000\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "blqUidv5Ixlu"
      },
      "source": [
        "## Clustering:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "eF5zTI_tk8cP"
      },
      "source": [
        "Finding similarities by clustering Columns like state, area code and phone number which are not used in model bulding in previous models will help and train model to learn  from those similarities. Using K_Means clustering to cluster 3 unique identification columns."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "gq_G1gZYltAt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "fb2a3a8c-2187-45be-a458-b76cac7677c2"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['state', 'account length', 'area code', 'phone number',\n",
              "       'international plan', 'voice mail plan', 'number vmail messages',\n",
              "       'total day minutes', 'total day calls', 'total day charge',\n",
              "       'total eve minutes', 'total eve calls', 'total eve charge',\n",
              "       'total night minutes', 'total night calls', 'total night charge',\n",
              "       'total intl minutes', 'total intl calls', 'total intl charge',\n",
              "       'customer service calls', 'churn'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 550
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "GlHXQJ-Cllfs",
        "colab": {}
      },
      "source": [
        " df2=df[['state','area code', 'phone number']] ## creating a dataframe with those three columns."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "cQD5s2Ttl1Wi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "a14c7ecc-1abd-41b9-944b-7fc5ff833951"
      },
      "source": [
        "df2.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>state</th>\n",
              "      <th>area code</th>\n",
              "      <th>phone number</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>KS</td>\n",
              "      <td>415</td>\n",
              "      <td>382-4657</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>OH</td>\n",
              "      <td>415</td>\n",
              "      <td>371-7191</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NJ</td>\n",
              "      <td>415</td>\n",
              "      <td>358-1921</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>OH</td>\n",
              "      <td>408</td>\n",
              "      <td>375-9999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>OK</td>\n",
              "      <td>415</td>\n",
              "      <td>330-6626</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  state area code phone number\n",
              "0    KS       415     382-4657\n",
              "1    OH       415     371-7191\n",
              "2    NJ       415     358-1921\n",
              "3    OH       408     375-9999\n",
              "4    OK       415     330-6626"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 552
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ZGHKORHzl3Bb",
        "colab": {}
      },
      "source": [
        "df3=df2.groupby(['area code','state']).agg('count').reset_index() ## grouping by on areacode and state putting count on howmany phone numbers at that instance."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "3_o9Zy1dmOYI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "68902dae-9180-4186-d269-fa25485a7f15"
      },
      "source": [
        "df3"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>area code</th>\n",
              "      <th>state</th>\n",
              "      <th>phone number</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>408</td>\n",
              "      <td>AK</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>408</td>\n",
              "      <td>AL</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>408</td>\n",
              "      <td>AR</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>408</td>\n",
              "      <td>AZ</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>408</td>\n",
              "      <td>CA</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>510</td>\n",
              "      <td>VT</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>510</td>\n",
              "      <td>WA</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>150</th>\n",
              "      <td>510</td>\n",
              "      <td>WI</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>151</th>\n",
              "      <td>510</td>\n",
              "      <td>WV</td>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>152</th>\n",
              "      <td>510</td>\n",
              "      <td>WY</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>153 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     area code state  phone number\n",
              "0          408    AK            14\n",
              "1          408    AL            25\n",
              "2          408    AR            13\n",
              "3          408    AZ            15\n",
              "4          408    CA             7\n",
              "..         ...   ...           ...\n",
              "148        510    VT            20\n",
              "149        510    WA            17\n",
              "150        510    WI            21\n",
              "151        510    WV            34\n",
              "152        510    WY            19\n",
              "\n",
              "[153 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 554
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "XA-tyTTQmPTE",
        "colab": {}
      },
      "source": [
        "df3['state']=df3['state'].astype('category') "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "nztkmi7imodz",
        "colab": {}
      },
      "source": [
        "df3['state'] = df3['state'].cat.codes ## .cat.codes coverts state category to numerical value "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ZPiFbwncmtvV",
        "colab": {}
      },
      "source": [
        "L=df3[['state','area code','phone number']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "nKmORTGtncrc",
        "colab": {}
      },
      "source": [
        "from sklearn.cluster import KMeans"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Lz8NEKnYnadU"
      },
      "source": [
        "Below loop iterates number of clusters ranging from 1 to 10 to know the opitmum value of K."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "YIGGy43inqho",
        "colab": {}
      },
      "source": [
        "WCSS = []\n",
        "for i in range(1, 10):\n",
        "    kmeans = KMeans(n_clusters=i,max_iter=300,random_state=42)\n",
        "    kmeans.fit(L)\n",
        "    WCSS.append(kmeans.inertia_) ## this particular syntax hleps us append the sum of suqares with in cluster using kmeans model.\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "FtHWAnKYn6Ml",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "f409ab1f-256e-48f6-c680-8f96bcd32695"
      },
      "source": [
        "plt.plot(range(1,10),WCSS)\n",
        "plt.title('Elbow curve')\n",
        "plt.xlabel('K')\n",
        "plt.ylabel('WCSS')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "UTKwc5a8ogym"
      },
      "source": [
        "Plotting elbow curve using values of sum of squares for different value of K show the curve begins to flatten between 4 and 5. Righnt now i'm using 5 clusters, which creates 5 different clusters using similarities in 3 columns."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "1RDVrbtDoJt9",
        "colab": {}
      },
      "source": [
        "kmeans = KMeans(n_clusters = 5,max_iter=300,random_state = 3565)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "-dRU2YVfoftc",
        "colab": {}
      },
      "source": [
        "Y_kmeans= kmeans.fit_predict(L)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "MuMNSGEmo8ug",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "fb99a9b7-465a-4847-f741-bfce9f9f8fb6"
      },
      "source": [
        "Y_kmeans ## Clusters have been created for on groupby dataframe of areacode and state."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
              "       3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
              "       3, 3, 3, 3, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0,\n",
              "       0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4,\n",
              "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
              "      dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 563
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "7fDyLI8_qITX",
        "colab": {}
      },
      "source": [
        "df4=df2.groupby(['area code','state']).agg('count').reset_index() ## new dataframe stroing the same areacode and state groupby data."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "6s5xB2iUpsSJ",
        "colab": {}
      },
      "source": [
        "df4['cluster']=Y_kmeans ## appending cluster column to the dataframe."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "aK-P7RIZp06Z",
        "colab": {}
      },
      "source": [
        "df5=pd.merge(df2,df4,on=['area code','state']) ## Merging df2 and df4 on areacode and state would allot cluster to all the rows of df2 dataframe and stroing it as df5."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "dYD5AvTxqSIT",
        "colab": {}
      },
      "source": [
        "df['cluster']=df5['cluster'] ## now appending cluster coulmn from df5 to original df."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "T1REvPLOqbOV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "outputId": "590b6920-cb16-4d7a-f4ab-d53f28828b5e"
      },
      "source": [
        "df.sample(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>state</th>\n",
              "      <th>account length</th>\n",
              "      <th>area code</th>\n",
              "      <th>phone number</th>\n",
              "      <th>international plan</th>\n",
              "      <th>voice mail plan</th>\n",
              "      <th>number vmail messages</th>\n",
              "      <th>total day minutes</th>\n",
              "      <th>total day calls</th>\n",
              "      <th>total day charge</th>\n",
              "      <th>total eve minutes</th>\n",
              "      <th>total eve calls</th>\n",
              "      <th>total eve charge</th>\n",
              "      <th>total night minutes</th>\n",
              "      <th>total night calls</th>\n",
              "      <th>total night charge</th>\n",
              "      <th>total intl minutes</th>\n",
              "      <th>total intl calls</th>\n",
              "      <th>total intl charge</th>\n",
              "      <th>customer service calls</th>\n",
              "      <th>churn</th>\n",
              "      <th>cluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2699</th>\n",
              "      <td>AL</td>\n",
              "      <td>136</td>\n",
              "      <td>510</td>\n",
              "      <td>352-6732</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "      <td>109.4</td>\n",
              "      <td>91</td>\n",
              "      <td>18.60</td>\n",
              "      <td>207.5</td>\n",
              "      <td>111</td>\n",
              "      <td>17.64</td>\n",
              "      <td>135.0</td>\n",
              "      <td>107</td>\n",
              "      <td>6.08</td>\n",
              "      <td>11.6</td>\n",
              "      <td>5</td>\n",
              "      <td>3.13</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>651</th>\n",
              "      <td>CT</td>\n",
              "      <td>105</td>\n",
              "      <td>408</td>\n",
              "      <td>357-2679</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "      <td>213.4</td>\n",
              "      <td>100</td>\n",
              "      <td>36.28</td>\n",
              "      <td>204.9</td>\n",
              "      <td>52</td>\n",
              "      <td>17.42</td>\n",
              "      <td>179.7</td>\n",
              "      <td>93</td>\n",
              "      <td>8.09</td>\n",
              "      <td>9.5</td>\n",
              "      <td>6</td>\n",
              "      <td>2.57</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1070</th>\n",
              "      <td>MN</td>\n",
              "      <td>75</td>\n",
              "      <td>415</td>\n",
              "      <td>379-7779</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "      <td>143.2</td>\n",
              "      <td>92</td>\n",
              "      <td>24.34</td>\n",
              "      <td>209.1</td>\n",
              "      <td>142</td>\n",
              "      <td>17.77</td>\n",
              "      <td>173.0</td>\n",
              "      <td>96</td>\n",
              "      <td>7.79</td>\n",
              "      <td>11.9</td>\n",
              "      <td>9</td>\n",
              "      <td>3.21</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2477</th>\n",
              "      <td>MA</td>\n",
              "      <td>96</td>\n",
              "      <td>415</td>\n",
              "      <td>359-9369</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "      <td>173.9</td>\n",
              "      <td>111</td>\n",
              "      <td>29.56</td>\n",
              "      <td>287.4</td>\n",
              "      <td>105</td>\n",
              "      <td>24.43</td>\n",
              "      <td>204.8</td>\n",
              "      <td>91</td>\n",
              "      <td>9.22</td>\n",
              "      <td>9.1</td>\n",
              "      <td>7</td>\n",
              "      <td>2.46</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>MN</td>\n",
              "      <td>62</td>\n",
              "      <td>415</td>\n",
              "      <td>409-2111</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "      <td>147.1</td>\n",
              "      <td>91</td>\n",
              "      <td>25.01</td>\n",
              "      <td>190.4</td>\n",
              "      <td>107</td>\n",
              "      <td>16.18</td>\n",
              "      <td>195.2</td>\n",
              "      <td>115</td>\n",
              "      <td>8.78</td>\n",
              "      <td>12.2</td>\n",
              "      <td>3</td>\n",
              "      <td>3.29</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     state  account length area code  ... customer service calls churn cluster\n",
              "2699    AL             136       510  ...                      0     0       3\n",
              "651     CT             105       408  ...                      1     0       0\n",
              "1070    MN              75       415  ...                      1     0       3\n",
              "2477    MA              96       415  ...                      1     0       0\n",
              "176     MN              62       415  ...                      0     0       4\n",
              "\n",
              "[5 rows x 22 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 568
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "q8vReS5iwgtG"
      },
      "source": [
        "## PCA"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "VOezATfvqwW_"
      },
      "source": [
        "Applying Dimensionality reduction on all the numerical columns and creating new components."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "9x6_F7b3t-IX",
        "colab": {}
      },
      "source": [
        "pca_df=df[[ 'account length','number vmail messages',\n",
        "       'total day minutes', 'total day calls', 'total day charge',\n",
        "       'total eve minutes', 'total eve calls', 'total eve charge',\n",
        "       'total night minutes', 'total night calls', 'total night charge',\n",
        "       'total intl minutes', 'total intl calls', 'total intl charge',\n",
        "       'customer service calls']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "9YJQlBdrtTrl",
        "colab": {}
      },
      "source": [
        "PCA_df=pd.DataFrame(scaler.fit_transform(pca_df))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "dCmCF_P_sBE-",
        "colab": {}
      },
      "source": [
        "from sklearn.decomposition import PCA\n",
        "pca = PCA(n_components=15)\n",
        "principalComponents = pca.fit_transform(PCA_df)\n",
        "principalDf = pd.DataFrame(data = principalComponents\n",
        "             , columns = ['principal component 1', 'principal component 2','principal component 3', 'principal component 4','principal component 5',\n",
        "                          'principal component 6', 'principal component 7','principal component 8', 'principal component 9','principal component 10',\n",
        "                          'principal component 11', 'principal component 12','principal component 13', 'principal component 14','principal component 15'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Ve8vQp_ItA0L",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2b4d0c48-b6e1-4645-b44f-da0b597b156e"
      },
      "source": [
        "principalDf.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3333, 15)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 572
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "a7rkWZuZuobg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "outputId": "6bf80d7a-f300-4bc2-8559-11639de41f24"
      },
      "source": [
        "principalDf.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>principal component 1</th>\n",
              "      <th>principal component 2</th>\n",
              "      <th>principal component 3</th>\n",
              "      <th>principal component 4</th>\n",
              "      <th>principal component 5</th>\n",
              "      <th>principal component 6</th>\n",
              "      <th>principal component 7</th>\n",
              "      <th>principal component 8</th>\n",
              "      <th>principal component 9</th>\n",
              "      <th>principal component 10</th>\n",
              "      <th>principal component 11</th>\n",
              "      <th>principal component 12</th>\n",
              "      <th>principal component 13</th>\n",
              "      <th>principal component 14</th>\n",
              "      <th>principal component 15</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.482446</td>\n",
              "      <td>-0.244075</td>\n",
              "      <td>0.056428</td>\n",
              "      <td>-0.102170</td>\n",
              "      <td>0.315060</td>\n",
              "      <td>0.138723</td>\n",
              "      <td>-0.044230</td>\n",
              "      <td>-0.079468</td>\n",
              "      <td>-0.077282</td>\n",
              "      <td>-0.050188</td>\n",
              "      <td>-0.000956</td>\n",
              "      <td>0.000095</td>\n",
              "      <td>-0.000052</td>\n",
              "      <td>-0.000090</td>\n",
              "      <td>-0.000046</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.134150</td>\n",
              "      <td>-0.225231</td>\n",
              "      <td>0.107302</td>\n",
              "      <td>-0.428732</td>\n",
              "      <td>0.327878</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>-0.058891</td>\n",
              "      <td>0.006598</td>\n",
              "      <td>-0.064854</td>\n",
              "      <td>-0.136329</td>\n",
              "      <td>-0.021520</td>\n",
              "      <td>-0.000035</td>\n",
              "      <td>-0.000091</td>\n",
              "      <td>0.000137</td>\n",
              "      <td>0.000041</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.194510</td>\n",
              "      <td>-0.147750</td>\n",
              "      <td>-0.630275</td>\n",
              "      <td>-0.059818</td>\n",
              "      <td>-0.109540</td>\n",
              "      <td>0.171244</td>\n",
              "      <td>-0.181306</td>\n",
              "      <td>0.010867</td>\n",
              "      <td>0.025773</td>\n",
              "      <td>-0.062845</td>\n",
              "      <td>-0.041289</td>\n",
              "      <td>0.000925</td>\n",
              "      <td>-0.000053</td>\n",
              "      <td>0.000197</td>\n",
              "      <td>-0.000031</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.546618</td>\n",
              "      <td>-0.519444</td>\n",
              "      <td>-0.573275</td>\n",
              "      <td>0.477306</td>\n",
              "      <td>-0.080491</td>\n",
              "      <td>-0.095121</td>\n",
              "      <td>0.051662</td>\n",
              "      <td>-0.070760</td>\n",
              "      <td>0.101382</td>\n",
              "      <td>0.201315</td>\n",
              "      <td>0.085631</td>\n",
              "      <td>0.000455</td>\n",
              "      <td>0.001893</td>\n",
              "      <td>-0.000015</td>\n",
              "      <td>-0.000043</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.141377</td>\n",
              "      <td>-0.137928</td>\n",
              "      <td>-0.255874</td>\n",
              "      <td>0.123721</td>\n",
              "      <td>-0.124226</td>\n",
              "      <td>-0.133966</td>\n",
              "      <td>0.137742</td>\n",
              "      <td>0.158108</td>\n",
              "      <td>-0.029571</td>\n",
              "      <td>-0.109820</td>\n",
              "      <td>-0.126728</td>\n",
              "      <td>-0.000531</td>\n",
              "      <td>-0.000203</td>\n",
              "      <td>-0.000041</td>\n",
              "      <td>-0.000009</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   principal component 1  ...  principal component 15\n",
              "0               0.482446  ...               -0.000046\n",
              "1              -0.134150  ...                0.000041\n",
              "2               0.194510  ...               -0.000031\n",
              "3               0.546618  ...               -0.000043\n",
              "4              -0.141377  ...               -0.000009\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 573
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "XoG2kk3J7FPi",
        "colab": {}
      },
      "source": [
        "churn_df = pd.read_csv(\"bigml_59c28831336c6604c800002a.csv\") ## reloading dataset as churn_df to append two pca components to churn and intl. plan and vmail plan columns."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "7mfK11wA7FUX",
        "colab": {}
      },
      "source": [
        "churn_df.dropna(axis=0,how='all',inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "zTqbAvbI7Fe7",
        "colab": {}
      },
      "source": [
        "churn_df = churn_df.reset_index()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "gSF7O1Mi7FNW",
        "colab": {}
      },
      "source": [
        "new_df=pd.DataFrame(principalDf)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "zTUomu9g7FLn",
        "colab": {}
      },
      "source": [
        "new_df['international plan']=churn_df['international plan']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "-BnP6B4x7vvI",
        "colab": {}
      },
      "source": [
        "new_df['voice mail plan']=churn_df['voice mail plan']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "nfD-tNy179KV",
        "colab": {}
      },
      "source": [
        "new_df['cluster']=df5['cluster'] ## appending cluster column to new_df dataframe"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "AcGRPs4x3SFC",
        "colab": {}
      },
      "source": [
        "pca_features=new_df[['international plan','voice mail plan','cluster',\n",
        "                     'principal component 1', 'principal component 2','principal component 3', 'principal component 4','principal component 5',\n",
        "                          'principal component 6', 'principal component 7','principal component 8', 'principal component 9','principal component 10',\n",
        "                          'principal component 11', 'principal component 12','principal component 13', 'principal component 14','principal component 15'\n",
        "                     ]]\n",
        "## creating new variable x for trainingg and tetsing"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "97oa4lSb8xZE",
        "colab": {}
      },
      "source": [
        "pca_features = pd.get_dummies(pca_features,columns=['international plan','voice mail plan','cluster'],drop_first=True) \n",
        "## creating dummies would convert the categorical columns to numerical by assigning 0 and 1."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "yhlybtlM-ZyW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "8d092ade-9c48-47bc-969d-94834048faa4"
      },
      "source": [
        "pca_features.reset_index"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method DataFrame.reset_index of       principal component 1  principal component 2  ...  cluster_3  cluster_4\n",
              "0                  0.482446              -0.244075  ...          1          0\n",
              "1                 -0.134150              -0.225231  ...          1          0\n",
              "2                  0.194510              -0.147750  ...          1          0\n",
              "3                  0.546618              -0.519444  ...          1          0\n",
              "4                 -0.141377              -0.137928  ...          1          0\n",
              "...                     ...                    ...  ...        ...        ...\n",
              "3328              -0.060019              -0.272682  ...          0          0\n",
              "3329               0.219225              -0.164527  ...          0          0\n",
              "3330               0.036973               0.390326  ...          0          0\n",
              "3331               0.199520               0.071812  ...          0          0\n",
              "3332               0.336091               0.066800  ...          0          0\n",
              "\n",
              "[3333 rows x 21 columns]>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 583
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "HpaqLMCR8z-V",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 227
        },
        "outputId": "d58ade1e-fd44-4e98-8f83-144efa4e9ee7"
      },
      "source": [
        "pca_features.sample(4)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\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>principal component 1</th>\n",
              "      <th>principal component 2</th>\n",
              "      <th>principal component 3</th>\n",
              "      <th>principal component 4</th>\n",
              "      <th>principal component 5</th>\n",
              "      <th>principal component 6</th>\n",
              "      <th>principal component 7</th>\n",
              "      <th>principal component 8</th>\n",
              "      <th>principal component 9</th>\n",
              "      <th>principal component 10</th>\n",
              "      <th>principal component 11</th>\n",
              "      <th>principal component 12</th>\n",
              "      <th>principal component 13</th>\n",
              "      <th>principal component 14</th>\n",
              "      <th>principal component 15</th>\n",
              "      <th>international plan_yes</th>\n",
              "      <th>voice mail plan_yes</th>\n",
              "      <th>cluster_1</th>\n",
              "      <th>cluster_2</th>\n",
              "      <th>cluster_3</th>\n",
              "      <th>cluster_4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1427</th>\n",
              "      <td>0.332505</td>\n",
              "      <td>0.085923</td>\n",
              "      <td>-0.403748</td>\n",
              "      <td>0.148942</td>\n",
              "      <td>-0.130392</td>\n",
              "      <td>0.372977</td>\n",
              "      <td>0.053413</td>\n",
              "      <td>-0.099817</td>\n",
              "      <td>0.069877</td>\n",
              "      <td>-0.019065</td>\n",
              "      <td>0.023074</td>\n",
              "      <td>0.000490</td>\n",
              "      <td>-0.000104</td>\n",
              "      <td>0.000194</td>\n",
              "      <td>0.000050</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1626</th>\n",
              "      <td>0.136343</td>\n",
              "      <td>0.356040</td>\n",
              "      <td>-0.316785</td>\n",
              "      <td>0.288352</td>\n",
              "      <td>-0.130981</td>\n",
              "      <td>0.239209</td>\n",
              "      <td>-0.057082</td>\n",
              "      <td>-0.130252</td>\n",
              "      <td>-0.042704</td>\n",
              "      <td>-0.071415</td>\n",
              "      <td>0.107127</td>\n",
              "      <td>0.000446</td>\n",
              "      <td>0.000103</td>\n",
              "      <td>-0.000287</td>\n",
              "      <td>0.000025</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>912</th>\n",
              "      <td>-0.233212</td>\n",
              "      <td>-0.125273</td>\n",
              "      <td>-0.456365</td>\n",
              "      <td>0.152957</td>\n",
              "      <td>-0.100720</td>\n",
              "      <td>-0.292870</td>\n",
              "      <td>0.048005</td>\n",
              "      <td>-0.043180</td>\n",
              "      <td>-0.082096</td>\n",
              "      <td>0.047532</td>\n",
              "      <td>0.064221</td>\n",
              "      <td>0.000465</td>\n",
              "      <td>0.000033</td>\n",
              "      <td>0.000331</td>\n",
              "      <td>-0.000060</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3193</th>\n",
              "      <td>-0.428792</td>\n",
              "      <td>0.163494</td>\n",
              "      <td>-0.293652</td>\n",
              "      <td>0.128429</td>\n",
              "      <td>-0.117472</td>\n",
              "      <td>-0.080430</td>\n",
              "      <td>-0.087458</td>\n",
              "      <td>0.313458</td>\n",
              "      <td>-0.066269</td>\n",
              "      <td>0.160822</td>\n",
              "      <td>-0.175195</td>\n",
              "      <td>-0.000756</td>\n",
              "      <td>0.000098</td>\n",
              "      <td>0.000166</td>\n",
              "      <td>-0.000010</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      principal component 1  principal component 2  ...  cluster_3  cluster_4\n",
              "1427               0.332505               0.085923  ...          0          0\n",
              "1626               0.136343               0.356040  ...          0          0\n",
              "912               -0.233212              -0.125273  ...          1          0\n",
              "3193              -0.428792               0.163494  ...          0          1\n",
              "\n",
              "[4 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 584
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "u0giacx73lNk",
        "colab": {}
      },
      "source": [
        "pca_target = target"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "9f2y9BQSN27z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "091411fe-8ea6-4a3a-9b49-7da159385175"
      },
      "source": [
        "pca_target.sample(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1139</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>900</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2105</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1178</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      churn\n",
              "1139      0\n",
              "900       0\n",
              "2105      0\n",
              "87        0\n",
              "1178      0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 586
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "tdx80yHm2zbh",
        "colab": {}
      },
      "source": [
        "## Splitting X and Y dataframes into train, test sets using train_test_split\n",
        "pca_features_train, pca_features_test, pca_target_train, pca_target_test = train_test_split(pca_features,pca_target, test_size=0.30, random_state=42)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "kV6gMz870d7a"
      },
      "source": [
        "Though PCA is meant for Dimensionality Reduction, I'm not really reducing the feature space by eliminating features or combining older onces to create fewer components. Generally we are supposed to drop new pca components which have lower impact on the dependent variable. But, i have decided not to alter the number of columns and created 15 new components using 15 older inputs columns which indeed gave me better predicitons than data with fewer pca components."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "hOY8gNzJ6OlS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "502c9b96-176c-484b-ea6f-f4fd128e7376"
      },
      "source": [
        "importance = RFclf.feature_importances_\n",
        "# summarize feature importance\n",
        "x = []\n",
        "for i,v in enumerate(importance):\n",
        "\tprint('Feature: %0d, Score: %.5f' % (i,v*100))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Feature: 0, Score: 2.86889\n",
            "Feature: 1, Score: 3.36371\n",
            "Feature: 2, Score: 3.00487\n",
            "Feature: 3, Score: 27.59983\n",
            "Feature: 4, Score: 2.54978\n",
            "Feature: 5, Score: 7.89916\n",
            "Feature: 6, Score: 2.93939\n",
            "Feature: 7, Score: 4.80679\n",
            "Feature: 8, Score: 3.31900\n",
            "Feature: 9, Score: 5.97216\n",
            "Feature: 10, Score: 18.66468\n",
            "Feature: 11, Score: 14.72940\n",
            "Feature: 12, Score: 2.28235\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "EZzp0JtJ523Q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "outputId": "c61f1ace-3867-4f04-8fad-840763b7cca9"
      },
      "source": [
        "importance = rfclf.feature_importances_\n",
        "# summarize feature importance\n",
        "for i,v in enumerate(importance):\n",
        "\tprint('Feature: %0d, Score: %.5f' % (i,v*100))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Feature: 0, Score: 23.52005\n",
            "Feature: 1, Score: 2.12697\n",
            "Feature: 2, Score: 2.80466\n",
            "Feature: 3, Score: 6.06059\n",
            "Feature: 4, Score: 9.43482\n",
            "Feature: 5, Score: 2.37889\n",
            "Feature: 6, Score: 18.65166\n",
            "Feature: 7, Score: 2.35064\n",
            "Feature: 8, Score: 3.83829\n",
            "Feature: 9, Score: 2.28727\n",
            "Feature: 10, Score: 1.86346\n",
            "Feature: 11, Score: 2.02063\n",
            "Feature: 12, Score: 2.36069\n",
            "Feature: 13, Score: 2.27999\n",
            "Feature: 14, Score: 3.09334\n",
            "Feature: 15, Score: 12.73489\n",
            "Feature: 16, Score: 1.64705\n",
            "Feature: 17, Score: 0.09374\n",
            "Feature: 18, Score: 0.16467\n",
            "Feature: 19, Score: 0.18506\n",
            "Feature: 20, Score: 0.10264\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "BtHoUZzUsPBx"
      },
      "source": [
        "## RnadomForest+PCA+Cluster"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "a2_UNaCG9dhN",
        "colab": {}
      },
      "source": [
        "rfclf = RandomForestClassifier(n_estimators=300,max_depth=7,min_samples_leaf=5,class_weight='balanced',random_state=42)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "rsgDv_583L_t",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "bb81ff15-5ed0-4897-9b09-7918cff4d42f"
      },
      "source": [
        "rfclf.fit(pca_features_train,pca_target_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight='balanced',\n",
              "                       criterion='gini', max_depth=7, max_features='auto',\n",
              "                       max_leaf_nodes=None, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=5, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=300,\n",
              "                       n_jobs=None, oob_score=False, random_state=42, verbose=0,\n",
              "                       warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 591
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "47LqthEM5Ctl",
        "colab": {}
      },
      "source": [
        "rfclf_Train_pred=rfclf.predict(pca_features_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "5fIrB7Yp5HMV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "dbff06e4-9a3d-4d1d-d6e4-8df089fbd2f9"
      },
      "source": [
        "print(classification_report(pca_target_train,rfclf_Train_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.98      0.98      1993\n",
            "           1       0.89      0.85      0.87       340\n",
            "\n",
            "    accuracy                           0.96      2333\n",
            "   macro avg       0.93      0.92      0.92      2333\n",
            "weighted avg       0.96      0.96      0.96      2333\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "vft6ZD-75K1V",
        "colab": {}
      },
      "source": [
        "rfclf_Test_pred=rfclf.predict(pca_features_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "z0wWvuIK5RfS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "f6194def-0c06-427d-f91c-310d7337b46c"
      },
      "source": [
        "print(classification_report(pca_target_test,rfclf_Test_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.97      0.97       857\n",
            "           1       0.82      0.83      0.82       143\n",
            "\n",
            "    accuracy                           0.95      1000\n",
            "   macro avg       0.89      0.90      0.90      1000\n",
            "weighted avg       0.95      0.95      0.95      1000\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "QCzHJa8crKoH"
      },
      "source": [
        "After all the hard work of creating clusters, PCA components and feeding it to RandomForest Classifier have resulted in 1% increase in accuracy on test data compared to that of simple RandomForest calssifier model. Though it's just 1%, model got better in predicting True churn data by 2% on testing data.\n",
        "\n",
        "Below are the accuracy comaprions of simple RF model and RandomForest model with PCA and Clusters on test data.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "MsqvZQM9r253",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "outputId": "6781d4ed-c5f1-4e75-9295-246a0851a3f5"
      },
      "source": [
        "print('calssification report of RandomForest Test Model',classification_report(target_test,RFclf_Test_pred))\n",
        "print('calssification report of CLuster+PCA+RandomForest Test Model',classification_report(pca_target_test,rfclf_Test_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "calssification report of RandomForest Test Model               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.96      0.97       857\n",
            "           1       0.78      0.83      0.80       143\n",
            "\n",
            "    accuracy                           0.94      1000\n",
            "   macro avg       0.87      0.89      0.88      1000\n",
            "weighted avg       0.94      0.94      0.94      1000\n",
            "\n",
            "calssification report of CLuster+PCA+RandomForest Test Model               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.97      0.97       857\n",
            "           1       0.82      0.83      0.82       143\n",
            "\n",
            "    accuracy                           0.95      1000\n",
            "   macro avg       0.89      0.90      0.90      1000\n",
            "weighted avg       0.95      0.95      0.95      1000\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "VUG7iUmlacHl",
        "colab": {}
      },
      "source": [
        "from sklearn.ensemble import BaggingClassifier"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "isYqP17CadO6",
        "colab": {}
      },
      "source": [
        "bag = BaggingClassifier(rfclf)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "6uAEh42yadUC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "outputId": "9bec7baf-09ed-4089-c81d-bcdd2b3ab7df"
      },
      "source": [
        "bag.fit(pca_features_train,pca_target_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/_bagging.py:645: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BaggingClassifier(base_estimator=RandomForestClassifier(bootstrap=True,\n",
              "                                                        ccp_alpha=0.0,\n",
              "                                                        class_weight='balanced',\n",
              "                                                        criterion='gini',\n",
              "                                                        max_depth=7,\n",
              "                                                        max_features='auto',\n",
              "                                                        max_leaf_nodes=None,\n",
              "                                                        max_samples=None,\n",
              "                                                        min_impurity_decrease=0.0,\n",
              "                                                        min_impurity_split=None,\n",
              "                                                        min_samples_leaf=5,\n",
              "                                                        min_samples_split=2,\n",
              "                                                        min_weight_fraction_leaf=0.0,\n",
              "                                                        n_estimators=300,\n",
              "                                                        n_jobs=None,\n",
              "                                                        oob_score=False,\n",
              "                                                        random_state=42,\n",
              "                                                        verbose=0,\n",
              "                                                        warm_start=False),\n",
              "                  bootstrap=True, bootstrap_features=False, max_features=1.0,\n",
              "                  max_samples=1.0, n_estimators=10, n_jobs=None,\n",
              "                  oob_score=False, random_state=None, verbose=0,\n",
              "                  warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 599
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "G8pof863adX9",
        "colab": {}
      },
      "source": [
        "bag_Train_pred=bag.predict(pca_features_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "eHIhNdvRadSQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "23a9cee9-6af4-47e7-9733-19099853cf55"
      },
      "source": [
        "print(classification_report(pca_target_train,bag_Train_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.98      0.98      1993\n",
            "           1       0.89      0.84      0.87       340\n",
            "\n",
            "    accuracy                           0.96      2333\n",
            "   macro avg       0.93      0.91      0.92      2333\n",
            "weighted avg       0.96      0.96      0.96      2333\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ylZRmfx3adNG",
        "colab": {}
      },
      "source": [
        "bag_Test_pred=bag.predict(pca_features_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "LZ9oNvYfbZb6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "9c11bd43-4a3e-4c59-879a-51aa519bc887"
      },
      "source": [
        "print(classification_report(pca_target_test,bag_Test_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.98      0.97       857\n",
            "           1       0.87      0.80      0.84       143\n",
            "\n",
            "    accuracy                           0.95      1000\n",
            "   macro avg       0.92      0.89      0.91      1000\n",
            "weighted avg       0.95      0.95      0.95      1000\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "DdcPcvBzcKQ3"
      },
      "source": [
        "Here, we can observe that accuracy after applying bagging is 95% on test data which is similar to that of previous model.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "_bd8qCsvcgBq"
      },
      "source": [
        "To evaluate the performance of the model, Learning curve is plotted."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "lsysaagzbZW8",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import learning_curve"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "QVFWegl3faDJ",
        "colab": {}
      },
      "source": [
        "BAG = BaggingClassifier(rfclf)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "tga9fMeBfBeN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "8de56733-64c6-4d99-df91-f3257dedd354"
      },
      "source": [
        "train_size, train_scores, test_scores = learning_curve(BAG, pca_features,pca_target,train_sizes=np.linspace(.1, 1.0, 5))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/_bagging.py:645: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/_bagging.py:645: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/_bagging.py:645: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/_bagging.py:645: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/_bagging.py:645: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/_bagging.py:645: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/_bagging.py:645: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/_bagging.py:645: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/_bagging.py:645: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/_bagging.py:645: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/_bagging.py:645: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/_bagging.py:645: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/_bagging.py:645: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/_bagging.py:645: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/_bagging.py:645: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/_bagging.py:645: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/_bagging.py:645: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/_bagging.py:645: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/_bagging.py:645: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/_bagging.py:645: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/_bagging.py:645: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/_bagging.py:645: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/_bagging.py:645: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/_bagging.py:645: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/_bagging.py:645: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "_pnKnBafjhb5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e6f4528e-41b1-4650-d2e2-ffbace8aca0b"
      },
      "source": [
        "train_size"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 266,  866, 1466, 2066, 2666])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 607
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "NjTI2XeHfBi2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "ec1b0dc9-e0d5-47c7-b7d2-97b1b90d7031"
      },
      "source": [
        "train_scores"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.94736842, 0.94736842, 0.95488722, 0.92481203, 0.94360902],\n",
              "       [0.97459584, 0.97921478, 0.97806005, 0.97228637, 0.96997691],\n",
              "       [0.97135061, 0.97748977, 0.9638472 , 0.96930423, 0.96248295],\n",
              "       [0.96805421, 0.96999032, 0.96708616, 0.96660213, 0.96805421],\n",
              "       [0.96624156, 0.96774194, 0.96249062, 0.96586647, 0.96436609]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 608
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "XO1YzEIMfBmY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "9a01acc9-7c52-447f-bbbd-5f50c99ba9a1"
      },
      "source": [
        "test_scores"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.85457271, 0.86206897, 0.85457271, 0.85735736, 0.86186186],\n",
              "       [0.89655172, 0.89205397, 0.90254873, 0.9039039 , 0.90690691],\n",
              "       [0.91754123, 0.92953523, 0.91454273, 0.92942943, 0.92792793],\n",
              "       [0.92503748, 0.92653673, 0.94152924, 0.92792793, 0.94294294],\n",
              "       [0.92503748, 0.92353823, 0.94602699, 0.94144144, 0.95645646]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 609
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "hVj8RYbFfBhX",
        "colab": {}
      },
      "source": [
        "def plot_learning_curve(train_sizes, train_scores, test_scores, title, alpha=0.1):\n",
        "    train_mean = np.mean(train_scores, axis=1)\n",
        "    train_std = np.std(train_scores, axis=1)\n",
        "    test_mean = np.mean(test_scores, axis=1)\n",
        "    test_std = np.std(test_scores, axis=1)\n",
        "    plt.plot(train_sizes, train_mean, label='train score', color='blue', marker='o')\n",
        "    plt.fill_between(train_sizes, train_mean + train_std,\n",
        "                     train_mean - train_std, color='blue', alpha=alpha)\n",
        "    plt.plot(train_sizes, test_mean, label='test score', color='red', marker='o')\n",
        "\n",
        "    plt.fill_between(train_sizes, test_mean + test_std, test_mean - test_std, color='red', alpha=alpha)\n",
        "    plt.title(title)\n",
        "    plt.xlabel('Number of training points')\n",
        "    plt.ylabel('f1-score')\n",
        "    plt.grid(ls='--')\n",
        "    plt.legend(loc='best')\n",
        "    plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "F84-XrzwkQq-",
        "colab": {}
      },
      "source": [
        "title= 'Performance Learning-Curve RandomForest Bagging Classifier'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "1Rpz3ViLfBcI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "6de6e3ea-488f-4b9c-877e-b304e4da4544"
      },
      "source": [
        "plot_learning_curve(train_size, train_scores, test_scores, title, alpha=0.1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "b9NBczNoCWJQ"
      },
      "source": [
        "\n",
        "\n",
        "A model of a given complexity will overfit a small dataset: this means the training score will be relatively high, while the validation score will be relatively low.\n",
        "\n",
        "The notable feature of the learning curve is the convergence to a particular score as the number of training samples grows. In particular, once you have enough points that a particular model has converged, adding more training data will not help you! The only way to increase model performance in this case is to use another (often more complex) model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "kXqxeBrQBuvP",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "param_grid = {\n",
        "    'bootstrap': [True],\n",
        "    'max_depth': [80, 90, 100, 110],\n",
        "    'max_features': [2, 3],\n",
        "    'min_samples_leaf': [3, 4, 5],\n",
        "    'min_samples_split': [8, 10, 12],\n",
        "    'n_estimators': [100, 200, 300, 1000]\n",
        "}\n",
        "\n",
        "# Instantiate the grid search model\n",
        "grid_search = GridSearchCV(estimator = rfclf, param_grid = param_grid, \n",
        "                          cv = 10, n_jobs = -1, verbose = 2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "9xLxcdrUCiyr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 646
        },
        "outputId": "a82b5d34-dea1-4447-933e-6226338ece89"
      },
      "source": [
        "grid_search.fit(pca_features_train,pca_target_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 10 folds for each of 288 candidates, totalling 2880 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  37 tasks      | elapsed:   43.5s\n",
            "[Parallel(n_jobs=-1)]: Done 158 tasks      | elapsed:  3.1min\n",
            "[Parallel(n_jobs=-1)]: Done 361 tasks      | elapsed:  7.1min\n",
            "[Parallel(n_jobs=-1)]: Done 644 tasks      | elapsed: 13.8min\n",
            "[Parallel(n_jobs=-1)]: Done 1009 tasks      | elapsed: 21.2min\n",
            "[Parallel(n_jobs=-1)]: Done 1454 tasks      | elapsed: 31.3min\n",
            "[Parallel(n_jobs=-1)]: Done 1981 tasks      | elapsed: 42.3min\n",
            "[Parallel(n_jobs=-1)]: Done 2588 tasks      | elapsed: 55.1min\n",
            "[Parallel(n_jobs=-1)]: Done 2880 out of 2880 | elapsed: 62.2min finished\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:739: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=10, error_score=nan,\n",
              "             estimator=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\n",
              "                                              class_weight='balanced',\n",
              "                                              criterion='gini', max_depth=7,\n",
              "                                              max_features='auto',\n",
              "                                              max_leaf_nodes=None,\n",
              "                                              max_samples=None,\n",
              "                                              min_impurity_decrease=0.0,\n",
              "                                              min_impurity_split=None,\n",
              "                                              min_samples_leaf=5,\n",
              "                                              min_samples_split=2,\n",
              "                                              min_weight_fraction_leaf=0.0,\n",
              "                                              n_estimators=300, n_jobs=None,\n",
              "                                              oob_score=False, random_state=42,\n",
              "                                              verbose=0, warm_start=False),\n",
              "             iid='deprecated', n_jobs=-1,\n",
              "             param_grid={'bootstrap': [True], 'max_depth': [80, 90, 100, 110],\n",
              "                         'max_features': [2, 3], 'min_samples_leaf': [3, 4, 5],\n",
              "                         'min_samples_split': [8, 10, 12],\n",
              "                         'n_estimators': [100, 200, 300, 1000]},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring=None, verbose=2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 614
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "B1TZh0WlUQ9h",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "16467b8b-ab20-42ee-a2d5-85784035623e"
      },
      "source": [
        "grid_search.score(pca_features_train,pca_target_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9892841834547793"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 615
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "v21Z3ZRhVlir",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4640d463-3f4c-48b1-ba8f-ca29b567e19e"
      },
      "source": [
        "grid_search.score(pca_features_test,pca_target_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.959"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 616
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "J4CqApBDcyIp"
      },
      "source": [
        "With Grid Search, after tuning hyper parameters the accuracy is recorded as 96% i.e increased by 2%.\n"
      ]
    }
  ]
}