[Project]_Classification_Analysis_with_Python.ipynb

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      "name": "[Project] Classification Analysis with Python",
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      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "## <font color='#2F4F4F'>1. Defining the Question</font>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XecOwPNorl2W"
      },
      "source": [
        "### a) Background and Problem Statement\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "You work as a Data Scientist for MTN Africa, a leading Telecommunications Company in\n",
        "Africa. In an effort to understand the behaviour of the company's customers, you are\n",
        "tasked to analyze relevant customer data and develop a solution that will help determine\n",
        "whether a customer will churn. Use the provided dataset to implement your solution. In\n",
        "addition, you will be expected to help relevant stakeholders understand the potential of\n",
        "your developed solution."
      ],
      "metadata": {
        "id": "-h700Wyx7RzS"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "### c) Metric for Success\n",
        "\n",
        "A model that will predict whether a customer will churn."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7KMRBJ7zr9HD"
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      "source": [
        "### d) Solution steps\n",
        "\n",
        "1. Data Importation\n",
        "2. Data Exploration\n",
        "3. Data Cleaning\n",
        "4. Data Analysis (Univariate and Bivariate)\n",
        "5. Data Preparation\n",
        "6. Data Modeling\n",
        "7. Model Evaluation\n",
        "8. Challenging your Solution\n",
        "9. Recommendations / Conclusion"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zSGyg6kWsBUl"
      },
      "source": [
        "### e) Data Relevance\n",
        "\n",
        "The provided data is relevant to the problem."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iUNbvIvnT7ep"
      },
      "source": [
        "## <font color='#2F4F4F'>2. Data Cleaning & Preparation</font>"
      ]
    },
    {
      "cell_type": "code",
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      "source": [
        "# load libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "%matplotlib inline\n",
        "\n",
        "# to display all columns\n",
        "pd.set_option('display.max.columns', None)\n",
        "\n",
        "# to display the entire contents of a cell\n",
        "pd.set_option('display.max_colwidth', None)\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": 1,
      "outputs": []
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      "source": [
        "# load and preview dataset\n",
        "df = pd.read_csv('/content/call-center-data-QueryResult.csv')\n",
        "df.sample(3)"
      ],
      "execution_count": 2,
      "outputs": [
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              "      recordid  customer_id  account_length  area_code  international_plan  \\\n",
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              "      total_intl_minutes  total_intl_calls  voice_mail_plan  \\\n",
              "9424                10.0                 3            False   \n",
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              "9989                10.7                 5             True   \n",
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              "      number_vmail_messages  total_day_minutes  total_day_calls  \\\n",
              "9424                      0              166.3               95   \n",
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              "9989                     28              105.9              132   \n",
              "\n",
              "      total_day_charge  total_eve_minutes  total_eve_calls  total_eve_charge  \\\n",
              "9424             28.27              239.3               87             20.34   \n",
              "5937             38.15              147.9               97             12.57   \n",
              "9989             18.00              231.7              107             19.69   \n",
              "\n",
              "      total_night_minutes  total_night_calls  total_night_charge  \\\n",
              "9424                123.2                108                5.54   \n",
              "5937                183.8                 74                8.27   \n",
              "9989                281.3                120               12.66   \n",
              "\n",
              "      total_intl_minutes_2  total_intl_calls_2  total_intl_charge  \\\n",
              "9424                  10.0                   3               2.70   \n",
              "5937                   6.7                   2               1.81   \n",
              "9989                  10.7                   5               2.89   \n",
              "\n",
              "      number_customer_service_calls  churn  \n",
              "9424                              2  False  \n",
              "5937                              2  False  \n",
              "9989                              1  False  "
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        "glossary = pd.read_csv('classification_analysis_glossary.csv',header = None)\n",
        "glossary"
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              "                                                                                         0\n",
              "0                                               account_length - Age of account in months.\n",
              "1                                             customer_id - Enterprise ID of the customer.\n",
              "2                                                                   area_code - Area code.\n",
              "3       international_plan - Rather or not the customer has an international calling plan.\n",
              "4                      voice_mail_plan - Rather or not the customer has a voice mail plan.\n",
              "5      number_vmail_messages - Number of VM messages customer currently has on the server.\n",
              "6                        total_day_minutes - Customers total usage of day minutes in plan.\n",
              "7                total_day_calls - Total number of calls customer has made during the day.\n",
              "8               total_day_charge - How much the customer has been charged for day minutes.\n",
              "9          total_eve_minutes - Total number of calls customer has made during the evening.\n",
              "10          total_eve_charge - How much the customer has been charged for evening minutes.\n",
              "11                   total_night_minutes - Customers total usage of night minutes in plan.\n",
              "12           total_night_calls - Total number of calls customer has made during the night.\n",
              "13          total_night_charge - How much the customer has been charged for night minutes.\n",
              "14                                       total_intl_minutes - Total international minutes.\n",
              "15                                           total_intl_calls - Total international calls.\n",
              "16                                        total_intl_charge - Total international charges.\n",
              "17  number_customer_service_calls - How many times the customer has called the IVR system.\n",
              "18                                                           churn - Customer has churned."
            ],
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              "      <th>1</th>\n",
              "      <td>customer_id - Enterprise ID of the customer.</td>\n",
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              "      <td>area_code - Area code.</td>\n",
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              "      <th>3</th>\n",
              "      <td>international_plan - Rather or not the customer has an international calling plan.</td>\n",
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              "      <th>4</th>\n",
              "      <td>voice_mail_plan - Rather or not the customer has a voice mail plan.</td>\n",
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              "      <th>6</th>\n",
              "      <td>total_day_minutes - Customers total usage of day minutes in plan.</td>\n",
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              "      <th>7</th>\n",
              "      <td>total_day_calls - Total number of calls customer has made during the day.</td>\n",
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              "      <td>total_day_charge - How much the customer has been charged for day minutes.</td>\n",
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              "      <th>10</th>\n",
              "      <td>total_eve_charge - How much the customer has been charged for evening minutes.</td>\n",
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              "      <th>11</th>\n",
              "      <td>total_night_minutes - Customers total usage of night minutes in plan.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>total_night_calls - Total number of calls customer has made during the night.</td>\n",
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              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>total_night_charge - How much the customer has been charged for night minutes.</td>\n",
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              "      <td>total_intl_minutes - Total international minutes.</td>\n",
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              "      <th>15</th>\n",
              "      <td>total_intl_calls - Total international calls.</td>\n",
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              "      <th>16</th>\n",
              "      <td>total_intl_charge - Total international charges.</td>\n",
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              "      <th>17</th>\n",
              "      <td>number_customer_service_calls - How many times the customer has called the IVR system.</td>\n",
              "    </tr>\n",
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              "      <th>18</th>\n",
              "      <td>churn - Customer has churned.</td>\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T3nO8Pr0asDa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "defa29ad-9d4a-4716-f54c-c30e41b0227b"
      },
      "source": [
        "# check dataset shape\n",
        "df.shape"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(12892, 23)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V0L7v6F2asDt"
      },
      "source": [
        "Our dataset has 12,892 records and 23 variables."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wj_MLc33asDv"
      },
      "source": [
        "We will drop 'recordid' and 'customer_id' since we have no need of them and they would interfere with our analysis."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OK4RrExBasD4"
      },
      "source": [
        "df.drop(columns = ['recordid', 'customer_id'], inplace = True)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t8WGzIueasEB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cab4a9c9-ac28-4e80-8c14-f5462d265778"
      },
      "source": [
        "# preview variable datatypes\n",
        "df.dtypes"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "account_length                     int64\n",
              "area_code                          int64\n",
              "international_plan                  bool\n",
              "total_intl_minutes               float64\n",
              "total_intl_calls                   int64\n",
              "voice_mail_plan                     bool\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_2             float64\n",
              "total_intl_calls_2                 int64\n",
              "total_intl_charge                float64\n",
              "number_customer_service_calls      int64\n",
              "churn                               bool\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W0swT5hLasEU"
      },
      "source": [
        "With the exception of the 'international_plan', 'voice_mail_plan', and 'churn' variables which are boolean, this dataset is numerical."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lncux1rmasEW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fc4bba5c-afdb-449f-ba1c-222bfcf79e91"
      },
      "source": [
        "# check for duplicates\n",
        "df.duplicated().sum()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7892"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sEH9EDx-asEZ"
      },
      "source": [
        "7,892 duplicated records are found. We will drop them."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UzmhHpehasEa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d52863d4-1805-4f9d-b86b-174bd02953ae"
      },
      "source": [
        "df = df.drop_duplicates()\n",
        "df.shape"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5000, 21)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7tOe_SdmasEd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4ba308f5-5267-480f-a400-af3d53b23c09"
      },
      "source": [
        "# check for missing values\n",
        "df.isna().sum()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "account_length                   0\n",
              "area_code                        0\n",
              "international_plan               0\n",
              "total_intl_minutes               0\n",
              "total_intl_calls                 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_2             0\n",
              "total_intl_calls_2               0\n",
              "total_intl_charge                0\n",
              "number_customer_service_calls    0\n",
              "churn                            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VUq1Soc9asEh"
      },
      "source": [
        "No missing values found. We will look at the unique values in each variable just to be safe."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sc73gdLSasEi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4e0bcd8e-dce1-45ef-8f05-d93607fd6610"
      },
      "source": [
        "columns = df.columns\n",
        "\n",
        "for col in columns:\n",
        "    print(\"Variable:\", col)\n",
        "    print(\"Number of unique values:\", df[col].nunique())\n",
        "    print(df[col].unique())\n",
        "    print()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Variable: account_length\n",
            "Number of unique values: 218\n",
            "[101 137 103  99 108 117  63  94 138 128 113 140 102  60  96 178  75 106\n",
            " 158 111  92  42  69  76  72 115  68  97  90  73  54 161 125 194 141  98\n",
            " 110  70  66  79  61  77 105 159  83  93 163 169  57  85 132 190  39  84\n",
            "  71 114 135  82  64 112 166 120 187 100  45  43  52  74  40  56 133 119\n",
            " 131 118 107 109 127  33 145  47  53 148  55 143 170  65 157  89  91 104\n",
            " 130  80 146  29  78 185 144  59 139  12 150 156  17 176  95 136 165 149\n",
            " 124  87  51 126  62  48  58  81  36 177  25 171 122  44 167  41 184  88\n",
            " 151 160 152  49  13 142   3  23  19 154 116 173  24  15  86 147  21 174\n",
            " 162 186  14   7   1  18  20  11 192 121 134  67 164 180  35 155  32 123\n",
            " 182 129  38  37  50  27 188 175   5  26   4  31 193 238  28 195 153 201\n",
            " 216 183 168 222 232 233  22  46  34   8 217   2 200 172  16  30 202 181\n",
            "  10   6 179 208 215 199 196 209 224 189 243 205   9 210 212 197 225 191\n",
            " 204 221]\n",
            "\n",
            "Variable: area_code\n",
            "Number of unique values: 3\n",
            "[510 408 415]\n",
            "\n",
            "Variable: international_plan\n",
            "Number of unique values: 2\n",
            "[False  True]\n",
            "\n",
            "Variable: total_intl_minutes\n",
            "Number of unique values: 170\n",
            "[10.6  9.5 13.7 15.7  7.7  6.9 12.9 11.1  9.9 11.9  9.  10.5  7.2  7.4\n",
            " 14.3 11.7 13.9  7.1 14.7  9.8  9.3 11.   5.2  9.2 10.1 12.3  9.1 12.6\n",
            " 10.  12.  10.7 13.4  8.3 13.6 12.5 10.2  4.4  8.6  6.8 11.8 16.2 13.2\n",
            "  8.  11.4 12.4 10.4  7.5 12.8 18.9  7.6  0.  11.3 13.8  7.   9.6  8.8\n",
            "  7.3 15.1  7.9 11.6  6.1  8.7 13.3 10.3  5.7 10.9  7.8  5.6 13.5  9.7\n",
            " 14.8  8.9  9.4  8.5 12.7 15.4  5.4 14.   8.2  6.6 11.5 12.1 13.1 11.2\n",
            "  8.4 14.9  6.   6.3  4.5  8.1  6.2  3.9  4.6 12.2 13.   6.7 15.6 14.2\n",
            " 18.7 10.8  5.8 15.   5.9  4.3  5.5 16.8 14.5  4.8  5.3  4.7 16.1 14.1\n",
            " 15.2 15.3 16.5 16.3  3.1  5.1  6.5  3.7  3.8 17.2  4.2  0.4 19.3 14.6\n",
            " 19.2  6.4 16.6  2.2 15.9  5.  14.4 15.5  4.9 19.7 16.  15.8 17.8  3.4\n",
            " 18.5  3.5  4.1 16.4  4.   1.1  2.  17.7 20.  17.6  2.7 18.  17.5 18.2\n",
            "  3.6 16.7  1.3 16.9 17.  17.1 17.9 17.3 18.4  2.9  3.3  2.6 18.3  2.1\n",
            "  2.4  2.5]\n",
            "\n",
            "Variable: total_intl_calls\n",
            "Number of unique values: 21\n",
            "[ 3  7  6  2  4  5  1  9  8 13  0 12 10 19 16 11 15 18 17 14 20]\n",
            "\n",
            "Variable: voice_mail_plan\n",
            "Number of unique values: 2\n",
            "[False  True]\n",
            "\n",
            "Variable: number_vmail_messages\n",
            "Number of unique values: 48\n",
            "[ 0 29 32 43 39 22 25 35 31 38 41  6 37 30 40 33 36 12 21 46 27 20 18 24\n",
            " 28 34 15 26 45 49 19 17 16 14 23 42 10 47 48 52 44 51 11  8  4 13 50  9]\n",
            "\n",
            "Variable: total_day_minutes\n",
            "Number of unique values: 1961\n",
            "[ 70.9 223.6 294.7 ... 224.1 124.9 321.1]\n",
            "\n",
            "Variable: total_day_calls\n",
            "Number of unique values: 123\n",
            "[123  86  95  78  85 124  97 117 100  77 109  93  66 105 121 142  88  99\n",
            " 101 111  94 110 125 106  74 131 112 104  76 113 122 127  65  91  48 120\n",
            " 102 103  81 108  83  56 118  75  57 132 114  92 126  98 119  87  82  96\n",
            " 107  90  64 133  89 115 147  58  72  80  53 128 130 149  79  68 143 141\n",
            "  61 116 148  84  62  60  51  73 135 137 151 136 134  50  71  70 129  52\n",
            " 138  69  59  67 144  63  47  34  39 156 157 146  44  46 140 152 139 160\n",
            "  49 145  55 158 163  54  36  40 150 165  30  42   0  45  35]\n",
            "\n",
            "Variable: total_day_charge\n",
            "Number of unique values: 1961\n",
            "[12.05 38.01 50.1  ... 38.1  21.23 54.59]\n",
            "\n",
            "Variable: total_eve_minutes\n",
            "Number of unique values: 1879\n",
            "[211.9 244.8 237.3 ... 153.4 288.8 265.9]\n",
            "\n",
            "Variable: total_eve_calls\n",
            "Number of unique values: 126\n",
            "[ 73 139 105  88 101  68 125 112  46  89  90 116  97 107 100  81 108 104\n",
            " 106 120  71  70 131 128  91 109 126  75  87  78  94 127  62 103 110  96\n",
            " 136  67 148 117  95  98  92 111  99  63 119 123  93 102  86 121  56 118\n",
            " 114 133  76  79  65  85  72 113 115 132  84 143 130  83 124 142 150  74\n",
            " 129 138  61 122  80  77 134  82 151 147  58  69 141  66  55  64  47 144\n",
            " 169 149  53 140 152  54 154  60 135 145 155  51  52 137  38  57  44 146\n",
            "  49 164  59  48 168  42  37  12  50 157  45  36 156 153  43   0 159 170]\n",
            "\n",
            "Variable: total_eve_charge\n",
            "Number of unique values: 1659\n",
            "[18.01 20.81 20.17 ... 13.04 24.55 22.6 ]\n",
            "\n",
            "Variable: total_night_minutes\n",
            "Number of unique values: 1853\n",
            "[236.   94.2 300.3 ... 131.9 280.9 279.1]\n",
            "\n",
            "Variable: total_night_calls\n",
            "Number of unique values: 131\n",
            "[ 73  81 127  82 107  90 120 106  71  92 100  94 124 119  89 114  99  98\n",
            "  95 104 121  80 125  78 105  96 103 115  79 101 132  72  85 110  83  84\n",
            " 109 118 134 116  64 113  88 123 135  56  68 148  76  93  91  97 141 102\n",
            " 139 136 131  58 129 112  49  48 128 122  77 151 145 108 130 117  69  53\n",
            "  66  40  87  70  75 111 168 149 137 161 133  67 138  65  60  74 126  61\n",
            " 150 143 140 146 159 142 160  57  86 170 158  63  62  52 147 154  42  41\n",
            "  59  50  38  46 155  12  55 165  54  43  51   0 152  44 153 175 157 144\n",
            " 166  33 156  36 164]\n",
            "\n",
            "Variable: total_night_charge\n",
            "Number of unique values: 1028\n",
            "[10.62  4.24 13.51 ...  5.12  5.65 12.37]\n",
            "\n",
            "Variable: total_intl_minutes_2\n",
            "Number of unique values: 170\n",
            "[10.6  9.5 13.7 15.7  7.7  6.9 12.9 11.1  9.9 11.9  9.  10.5  7.2  7.4\n",
            " 14.3 11.7 13.9  7.1 14.7  9.8  9.3 11.   5.2  9.2 10.1 12.3  9.1 12.6\n",
            " 10.  12.  10.7 13.4  8.3 13.6 12.5 10.2  4.4  8.6  6.8 11.8 16.2 13.2\n",
            "  8.  11.4 12.4 10.4  7.5 12.8 18.9  7.6  0.  11.3 13.8  7.   9.6  8.8\n",
            "  7.3 15.1  7.9 11.6  6.1  8.7 13.3 10.3  5.7 10.9  7.8  5.6 13.5  9.7\n",
            " 14.8  8.9  9.4  8.5 12.7 15.4  5.4 14.   8.2  6.6 11.5 12.1 13.1 11.2\n",
            "  8.4 14.9  6.   6.3  4.5  8.1  6.2  3.9  4.6 12.2 13.   6.7 15.6 14.2\n",
            " 18.7 10.8  5.8 15.   5.9  4.3  5.5 16.8 14.5  4.8  5.3  4.7 16.1 14.1\n",
            " 15.2 15.3 16.5 16.3  3.1  5.1  6.5  3.7  3.8 17.2  4.2  0.4 19.3 14.6\n",
            " 19.2  6.4 16.6  2.2 15.9  5.  14.4 15.5  4.9 19.7 16.  15.8 17.8  3.4\n",
            " 18.5  3.5  4.1 16.4  4.   1.1  2.  17.7 20.  17.6  2.7 18.  17.5 18.2\n",
            "  3.6 16.7  1.3 16.9 17.  17.1 17.9 17.3 18.4  2.9  3.3  2.6 18.3  2.1\n",
            "  2.4  2.5]\n",
            "\n",
            "Variable: total_intl_calls_2\n",
            "Number of unique values: 21\n",
            "[ 3  7  6  2  4  5  1  9  8 13  0 12 10 19 16 11 15 18 17 14 20]\n",
            "\n",
            "Variable: total_intl_charge\n",
            "Number of unique values: 170\n",
            "[2.86 2.57 3.7  4.24 2.08 1.86 3.48 3.   2.67 3.21 2.43 2.84 1.94 2.\n",
            " 3.86 3.16 3.75 1.92 3.97 2.65 2.51 2.97 1.4  2.48 2.73 3.32 2.46 3.4\n",
            " 2.7  3.24 2.89 3.62 2.24 3.67 3.38 2.75 1.19 2.32 1.84 3.19 4.37 3.56\n",
            " 2.16 3.08 3.35 2.81 2.03 3.46 5.1  2.05 0.   3.05 3.73 1.89 2.59 2.38\n",
            " 1.97 4.08 2.13 3.13 1.65 2.35 3.59 2.78 1.54 2.94 2.11 1.51 3.65 2.62\n",
            " 4.   2.4  2.54 2.3  3.43 4.16 1.46 3.78 2.21 1.78 3.11 3.27 3.54 3.02\n",
            " 2.27 4.02 1.62 1.7  1.22 2.19 1.67 1.05 1.24 3.29 3.51 1.81 4.21 3.83\n",
            " 5.05 2.92 1.57 4.05 1.59 1.16 1.49 4.54 3.92 1.3  1.43 1.27 4.35 3.81\n",
            " 4.1  4.13 4.46 4.4  0.84 1.38 1.76 1.   1.03 4.64 1.13 0.11 5.21 3.94\n",
            " 5.18 1.73 4.48 0.59 4.29 1.35 3.89 4.19 1.32 5.32 4.32 4.27 4.81 0.92\n",
            " 5.   0.95 1.11 4.43 1.08 0.3  0.54 4.78 5.4  4.75 0.73 4.86 4.73 4.91\n",
            " 0.97 4.51 0.35 4.56 4.59 4.62 4.83 4.67 4.97 0.78 0.89 0.7  4.94 0.57\n",
            " 0.65 0.68]\n",
            "\n",
            "Variable: number_customer_service_calls\n",
            "Number of unique values: 10\n",
            "[3 0 1 2 4 5 6 7 9 8]\n",
            "\n",
            "Variable: churn\n",
            "Number of unique values: 2\n",
            "[False  True]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hFhi0sj6asEm"
      },
      "source": [
        "We can confirm that there are no missing values in this dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W2ztYk64asEq"
      },
      "source": [
        "An anomaly has been noted: there appears to be duplicated columns between 'total_intl_minutes' and 'total_intl_minutes_2', and 'total_intl_calls' and 'total_intl_calls_2'. Let's preview them:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "44g9GeRSasEr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "ebbe4454-5807-4a69-df5b-8918197a0e6c"
      },
      "source": [
        "# previewing the possibly duplicated columns\n",
        "df[['total_intl_minutes', 'total_intl_calls', 'total_intl_minutes_2', 'total_intl_calls_2']]"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      total_intl_minutes  total_intl_calls  total_intl_minutes_2  \\\n",
              "0                   10.6                 3                  10.6   \n",
              "1                    9.5                 7                   9.5   \n",
              "2                   13.7                 6                  13.7   \n",
              "3                   15.7                 2                  15.7   \n",
              "4                    7.7                 4                   7.7   \n",
              "...                  ...               ...                   ...   \n",
              "4995                 9.9                 6                   9.9   \n",
              "4996                 9.6                 4                   9.6   \n",
              "4997                14.1                 6                  14.1   \n",
              "4998                 5.0                10                   5.0   \n",
              "4999                13.7                 4                  13.7   \n",
              "\n",
              "      total_intl_calls_2  \n",
              "0                      3  \n",
              "1                      7  \n",
              "2                      6  \n",
              "3                      2  \n",
              "4                      4  \n",
              "...                  ...  \n",
              "4995                   6  \n",
              "4996                   4  \n",
              "4997                   6  \n",
              "4998                  10  \n",
              "4999                   4  \n",
              "\n",
              "[5000 rows x 4 columns]"
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              "<p>5000 rows × 4 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1g82qmkGasEx"
      },
      "source": [
        "The last two columns appear to be complete copies of the first two. We will confirm this so that we do not blindly drop them."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gZsoHsPbasE0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "99ad4e36-60bb-46cf-ff97-dfa53f9b11fc"
      },
      "source": [
        "# selecting the total number of records where the values of 'total_intl_minutes' are equal to the values of \n",
        "# 'total_intl_minutes_2', AND the values of 'total_intl_calls' are equal to the values of 'total_intl_calls_2'\n",
        "df[(df['total_intl_minutes'] == df['total_intl_minutes_2']) & (df['total_intl_calls'] == df['total_intl_calls_2'])].count()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "account_length                   5000\n",
              "area_code                        5000\n",
              "international_plan               5000\n",
              "total_intl_minutes               5000\n",
              "total_intl_calls                 5000\n",
              "voice_mail_plan                  5000\n",
              "number_vmail_messages            5000\n",
              "total_day_minutes                5000\n",
              "total_day_calls                  5000\n",
              "total_day_charge                 5000\n",
              "total_eve_minutes                5000\n",
              "total_eve_calls                  5000\n",
              "total_eve_charge                 5000\n",
              "total_night_minutes              5000\n",
              "total_night_calls                5000\n",
              "total_night_charge               5000\n",
              "total_intl_minutes_2             5000\n",
              "total_intl_calls_2               5000\n",
              "total_intl_charge                5000\n",
              "number_customer_service_calls    5000\n",
              "churn                            5000\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rVFYq4WoasE7"
      },
      "source": [
        "We see that the columns are indeed duplicates so we can safely drop them."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AqsIQOJ7asE8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "31837794-53a5-44bd-cf7c-1ea1f35ea4a4"
      },
      "source": [
        "df = df.drop(columns = ['total_intl_minutes_2', 'total_intl_calls_2'])\n",
        "df.shape"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5000, 19)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xNmGe1f1asFW"
      },
      "source": [
        "Another anomaly noted is that where there are records of 'total_intl_minutes', 'total_int_calls', and 'total_intl_charge' when 'international_plan' is False."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RLfEw2Q0asFX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "b2dafbfb-e568-45a3-f78f-e58bd803c1c6"
      },
      "source": [
        "df[(df['international_plan'] == False) & ((df['total_intl_minutes'] > 0) | (df['total_intl_calls'] > 0) |\n",
        "                                         df['total_intl_charge'] > 0)]"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      account_length  area_code  international_plan  total_intl_minutes  \\\n",
              "0                101        510               False                10.6   \n",
              "1                137        510               False                 9.5   \n",
              "2                103        408               False                13.7   \n",
              "3                 99        415               False                15.7   \n",
              "4                108        415               False                 7.7   \n",
              "...              ...        ...                 ...                 ...   \n",
              "4994              79        415               False                11.8   \n",
              "4995             192        415               False                 9.9   \n",
              "4996              68        415               False                 9.6   \n",
              "4997              28        510               False                14.1   \n",
              "4999              74        415               False                13.7   \n",
              "\n",
              "      total_intl_calls  voice_mail_plan  number_vmail_messages  \\\n",
              "0                    3            False                      0   \n",
              "1                    7            False                      0   \n",
              "2                    6             True                     29   \n",
              "3                    2            False                      0   \n",
              "4                    4            False                      0   \n",
              "...                ...              ...                    ...   \n",
              "4994                 5            False                      0   \n",
              "4995                 6             True                     36   \n",
              "4996                 4            False                      0   \n",
              "4997                 6            False                      0   \n",
              "4999                 4             True                     25   \n",
              "\n",
              "      total_day_minutes  total_day_calls  total_day_charge  total_eve_minutes  \\\n",
              "0                  70.9              123             12.05              211.9   \n",
              "1                 223.6               86             38.01              244.8   \n",
              "2                 294.7               95             50.10              237.3   \n",
              "3                 216.8              123             36.86              126.4   \n",
              "4                 197.4               78             33.56              124.0   \n",
              "...                 ...              ...               ...                ...   \n",
              "4994              134.7               98             22.90              189.7   \n",
              "4995              156.2               77             26.55              215.5   \n",
              "4996              231.1               57             39.29              153.4   \n",
              "4997              180.8              109             30.74              288.8   \n",
              "4999              234.4              113             39.85              265.9   \n",
              "\n",
              "      total_eve_calls  total_eve_charge  total_night_minutes  \\\n",
              "0                  73             18.01                236.0   \n",
              "1                 139             20.81                 94.2   \n",
              "2                 105             20.17                300.3   \n",
              "3                  88             10.74                220.6   \n",
              "4                 101             10.54                204.5   \n",
              "...               ...               ...                  ...   \n",
              "4994               68             16.12                221.4   \n",
              "4995              126             18.32                279.1   \n",
              "4996               55             13.04                191.3   \n",
              "4997               58             24.55                191.9   \n",
              "4999               82             22.60                241.4   \n",
              "\n",
              "      total_night_calls  total_night_charge  total_intl_charge  \\\n",
              "0                    73               10.62               2.86   \n",
              "1                    81                4.24               2.57   \n",
              "2                   127               13.51               3.70   \n",
              "3                    82                9.93               4.24   \n",
              "4                   107                9.20               2.08   \n",
              "...                 ...                 ...                ...   \n",
              "4994                128                9.96               3.19   \n",
              "4995                 83               12.56               2.67   \n",
              "4996                123                8.61               2.59   \n",
              "4997                 91                8.64               3.81   \n",
              "4999                 77               10.86               3.70   \n",
              "\n",
              "      number_customer_service_calls  churn  \n",
              "0                                 3  False  \n",
              "1                                 0  False  \n",
              "2                                 1  False  \n",
              "3                                 1  False  \n",
              "4                                 2  False  \n",
              "...                             ...    ...  \n",
              "4994                              2  False  \n",
              "4995                              2  False  \n",
              "4996                              3  False  \n",
              "4997                              2  False  \n",
              "4999                              0  False  \n",
              "\n",
              "[4504 rows x 19 columns]"
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              "      <td>95</td>\n",
              "      <td>50.10</td>\n",
              "      <td>237.3</td>\n",
              "      <td>105</td>\n",
              "      <td>20.17</td>\n",
              "      <td>300.3</td>\n",
              "      <td>127</td>\n",
              "      <td>13.51</td>\n",
              "      <td>3.70</td>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>99</td>\n",
              "      <td>415</td>\n",
              "      <td>False</td>\n",
              "      <td>15.7</td>\n",
              "      <td>2</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "      <td>216.8</td>\n",
              "      <td>123</td>\n",
              "      <td>36.86</td>\n",
              "      <td>126.4</td>\n",
              "      <td>88</td>\n",
              "      <td>10.74</td>\n",
              "      <td>220.6</td>\n",
              "      <td>82</td>\n",
              "      <td>9.93</td>\n",
              "      <td>4.24</td>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>108</td>\n",
              "      <td>415</td>\n",
              "      <td>False</td>\n",
              "      <td>7.7</td>\n",
              "      <td>4</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "      <td>197.4</td>\n",
              "      <td>78</td>\n",
              "      <td>33.56</td>\n",
              "      <td>124.0</td>\n",
              "      <td>101</td>\n",
              "      <td>10.54</td>\n",
              "      <td>204.5</td>\n",
              "      <td>107</td>\n",
              "      <td>9.20</td>\n",
              "      <td>2.08</td>\n",
              "      <td>2</td>\n",
              "      <td>False</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4994</th>\n",
              "      <td>79</td>\n",
              "      <td>415</td>\n",
              "      <td>False</td>\n",
              "      <td>11.8</td>\n",
              "      <td>5</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "      <td>134.7</td>\n",
              "      <td>98</td>\n",
              "      <td>22.90</td>\n",
              "      <td>189.7</td>\n",
              "      <td>68</td>\n",
              "      <td>16.12</td>\n",
              "      <td>221.4</td>\n",
              "      <td>128</td>\n",
              "      <td>9.96</td>\n",
              "      <td>3.19</td>\n",
              "      <td>2</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4995</th>\n",
              "      <td>192</td>\n",
              "      <td>415</td>\n",
              "      <td>False</td>\n",
              "      <td>9.9</td>\n",
              "      <td>6</td>\n",
              "      <td>True</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>2.67</td>\n",
              "      <td>2</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4996</th>\n",
              "      <td>68</td>\n",
              "      <td>415</td>\n",
              "      <td>False</td>\n",
              "      <td>9.6</td>\n",
              "      <td>4</td>\n",
              "      <td>False</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>2.59</td>\n",
              "      <td>3</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4997</th>\n",
              "      <td>28</td>\n",
              "      <td>510</td>\n",
              "      <td>False</td>\n",
              "      <td>14.1</td>\n",
              "      <td>6</td>\n",
              "      <td>False</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>3.81</td>\n",
              "      <td>2</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4999</th>\n",
              "      <td>74</td>\n",
              "      <td>415</td>\n",
              "      <td>False</td>\n",
              "      <td>13.7</td>\n",
              "      <td>4</td>\n",
              "      <td>True</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>3.70</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4504 rows × 19 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cb7820cc-c1fb-44bc-81bf-aa263edb2095')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FyVdZxRUasFc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "64c0a59b-8140-49db-e137-98e470ed052d"
      },
      "source": [
        "df.international_plan.value_counts()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False    4527\n",
              "True      473\n",
              "Name: international_plan, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s6KzIz33asFf"
      },
      "source": [
        "Much as we'd like to remove these invalid variables, doing so would result in a huge loss of data. We will therefore leave them as is, but flag them for future work."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jO0WlCpaasFi"
      },
      "source": [
        "We will check to confirm that there are no 'number_vmail_messages' when 'voice_mail_plan' is set to False."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Vqb2AsjasFi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113
        },
        "outputId": "3bc74e43-bbc0-4a16-825b-e875ef3aad2d"
      },
      "source": [
        "df[(df['voice_mail_plan'] == False) & (df['number_vmail_messages'] > 0)]"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [account_length, area_code, international_plan, total_intl_minutes, total_intl_calls, voice_mail_plan, 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_charge, number_customer_service_calls, churn]\n",
              "Index: []"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-54f6826b-df49-4aba-a80e-c8cf7d6b732c\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>account_length</th>\n",
              "      <th>area_code</th>\n",
              "      <th>international_plan</th>\n",
              "      <th>total_intl_minutes</th>\n",
              "      <th>total_intl_calls</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_charge</th>\n",
              "      <th>number_customer_service_calls</th>\n",
              "      <th>churn</th>\n",
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              "\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "neiYQ2LgasFm"
      },
      "source": [
        "After confirming this, we can now drop the 'voice_mail_plan' column."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rbzi1m9EasFm"
      },
      "source": [
        "df.drop(columns = ['voice_mail_plan'], inplace = True)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Fwke7e9asFu",
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0fcca694-c846-417b-cbcc-245bb8e66397"
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "account_length                     int64\n",
              "area_code                          int64\n",
              "international_plan                  bool\n",
              "total_intl_minutes               float64\n",
              "total_intl_calls                   int64\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_charge                float64\n",
              "number_customer_service_calls      int64\n",
              "churn                               bool\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
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        "id": "aCas6x9RasF1",
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        },
        "outputId": "3cf3087d-1bba-4a5a-a404-bb8dc0626811"
      },
      "source": [
        "# looking for outliers\n",
        "num_cols = df.columns.to_list()\n",
        "num_cols.remove('international_plan')\n",
        "num_cols.remove('churn')\n",
        "\n",
        "plt.figure(figsize = (14, 6))\n",
        "df.boxplot(num_cols)\n",
        "plt.xticks(rotation = 45)\n",
        "plt.show()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iSHmG33KasF4"
      },
      "source": [
        "We see that all the remaining variables have outliers, which we will not drop."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mELIvxYqasF8"
      },
      "source": [
        "We now save our clean dataset to a new CSV file."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t6cDaIdSasF9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "outputId": "40064372-1392-4279-fd48-c3d899f5712c"
      },
      "source": [
        "# save the data set to a clean CSV file\n",
        "\n",
        "df.to_csv('call_center_clean.csv', index = False)\n",
        "\n",
        "df = pd.read_csv('call_center_clean.csv')\n",
        "df.head()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   account_length  area_code  international_plan  total_intl_minutes  \\\n",
              "0             101        510               False                10.6   \n",
              "1             137        510               False                 9.5   \n",
              "2             103        408               False                13.7   \n",
              "3              99        415               False                15.7   \n",
              "4             108        415               False                 7.7   \n",
              "\n",
              "   total_intl_calls  number_vmail_messages  total_day_minutes  \\\n",
              "0                 3                      0               70.9   \n",
              "1                 7                      0              223.6   \n",
              "2                 6                     29              294.7   \n",
              "3                 2                      0              216.8   \n",
              "4                 4                      0              197.4   \n",
              "\n",
              "   total_day_calls  total_day_charge  total_eve_minutes  total_eve_calls  \\\n",
              "0              123             12.05              211.9               73   \n",
              "1               86             38.01              244.8              139   \n",
              "2               95             50.10              237.3              105   \n",
              "3              123             36.86              126.4               88   \n",
              "4               78             33.56              124.0              101   \n",
              "\n",
              "   total_eve_charge  total_night_minutes  total_night_calls  \\\n",
              "0             18.01                236.0                 73   \n",
              "1             20.81                 94.2                 81   \n",
              "2             20.17                300.3                127   \n",
              "3             10.74                220.6                 82   \n",
              "4             10.54                204.5                107   \n",
              "\n",
              "   total_night_charge  total_intl_charge  number_customer_service_calls  churn  \n",
              "0               10.62               2.86                              3  False  \n",
              "1                4.24               2.57                              0  False  \n",
              "2               13.51               3.70                              1  False  \n",
              "3                9.93               4.24                              1  False  \n",
              "4                9.20               2.08                              2  False  "
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          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eU3KA88JasGD"
      },
      "source": [
        "## <font color='#2F4F4F'>3. Data Analysis</font>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8AC0Ib2lasGE"
      },
      "source": [
        "### 3.1 Univariate Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mF2m8qlfasGE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "581ec5ad-8bad-43f3-b69c-be675e459ba2"
      },
      "source": [
        "# get the summary statistics\n",
        "df.describe()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       account_length    area_code  total_intl_minutes  total_intl_calls  \\\n",
              "count      5000.00000  5000.000000         5000.000000       5000.000000   \n",
              "mean        100.25860   436.911400           10.261780          4.435200   \n",
              "std          39.69456    42.209182            2.761396          2.456788   \n",
              "min           1.00000   408.000000            0.000000          0.000000   \n",
              "25%          73.00000   408.000000            8.500000          3.000000   \n",
              "50%         100.00000   415.000000           10.300000          4.000000   \n",
              "75%         127.00000   415.000000           12.000000          6.000000   \n",
              "max         243.00000   510.000000           20.000000         20.000000   \n",
              "\n",
              "       number_vmail_messages  total_day_minutes  total_day_calls  \\\n",
              "count            5000.000000        5000.000000      5000.000000   \n",
              "mean                7.755200         180.288900       100.029400   \n",
              "std                13.546393          53.894699        19.831197   \n",
              "min                 0.000000           0.000000         0.000000   \n",
              "25%                 0.000000         143.700000        87.000000   \n",
              "50%                 0.000000         180.100000       100.000000   \n",
              "75%                17.000000         216.200000       113.000000   \n",
              "max                52.000000         351.500000       165.000000   \n",
              "\n",
              "       total_day_charge  total_eve_minutes  total_eve_calls  total_eve_charge  \\\n",
              "count       5000.000000        5000.000000      5000.000000       5000.000000   \n",
              "mean          30.649668         200.636560       100.191000         17.054322   \n",
              "std            9.162069          50.551309        19.826496          4.296843   \n",
              "min            0.000000           0.000000         0.000000          0.000000   \n",
              "25%           24.430000         166.375000        87.000000         14.140000   \n",
              "50%           30.620000         201.000000       100.000000         17.090000   \n",
              "75%           36.750000         234.100000       114.000000         19.900000   \n",
              "max           59.760000         363.700000       170.000000         30.910000   \n",
              "\n",
              "       total_night_minutes  total_night_calls  total_night_charge  \\\n",
              "count          5000.000000        5000.000000         5000.000000   \n",
              "mean            200.391620          99.919200            9.017732   \n",
              "std              50.527789          19.958686            2.273763   \n",
              "min               0.000000           0.000000            0.000000   \n",
              "25%             166.900000          87.000000            7.510000   \n",
              "50%             200.400000         100.000000            9.020000   \n",
              "75%             234.700000         113.000000           10.560000   \n",
              "max             395.000000         175.000000           17.770000   \n",
              "\n",
              "       total_intl_charge  number_customer_service_calls  \n",
              "count        5000.000000                    5000.000000  \n",
              "mean            2.771196                       1.570400  \n",
              "std             0.745514                       1.306363  \n",
              "min             0.000000                       0.000000  \n",
              "25%             2.300000                       1.000000  \n",
              "50%             2.780000                       1.000000  \n",
              "75%             3.240000                       2.000000  \n",
              "max             5.400000                       9.000000  "
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th>account_length</th>\n",
              "      <th>area_code</th>\n",
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              "      <th>total_night_calls</th>\n",
              "      <th>total_night_charge</th>\n",
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              "      <th>number_customer_service_calls</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>5000.00000</td>\n",
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              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>100.25860</td>\n",
              "      <td>436.911400</td>\n",
              "      <td>10.261780</td>\n",
              "      <td>4.435200</td>\n",
              "      <td>7.755200</td>\n",
              "      <td>180.288900</td>\n",
              "      <td>100.029400</td>\n",
              "      <td>30.649668</td>\n",
              "      <td>200.636560</td>\n",
              "      <td>100.191000</td>\n",
              "      <td>17.054322</td>\n",
              "      <td>200.391620</td>\n",
              "      <td>99.919200</td>\n",
              "      <td>9.017732</td>\n",
              "      <td>2.771196</td>\n",
              "      <td>1.570400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>39.69456</td>\n",
              "      <td>42.209182</td>\n",
              "      <td>2.761396</td>\n",
              "      <td>2.456788</td>\n",
              "      <td>13.546393</td>\n",
              "      <td>53.894699</td>\n",
              "      <td>19.831197</td>\n",
              "      <td>9.162069</td>\n",
              "      <td>50.551309</td>\n",
              "      <td>19.826496</td>\n",
              "      <td>4.296843</td>\n",
              "      <td>50.527789</td>\n",
              "      <td>19.958686</td>\n",
              "      <td>2.273763</td>\n",
              "      <td>0.745514</td>\n",
              "      <td>1.306363</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.00000</td>\n",
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              "      <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>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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>73.00000</td>\n",
              "      <td>408.000000</td>\n",
              "      <td>8.500000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>143.700000</td>\n",
              "      <td>87.000000</td>\n",
              "      <td>24.430000</td>\n",
              "      <td>166.375000</td>\n",
              "      <td>87.000000</td>\n",
              "      <td>14.140000</td>\n",
              "      <td>166.900000</td>\n",
              "      <td>87.000000</td>\n",
              "      <td>7.510000</td>\n",
              "      <td>2.300000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>100.00000</td>\n",
              "      <td>415.000000</td>\n",
              "      <td>10.300000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>180.100000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>30.620000</td>\n",
              "      <td>201.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>17.090000</td>\n",
              "      <td>200.400000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>9.020000</td>\n",
              "      <td>2.780000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>127.00000</td>\n",
              "      <td>415.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>216.200000</td>\n",
              "      <td>113.000000</td>\n",
              "      <td>36.750000</td>\n",
              "      <td>234.100000</td>\n",
              "      <td>114.000000</td>\n",
              "      <td>19.900000</td>\n",
              "      <td>234.700000</td>\n",
              "      <td>113.000000</td>\n",
              "      <td>10.560000</td>\n",
              "      <td>3.240000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>243.00000</td>\n",
              "      <td>510.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>351.500000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>59.760000</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>5.400000</td>\n",
              "      <td>9.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bf66af55-aafb-4e8c-b41d-0b146ebb718d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-bf66af55-aafb-4e8c-b41d-0b146ebb718d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-bf66af55-aafb-4e8c-b41d-0b146ebb718d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r_2FDdrCasGP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "outputId": "ca57554c-031f-4390-feed-636011d2aab5"
      },
      "source": [
        "print(df.area_code.value_counts())\n",
        "\n",
        "plt.figure(figsize = (6, 6))\n",
        "df.area_code.value_counts().plot(kind = 'pie', autopct = '%1.1f%%')\n",
        "plt.title('Pie Chart of Area Code')\n",
        "plt.show()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "415    2495\n",
            "408    1259\n",
            "510    1246\n",
            "Name: area_code, dtype: int64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XUTXcPj8asGT"
      },
      "source": [
        "Area Code 415 consists of almost half of the area codes in this dataset. Area Code 510 very slightly outnumbers Area Code 408."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3dS-KVIkasGU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "outputId": "2e000ed4-2473-4728-ee92-38ba4606911f"
      },
      "source": [
        "print(df.international_plan.value_counts())\n",
        "\n",
        "plt.figure(figsize = (6, 6))\n",
        "df.international_plan.value_counts().plot(kind = 'bar', rot = 0, color = ['skyblue', 'darkorange'])\n",
        "plt.title('Distribution of International Plan')\n",
        "plt.xlabel('International Plan')\n",
        "plt.show()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "False    4527\n",
            "True      473\n",
            "Name: international_plan, dtype: int64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XnAsY6f8asGY"
      },
      "source": [
        "Very few of the customers are subscribed to an international plan."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bXumquNYasGZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 688
        },
        "outputId": "4c203d02-ce13-46b4-8e11-b8d1b7808328"
      },
      "source": [
        "print(df.number_customer_service_calls.value_counts())\n",
        "\n",
        "plt.figure(figsize = (8, 8))\n",
        "df.number_customer_service_calls.value_counts().plot(kind = 'bar', rot = 0)\n",
        "plt.xlabel(\"Number of Calls to Customer Service\")\n",
        "plt.show()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1    1786\n",
            "2    1127\n",
            "0    1023\n",
            "3     665\n",
            "4     252\n",
            "5      96\n",
            "6      34\n",
            "7      13\n",
            "9       2\n",
            "8       2\n",
            "Name: number_customer_service_calls, dtype: int64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kkP0E4MbasGf"
      },
      "source": [
        "Most customers made exactly 1 call to customer service followed by those who made 2 calls, and then those who made 0 calls. Those who made more than 5 calls make up the minority."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k5GGY5BbasGf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 440
        },
        "outputId": "38b980e7-bf9d-4034-a099-ca148de1712f"
      },
      "source": [
        "print(df.churn.value_counts())\n",
        "\n",
        "plt.figure(figsize = (6, 6))\n",
        "df.churn.value_counts().plot(kind ='bar', rot = 0, color = ['darkgreen', 'darkred'])\n",
        "plt.xlabel(\"Churn\")\n",
        "plt.show()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "False    4293\n",
            "True      707\n",
            "Name: churn, dtype: int64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAFzCAYAAADSc9khAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAQmUlEQVR4nO3df4xlZX3H8fdHVtDUVFCmSHbRRaWx2FTFLdISW5UW8EcEU7VYLVtDuv/QRNNqK40J/qLVxoqaVC0KcTVGRK1CLQndgMa0UWBRFIFSVoWyRGFxgZaqlF2//WOe1csyszPLzt5h9/t+JZM55znn3nlucvd9T86cOZuqQpLUw6OWewKSpOkx+pLUiNGXpEaMviQ1YvQlqRGjL0mNrFjuCezKoYceWqtXr17uaUjSPuWaa665q6pm5tr2iI7+6tWr2bhx43JPQ5L2KUlunW+bp3ckqRGjL0mNGH1JasToS1IjRl+SGjH6ktSI0ZekRoy+JDVi9CWpEaMvSY0YfUlqxOhLUiNGX5IaeUTfZXNfkT/Nck9hv1IfreWegrTf8khfkhox+pLUiNGXpEaMviQ1YvQlqRGjL0mNGH1JasToS1IjRl+SGjH6ktSI0ZekRoy+JDVi9CWpEaMvSY0YfUlqxOhLUiNGX5IaWXT0kxyQ5JtJvjTWj0xyZZJNST6T5MAxftBY3zS2r554jrPG+E1JTlrqFyNJ2rXdOdJ/A3DjxPp7gHOr6unA3cAZY/wM4O4xfu7YjyRHA6cBzwROBj6U5IA9m74kaXcsKvpJVgEvBT421gO8CPjc2GU9cOpYPmWsM7afMPY/Bbiwqu6vqu8Dm4Bjl+JFSJIWZ7FH+u8H/hL42Vh/InBPVW0b65uBlWN5JXAbwNh+79j/5+NzPObnkqxLsjHJxi1btuzGS5EkLWTB6Cd5GXBnVV0zhflQVedV1ZqqWjMzMzONHylJbaxYxD7HAy9P8hLgMcAvAx8ADk6yYhzNrwJuH/vfDhwBbE6yAng88KOJ8R0mHyNJmoIFj/Sr6qyqWlVVq5n9RewVVfVa4MvAK8dua4GLx/IlY52x/YqqqjF+2ri650jgKOCqJXslkqQFLeZIfz5/BVyY5F3AN4Hzx/j5wCeTbAK2MvtBQVVdn+Qi4AZgG3BmVW3fg58vSdpNuxX9qvoK8JWx/D3muPqmqn4KvGqex58DnLO7k5QkLQ3/IleSGjH6ktSI0ZekRoy+JDVi9CWpEaMvSY0YfUlqxOhLUiNGX5IaMfqS1IjRl6RGjL4kNWL0JakRoy9JjRh9SWrE6EtSI0Zfkhox+pLUiNGXpEaMviQ1YvQlqRGjL0mNGH1JasToS1IjRl+SGjH6ktSI0ZekRoy+JDVi9CWpEaMvSY0YfUlqxOhLUiNGX5IaMfqS1IjRl6RGjL4kNWL0JakRoy9JjRh9SWrE6EtSI0Zfkhox+pLUiNGXpEaMviQ1YvQlqRGjL0mNGH1JasToS1IjRl+SGjH6ktSI0ZekRoy+JDVi9CWpEaMvSY0YfUlqxOhLUiNGX5IaMfqS1IjRl6RGjL4kNWL0JakRoy9JjRh9SWpkwegneUySq5J8K8n1Sd4+xo9McmWSTUk+k+TAMX7QWN80tq+eeK6zxvhNSU7aWy9KkjS3xRzp3w+8qKqeBTwbODnJccB7gHOr6unA3cAZY/8zgLvH+LljP5IcDZwGPBM4GfhQkgOW8sVIknZtwejXrPvG6qPHVwEvAj43xtcDp47lU8Y6Y/sJSTLGL6yq+6vq+8Am4NgleRWSpEVZ1Dn9JAckuRa4E9gAfBe4p6q2jV02AyvH8krgNoCx/V7giZPjczxGkjQFi4p+VW2vqmcDq5g9On/G3ppQknVJNibZuGXLlr31YySppd26eqeq7gG+DPwWcHCSFWPTKuD2sXw7cATA2P544EeT43M8ZvJnnFdVa6pqzczMzO5MT5K0gMVcvTOT5OCx/Fjg94EbmY3/K8dua4GLx/IlY52x/YqqqjF+2ri650jgKOCqpXohkqSFrVh4Fw4H1o8rbR4FXFRVX0pyA3BhkncB3wTOH/ufD3wyySZgK7NX7FBV1ye5CLgB2AacWVXbl/blSJJ2ZcHoV9W3gefMMf495rj6pqp+Crxqnuc6Bzhn96cpSVoK/kWuJDVi9CWpEaMvSY0YfUlqxOhLUiNGX5IaMfqS1IjRl6RGjL4kNWL0JakRoy9JjRh9SWrE6EtSI0Zfkhox+pLUiNGXpEaMviQ1YvQlqRGjL0mNGH1JasToS1IjRl+SGjH6ktSI0ZekRoy+JDVi9CWpEaMvSY0YfUlqxOhLUiNGX5IaMfqS1IjRl6RGjL4kNWL0JakRoy9JjRh9SWrE6EtSI0Zfkhox+pLUiNGXpEaMviQ1YvQlqRGjL0mNGH1JasToS1IjRl+SGjH6ktSI0ZekRoy+JDVi9CWpEaMvSY0YfUlqxOhLUiNGX5IaMfqS1IjRl6RGjL4kNWL0JakRoy9JjRh9SWrE6EtSI0Zfkhox+pLUiNGXpEYWjH6SI5J8OckNSa5P8oYx/oQkG5LcPL4fMsaT5INJNiX5dpJjJp5r7dj/5iRr997LkiTNZTFH+tuAv6iqo4HjgDOTHA28Bbi8qo4CLh/rAC8Gjhpf64APw+yHBHA28DzgWODsHR8UkqTpWDD6VfWDqvrGWP4f4EZgJXAKsH7sth44dSyfAnyiZn0dODjJ4cBJwIaq2lpVdwMbgJOX9NVIknZpt87pJ1kNPAe4Ejisqn4wNv0QOGwsrwRum3jY5jE23/jOP2Ndko1JNm7ZsmV3pidJWsCio5/kccDngTdW1X9PbquqAmopJlRV51XVmqpaMzMzsxRPKUkaFhX9JI9mNvifqqp/GsN3jNM2jO93jvHbgSMmHr5qjM03LkmaksVcvRPgfODGqnrfxKZLgB1X4KwFLp4YP31cxXMccO84DXQZcGKSQ8YvcE8cY5KkKVmxiH2OB/4YuC7JtWPsr4F3AxclOQO4FXj12HYp8BJgE/Bj4PUAVbU1yTuBq8d+76iqrUvyKiRJi7Jg9Kvq34DMs/mEOfYv4Mx5nusC4ILdmaAkaen4F7mS1IjRl6RGjL4kNWL0JakRoy9JjRh9SWrE6EtSI0Zfkhox+pLUiNGXpEaMviQ1YvQlqRGjL0mNGH1JasToS1IjRl+SGjH6ktSI0ZekRoy+JDVi9CWpEaMvSY0YfUlqxOhLUiNGX5IaMfqS1IjRl6RGjL4kNWL0JakRoy9JjRh9SWrE6EtSI0Zfkhox+pLUiNGXpEaMviQ1YvQlqRGjL0mNGH1JasToS1IjRl+SGjH6ktSI0ZekRoy+JDVi9CWpEaMvSY0YfUlqxOhLUiNGX5IaMfqS1IjRl6RGjL4kNWL0JakRoy9JjRh9SWrE6EtSI0Zfkhox+pLUiNGXpEaMviQ1YvQlqRGjL0mNGH1JasToS1IjC0Y/yQVJ7kzynYmxJyTZkOTm8f2QMZ4kH0yyKcm3kxwz8Zi1Y/+bk6zdOy9HkrQriznS/zhw8k5jbwEur6qjgMvHOsCLgaPG1zrgwzD7IQGcDTwPOBY4e8cHhSRpehaMflV9Fdi60/ApwPqxvB44dWL8EzXr68DBSQ4HTgI2VNXWqrob2MBDP0gkSXvZwz2nf1hV/WAs/xA4bCyvBG6b2G/zGJtv/CGSrEuyMcnGLVu2PMzpSZLmsse/yK2qAmoJ5rLj+c6rqjVVtWZmZmapnlaSxMOP/h3jtA3j+51j/HbgiIn9Vo2x+cYlSVP0cKN/CbDjCpy1wMUT46ePq3iOA+4dp4EuA05Mcsj4Be6JY0ySNEUrFtohyaeBFwCHJtnM7FU47wYuSnIGcCvw6rH7pcBLgE3Aj4HXA1TV1iTvBK4e+72jqnb+5bAkaS9bMPpV9Zp5Np0wx74FnDnP81wAXLBbs5MkLSn/IleSGjH6ktSI0ZekRoy+JDVi9CWpEaMvSY0YfUlqxOhLUiNGX5IaMfqS1IjRl6RGjL4kNWL0JakRoy9JjRh9SWrE6EtSIwv+JyqS9m3vTZZ7CvuNN1Ut9xT2mEf6ktSI0ZekRoy+JDVi9CWpEaMvSY0YfUlqxOhLUiNGX5IaMfqS1IjRl6RGjL4kNWL0JakRoy9JjRh9SWrE6EtSI0Zfkhox+pLUiNGXpEaMviQ1YvQlqRGjL0mNGH1JasToS1IjRl+SGjH6ktSI0ZekRoy+JDVi9CWpEaMvSY0YfUlqxOhLUiNGX5IaMfqS1IjRl6RGjL4kNWL0JakRoy9JjRh9SWrE6EtSI0Zfkhox+pLUiNGXpEaMviQ1YvQlqRGjL0mNGH1JamTq0U9ycpKbkmxK8pZp/3xJ6myq0U9yAPAPwIuBo4HXJDl6mnOQpM6mfaR/LLCpqr5XVf8HXAicMuU5SFJb047+SuC2ifXNY0ySNAUrlnsCO0uyDlg3Vu9LctNyzmc/cyhw13JPYiH5WJZ7Cpq+feK9+ebsM+/Np8y3YdrRvx04YmJ91Rj7uao6DzhvmpPqIsnGqlqz3POQduZ7c3qmfXrnauCoJEcmORA4DbhkynOQpLameqRfVduS/BlwGXAAcEFVXT/NOUhSZ1M/p19VlwKXTvvnCvC0mR65fG9OSapquecgSZoSb8MgSY084i7Z1OIl2Q5cNzF0alXdMs++91XV46YyMWlI8kTg8rH6JGA7sGWsHzv+SFNT5OmdfdjuhNzoa7kleRtwX1W9d2JsRVVtW75Z9ePpnf1IkscluTzJN5Jcl+Qht7hIcniSrya5Nsl3kjx/jJ+Y5GvjsZ9N4geE9ookH0/ykSRXAn+X5G1J3jSx/TtJVo/l1yW5arxf/3Hcv0t7wOjv2x47/jFcm+QLwE+BV1TVMcALgb9PHvInhH8EXFZVzwaeBVyb5FDgrcDvjcduBP58ei9DDa0Cfruq5n2fJfk14A+B48f7dTvw2inNb7/lOf1920/GPwYAkjwa+JskvwP8jNn7Gh0G/HDiMVcDF4x9v1hV1yb5XWbvevrv4zPiQOBrU3oN6umzVbV9gX1OAJ4LXD3el48F7tzbE9vfGf39y2uBGeC5VfVAkluAx0zuUFVfHR8KLwU+nuR9wN3Ahqp6zbQnrLb+d2J5Gw8+67DjPRtgfVWdNbVZNeDpnf3L44E7R/BfyBw3XUryFOCOqvoo8DHgGODrwPFJnj72+aUkvzrFeau3W5h9H5LkGODIMX458MokvzK2PWG8f7UHPNLfv3wK+Ock1zF7Xv4/5tjnBcCbkzwA3AecXlVbkvwJ8OkkB4393gr8596fssTngdOTXA9cyXjfVdUNSd4K/GuSRwEPAGcCty7bTPcDXrIpSY14ekeSGjH6ktSI0ZekRoy+JDVi9CWpES/ZVHtJngS8H/hN4B7gDuCLwMur6mXLOTdpqXmkr9bGvYm+AHylqp5WVc8FzmL29hV78rweUOkRyTemunsh8EBVfWTHQFV9K8khwAlJPgf8OnAN8LqqqnF7izVVdVeSNcB7q+oF49bBTwOeCvxXkpuAJ4/1JwPvr6oPTvPFSTvzSF/d7Qj6XJ4DvJHZm9E9FTh+Ec93NLN3K91xH6NnACcBxwJnjxvdScvG6Evzu6qqNlfVz4BrgdWLeMwlVfWTifV/qar7q+ouZu8QuUenjaQ9ZfTV3fXM3r53LvdPLG/nF6dDJ+8K+aC7mPLgu0fu6jmkZWH01d0VwEFJ1u0YSPIbwPN38Zhb+MUHxR/svalJS8/oq7WavePgK4DfS/LdcafHv+XB//HMzt4OfCDJRmaP3qV9hnfZlKRGPNKXpEaMviQ1YvQlqRGjL0mNGH1JasToS1IjRl+SGjH6ktTI/wPXWpCb2wZm4AAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K1kdL5KTasGi"
      },
      "source": [
        "Majority of the customers in this dataset have not churned, thereby making this dataset very biased."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "uYjrFyjMasGl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "fe112253-9699-484b-8368-7a9279d727e8"
      },
      "source": [
        "# plotting the histograms of all our numerical variables with the\n",
        "# exception of 'area_code' and 'number_customer_service_calls'\n",
        "num_cols.remove('area_code')\n",
        "num_cols.remove('number_customer_service_calls')\n",
        "\n",
        "fig, axes = plt.subplots(nrows = 7, ncols = 2, figsize = (14, 30))\n",
        "plt.suptitle('Countplots of Tests Measured', fontsize = 20, y = 1.01, color = 'blue')\n",
        "\n",
        "colors = ['#00FF7F', '#8B0000', '#C71585', '#0000FF', '#DB7093', '#FFFF00', '#FF4500',\n",
        "          '#7B68EE', '#FF00FF', '#ADFF2F', '#FFD700', '#A52A2A', '#2F4F4F', '#8B008B']\n",
        "for ax, column, color in zip(axes.flatten(), num_cols, colors):\n",
        "    sns.distplot(df[column], ax = ax, color = color, hist_kws = dict(alpha = 0.75))\n",
        "    \n",
        "plt.tight_layout()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x2160 with 14 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nf3xFgCWasGs"
      },
      "source": [
        "Majority of the numerical variables have normal distributions. Apart from having most of its values in the 0-5 bin, the 'number_vmail_messages' variable appears to be normally distributed. The 'total_intl_calls' variable is skewed to the right and is not continuous."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M31MLioIasGt"
      },
      "source": [
        "### 3.2 Bivariata Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2u9K_UW_asGu"
      },
      "source": [
        "We will make 'churn' our target variable and look at how the other variables relate to it."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5eN2qk6XasGv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "outputId": "9d409211-df26-4350-e975-e2a42825f819"
      },
      "source": [
        "# churn by area code\n",
        "plt.figure(figsize = (8, 6))\n",
        "churn_area_code = sns.countplot('area_code', hue = 'churn', data = df)\n",
        "churn_area_code.set(title = \"Churn by Area Code\", ylabel = 'Area Code')\n",
        "plt.show()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "54N-Z7w0asGy"
      },
      "source": [
        "Area code 415 reports the highest churn rates."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L2FNasXSasGy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "outputId": "2c96076c-2461-4511-e2ac-aed2560dc519"
      },
      "source": [
        "# churn by international plan\n",
        "plt.figure(figsize = (8, 6))\n",
        "churn_area_code = sns.countplot('international_plan', hue = 'churn', data = df)\n",
        "churn_area_code.set(title = \"Churn by International Plan\", ylabel = 'International Plan')\n",
        "plt.show()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kNprofaYasG1"
      },
      "source": [
        "Those without international plans churned more than those with international plans."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bDY0XJOFasG2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "outputId": "3c416b3f-0184-441f-ab36-36939b6a6326"
      },
      "source": [
        "# churn by number of customer service calls\n",
        "plt.figure(figsize = (8, 6))\n",
        "churn_area_code = sns.countplot('number_customer_service_calls', hue = 'churn', data = df)\n",
        "churn_area_code.set(title = \"Churn by Number of Customer Service Calls\",\n",
        "                    ylabel = 'Number of Customer Service Calls')\n",
        "plt.show()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_O6DZfCWasG_"
      },
      "source": [
        "The interesting thing to note here is that those who made 0 or 2 calls to customer service churned at around the same rate. Similarly, those who made more than 3 calls reported high churn rates."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZQjql-lgasHJ"
      },
      "source": [
        "### 3.3 Feature Engineering & Test for Multicollinearity"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zKvXZkbLasHJ"
      },
      "source": [
        "Before we can carry out the test for multicollinearity (a requirement for logistic regression), we need to convert the values of 'international_plan' and 'churn' to binary."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "300tIugMasHT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "outputId": "398dcd7b-73db-4d48-d1fc-29534a08bc99"
      },
      "source": [
        "df['international_plan'] = df['international_plan'].replace({False : 0, True : 1})\n",
        "df['churn'] = df['churn'].replace({False : 0, True : 1})\n",
        "df.head()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   account_length  area_code  international_plan  total_intl_minutes  \\\n",
              "0             101        510                   0                10.6   \n",
              "1             137        510                   0                 9.5   \n",
              "2             103        408                   0                13.7   \n",
              "3              99        415                   0                15.7   \n",
              "4             108        415                   0                 7.7   \n",
              "\n",
              "   total_intl_calls  number_vmail_messages  total_day_minutes  \\\n",
              "0                 3                      0               70.9   \n",
              "1                 7                      0              223.6   \n",
              "2                 6                     29              294.7   \n",
              "3                 2                      0              216.8   \n",
              "4                 4                      0              197.4   \n",
              "\n",
              "   total_day_calls  total_day_charge  total_eve_minutes  total_eve_calls  \\\n",
              "0              123             12.05              211.9               73   \n",
              "1               86             38.01              244.8              139   \n",
              "2               95             50.10              237.3              105   \n",
              "3              123             36.86              126.4               88   \n",
              "4               78             33.56              124.0              101   \n",
              "\n",
              "   total_eve_charge  total_night_minutes  total_night_calls  \\\n",
              "0             18.01                236.0                 73   \n",
              "1             20.81                 94.2                 81   \n",
              "2             20.17                300.3                127   \n",
              "3             10.74                220.6                 82   \n",
              "4             10.54                204.5                107   \n",
              "\n",
              "   total_night_charge  total_intl_charge  number_customer_service_calls  churn  \n",
              "0               10.62               2.86                              3      0  \n",
              "1                4.24               2.57                              0      0  \n",
              "2               13.51               3.70                              1      0  \n",
              "3                9.93               4.24                              1      0  \n",
              "4                9.20               2.08                              2      0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-27bdc0ae-cd9a-4d00-9192-29bb23f7e0e8\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>account_length</th>\n",
              "      <th>area_code</th>\n",
              "      <th>international_plan</th>\n",
              "      <th>total_intl_minutes</th>\n",
              "      <th>total_intl_calls</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_charge</th>\n",
              "      <th>number_customer_service_calls</th>\n",
              "      <th>churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>101</td>\n",
              "      <td>510</td>\n",
              "      <td>0</td>\n",
              "      <td>10.6</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>70.9</td>\n",
              "      <td>123</td>\n",
              "      <td>12.05</td>\n",
              "      <td>211.9</td>\n",
              "      <td>73</td>\n",
              "      <td>18.01</td>\n",
              "      <td>236.0</td>\n",
              "      <td>73</td>\n",
              "      <td>10.62</td>\n",
              "      <td>2.86</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>137</td>\n",
              "      <td>510</td>\n",
              "      <td>0</td>\n",
              "      <td>9.5</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>223.6</td>\n",
              "      <td>86</td>\n",
              "      <td>38.01</td>\n",
              "      <td>244.8</td>\n",
              "      <td>139</td>\n",
              "      <td>20.81</td>\n",
              "      <td>94.2</td>\n",
              "      <td>81</td>\n",
              "      <td>4.24</td>\n",
              "      <td>2.57</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>103</td>\n",
              "      <td>408</td>\n",
              "      <td>0</td>\n",
              "      <td>13.7</td>\n",
              "      <td>6</td>\n",
              "      <td>29</td>\n",
              "      <td>294.7</td>\n",
              "      <td>95</td>\n",
              "      <td>50.10</td>\n",
              "      <td>237.3</td>\n",
              "      <td>105</td>\n",
              "      <td>20.17</td>\n",
              "      <td>300.3</td>\n",
              "      <td>127</td>\n",
              "      <td>13.51</td>\n",
              "      <td>3.70</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>99</td>\n",
              "      <td>415</td>\n",
              "      <td>0</td>\n",
              "      <td>15.7</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>216.8</td>\n",
              "      <td>123</td>\n",
              "      <td>36.86</td>\n",
              "      <td>126.4</td>\n",
              "      <td>88</td>\n",
              "      <td>10.74</td>\n",
              "      <td>220.6</td>\n",
              "      <td>82</td>\n",
              "      <td>9.93</td>\n",
              "      <td>4.24</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>108</td>\n",
              "      <td>415</td>\n",
              "      <td>0</td>\n",
              "      <td>7.7</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>197.4</td>\n",
              "      <td>78</td>\n",
              "      <td>33.56</td>\n",
              "      <td>124.0</td>\n",
              "      <td>101</td>\n",
              "      <td>10.54</td>\n",
              "      <td>204.5</td>\n",
              "      <td>107</td>\n",
              "      <td>9.20</td>\n",
              "      <td>2.08</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-27bdc0ae-cd9a-4d00-9192-29bb23f7e0e8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-27bdc0ae-cd9a-4d00-9192-29bb23f7e0e8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-27bdc0ae-cd9a-4d00-9192-29bb23f7e0e8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Io_efH3JasHb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "4e06a36a-f479-4d34-c667-8b0a698b7754"
      },
      "source": [
        "# checking the correlations between the numerical variables\n",
        "df.describe()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       account_length    area_code  international_plan  total_intl_minutes  \\\n",
              "count      5000.00000  5000.000000         5000.000000         5000.000000   \n",
              "mean        100.25860   436.911400            0.094600           10.261780   \n",
              "std          39.69456    42.209182            0.292691            2.761396   \n",
              "min           1.00000   408.000000            0.000000            0.000000   \n",
              "25%          73.00000   408.000000            0.000000            8.500000   \n",
              "50%         100.00000   415.000000            0.000000           10.300000   \n",
              "75%         127.00000   415.000000            0.000000           12.000000   \n",
              "max         243.00000   510.000000            1.000000           20.000000   \n",
              "\n",
              "       total_intl_calls  number_vmail_messages  total_day_minutes  \\\n",
              "count       5000.000000            5000.000000        5000.000000   \n",
              "mean           4.435200               7.755200         180.288900   \n",
              "std            2.456788              13.546393          53.894699   \n",
              "min            0.000000               0.000000           0.000000   \n",
              "25%            3.000000               0.000000         143.700000   \n",
              "50%            4.000000               0.000000         180.100000   \n",
              "75%            6.000000              17.000000         216.200000   \n",
              "max           20.000000              52.000000         351.500000   \n",
              "\n",
              "       total_day_calls  total_day_charge  total_eve_minutes  total_eve_calls  \\\n",
              "count      5000.000000       5000.000000        5000.000000      5000.000000   \n",
              "mean        100.029400         30.649668         200.636560       100.191000   \n",
              "std          19.831197          9.162069          50.551309        19.826496   \n",
              "min           0.000000          0.000000           0.000000         0.000000   \n",
              "25%          87.000000         24.430000         166.375000        87.000000   \n",
              "50%         100.000000         30.620000         201.000000       100.000000   \n",
              "75%         113.000000         36.750000         234.100000       114.000000   \n",
              "max         165.000000         59.760000         363.700000       170.000000   \n",
              "\n",
              "       total_eve_charge  total_night_minutes  total_night_calls  \\\n",
              "count       5000.000000          5000.000000        5000.000000   \n",
              "mean          17.054322           200.391620          99.919200   \n",
              "std            4.296843            50.527789          19.958686   \n",
              "min            0.000000             0.000000           0.000000   \n",
              "25%           14.140000           166.900000          87.000000   \n",
              "50%           17.090000           200.400000         100.000000   \n",
              "75%           19.900000           234.700000         113.000000   \n",
              "max           30.910000           395.000000         175.000000   \n",
              "\n",
              "       total_night_charge  total_intl_charge  number_customer_service_calls  \\\n",
              "count         5000.000000        5000.000000                    5000.000000   \n",
              "mean             9.017732           2.771196                       1.570400   \n",
              "std              2.273763           0.745514                       1.306363   \n",
              "min              0.000000           0.000000                       0.000000   \n",
              "25%              7.510000           2.300000                       1.000000   \n",
              "50%              9.020000           2.780000                       1.000000   \n",
              "75%             10.560000           3.240000                       2.000000   \n",
              "max             17.770000           5.400000                       9.000000   \n",
              "\n",
              "             churn  \n",
              "count  5000.000000  \n",
              "mean      0.141400  \n",
              "std       0.348469  \n",
              "min       0.000000  \n",
              "25%       0.000000  \n",
              "50%       0.000000  \n",
              "75%       0.000000  \n",
              "max       1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f3da4487-7c49-4ac9-be1b-e73dea87610d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\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>area_code</th>\n",
              "      <th>international_plan</th>\n",
              "      <th>total_intl_minutes</th>\n",
              "      <th>total_intl_calls</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_charge</th>\n",
              "      <th>number_customer_service_calls</th>\n",
              "      <th>churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>5000.00000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>100.25860</td>\n",
              "      <td>436.911400</td>\n",
              "      <td>0.094600</td>\n",
              "      <td>10.261780</td>\n",
              "      <td>4.435200</td>\n",
              "      <td>7.755200</td>\n",
              "      <td>180.288900</td>\n",
              "      <td>100.029400</td>\n",
              "      <td>30.649668</td>\n",
              "      <td>200.636560</td>\n",
              "      <td>100.191000</td>\n",
              "      <td>17.054322</td>\n",
              "      <td>200.391620</td>\n",
              "      <td>99.919200</td>\n",
              "      <td>9.017732</td>\n",
              "      <td>2.771196</td>\n",
              "      <td>1.570400</td>\n",
              "      <td>0.141400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>39.69456</td>\n",
              "      <td>42.209182</td>\n",
              "      <td>0.292691</td>\n",
              "      <td>2.761396</td>\n",
              "      <td>2.456788</td>\n",
              "      <td>13.546393</td>\n",
              "      <td>53.894699</td>\n",
              "      <td>19.831197</td>\n",
              "      <td>9.162069</td>\n",
              "      <td>50.551309</td>\n",
              "      <td>19.826496</td>\n",
              "      <td>4.296843</td>\n",
              "      <td>50.527789</td>\n",
              "      <td>19.958686</td>\n",
              "      <td>2.273763</td>\n",
              "      <td>0.745514</td>\n",
              "      <td>1.306363</td>\n",
              "      <td>0.348469</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.00000</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>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>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>73.00000</td>\n",
              "      <td>408.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8.500000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>143.700000</td>\n",
              "      <td>87.000000</td>\n",
              "      <td>24.430000</td>\n",
              "      <td>166.375000</td>\n",
              "      <td>87.000000</td>\n",
              "      <td>14.140000</td>\n",
              "      <td>166.900000</td>\n",
              "      <td>87.000000</td>\n",
              "      <td>7.510000</td>\n",
              "      <td>2.300000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>100.00000</td>\n",
              "      <td>415.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>10.300000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>180.100000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>30.620000</td>\n",
              "      <td>201.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>17.090000</td>\n",
              "      <td>200.400000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>9.020000</td>\n",
              "      <td>2.780000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>127.00000</td>\n",
              "      <td>415.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>216.200000</td>\n",
              "      <td>113.000000</td>\n",
              "      <td>36.750000</td>\n",
              "      <td>234.100000</td>\n",
              "      <td>114.000000</td>\n",
              "      <td>19.900000</td>\n",
              "      <td>234.700000</td>\n",
              "      <td>113.000000</td>\n",
              "      <td>10.560000</td>\n",
              "      <td>3.240000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>243.00000</td>\n",
              "      <td>510.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>351.500000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>59.760000</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>5.400000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f3da4487-7c49-4ac9-be1b-e73dea87610d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f3da4487-7c49-4ac9-be1b-e73dea87610d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f3da4487-7c49-4ac9-be1b-e73dea87610d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plotting the correlations onto a heatmap\n",
        "plt.figure(figsize = (16, 10))\n",
        "sns.heatmap(df.corr(), annot = True, cmap=\"YlGnBu\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 648
        },
        "id": "fwAKzkVUWtAA",
        "outputId": "27386f9f-05b5-44da-c9f4-b6477bc29693"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "phY6GZ1_asHk"
      },
      "source": [
        "We see some perfect correlations between the following variables:\n",
        "- 'total_day_minutes' and 'total_day_charge'\n",
        "- 'total_eve_minutes' and 'total_eve_charge'\n",
        "- 'total_night_minutes' and 'total_night_charge'\n",
        "- 'total_intl_minutes' and 'total_intl_charge'\n",
        "\n",
        "We will drop the minutes."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xp_78zjxasHn"
      },
      "source": [
        "# drop the columns with minutes, e.g., 'total_day_minutes', etc.\n",
        "df.drop(columns = ['total_day_minutes', 'total_eve_minutes', 'total_night_minutes', 'total_intl_minutes'], inplace = True)"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vbY0MHVkasHz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "fd2ac2a7-7f38-417d-b4b3-29262e61688b"
      },
      "source": [
        "# checking the correlations between the numerical variables\n",
        "df.describe()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       account_length    area_code  international_plan  total_intl_calls  \\\n",
              "count      5000.00000  5000.000000         5000.000000       5000.000000   \n",
              "mean        100.25860   436.911400            0.094600          4.435200   \n",
              "std          39.69456    42.209182            0.292691          2.456788   \n",
              "min           1.00000   408.000000            0.000000          0.000000   \n",
              "25%          73.00000   408.000000            0.000000          3.000000   \n",
              "50%         100.00000   415.000000            0.000000          4.000000   \n",
              "75%         127.00000   415.000000            0.000000          6.000000   \n",
              "max         243.00000   510.000000            1.000000         20.000000   \n",
              "\n",
              "       number_vmail_messages  total_day_calls  total_day_charge  \\\n",
              "count            5000.000000      5000.000000       5000.000000   \n",
              "mean                7.755200       100.029400         30.649668   \n",
              "std                13.546393        19.831197          9.162069   \n",
              "min                 0.000000         0.000000          0.000000   \n",
              "25%                 0.000000        87.000000         24.430000   \n",
              "50%                 0.000000       100.000000         30.620000   \n",
              "75%                17.000000       113.000000         36.750000   \n",
              "max                52.000000       165.000000         59.760000   \n",
              "\n",
              "       total_eve_calls  total_eve_charge  total_night_calls  \\\n",
              "count      5000.000000       5000.000000        5000.000000   \n",
              "mean        100.191000         17.054322          99.919200   \n",
              "std          19.826496          4.296843          19.958686   \n",
              "min           0.000000          0.000000           0.000000   \n",
              "25%          87.000000         14.140000          87.000000   \n",
              "50%         100.000000         17.090000         100.000000   \n",
              "75%         114.000000         19.900000         113.000000   \n",
              "max         170.000000         30.910000         175.000000   \n",
              "\n",
              "       total_night_charge  total_intl_charge  number_customer_service_calls  \\\n",
              "count         5000.000000        5000.000000                    5000.000000   \n",
              "mean             9.017732           2.771196                       1.570400   \n",
              "std              2.273763           0.745514                       1.306363   \n",
              "min              0.000000           0.000000                       0.000000   \n",
              "25%              7.510000           2.300000                       1.000000   \n",
              "50%              9.020000           2.780000                       1.000000   \n",
              "75%             10.560000           3.240000                       2.000000   \n",
              "max             17.770000           5.400000                       9.000000   \n",
              "\n",
              "             churn  \n",
              "count  5000.000000  \n",
              "mean      0.141400  \n",
              "std       0.348469  \n",
              "min       0.000000  \n",
              "25%       0.000000  \n",
              "50%       0.000000  \n",
              "75%       0.000000  \n",
              "max       1.000000  "
            ],
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              "\n",
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              "      <th>account_length</th>\n",
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              "      <th>international_plan</th>\n",
              "      <th>total_intl_calls</th>\n",
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              "      <th>number_customer_service_calls</th>\n",
              "      <th>churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>5000.00000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
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              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
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              "      <td>436.911400</td>\n",
              "      <td>0.094600</td>\n",
              "      <td>4.435200</td>\n",
              "      <td>7.755200</td>\n",
              "      <td>100.029400</td>\n",
              "      <td>30.649668</td>\n",
              "      <td>100.191000</td>\n",
              "      <td>17.054322</td>\n",
              "      <td>99.919200</td>\n",
              "      <td>9.017732</td>\n",
              "      <td>2.771196</td>\n",
              "      <td>1.570400</td>\n",
              "      <td>0.141400</td>\n",
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              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>39.69456</td>\n",
              "      <td>42.209182</td>\n",
              "      <td>0.292691</td>\n",
              "      <td>2.456788</td>\n",
              "      <td>13.546393</td>\n",
              "      <td>19.831197</td>\n",
              "      <td>9.162069</td>\n",
              "      <td>19.826496</td>\n",
              "      <td>4.296843</td>\n",
              "      <td>19.958686</td>\n",
              "      <td>2.273763</td>\n",
              "      <td>0.745514</td>\n",
              "      <td>1.306363</td>\n",
              "      <td>0.348469</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.00000</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>0.000000</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>73.00000</td>\n",
              "      <td>408.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>87.000000</td>\n",
              "      <td>24.430000</td>\n",
              "      <td>87.000000</td>\n",
              "      <td>14.140000</td>\n",
              "      <td>87.000000</td>\n",
              "      <td>7.510000</td>\n",
              "      <td>2.300000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>100.00000</td>\n",
              "      <td>415.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>30.620000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>17.090000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>9.020000</td>\n",
              "      <td>2.780000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>127.00000</td>\n",
              "      <td>415.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>113.000000</td>\n",
              "      <td>36.750000</td>\n",
              "      <td>114.000000</td>\n",
              "      <td>19.900000</td>\n",
              "      <td>113.000000</td>\n",
              "      <td>10.560000</td>\n",
              "      <td>3.240000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>243.00000</td>\n",
              "      <td>510.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>59.760000</td>\n",
              "      <td>170.000000</td>\n",
              "      <td>30.910000</td>\n",
              "      <td>175.000000</td>\n",
              "      <td>17.770000</td>\n",
              "      <td>5.400000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "                                                     [key], {});\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plotting the correlations onto a heatmap\n",
        "plt.figure(figsize = (16, 10))\n",
        "sns.heatmap(df.corr(), annot = True, cmap=\"YlGnBu\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 648
        },
        "id": "X3sv2zFeY2dF",
        "outputId": "9191cdfc-03f5-4d1a-e02e-49a22dede75f"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x720 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PpvlSJG-asH2"
      },
      "source": [
        "We will then check the Variance Inflation Factor (VIF) scores to ensure there is no high multicollinearity."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.stats.outliers_influence import variance_inflation_factor as vif"
      ],
      "metadata": {
        "id": "bfqYYYUwaiMT"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# VIF dataframe\n",
        "vif_data = pd.DataFrame()\n",
        "vif_data[\"feature\"] = df.columns\n",
        "\n",
        "# calculating VIF for each feature\n",
        "vif_data[\"VIF\"] = [vif(df.values, i)\n",
        "                          for i in range(len(df.columns))]\n",
        "  \n",
        "vif_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "fsdVjehQeu_H",
        "outputId": "880050d0-b578-43d6-e6df-d30971ae36fa"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                          feature        VIF\n",
              "0                  account_length   7.201962\n",
              "1                       area_code  61.657697\n",
              "2              international_plan   1.197328\n",
              "3                total_intl_calls   4.225636\n",
              "4           number_vmail_messages   1.345232\n",
              "5                 total_day_calls  23.769646\n",
              "6                total_day_charge  12.112808\n",
              "7                 total_eve_calls  23.815939\n",
              "8                total_eve_charge  15.870252\n",
              "9               total_night_calls  23.768009\n",
              "10             total_night_charge  15.812476\n",
              "11              total_intl_charge  14.038428\n",
              "12  number_customer_service_calls   2.566336\n",
              "13                          churn   1.411394"
            ],
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              "      <th>feature</th>\n",
              "      <th>VIF</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>account_length</td>\n",
              "      <td>7.201962</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>area_code</td>\n",
              "      <td>61.657697</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>international_plan</td>\n",
              "      <td>1.197328</td>\n",
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              "      <th>3</th>\n",
              "      <td>total_intl_calls</td>\n",
              "      <td>4.225636</td>\n",
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              "      <th>4</th>\n",
              "      <td>number_vmail_messages</td>\n",
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              "      <td>total_eve_charge</td>\n",
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              "    <tr>\n",
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              "      <td>total_night_calls</td>\n",
              "      <td>23.768009</td>\n",
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              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>total_night_charge</td>\n",
              "      <td>15.812476</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>total_intl_charge</td>\n",
              "      <td>14.038428</td>\n",
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              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>number_customer_service_calls</td>\n",
              "      <td>2.566336</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>churn</td>\n",
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              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-68c5705c-0f27-4b48-9919-f39a93df5561 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-68c5705c-0f27-4b48-9919-f39a93df5561');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A VIF of 1 indicates no correlation, a VIF between 1 and 5 indicates a moderate correlation, and a VIF above 5 indicates a high correlation.\n",
        "\n",
        "As result shows we have 9 highly correlated and 5 moderately correlated variables."
      ],
      "metadata": {
        "id": "12U3MjO_fFsv"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z58VpwHoasH_"
      },
      "source": [
        "## <font color='#2F4F4F'>4. Data Modeling</font>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WKm33MJYasH_"
      },
      "source": [
        "We will carry out 5 types of classification analysis, namely:\n",
        "1. Logistic Regression\n",
        "2. Gaussian Naive Bayes (NB) classification\n",
        "3. Decision Trees Classification\n",
        "4. K-Nearest Neighbors (KNN) Classification\n",
        "5. Support Vector Machine (SVM) Classification\n",
        "\n",
        "We will then compare the different classification models to assess the best performing one(s)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wCVbM0xcasIL"
      },
      "source": [
        "# divide our dataset into features (X) and target (Y)\n",
        "X = df.drop(['churn'], axis=1)\n",
        "Y = df['churn']"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kByQ2gGOasIN"
      },
      "source": [
        "# split into 80-20 train-test sets\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, train_size=0.8, random_state=0)"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cLa8VQrUasIS"
      },
      "source": [
        "# perform feature scaling on training data\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "\n",
        "# fit and transform X_train, transform X_test\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# load the classification libraries\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.svm import SVC"
      ],
      "metadata": {
        "id": "bYoeMewshu6e"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# instantiating our classifiers\n",
        "lg = LogisticRegression(random_state = 0, solver='lbfgs')\n",
        "nb = GaussianNB() \n",
        "tree = DecisionTreeClassifier()\n",
        "knn = KNeighborsClassifier(n_neighbors=5)\n",
        "svm = SVC()"
      ],
      "metadata": {
        "id": "_QUwlPBjhlAX"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# fitting our classifiers to the training data\n",
        "lg.fit(X_train, Y_train)\n",
        "nb.fit(X_train, Y_train)\n",
        "tree.fit(X_train, Y_train)\n",
        "knn.fit(X_train, Y_train)\n",
        "svm.fit(X_train, Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ezdZXRTQhmLl",
        "outputId": "bf839dcd-3ea1-4e9e-a2d4-7120d1aff876"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC()"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# making predictions\n",
        "lg_pred = lg.predict(X_test)\n",
        "nb_pred = nb.predict(X_test)\n",
        "tree_pred = tree.predict(X_test)\n",
        "knn_pred = knn.predict(X_test)\n",
        "svm_pred = svm.predict(X_test)"
      ],
      "metadata": {
        "id": "GVf85UmOhm7p"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KfXCA87-asIf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a9562201-26c2-4eae-b6f6-04565d99673e"
      },
      "source": [
        "# printing the classification report for each classifier to assess performance\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "# classification report for Logistic Regression\n",
        "print(\"Logistic Regression classification report:\")\n",
        "print(classification_report(Y_test, lg_pred))\n",
        "\n",
        "# classification report for Gaussian Naive Bayes Classifier\n",
        "print(\"Gaussian Naive Bayes classification report:\")\n",
        "print(classification_report(Y_test, nb_pred))\n",
        "\n",
        "# classification report for Decision Tree Classifier\n",
        "print(\"Decision Tree classification report:\")\n",
        "print(classification_report(Y_test, tree_pred))\n",
        "\n",
        "# classification report for K-Nearest Neighbors Classifier\n",
        "print(\"K-Nearest Neighbors classification report:\")\n",
        "print(classification_report(Y_test, knn_pred))\n",
        "\n",
        "# classification report for Support Vector Machine Classifier\n",
        "print(\"Support Vector Machine classification report:\")\n",
        "print(classification_report(Y_test, svm_pred))"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic Regression classification report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.87      0.98      0.92       849\n",
            "           1       0.58      0.19      0.29       151\n",
            "\n",
            "    accuracy                           0.86      1000\n",
            "   macro avg       0.73      0.58      0.60      1000\n",
            "weighted avg       0.83      0.86      0.83      1000\n",
            "\n",
            "Gaussian Naive Bayes classification report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.90      0.94      0.91       849\n",
            "           1       0.51      0.38      0.44       151\n",
            "\n",
            "    accuracy                           0.85      1000\n",
            "   macro avg       0.70      0.66      0.68      1000\n",
            "weighted avg       0.84      0.85      0.84      1000\n",
            "\n",
            "Decision Tree classification report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.96      0.96       849\n",
            "           1       0.78      0.75      0.77       151\n",
            "\n",
            "    accuracy                           0.93      1000\n",
            "   macro avg       0.87      0.86      0.86      1000\n",
            "weighted avg       0.93      0.93      0.93      1000\n",
            "\n",
            "K-Nearest Neighbors classification report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.89      0.99      0.93       849\n",
            "           1       0.81      0.29      0.43       151\n",
            "\n",
            "    accuracy                           0.88      1000\n",
            "   macro avg       0.85      0.64      0.68      1000\n",
            "weighted avg       0.88      0.88      0.86      1000\n",
            "\n",
            "Support Vector Machine classification report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.91      0.99      0.95       849\n",
            "           1       0.87      0.44      0.58       151\n",
            "\n",
            "    accuracy                           0.91      1000\n",
            "   macro avg       0.89      0.71      0.76      1000\n",
            "weighted avg       0.90      0.91      0.89      1000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UopMes2SasIl"
      },
      "source": [
        "KNN classifier has the worst accuracy of 84%. Logistic Regression, Naive Bayes and SVM classifiers have a slightly higher accuracy than KNN at 85%. \n",
        "\n",
        "Decision Tree is the best performer with an accuracy score of 92%."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vTbdjSrhVIiT"
      },
      "source": [
        "## <font color='#2F4F4F'>5. Summary of Findings</font>\n",
        "\n",
        "* Customers without an international plan churned more compared to those with an international plan.\n",
        "* Customers who made 0 or 2 calls to customer service churned at around the same rate.\n",
        "* Area code 415 had the highest churn compared to area codes 408 and 510.\n",
        "* Of the 5 models, Decision tree classifier has the highest accuracy."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "noZz5YRQXKzi"
      },
      "source": [
        "## <font color='#2F4F4F'>6. Recommendations</font>\n",
        "\n",
        "Look more into why customers didn't have international plans despite having making internation calls. Could it be that they aren't aware of the plans hence end up spending more than necessary?\n",
        "\n",
        "Look into why area code 415 had the highest churn rate and perhaps devise anti-churn strategies in order to retain customers."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xrmHVMVsVS--"
      },
      "source": [
        "## <font color='#2F4F4F'>7. Challenging your Solution</font>\n",
        "\n",
        "The solution can be imroved by performing feature engineering, boosting"
      ]
    }
  ]
}