Internship_Task1.ipynb

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    "colab": {
      "name": "Internship_Task1.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SZwJ6AfBX_63"
      },
      "source": [
        "#**Internship Task1**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "60gMkNxUVE50"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns\n"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2cAxK9Z1Wfhh",
        "outputId": "ab909842-a061-45e2-a4bd-43de019f8cb0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "dataset = pd.read_csv(\"taskdata.csv\")\n",
        "print(dataset.shape)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(4407, 17)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HLoO9cwsYT7s",
        "outputId": "811b38a9-ecc4-4073-aa4b-06a37b5230e1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        }
      },
      "source": [
        "dataset.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>user_id</th>\n",
              "      <th>account_id</th>\n",
              "      <th>max_return_days</th>\n",
              "      <th>average_return_days</th>\n",
              "      <th>customer_since_months</th>\n",
              "      <th>total_revenue</th>\n",
              "      <th>average_revenue</th>\n",
              "      <th>avg_vas_count</th>\n",
              "      <th>avg_used_vas_value</th>\n",
              "      <th>business_type</th>\n",
              "      <th>avg_wallet_bonus</th>\n",
              "      <th>avg_active_ads</th>\n",
              "      <th>avg_new_ads</th>\n",
              "      <th>avg_email_replies</th>\n",
              "      <th>avg_call_replies</th>\n",
              "      <th>avg_sms_replies</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>410039</td>\n",
              "      <td>496.0</td>\n",
              "      <td>8</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>14</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.666667</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>0</td>\n",
              "      <td>300.0</td>\n",
              "      <td>79.333333</td>\n",
              "      <td>15.250000</td>\n",
              "      <td>32.416667</td>\n",
              "      <td>27.916667</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>410240</td>\n",
              "      <td>499.0</td>\n",
              "      <td>37</td>\n",
              "      <td>8.200000</td>\n",
              "      <td>10</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.900000</td>\n",
              "      <td>114.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>232.100000</td>\n",
              "      <td>18.300000</td>\n",
              "      <td>16.200000</td>\n",
              "      <td>13.800000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>411764</td>\n",
              "      <td>493.0</td>\n",
              "      <td>9</td>\n",
              "      <td>1.533333</td>\n",
              "      <td>15</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.333333</td>\n",
              "      <td>106.666667</td>\n",
              "      <td>0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>55.083333</td>\n",
              "      <td>3.750000</td>\n",
              "      <td>20.250000</td>\n",
              "      <td>24.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>411985</td>\n",
              "      <td>262.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>14</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.307692</td>\n",
              "      <td>176.153846</td>\n",
              "      <td>0</td>\n",
              "      <td>1481.0</td>\n",
              "      <td>409.538462</td>\n",
              "      <td>80.769231</td>\n",
              "      <td>87.461538</td>\n",
              "      <td>57.615385</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>412071</td>\n",
              "      <td>496.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>18</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.846154</td>\n",
              "      <td>109.230769</td>\n",
              "      <td>0</td>\n",
              "      <td>41.5</td>\n",
              "      <td>48.230769</td>\n",
              "      <td>1.692308</td>\n",
              "      <td>17.461538</td>\n",
              "      <td>25.769231</td>\n",
              "      <td>0.230769</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   user_id  account_id  ...  avg_sms_replies  target\n",
              "0   410039       496.0  ...         0.250000       0\n",
              "1   410240       499.0  ...         0.000000       0\n",
              "2   411764       493.0  ...         0.000000       0\n",
              "3   411985       262.0  ...         0.000000       1\n",
              "4   412071       496.0  ...         0.230769       0\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "smNF5Oy5cHVe"
      },
      "source": [
        "## **Misssing Values**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ol9xjwpZ0aU4",
        "outputId": "ec5914bf-6f1b-497c-b52c-e70c0a8ef060",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        }
      },
      "source": [
        "dataset.isnull()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>user_id</th>\n",
              "      <th>account_id</th>\n",
              "      <th>max_return_days</th>\n",
              "      <th>average_return_days</th>\n",
              "      <th>customer_since_months</th>\n",
              "      <th>total_revenue</th>\n",
              "      <th>average_revenue</th>\n",
              "      <th>avg_vas_count</th>\n",
              "      <th>avg_used_vas_value</th>\n",
              "      <th>business_type</th>\n",
              "      <th>avg_wallet_bonus</th>\n",
              "      <th>avg_active_ads</th>\n",
              "      <th>avg_new_ads</th>\n",
              "      <th>avg_email_replies</th>\n",
              "      <th>avg_call_replies</th>\n",
              "      <th>avg_sms_replies</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4402</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4403</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4404</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4405</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4406</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4407 rows × 17 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      user_id  account_id  ...  avg_sms_replies  target\n",
              "0       False       False  ...            False   False\n",
              "1       False       False  ...            False   False\n",
              "2       False       False  ...            False   False\n",
              "3       False       False  ...            False   False\n",
              "4       False       False  ...            False   False\n",
              "...       ...         ...  ...              ...     ...\n",
              "4402    False       False  ...            False   False\n",
              "4403    False       False  ...            False   False\n",
              "4404    False       False  ...            False   False\n",
              "4405    False       False  ...            False   False\n",
              "4406    False       False  ...            False   False\n",
              "\n",
              "[4407 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MBK-o-63aIYK",
        "outputId": "79ff71f9-4e6c-42e3-9da2-a47b2602ae6a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "## Here we will check % of nan values present in each feature of dataset\n",
        "features_with_na = [features for features in dataset.columns if dataset[features].isnull().sum() > 1]\n",
        "\n",
        "for feature in features_with_na :\n",
        "  print(feature, np.round(dataset[feature].isnull().mean(), 4), '% missing values')"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "account_id 0.0016 % missing values\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PMU3qvHV3Eso",
        "outputId": "78470855-4a9b-4291-dbda-b437d7e6558a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        }
      },
      "source": [
        "sns.heatmap(dataset.isnull(), yticklabels = False, cbar = False, cmap = 'viridis')"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa498cc2c18>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BtqlZrCF3XLi",
        "outputId": "617b2cfd-50d4-4aaf-b07c-563bd94220e4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        }
      },
      "source": [
        "#dropping the NaN values present in account_id of dataset\n",
        "dataset1 = dataset.dropna()\n",
        "dataset1"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>user_id</th>\n",
              "      <th>account_id</th>\n",
              "      <th>max_return_days</th>\n",
              "      <th>average_return_days</th>\n",
              "      <th>customer_since_months</th>\n",
              "      <th>total_revenue</th>\n",
              "      <th>average_revenue</th>\n",
              "      <th>avg_vas_count</th>\n",
              "      <th>avg_used_vas_value</th>\n",
              "      <th>business_type</th>\n",
              "      <th>avg_wallet_bonus</th>\n",
              "      <th>avg_active_ads</th>\n",
              "      <th>avg_new_ads</th>\n",
              "      <th>avg_email_replies</th>\n",
              "      <th>avg_call_replies</th>\n",
              "      <th>avg_sms_replies</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>410039</td>\n",
              "      <td>496.0</td>\n",
              "      <td>8</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>14</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>3.666667</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>0</td>\n",
              "      <td>300.0</td>\n",
              "      <td>79.333333</td>\n",
              "      <td>15.250000</td>\n",
              "      <td>32.416667</td>\n",
              "      <td>27.916667</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>410240</td>\n",
              "      <td>499.0</td>\n",
              "      <td>37</td>\n",
              "      <td>8.200000</td>\n",
              "      <td>10</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>4.900000</td>\n",
              "      <td>114.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>232.100000</td>\n",
              "      <td>18.300000</td>\n",
              "      <td>16.200000</td>\n",
              "      <td>13.800000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>411764</td>\n",
              "      <td>493.0</td>\n",
              "      <td>9</td>\n",
              "      <td>1.533333</td>\n",
              "      <td>15</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>5.333333</td>\n",
              "      <td>106.666667</td>\n",
              "      <td>0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>55.083333</td>\n",
              "      <td>3.750000</td>\n",
              "      <td>20.250000</td>\n",
              "      <td>24.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>411985</td>\n",
              "      <td>262.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>14</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>6.307692</td>\n",
              "      <td>176.153846</td>\n",
              "      <td>0</td>\n",
              "      <td>1481.0</td>\n",
              "      <td>409.538462</td>\n",
              "      <td>80.769231</td>\n",
              "      <td>87.461538</td>\n",
              "      <td>57.615385</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>412071</td>\n",
              "      <td>496.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>18</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>5.846154</td>\n",
              "      <td>109.230769</td>\n",
              "      <td>0</td>\n",
              "      <td>41.5</td>\n",
              "      <td>48.230769</td>\n",
              "      <td>1.692308</td>\n",
              "      <td>17.461538</td>\n",
              "      <td>25.769231</td>\n",
              "      <td>0.230769</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4402</th>\n",
              "      <td>73498690100</td>\n",
              "      <td>113.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.666667</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>9.666667</td>\n",
              "      <td>14.666667</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4403</th>\n",
              "      <td>73519239100</td>\n",
              "      <td>1017.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>153.000000</td>\n",
              "      <td>76.500000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>11.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4404</th>\n",
              "      <td>73525763100</td>\n",
              "      <td>226.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</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</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4405</th>\n",
              "      <td>73538605100</td>\n",
              "      <td>226.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2</td>\n",
              "      <td>551.19</td>\n",
              "      <td>551.19</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4406</th>\n",
              "      <td>73560305100</td>\n",
              "      <td>1017.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>240.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4400 rows × 17 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          user_id  account_id  ...  avg_sms_replies  target\n",
              "0          410039       496.0  ...         0.250000       0\n",
              "1          410240       499.0  ...         0.000000       0\n",
              "2          411764       493.0  ...         0.000000       0\n",
              "3          411985       262.0  ...         0.000000       1\n",
              "4          412071       496.0  ...         0.230769       0\n",
              "...           ...         ...  ...              ...     ...\n",
              "4402  73498690100       113.0  ...         0.333333       0\n",
              "4403  73519239100      1017.0  ...         0.000000       0\n",
              "4404  73525763100       226.0  ...         0.000000       0\n",
              "4405  73538605100       226.0  ...         0.000000       0\n",
              "4406  73560305100      1017.0  ...         0.000000       0\n",
              "\n",
              "[4400 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9fdinL2c96NT",
        "outputId": "ec71a48e-c135-4cc7-ec79-65ba4794d512",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        }
      },
      "source": [
        "sns.heatmap(dataset1.isnull(), yticklabels = False, cbar = False, cmap = 'viridis')"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa494cd1b70>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kg_lqKZ7DQb4"
      },
      "source": [
        "## **EDA on the given data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2_siBJgq3FEU",
        "outputId": "bccdcf51-cd85-477f-8197-2b21f510bfe4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        }
      },
      "source": [
        "dataset1.info()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 4400 entries, 0 to 4406\n",
            "Data columns (total 17 columns):\n",
            " #   Column                 Non-Null Count  Dtype  \n",
            "---  ------                 --------------  -----  \n",
            " 0   user_id                4400 non-null   int64  \n",
            " 1   account_id             4400 non-null   float64\n",
            " 2   max_return_days        4400 non-null   int64  \n",
            " 3   average_return_days    4400 non-null   float64\n",
            " 4   customer_since_months  4400 non-null   int64  \n",
            " 5   total_revenue          4400 non-null   float64\n",
            " 6   average_revenue        4400 non-null   float64\n",
            " 7   avg_vas_count          4400 non-null   float64\n",
            " 8   avg_used_vas_value     4400 non-null   float64\n",
            " 9   business_type          4400 non-null   int64  \n",
            " 10  avg_wallet_bonus       4400 non-null   float64\n",
            " 11  avg_active_ads         4400 non-null   float64\n",
            " 12  avg_new_ads            4400 non-null   float64\n",
            " 13  avg_email_replies      4400 non-null   float64\n",
            " 14  avg_call_replies       4400 non-null   float64\n",
            " 15  avg_sms_replies        4400 non-null   float64\n",
            " 16  target                 4400 non-null   int64  \n",
            "dtypes: float64(12), int64(5)\n",
            "memory usage: 618.8 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ISM4dr8-3XI7",
        "outputId": "d4911387-89bf-45bc-f653-e65f699aa07a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        }
      },
      "source": [
        "dataset1.describe()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>user_id</th>\n",
              "      <th>account_id</th>\n",
              "      <th>max_return_days</th>\n",
              "      <th>average_return_days</th>\n",
              "      <th>customer_since_months</th>\n",
              "      <th>total_revenue</th>\n",
              "      <th>average_revenue</th>\n",
              "      <th>avg_vas_count</th>\n",
              "      <th>avg_used_vas_value</th>\n",
              "      <th>business_type</th>\n",
              "      <th>avg_wallet_bonus</th>\n",
              "      <th>avg_active_ads</th>\n",
              "      <th>avg_new_ads</th>\n",
              "      <th>avg_email_replies</th>\n",
              "      <th>avg_call_replies</th>\n",
              "      <th>avg_sms_replies</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4.400000e+03</td>\n",
              "      <td>4400.000000</td>\n",
              "      <td>4400.000000</td>\n",
              "      <td>4400.000000</td>\n",
              "      <td>4400.000000</td>\n",
              "      <td>4400.000000</td>\n",
              "      <td>4400.000000</td>\n",
              "      <td>4400.000000</td>\n",
              "      <td>4400.000000</td>\n",
              "      <td>4400.000000</td>\n",
              "      <td>4400.000000</td>\n",
              "      <td>4400.000000</td>\n",
              "      <td>4400.000000</td>\n",
              "      <td>4400.000000</td>\n",
              "      <td>4400.000000</td>\n",
              "      <td>4400.000000</td>\n",
              "      <td>4400.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4.106191e+09</td>\n",
              "      <td>506.553864</td>\n",
              "      <td>22.362500</td>\n",
              "      <td>7.174561</td>\n",
              "      <td>9.884545</td>\n",
              "      <td>194.439208</td>\n",
              "      <td>16.969570</td>\n",
              "      <td>3.253853</td>\n",
              "      <td>72.217150</td>\n",
              "      <td>0.496591</td>\n",
              "      <td>339.299218</td>\n",
              "      <td>98.992841</td>\n",
              "      <td>16.768863</td>\n",
              "      <td>25.711065</td>\n",
              "      <td>27.655640</td>\n",
              "      <td>0.162266</td>\n",
              "      <td>0.103182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>6.798984e+09</td>\n",
              "      <td>326.072756</td>\n",
              "      <td>43.618703</td>\n",
              "      <td>19.219665</td>\n",
              "      <td>5.163509</td>\n",
              "      <td>1549.573607</td>\n",
              "      <td>128.369056</td>\n",
              "      <td>5.512943</td>\n",
              "      <td>117.082250</td>\n",
              "      <td>1.198236</td>\n",
              "      <td>832.312947</td>\n",
              "      <td>297.898133</td>\n",
              "      <td>67.975094</td>\n",
              "      <td>55.361244</td>\n",
              "      <td>61.110202</td>\n",
              "      <td>0.518064</td>\n",
              "      <td>0.304231</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.100390e+05</td>\n",
              "      <td>111.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>1.000000</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>6.184590e+08</td>\n",
              "      <td>226.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.153846</td>\n",
              "      <td>2.964286</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>13.538462</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>4.490385</td>\n",
              "      <td>4.412500</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>7.293982e+08</td>\n",
              "      <td>493.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.076923</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>41.500000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>6.333333</td>\n",
              "      <td>12.307692</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>7.292738e+09</td>\n",
              "      <td>1005.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>5.666667</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.358766</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>328.275000</td>\n",
              "      <td>101.333333</td>\n",
              "      <td>14.250000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>30.804545</td>\n",
              "      <td>0.153846</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>7.356031e+10</td>\n",
              "      <td>1017.000000</td>\n",
              "      <td>354.000000</td>\n",
              "      <td>354.000000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>43957.172700</td>\n",
              "      <td>3852.727273</td>\n",
              "      <td>156.583333</td>\n",
              "      <td>3357.500000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>15606.500000</td>\n",
              "      <td>9183.866667</td>\n",
              "      <td>2606.000000</td>\n",
              "      <td>1716.400000</td>\n",
              "      <td>1743.266667</td>\n",
              "      <td>14.636364</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            user_id   account_id  ...  avg_sms_replies       target\n",
              "count  4.400000e+03  4400.000000  ...      4400.000000  4400.000000\n",
              "mean   4.106191e+09   506.553864  ...         0.162266     0.103182\n",
              "std    6.798984e+09   326.072756  ...         0.518064     0.304231\n",
              "min    4.100390e+05   111.000000  ...         0.000000     0.000000\n",
              "25%    6.184590e+08   226.000000  ...         0.000000     0.000000\n",
              "50%    7.293982e+08   493.000000  ...         0.000000     0.000000\n",
              "75%    7.292738e+09  1005.000000  ...         0.153846     0.000000\n",
              "max    7.356031e+10  1017.000000  ...        14.636364     1.000000\n",
              "\n",
              "[8 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e6R1xlrg4TKV",
        "outputId": "4cf7449f-1bbf-4a49-ef96-618debce8046",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        }
      },
      "source": [
        "\n",
        "sns.distplot( dataset1.max_return_days ,kde = False)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa4944667b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cmgu_tKu4TQK",
        "outputId": "8359d26b-0875-48ab-fb06-194f505adea5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 690
        }
      },
      "source": [
        "dataset1.groupby('customer_since_months').average_return_days.hist(alpha = 1)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "customer_since_months\n",
              "1     AxesSubplot(0.125,0.125;0.775x0.755)\n",
              "2     AxesSubplot(0.125,0.125;0.775x0.755)\n",
              "3     AxesSubplot(0.125,0.125;0.775x0.755)\n",
              "4     AxesSubplot(0.125,0.125;0.775x0.755)\n",
              "5     AxesSubplot(0.125,0.125;0.775x0.755)\n",
              "6     AxesSubplot(0.125,0.125;0.775x0.755)\n",
              "7     AxesSubplot(0.125,0.125;0.775x0.755)\n",
              "8     AxesSubplot(0.125,0.125;0.775x0.755)\n",
              "9     AxesSubplot(0.125,0.125;0.775x0.755)\n",
              "10    AxesSubplot(0.125,0.125;0.775x0.755)\n",
              "11    AxesSubplot(0.125,0.125;0.775x0.755)\n",
              "12    AxesSubplot(0.125,0.125;0.775x0.755)\n",
              "13    AxesSubplot(0.125,0.125;0.775x0.755)\n",
              "14    AxesSubplot(0.125,0.125;0.775x0.755)\n",
              "15    AxesSubplot(0.125,0.125;0.775x0.755)\n",
              "16    AxesSubplot(0.125,0.125;0.775x0.755)\n",
              "17    AxesSubplot(0.125,0.125;0.775x0.755)\n",
              "18    AxesSubplot(0.125,0.125;0.775x0.755)\n",
              "19    AxesSubplot(0.125,0.125;0.775x0.755)\n",
              "20    AxesSubplot(0.125,0.125;0.775x0.755)\n",
              "21    AxesSubplot(0.125,0.125;0.775x0.755)\n",
              "23    AxesSubplot(0.125,0.125;0.775x0.755)\n",
              "24    AxesSubplot(0.125,0.125;0.775x0.755)\n",
              "Name: average_return_days, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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CfDMzb2zHdiZi3fzlfPk1WHbCb7H5M42j+Cuun8bPTtl9eWQnLpVcN385753xJuvmL5/U/VZ9ddmktzXWLwRJB4+Wt0BEHAIsB34DeBH4YUSszsynW72tyZg35xRWsfuoefqD23j7opO6mOjA1O6/GMbiXwwa1Q1HNW4/+BW4YVEHtvd6+7fRIu04xDsb2JKZzwNExCCwCGhbuX8y7uOuCy/jDOCDf3YoAxfNYP2D1/LMhVfz41Vw2hX/iq8D//e/wI9X/QmrvrqMK66/jhl/A1/gV3Z9C9OXPvQXzJtzCsy5jzc237jnm7HlMvob+Lcc/9Pzdg3/7IRHuOPtjwCNLwYZ+Qvjrrxs118C//SQAc6deeiuZSMvs5x+9Bf3em6TPdIHuOb7sKhpO832eWUQcPxPz+PtHV/j2IsO4+oHJ//tVDstfOK5cdeZzF8Yzb9Urvn+N/ZY9t5/9ua4n+0DcNu5n5/w9ppN5Vu6mh0039K1s2gPQPPmnDKxFVtwcDPylGe7DlwiM1v7gBGXAxdn5u+V+U8BH83Mz45YbymwtMx+EHhmips8Dvj5FO/baWZtD7O2h1nbo5VZ35eZx4+2oGsnZzNzBbBifx8nItZn5r4/sL1HmLU9zNoeZm2PTmVtx6WQ24BZTfMnlzFJUoe0o9x/CJwaEXMi4jDgSmB1G7YjSRpDy0/LZOa7EfFZ4EEal0J+KzOfavV2muz3qZ0OMmt7mLU9zNoeHcna8jdUJUndV83HD0iSdrPcJalCB2y5R8TFEfFMRGyJiOu6nWekiPhJRGyMiMcjYn0ZOyYiHoqIZ8vt0V3M962I2B4Rm5rGRs0XDbeUff1kRJzZA1lviIhtZf8+HhELm5ZdX7I+ExEXdTDnrIh4OCKejoinIuLzZbzn9us+svbcfi3bnh4RP4iIJ0rer5TxORHxWMl1T7mIg4g4vMxvKctn90DWOyLihaZ9e0YZb8/rIDMPuB8ab9Q+B7wfOAx4Apjb7VwjMv4EOG7E2H8CrivT1wE3dTHfecCZwKbx8gELge8CAZwDPNYDWW8Afn+UdeeW18PhwJzyOjmkQzlPBM4s0zOAvy55em6/7iNrz+3Xsv0A+sr0ocBjZZ+tAq4s47cBnynT/xq4rUxfCdzTA1nvAC4fZf22vA4O1CP3XR9xkJl/D+z8iINetwhYWaZXApd2K0hmPgK8OmJ4rHyLgG9nw6PAzIg4sTNJx8w6lkXAYGa+k5kvAFtovF7aLjNfysy/KtNvAJuBk+jB/bqPrGPp2n4FKPtouMweWn4SOB+4t4yP3Lc79/m9wAUREV3OOpa2vA4O1HI/CfjbpvkX2fcLsxsS+LOI2FA+agGgPzNfKtM/Bfq7E21MY+Xr1f392fJn7LeaTnH1RNZyGuDXaBy19fR+HZEVenS/RsQhEfE4sB14iMZfD69l5s5PBGzOtCtvWf46cGy3smbmzn37H8u+vTkiDh+ZtWjJvj1Qy/1A8LHMPBO4BFgWEec1L8zG32M9ex1qr+cDbgX+CXAG8BLwx92Ns1tE9AH3AV/IzL9rXtZr+3WUrD27XzPzl5l5Bo1/9X42cFqXI41pZNaIOB24nkbmjwDHAGN/eXMLHKjl3vMfcZCZ28rtduA7NF6ML+/8c6vcbu9ewlGNla/n9ndmvlz+B/oH4E/YfYqgq1kj4lAaZXlXZv5pGe7J/Tpa1l7dr80y8zXgYeBcGqcwdv5jzOZMu/KW5UcBr3Q4anPWi8upsMzMd4D/Rpv37YFa7j39EQcRcWREzNg5DVwIbKKRcXFZbTFwf3cSjmmsfKuBq8q7+ucArzedZuiKEeckf4vG/oVG1ivL1RJzgFOBH3QoUwC3A5sz82tNi3puv46VtRf3a8l1fETMLNNH0Pi+iM00ivPystrIfbtzn18OrCt/NXUr64+bfsEHjfcGmvdt618H7XzXuJ0/NN5h/msa593+oNt5RmR7P40rC54AntqZj8Y5v7XAs8D/AY7pYsa7afzZ/f9onONbMlY+Gu/iLy/7eiMw0ANZ7yxZniz/c5zYtP4flKzPAJd0MOfHaJxyeRJ4vPws7MX9uo+sPbdfy7Y/DPyo5NoE/Psy/n4av2S2AP8TOLyMTy/zW8ry9/dA1nVl324C/ju7r6hpy+vAjx+QpAodqKdlJEn7YLlLUoUsd0mqkOUuSRWy3CWpQpa7JFXIcpekCv1/n/5gpPR0XOYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3VRQpuoIAV-_",
        "outputId": "ee73d829-0742-430a-fb02-d82c953b1805",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 596
        }
      },
      "source": [
        "dataset1.corr()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>user_id</th>\n",
              "      <th>account_id</th>\n",
              "      <th>max_return_days</th>\n",
              "      <th>average_return_days</th>\n",
              "      <th>customer_since_months</th>\n",
              "      <th>total_revenue</th>\n",
              "      <th>average_revenue</th>\n",
              "      <th>avg_vas_count</th>\n",
              "      <th>avg_used_vas_value</th>\n",
              "      <th>business_type</th>\n",
              "      <th>avg_wallet_bonus</th>\n",
              "      <th>avg_active_ads</th>\n",
              "      <th>avg_new_ads</th>\n",
              "      <th>avg_email_replies</th>\n",
              "      <th>avg_call_replies</th>\n",
              "      <th>avg_sms_replies</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>user_id</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.056700</td>\n",
              "      <td>0.028626</td>\n",
              "      <td>0.027063</td>\n",
              "      <td>-0.181671</td>\n",
              "      <td>-0.033228</td>\n",
              "      <td>-0.016299</td>\n",
              "      <td>-0.101715</td>\n",
              "      <td>-0.113532</td>\n",
              "      <td>0.042191</td>\n",
              "      <td>-0.096223</td>\n",
              "      <td>-0.061172</td>\n",
              "      <td>-0.003543</td>\n",
              "      <td>-0.105052</td>\n",
              "      <td>-0.098219</td>\n",
              "      <td>-0.070978</td>\n",
              "      <td>0.048218</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>account_id</th>\n",
              "      <td>0.056700</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.186045</td>\n",
              "      <td>0.114084</td>\n",
              "      <td>-0.138623</td>\n",
              "      <td>-0.056088</td>\n",
              "      <td>-0.063251</td>\n",
              "      <td>-0.123744</td>\n",
              "      <td>-0.137692</td>\n",
              "      <td>-0.023560</td>\n",
              "      <td>-0.115246</td>\n",
              "      <td>-0.104818</td>\n",
              "      <td>-0.049433</td>\n",
              "      <td>-0.111508</td>\n",
              "      <td>-0.115326</td>\n",
              "      <td>-0.082611</td>\n",
              "      <td>0.121939</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max_return_days</th>\n",
              "      <td>0.028626</td>\n",
              "      <td>0.186045</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.749133</td>\n",
              "      <td>-0.187866</td>\n",
              "      <td>-0.001889</td>\n",
              "      <td>0.013184</td>\n",
              "      <td>-0.112382</td>\n",
              "      <td>-0.119129</td>\n",
              "      <td>-0.018485</td>\n",
              "      <td>-0.131552</td>\n",
              "      <td>-0.042902</td>\n",
              "      <td>-0.009146</td>\n",
              "      <td>-0.075940</td>\n",
              "      <td>-0.079302</td>\n",
              "      <td>-0.050359</td>\n",
              "      <td>0.275500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>average_return_days</th>\n",
              "      <td>0.027063</td>\n",
              "      <td>0.114084</td>\n",
              "      <td>0.749133</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.257220</td>\n",
              "      <td>-0.014447</td>\n",
              "      <td>-0.006567</td>\n",
              "      <td>-0.108128</td>\n",
              "      <td>-0.114067</td>\n",
              "      <td>0.027346</td>\n",
              "      <td>-0.103593</td>\n",
              "      <td>-0.047031</td>\n",
              "      <td>-0.017632</td>\n",
              "      <td>-0.070784</td>\n",
              "      <td>-0.070982</td>\n",
              "      <td>-0.038793</td>\n",
              "      <td>0.166119</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>customer_since_months</th>\n",
              "      <td>-0.181671</td>\n",
              "      <td>-0.138623</td>\n",
              "      <td>-0.187866</td>\n",
              "      <td>-0.257220</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.090466</td>\n",
              "      <td>0.063811</td>\n",
              "      <td>0.320958</td>\n",
              "      <td>0.350325</td>\n",
              "      <td>-0.217427</td>\n",
              "      <td>0.351896</td>\n",
              "      <td>0.138805</td>\n",
              "      <td>0.020578</td>\n",
              "      <td>0.206001</td>\n",
              "      <td>0.206257</td>\n",
              "      <td>0.122789</td>\n",
              "      <td>-0.235238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_revenue</th>\n",
              "      <td>-0.033228</td>\n",
              "      <td>-0.056088</td>\n",
              "      <td>-0.001889</td>\n",
              "      <td>-0.014447</td>\n",
              "      <td>0.090466</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.950357</td>\n",
              "      <td>0.608638</td>\n",
              "      <td>0.642191</td>\n",
              "      <td>-0.017400</td>\n",
              "      <td>0.075885</td>\n",
              "      <td>0.341788</td>\n",
              "      <td>0.131101</td>\n",
              "      <td>0.214032</td>\n",
              "      <td>0.214258</td>\n",
              "      <td>0.213352</td>\n",
              "      <td>-0.032580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>average_revenue</th>\n",
              "      <td>-0.016299</td>\n",
              "      <td>-0.063251</td>\n",
              "      <td>0.013184</td>\n",
              "      <td>-0.006567</td>\n",
              "      <td>0.063811</td>\n",
              "      <td>0.950357</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.586291</td>\n",
              "      <td>0.612613</td>\n",
              "      <td>-0.001530</td>\n",
              "      <td>0.070007</td>\n",
              "      <td>0.354482</td>\n",
              "      <td>0.132759</td>\n",
              "      <td>0.209668</td>\n",
              "      <td>0.209233</td>\n",
              "      <td>0.214889</td>\n",
              "      <td>-0.025354</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>avg_vas_count</th>\n",
              "      <td>-0.101715</td>\n",
              "      <td>-0.123744</td>\n",
              "      <td>-0.112382</td>\n",
              "      <td>-0.108128</td>\n",
              "      <td>0.320958</td>\n",
              "      <td>0.608638</td>\n",
              "      <td>0.586291</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.962294</td>\n",
              "      <td>-0.119417</td>\n",
              "      <td>0.130987</td>\n",
              "      <td>0.413871</td>\n",
              "      <td>0.192561</td>\n",
              "      <td>0.360600</td>\n",
              "      <td>0.342573</td>\n",
              "      <td>0.240456</td>\n",
              "      <td>-0.117154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>avg_used_vas_value</th>\n",
              "      <td>-0.113532</td>\n",
              "      <td>-0.137692</td>\n",
              "      <td>-0.119129</td>\n",
              "      <td>-0.114067</td>\n",
              "      <td>0.350325</td>\n",
              "      <td>0.642191</td>\n",
              "      <td>0.612613</td>\n",
              "      <td>0.962294</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.119371</td>\n",
              "      <td>0.156046</td>\n",
              "      <td>0.407629</td>\n",
              "      <td>0.186521</td>\n",
              "      <td>0.362861</td>\n",
              "      <td>0.347016</td>\n",
              "      <td>0.244097</td>\n",
              "      <td>-0.128464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>business_type</th>\n",
              "      <td>0.042191</td>\n",
              "      <td>-0.023560</td>\n",
              "      <td>-0.018485</td>\n",
              "      <td>0.027346</td>\n",
              "      <td>-0.217427</td>\n",
              "      <td>-0.017400</td>\n",
              "      <td>-0.001530</td>\n",
              "      <td>-0.119417</td>\n",
              "      <td>-0.119371</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.107314</td>\n",
              "      <td>-0.076037</td>\n",
              "      <td>-0.004429</td>\n",
              "      <td>-0.050023</td>\n",
              "      <td>-0.053766</td>\n",
              "      <td>0.005707</td>\n",
              "      <td>0.005954</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>avg_wallet_bonus</th>\n",
              "      <td>-0.096223</td>\n",
              "      <td>-0.115246</td>\n",
              "      <td>-0.131552</td>\n",
              "      <td>-0.103593</td>\n",
              "      <td>0.351896</td>\n",
              "      <td>0.075885</td>\n",
              "      <td>0.070007</td>\n",
              "      <td>0.130987</td>\n",
              "      <td>0.156046</td>\n",
              "      <td>-0.107314</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.256529</td>\n",
              "      <td>0.121930</td>\n",
              "      <td>0.273732</td>\n",
              "      <td>0.284304</td>\n",
              "      <td>0.237666</td>\n",
              "      <td>-0.076783</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>avg_active_ads</th>\n",
              "      <td>-0.061172</td>\n",
              "      <td>-0.104818</td>\n",
              "      <td>-0.042902</td>\n",
              "      <td>-0.047031</td>\n",
              "      <td>0.138805</td>\n",
              "      <td>0.341788</td>\n",
              "      <td>0.354482</td>\n",
              "      <td>0.413871</td>\n",
              "      <td>0.407629</td>\n",
              "      <td>-0.076037</td>\n",
              "      <td>0.256529</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.651254</td>\n",
              "      <td>0.704153</td>\n",
              "      <td>0.677130</td>\n",
              "      <td>0.433443</td>\n",
              "      <td>-0.021826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>avg_new_ads</th>\n",
              "      <td>-0.003543</td>\n",
              "      <td>-0.049433</td>\n",
              "      <td>-0.009146</td>\n",
              "      <td>-0.017632</td>\n",
              "      <td>0.020578</td>\n",
              "      <td>0.131101</td>\n",
              "      <td>0.132759</td>\n",
              "      <td>0.192561</td>\n",
              "      <td>0.186521</td>\n",
              "      <td>-0.004429</td>\n",
              "      <td>0.121930</td>\n",
              "      <td>0.651254</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.413935</td>\n",
              "      <td>0.379151</td>\n",
              "      <td>0.261525</td>\n",
              "      <td>0.014593</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>avg_email_replies</th>\n",
              "      <td>-0.105052</td>\n",
              "      <td>-0.111508</td>\n",
              "      <td>-0.075940</td>\n",
              "      <td>-0.070784</td>\n",
              "      <td>0.206001</td>\n",
              "      <td>0.214032</td>\n",
              "      <td>0.209668</td>\n",
              "      <td>0.360600</td>\n",
              "      <td>0.362861</td>\n",
              "      <td>-0.050023</td>\n",
              "      <td>0.273732</td>\n",
              "      <td>0.704153</td>\n",
              "      <td>0.413935</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.955301</td>\n",
              "      <td>0.716719</td>\n",
              "      <td>-0.070678</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>avg_call_replies</th>\n",
              "      <td>-0.098219</td>\n",
              "      <td>-0.115326</td>\n",
              "      <td>-0.079302</td>\n",
              "      <td>-0.070982</td>\n",
              "      <td>0.206257</td>\n",
              "      <td>0.214258</td>\n",
              "      <td>0.209233</td>\n",
              "      <td>0.342573</td>\n",
              "      <td>0.347016</td>\n",
              "      <td>-0.053766</td>\n",
              "      <td>0.284304</td>\n",
              "      <td>0.677130</td>\n",
              "      <td>0.379151</td>\n",
              "      <td>0.955301</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.799181</td>\n",
              "      <td>-0.075218</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>avg_sms_replies</th>\n",
              "      <td>-0.070978</td>\n",
              "      <td>-0.082611</td>\n",
              "      <td>-0.050359</td>\n",
              "      <td>-0.038793</td>\n",
              "      <td>0.122789</td>\n",
              "      <td>0.213352</td>\n",
              "      <td>0.214889</td>\n",
              "      <td>0.240456</td>\n",
              "      <td>0.244097</td>\n",
              "      <td>0.005707</td>\n",
              "      <td>0.237666</td>\n",
              "      <td>0.433443</td>\n",
              "      <td>0.261525</td>\n",
              "      <td>0.716719</td>\n",
              "      <td>0.799181</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.062319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
              "      <td>0.048218</td>\n",
              "      <td>0.121939</td>\n",
              "      <td>0.275500</td>\n",
              "      <td>0.166119</td>\n",
              "      <td>-0.235238</td>\n",
              "      <td>-0.032580</td>\n",
              "      <td>-0.025354</td>\n",
              "      <td>-0.117154</td>\n",
              "      <td>-0.128464</td>\n",
              "      <td>0.005954</td>\n",
              "      <td>-0.076783</td>\n",
              "      <td>-0.021826</td>\n",
              "      <td>0.014593</td>\n",
              "      <td>-0.070678</td>\n",
              "      <td>-0.075218</td>\n",
              "      <td>-0.062319</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        user_id  account_id  ...  avg_sms_replies    target\n",
              "user_id                1.000000    0.056700  ...        -0.070978  0.048218\n",
              "account_id             0.056700    1.000000  ...        -0.082611  0.121939\n",
              "max_return_days        0.028626    0.186045  ...        -0.050359  0.275500\n",
              "average_return_days    0.027063    0.114084  ...        -0.038793  0.166119\n",
              "customer_since_months -0.181671   -0.138623  ...         0.122789 -0.235238\n",
              "total_revenue         -0.033228   -0.056088  ...         0.213352 -0.032580\n",
              "average_revenue       -0.016299   -0.063251  ...         0.214889 -0.025354\n",
              "avg_vas_count         -0.101715   -0.123744  ...         0.240456 -0.117154\n",
              "avg_used_vas_value    -0.113532   -0.137692  ...         0.244097 -0.128464\n",
              "business_type          0.042191   -0.023560  ...         0.005707  0.005954\n",
              "avg_wallet_bonus      -0.096223   -0.115246  ...         0.237666 -0.076783\n",
              "avg_active_ads        -0.061172   -0.104818  ...         0.433443 -0.021826\n",
              "avg_new_ads           -0.003543   -0.049433  ...         0.261525  0.014593\n",
              "avg_email_replies     -0.105052   -0.111508  ...         0.716719 -0.070678\n",
              "avg_call_replies      -0.098219   -0.115326  ...         0.799181 -0.075218\n",
              "avg_sms_replies       -0.070978   -0.082611  ...         1.000000 -0.062319\n",
              "target                 0.048218    0.121939  ...        -0.062319  1.000000\n",
              "\n",
              "[17 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YuuCsuOgmd-p",
        "outputId": "8a2f3234-3216-421b-a2ca-a0dc890fbd0f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        }
      },
      "source": [
        "sns.heatmap(dataset1.corr())"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa493fc9b00>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xWjUVCUYCuQs",
        "outputId": "ff490bb7-c73e-40f8-9389-65bb73302c38",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        }
      },
      "source": [
        "sns.countplot(x = 'customer_since_months', data = dataset1);"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tZ2OS8e5jVSb",
        "outputId": "9965b795-baba-470d-b12e-c75b0de063c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        }
      },
      "source": [
        "sns.countplot(x = 'business_type', data = dataset1);"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pBfLoLSEpKJk",
        "outputId": "4e9b619a-7ff3-4765-d324-f274838b62aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "sns.countplot(x = 'target', data = dataset1);"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lEc25iHqk3k-",
        "outputId": "1470ea13-842f-4116-a10a-aa9344e534b5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        }
      },
      "source": [
        "sns.countplot(x = 'customer_since_months',hue='target', data = dataset1);"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eSbUoHtolgkQ",
        "outputId": "20381490-9f78-4e0e-eeb0-d9d4dc49f64c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        }
      },
      "source": [
        "#dropping the useless feature\n",
        "columns_to_drop = ['user_id','account_id','avg_email_replies','avg_call_replies',\n",
        "                   'avg_sms_replies']\n",
        "\n",
        "dataset1.drop(columns_to_drop, axis = 1 ,inplace = True)\n",
        "dataset1.head()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py:4170: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  errors=errors,\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>max_return_days</th>\n",
              "      <th>average_return_days</th>\n",
              "      <th>customer_since_months</th>\n",
              "      <th>total_revenue</th>\n",
              "      <th>average_revenue</th>\n",
              "      <th>avg_vas_count</th>\n",
              "      <th>avg_used_vas_value</th>\n",
              "      <th>business_type</th>\n",
              "      <th>avg_wallet_bonus</th>\n",
              "      <th>avg_active_ads</th>\n",
              "      <th>avg_new_ads</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>14</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.666667</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>0</td>\n",
              "      <td>300.0</td>\n",
              "      <td>79.333333</td>\n",
              "      <td>15.250000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>37</td>\n",
              "      <td>8.200000</td>\n",
              "      <td>10</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.900000</td>\n",
              "      <td>114.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>232.100000</td>\n",
              "      <td>18.300000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9</td>\n",
              "      <td>1.533333</td>\n",
              "      <td>15</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.333333</td>\n",
              "      <td>106.666667</td>\n",
              "      <td>0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>55.083333</td>\n",
              "      <td>3.750000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>14</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.307692</td>\n",
              "      <td>176.153846</td>\n",
              "      <td>0</td>\n",
              "      <td>1481.0</td>\n",
              "      <td>409.538462</td>\n",
              "      <td>80.769231</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>18</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.846154</td>\n",
              "      <td>109.230769</td>\n",
              "      <td>0</td>\n",
              "      <td>41.5</td>\n",
              "      <td>48.230769</td>\n",
              "      <td>1.692308</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   max_return_days  average_return_days  ...  avg_new_ads  target\n",
              "0                8             1.500000  ...    15.250000       0\n",
              "1               37             8.200000  ...    18.300000       0\n",
              "2                9             1.533333  ...     3.750000       0\n",
              "3                1             1.000000  ...    80.769231       1\n",
              "4                1             1.000000  ...     1.692308       0\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oZhgbJ3oo4RC",
        "outputId": "890c894b-59ef-4fff-82bd-4b71c2ff2254",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "source": [
        "dataset1.nunique()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "max_return_days           229\n",
              "average_return_days       917\n",
              "customer_since_months      23\n",
              "total_revenue             434\n",
              "average_revenue           510\n",
              "avg_vas_count             677\n",
              "avg_used_vas_value       1027\n",
              "business_type               6\n",
              "avg_wallet_bonus          989\n",
              "avg_active_ads           2755\n",
              "avg_new_ads              1421\n",
              "target                      2\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PQLXElo6xXoN"
      },
      "source": [
        "## **Training the classifier**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-jfL1LH6plhw",
        "outputId": "ba9e6e32-cd7a-47cc-8094-e6e92e4067ce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        }
      },
      "source": [
        "data = pd.DataFrame(dataset1)\n",
        "data.head()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>max_return_days</th>\n",
              "      <th>average_return_days</th>\n",
              "      <th>customer_since_months</th>\n",
              "      <th>total_revenue</th>\n",
              "      <th>average_revenue</th>\n",
              "      <th>avg_vas_count</th>\n",
              "      <th>avg_used_vas_value</th>\n",
              "      <th>business_type</th>\n",
              "      <th>avg_wallet_bonus</th>\n",
              "      <th>avg_active_ads</th>\n",
              "      <th>avg_new_ads</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>14</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.666667</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>0</td>\n",
              "      <td>300.0</td>\n",
              "      <td>79.333333</td>\n",
              "      <td>15.250000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>37</td>\n",
              "      <td>8.200000</td>\n",
              "      <td>10</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.900000</td>\n",
              "      <td>114.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>232.100000</td>\n",
              "      <td>18.300000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9</td>\n",
              "      <td>1.533333</td>\n",
              "      <td>15</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.333333</td>\n",
              "      <td>106.666667</td>\n",
              "      <td>0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>55.083333</td>\n",
              "      <td>3.750000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>14</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.307692</td>\n",
              "      <td>176.153846</td>\n",
              "      <td>0</td>\n",
              "      <td>1481.0</td>\n",
              "      <td>409.538462</td>\n",
              "      <td>80.769231</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>18</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.846154</td>\n",
              "      <td>109.230769</td>\n",
              "      <td>0</td>\n",
              "      <td>41.5</td>\n",
              "      <td>48.230769</td>\n",
              "      <td>1.692308</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   max_return_days  average_return_days  ...  avg_new_ads  target\n",
              "0                8             1.500000  ...    15.250000       0\n",
              "1               37             8.200000  ...    18.300000       0\n",
              "2                9             1.533333  ...     3.750000       0\n",
              "3                1             1.000000  ...    80.769231       1\n",
              "4                1             1.000000  ...     1.692308       0\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5XZqlOrtwmcZ",
        "outputId": "e17fa08d-1f7d-4fd8-c34b-10359e6eeb57",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "print(data.columns)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index(['max_return_days', 'average_return_days', 'customer_since_months',\n",
            "       'total_revenue', 'average_revenue', 'avg_vas_count',\n",
            "       'avg_used_vas_value', 'business_type', 'avg_wallet_bonus',\n",
            "       'avg_active_ads', 'avg_new_ads', 'target'],\n",
            "      dtype='object')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dJ3fteSHwuQu",
        "outputId": "43f1d6c7-699c-4f59-e16b-82b68a45492e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        }
      },
      "source": [
        "data.info()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 4400 entries, 0 to 4406\n",
            "Data columns (total 12 columns):\n",
            " #   Column                 Non-Null Count  Dtype  \n",
            "---  ------                 --------------  -----  \n",
            " 0   max_return_days        4400 non-null   int64  \n",
            " 1   average_return_days    4400 non-null   float64\n",
            " 2   customer_since_months  4400 non-null   int64  \n",
            " 3   total_revenue          4400 non-null   float64\n",
            " 4   average_revenue        4400 non-null   float64\n",
            " 5   avg_vas_count          4400 non-null   float64\n",
            " 6   avg_used_vas_value     4400 non-null   float64\n",
            " 7   business_type          4400 non-null   int64  \n",
            " 8   avg_wallet_bonus       4400 non-null   float64\n",
            " 9   avg_active_ads         4400 non-null   float64\n",
            " 10  avg_new_ads            4400 non-null   float64\n",
            " 11  target                 4400 non-null   int64  \n",
            "dtypes: float64(8), int64(4)\n",
            "memory usage: 606.9 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rOpK8eM3xwU5",
        "outputId": "c31fd493-f0a3-49c2-aa0f-58d4c7356baf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        }
      },
      "source": [
        "data.describe()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>max_return_days</th>\n",
              "      <th>average_return_days</th>\n",
              "      <th>customer_since_months</th>\n",
              "      <th>total_revenue</th>\n",
              "      <th>average_revenue</th>\n",
              "      <th>avg_vas_count</th>\n",
              "      <th>avg_used_vas_value</th>\n",
              "      <th>business_type</th>\n",
              "      <th>avg_wallet_bonus</th>\n",
              "      <th>avg_active_ads</th>\n",
              "      <th>avg_new_ads</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4400.000000</td>\n",
              "      <td>4400.000000</td>\n",
              "      <td>4400.000000</td>\n",
              "      <td>4400.000000</td>\n",
              "      <td>4400.000000</td>\n",
              "      <td>4400.000000</td>\n",
              "      <td>4400.000000</td>\n",
              "      <td>4400.000000</td>\n",
              "      <td>4400.000000</td>\n",
              "      <td>4400.000000</td>\n",
              "      <td>4400.000000</td>\n",
              "      <td>4400.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>22.362500</td>\n",
              "      <td>7.174561</td>\n",
              "      <td>9.884545</td>\n",
              "      <td>194.439208</td>\n",
              "      <td>16.969570</td>\n",
              "      <td>3.253853</td>\n",
              "      <td>72.217150</td>\n",
              "      <td>0.496591</td>\n",
              "      <td>339.299218</td>\n",
              "      <td>98.992841</td>\n",
              "      <td>16.768863</td>\n",
              "      <td>0.103182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>43.618703</td>\n",
              "      <td>19.219665</td>\n",
              "      <td>5.163509</td>\n",
              "      <td>1549.573607</td>\n",
              "      <td>128.369056</td>\n",
              "      <td>5.512943</td>\n",
              "      <td>117.082250</td>\n",
              "      <td>1.198236</td>\n",
              "      <td>832.312947</td>\n",
              "      <td>297.898133</td>\n",
              "      <td>67.975094</td>\n",
              "      <td>0.304231</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>1.000000</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>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.153846</td>\n",
              "      <td>2.964286</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>13.538462</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.076923</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>41.500000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>6.333333</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>30.000000</td>\n",
              "      <td>5.666667</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.358766</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>328.275000</td>\n",
              "      <td>101.333333</td>\n",
              "      <td>14.250000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>354.000000</td>\n",
              "      <td>354.000000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>43957.172700</td>\n",
              "      <td>3852.727273</td>\n",
              "      <td>156.583333</td>\n",
              "      <td>3357.500000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>15606.500000</td>\n",
              "      <td>9183.866667</td>\n",
              "      <td>2606.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       max_return_days  average_return_days  ...  avg_new_ads       target\n",
              "count      4400.000000          4400.000000  ...  4400.000000  4400.000000\n",
              "mean         22.362500             7.174561  ...    16.768863     0.103182\n",
              "std          43.618703            19.219665  ...    67.975094     0.304231\n",
              "min           1.000000             0.500000  ...     0.000000     0.000000\n",
              "25%           1.000000             1.000000  ...     2.500000     0.000000\n",
              "50%           2.000000             1.076923  ...     6.333333     0.000000\n",
              "75%          30.000000             5.666667  ...    14.250000     0.000000\n",
              "max         354.000000           354.000000  ...  2606.000000     1.000000\n",
              "\n",
              "[8 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TWfBFNfVxzst"
      },
      "source": [
        "import xgboost as xgb\n",
        "from sklearn.metrics import mean_squared_error\n"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DjPOXsvKyIqd"
      },
      "source": [
        "X, y = data.iloc[:,:-1] , data.iloc[:,-1]"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DpwSExObyUvQ"
      },
      "source": [
        "data_matrix = xgb.DMatrix(data = X, label = y)"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "adFTxNFdykSQ"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 123)"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cYD5pO33zXGq"
      },
      "source": [
        "xg_reg = xgb.XGBRegressor(objective= 'reg:squarederror', colsample_bytree=0.3, learning_rate=0.1,\n",
        "                          alpha = 10, max_depth = 5, n_estimators = 10  )"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S8ClHQsWz4WD"
      },
      "source": [
        "xg_reg.fit(X_train,y_train)\n",
        "\n",
        "preds = xg_reg.predict(X_test)"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IEhna4Mf0qjO",
        "outputId": "7df2f13b-54b1-41cd-d9f5-9eb616797fba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "rmse = np.sqrt(mean_squared_error(y_test, preds))\n",
        "print(\"RMSE: %f\" % (rmse))"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RMSE: 0.318091\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eNYQiteM08Pi"
      },
      "source": [
        "params = {\"objective\" : \"reg:squarederror\", \"colsample_bytree\" : 0.3, \"learning_rate\" : 0.1,\n",
        "          \"max_depth\" : 5, \"alpha\" : 10}\n",
        "\n",
        "cv_results = xgb.cv(dtrain = data_matrix, params = params, nfold = 3,\n",
        "                    num_boost_round = 50, early_stopping_rounds = 10, metrics = \"rmse\",as_pandas =True, seed = 123 )"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OC_XWqzycRtb",
        "outputId": "c039f6bb-7383-44ed-d131-d51ec6649c05",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "cv_results.head()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>train-rmse-mean</th>\n",
              "      <th>train-rmse-std</th>\n",
              "      <th>test-rmse-mean</th>\n",
              "      <th>test-rmse-std</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.468143</td>\n",
              "      <td>0.000635</td>\n",
              "      <td>0.468227</td>\n",
              "      <td>0.000401</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.440088</td>\n",
              "      <td>0.001045</td>\n",
              "      <td>0.440440</td>\n",
              "      <td>0.001197</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.416115</td>\n",
              "      <td>0.001578</td>\n",
              "      <td>0.416656</td>\n",
              "      <td>0.002133</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.395464</td>\n",
              "      <td>0.002651</td>\n",
              "      <td>0.396307</td>\n",
              "      <td>0.002835</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.377649</td>\n",
              "      <td>0.003265</td>\n",
              "      <td>0.378712</td>\n",
              "      <td>0.003581</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   train-rmse-mean  train-rmse-std  test-rmse-mean  test-rmse-std\n",
              "0         0.468143        0.000635        0.468227       0.000401\n",
              "1         0.440088        0.001045        0.440440       0.001197\n",
              "2         0.416115        0.001578        0.416656       0.002133\n",
              "3         0.395464        0.002651        0.396307       0.002835\n",
              "4         0.377649        0.003265        0.378712       0.003581"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pIGcSN5xcYgh",
        "outputId": "854c02ea-a7a7-447b-de88-d82b6af37397",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "#printing the final boosting round metric\n",
        "print((cv_results[\"test-rmse-mean\"]).tail(1))"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "49    0.275265\n",
            "Name: test-rmse-mean, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IVz78Rprdez_"
      },
      "source": [
        "## **Visualizing Boosting Trees and Feature Importance**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y5UsQb7Fc_E7"
      },
      "source": [
        "xg_reg = xgb.train(params = params, dtrain= data_matrix, num_boost_round = 10)"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r55Xin3hdWyw",
        "outputId": "4b6d0cbe-3b39-4fd2-a6ba-17baad1f7c87",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        }
      },
      "source": [
        "#plotting the first tree using matplotlib library\n",
        "\n",
        "xgb.plot_tree(xg_reg, num_trees=0)\n",
        "plt.rcParams['figure.figsize'] = [50,20]\n",
        "plt.show()"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JtPsHYyoeRP5",
        "outputId": "f6ec8986-9115-442a-c28e-bc96660b66cf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        }
      },
      "source": [
        "xgb.plot_importance(xg_reg)\n",
        "plt.rcParams['figure.figsize'] = [8,8]\n",
        "plt.show()"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pJpLojiwfeoY"
      },
      "source": [
        "The feature **max_return_days** has been given the highest importance score among all the features."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3mQQVAjFencR"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}