customer_conversion_prediction.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4hD_h7HZLgWS"
      },
      "source": [
        "## Importing And Installing Necessary Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nLDU3i_VF6Tl",
        "outputId": "2927f7e4-c8ef-4af8-87de-41084a595b93"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting statistics\n",
            "  Downloading statistics-1.0.3.5.tar.gz (8.3 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: docutils>=0.3 in /usr/local/lib/python3.10/dist-packages (from statistics) (0.16)\n",
            "Building wheels for collected packages: statistics\n",
            "  Building wheel for statistics (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for statistics: filename=statistics-1.0.3.5-py3-none-any.whl size=7438 sha256=725272b1ca8536803b0f84922a87493eff64b4b2858d6a164e648259fe922948\n",
            "  Stored in directory: /root/.cache/pip/wheels/59/bd/aa/41ad56fbb723d36e457b5be74feb992cdac2855a6999ba34a1\n",
            "Successfully built statistics\n",
            "Installing collected packages: statistics\n",
            "Successfully installed statistics-1.0.3.5\n"
          ]
        }
      ],
      "source": [
        "!pip install statistics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MZlbfGqjFLJp"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import statistics as st\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XIiv9aB6Lrhg"
      },
      "source": [
        "## Insights Of The Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f1y1Gr6MGICV"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv('/content/raw_data.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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          "height": 424
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        "id": "3zFTebEXGORD",
        "outputId": "731d9351-458b-485a-fcb7-83a9194d4fe5"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       age           job   marital education_qual  call_type  day  mon   dur  \\\n",
              "0       58    management   married       tertiary    unknown    5  may   261   \n",
              "1       44    technician    single      secondary    unknown    5  may   151   \n",
              "2       33  entrepreneur   married      secondary    unknown    5  may    76   \n",
              "3       47   blue-collar   married        unknown    unknown    5  may    92   \n",
              "4       33       unknown    single        unknown    unknown    5  may   198   \n",
              "...    ...           ...       ...            ...        ...  ...  ...   ...   \n",
              "45206   51    technician   married       tertiary   cellular   17  nov   977   \n",
              "45207   71       retired  divorced        primary   cellular   17  nov   456   \n",
              "45208   72       retired   married      secondary   cellular   17  nov  1127   \n",
              "45209   57   blue-collar   married      secondary  telephone   17  nov   508   \n",
              "45210   37  entrepreneur   married      secondary   cellular   17  nov   361   \n",
              "\n",
              "       num_calls prev_outcome    y  \n",
              "0              1      unknown   no  \n",
              "1              1      unknown   no  \n",
              "2              1      unknown   no  \n",
              "3              1      unknown   no  \n",
              "4              1      unknown   no  \n",
              "...          ...          ...  ...  \n",
              "45206          3      unknown  yes  \n",
              "45207          2      unknown  yes  \n",
              "45208          5      success  yes  \n",
              "45209          4      unknown   no  \n",
              "45210          2        other   no  \n",
              "\n",
              "[45211 rows x 11 columns]"
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              "      <th>prev_outcome</th>\n",
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              "      <td>58</td>\n",
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              "      <th>45206</th>\n",
              "      <td>51</td>\n",
              "      <td>technician</td>\n",
              "      <td>married</td>\n",
              "      <td>tertiary</td>\n",
              "      <td>cellular</td>\n",
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              "      <td>nov</td>\n",
              "      <td>977</td>\n",
              "      <td>3</td>\n",
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              "      <th>45207</th>\n",
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              "      <td>cellular</td>\n",
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              "      <td>nov</td>\n",
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              "      <td>yes</td>\n",
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              "    <tr>\n",
              "      <th>45208</th>\n",
              "      <td>72</td>\n",
              "      <td>retired</td>\n",
              "      <td>married</td>\n",
              "      <td>secondary</td>\n",
              "      <td>cellular</td>\n",
              "      <td>17</td>\n",
              "      <td>nov</td>\n",
              "      <td>1127</td>\n",
              "      <td>5</td>\n",
              "      <td>success</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45209</th>\n",
              "      <td>57</td>\n",
              "      <td>blue-collar</td>\n",
              "      <td>married</td>\n",
              "      <td>secondary</td>\n",
              "      <td>telephone</td>\n",
              "      <td>17</td>\n",
              "      <td>nov</td>\n",
              "      <td>508</td>\n",
              "      <td>4</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
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              "    <tr>\n",
              "      <th>45210</th>\n",
              "      <td>37</td>\n",
              "      <td>entrepreneur</td>\n",
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              "      <td>secondary</td>\n",
              "      <td>cellular</td>\n",
              "      <td>17</td>\n",
              "      <td>nov</td>\n",
              "      <td>361</td>\n",
              "      <td>2</td>\n",
              "      <td>other</td>\n",
              "      <td>no</td>\n",
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              "<p>45211 rows × 11 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "pd.set_option('display.max_columns', None)\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Insights of the data\n",
        "\n",
        "### Features\n",
        "age - continuous feature. Age of the customer\n",
        "\n",
        "job - multi class categorical feature. Job type of the customer\n",
        "\n",
        "marital - multi class categorical feature. marital status of the customer whether he/she is single or married or divorced\n",
        "\n",
        "education_qual - multi class categorical feature. education qualification of the customer\n",
        "\n",
        "call_type - multi class categorical feature. call type in which the company employees contacted the customer\n",
        "\n",
        "day - continuous feature. which day of the month the employees contacted the customer\n",
        "\n",
        "mon - multi class categorical feature. which month the employees contacted the customer\n",
        "\n",
        "dur - continuous feature. duration of the call in seconds. Longer the duration, more chance the customer takes insurance\n",
        "\n",
        "num_calls - continuous feature. number of calls made to the customer by the employees\n",
        "\n",
        "prev_outcome - multi class categorical feature. the status of the previous call made to the customer\n",
        "\n",
        "### Target\n",
        "\n",
        "y - Binary categorical target variable. The customer takes insurance or not. Imbalnced target variable. Need to balnce it"
      ],
      "metadata": {
        "id": "nVT6Pq-itAcO"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "peBeR4KKLTY1"
      },
      "source": [
        "## Generic Cleaning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gVpRSTPjJnzA",
        "outputId": "67cea93b-44c8-4ed6-a617-d3bb16bca51a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(45211, 11)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HQlzZKCTJrQh",
        "outputId": "276d8a1b-3025-4af1-b6dc-26418dbd3d22"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['age', 'job', 'marital', 'education_qual', 'call_type', 'day', 'mon',\n",
              "       'dur', 'num_calls', 'prev_outcome', 'y'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "df.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U85cqJPbJxM1",
        "outputId": "2f445541-30d4-4edd-8be0-70309cae9772"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age               0\n",
              "job               0\n",
              "marital           0\n",
              "education_qual    0\n",
              "call_type         0\n",
              "day               0\n",
              "mon               0\n",
              "dur               0\n",
              "num_calls         0\n",
              "prev_outcome      0\n",
              "y                 0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "df.isnull().sum()\n",
        "# no null values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cNYusjFzJ_qt",
        "outputId": "851c1fa8-3a17-4b8c-a66a-d56a25f516f3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age                int64\n",
              "job               object\n",
              "marital           object\n",
              "education_qual    object\n",
              "call_type         object\n",
              "day                int64\n",
              "mon               object\n",
              "dur                int64\n",
              "num_calls          int64\n",
              "prev_outcome      object\n",
              "y                 object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "df.dtypes\n",
        "# All the features and the target are in correct form of datatype"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TVOgmiTIKV1w"
      },
      "outputs": [],
      "source": [
        "df = df.drop_duplicates()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CwKXckGaLauK"
      },
      "source": [
        "## Column Wise Cleaning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6FVlAsryOcIz"
      },
      "source": [
        "#### 'age' Column"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FSQra9HcK9tB",
        "outputId": "b9fc1846-38e1-493c-f160-517ddfa8ec26"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "32    2084\n",
              "31    1996\n",
              "33    1972\n",
              "34    1929\n",
              "35    1894\n",
              "      ... \n",
              "93       2\n",
              "90       2\n",
              "95       2\n",
              "88       2\n",
              "94       1\n",
              "Name: age, Length: 77, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "df['age'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s2DSAtzNL-GN",
        "outputId": "b55912eb-80d5-4637-cfa8-6b4ce69158b5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    45205.000000\n",
              "mean        40.937087\n",
              "std         10.619130\n",
              "min         18.000000\n",
              "25%         33.000000\n",
              "50%         39.000000\n",
              "75%         48.000000\n",
              "max         95.000000\n",
              "Name: age, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "df['age'].describe()\n",
        "# Comparing value_counts and the summary, the aged persons can be considered as outliers here and these outliers are to be clipped."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4dxcoiw9NEtz"
      },
      "outputs": [],
      "source": [
        "q1 = df['age'].quantile(0.25)\n",
        "q3 = df['age'].quantile(0.75)\n",
        "iqr = q3 - q1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "n0u4ti6nNUQR"
      },
      "outputs": [],
      "source": [
        "lower_threshold = round(q1-(1.5*iqr))\n",
        "upper_threshold = round(q3+(1.5*iqr))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aMpTCRaQNrh2",
        "outputId": "6a8376b8-dfe6-4d2d-d6c6-c3884c07542a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10, 70)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "lower_threshold, upper_threshold"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TiLfmZs7NudM",
        "outputId": "d212ebee-ec25-4a50-a4f2-9f58c94ad6bd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-16-6e832abf6844>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\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",
            "  df['age'] = df['age'].clip(lower_threshold, upper_threshold)\n"
          ]
        }
      ],
      "source": [
        "# clipping the outliers in 'age' column\n",
        "df['age'] = df['age'].clip(lower_threshold, upper_threshold)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cLzproMjOCvc",
        "outputId": "cf46a6c8-e244-4395-f1d3-a0c284e95ab2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    45205.000000\n",
              "mean        40.863666\n",
              "std         10.380009\n",
              "min         18.000000\n",
              "25%         33.000000\n",
              "50%         39.000000\n",
              "75%         48.000000\n",
              "max         70.000000\n",
              "Name: age, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "df['age'].describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yqJoqB9yOlid"
      },
      "source": [
        "#### 'job' Column"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aWa1sPXgOJ-o",
        "outputId": "c8324616-fb7c-434b-a109-4b40cf0bfa34"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "blue-collar      9730\n",
              "management       9457\n",
              "technician       7596\n",
              "admin.           5170\n",
              "services         4153\n",
              "retired          2264\n",
              "self-employed    1579\n",
              "entrepreneur     1487\n",
              "unemployed       1303\n",
              "housemaid        1240\n",
              "student           938\n",
              "unknown           288\n",
              "Name: job, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "df['job'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yz-Uf__XRkCP"
      },
      "outputs": [],
      "source": [
        "df.loc[df['job'] == 'unknown', 'job'] = np.nan"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TL6c75vOR1Vh",
        "outputId": "d303127d-77a4-4750-9983-3f381022f3ce"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "288"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "df['job'].isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Hnxj0JDVR92S"
      },
      "outputs": [],
      "source": [
        "# Imputing the missing values with mode\n",
        "df = df.fillna({'job' : st.mode(df['job'])})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "snGpCFMAVpio",
        "outputId": "3ab1f4f9-dfbd-448e-a749-0bcde593a4cc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "blue-collar      10018\n",
              "management        9457\n",
              "technician        7596\n",
              "admin.            5170\n",
              "services          4153\n",
              "retired           2264\n",
              "self-employed     1579\n",
              "entrepreneur      1487\n",
              "unemployed        1303\n",
              "housemaid         1240\n",
              "student            938\n",
              "Name: job, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "df['job'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LMw46rGFWN91"
      },
      "source": [
        "#### 'marital' Column"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xZTSbOMuVtCy",
        "outputId": "fa29083c-99cd-488c-bf79-4511928f34b2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "married     27210\n",
              "single      12788\n",
              "divorced     5207\n",
              "Name: marital, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "df['marital'].value_counts()\n",
        "# this column need no imputations or corrections"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "43_hCxmzYJA5"
      },
      "source": [
        "#### 'education_qual' Column"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TeUKHWGJX7Cl",
        "outputId": "b5c8f40c-71fd-4173-fa9e-4bd5009392c2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "secondary    23199\n",
              "tertiary     13299\n",
              "primary       6850\n",
              "unknown       1857\n",
              "Name: education_qual, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "df['education_qual'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SYX79V3GYFkn"
      },
      "outputs": [],
      "source": [
        "# imputing 'unknown' with mode\n",
        "df.loc[df['education_qual'] == 'unknown', 'education_qual'] = st.mode(df['education_qual'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wfvbG3ByYoGB",
        "outputId": "30a3671a-3b2a-470c-8031-bd32610b8531"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "secondary    25056\n",
              "tertiary     13299\n",
              "primary       6850\n",
              "Name: education_qual, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "df['education_qual'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AEvgxEfffUVg"
      },
      "source": [
        "#### 'call_type' Column"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "muVmrIKEfXNx",
        "outputId": "25eb2514-8828-49af-c633-474ea40e0602"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "cellular     29282\n",
              "unknown      13017\n",
              "telephone     2906\n",
              "Name: call_type, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "df['call_type'].value_counts()\n",
        "# the 'unknown' in this column are reasonable values in this column. They cannot be considered as null values\n",
        "# So, this column need no imputations or corrections"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z8dwfVC2g3sK"
      },
      "source": [
        "#### 'day' Column"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kYDTngFtfeS0",
        "outputId": "c2be60f9-5daa-414c-8237-7e3d86e6f6a2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20    2752\n",
              "18    2308\n",
              "21    2026\n",
              "17    1939\n",
              "6     1932\n",
              "5     1910\n",
              "14    1848\n",
              "8     1840\n",
              "28    1829\n",
              "7     1817\n",
              "19    1756\n",
              "29    1745\n",
              "15    1703\n",
              "12    1603\n",
              "13    1585\n",
              "30    1566\n",
              "9     1560\n",
              "11    1479\n",
              "4     1445\n",
              "16    1415\n",
              "2     1292\n",
              "27    1121\n",
              "3     1079\n",
              "26    1035\n",
              "23     939\n",
              "22     905\n",
              "25     840\n",
              "31     643\n",
              "10     524\n",
              "24     447\n",
              "1      322\n",
              "Name: day, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "df['day'].value_counts()\n",
        "# There are no unstructured data in this column. And\n",
        "# this column need no imputations or corrections"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A9D1-IVqhczq"
      },
      "source": [
        "#### 'mon' Column"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hobigtCtg_nU",
        "outputId": "3773f7b2-02f0-4fe6-981f-ee81affa4ebe"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "may    13765\n",
              "jul     6894\n",
              "aug     6245\n",
              "jun     5339\n",
              "nov     3970\n",
              "apr     2932\n",
              "feb     2649\n",
              "jan     1403\n",
              "oct      738\n",
              "sep      579\n",
              "mar      477\n",
              "dec      214\n",
              "Name: mon, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "df['mon'].value_counts()\n",
        "# There are no unstructured data in this column. And\n",
        "# this column need no imputations or corrections"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nnbSitjKhzB7"
      },
      "source": [
        "#### 'dur' Column"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5eQD9b0QhqAM",
        "outputId": "3df4f43c-9868-4e1b-fa8b-011f11b5517e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    45205.000000\n",
              "mean       258.183055\n",
              "std        257.538504\n",
              "min          0.000000\n",
              "25%        103.000000\n",
              "50%        180.000000\n",
              "75%        319.000000\n",
              "max       4918.000000\n",
              "Name: dur, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "df['dur'].describe()\n",
        "# The max value is very high compared to q3. So, clipping of outliers required"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0JGm9FOYiAeL"
      },
      "outputs": [],
      "source": [
        "q1 = df['dur'].quantile(0.25)\n",
        "q3 = df['dur'].quantile(0.75)\n",
        "iqr = q3-q1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XsysPiU-iPHK"
      },
      "outputs": [],
      "source": [
        "lower_threshold = round(q1-(1.5*iqr))\n",
        "upper_threshold  = round(q3+(1.5*iqr))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kSsqjmHViXMB",
        "outputId": "5c951f93-9687-4bf5-a903-66c0eb258662"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-221, 643)"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "lower_threshold, upper_threshold"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hwHBlrtniuVg"
      },
      "outputs": [],
      "source": [
        "df['dur'] = df['dur'].clip(lower_threshold, upper_threshold)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XKd2udGRjGVZ",
        "outputId": "a5102c83-bfc5-4049-ac75-d96531e0d851"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    45205.00000\n",
              "mean       234.95620\n",
              "std        176.75476\n",
              "min          0.00000\n",
              "25%        103.00000\n",
              "50%        180.00000\n",
              "75%        319.00000\n",
              "max        643.00000\n",
              "Name: dur, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "df['dur'].describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ieBcolCrjqSg"
      },
      "source": [
        "#### 'num_calls' Column"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uMN_EkSzjKt8",
        "outputId": "7baf40be-3828-4bed-8ea3-bd539b31cf30"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1     17542\n",
              "2     12503\n",
              "3      5521\n",
              "4      3520\n",
              "5      1764\n",
              "6      1291\n",
              "7       735\n",
              "8       540\n",
              "9       327\n",
              "10      266\n",
              "11      201\n",
              "12      155\n",
              "13      133\n",
              "14       93\n",
              "15       84\n",
              "16       79\n",
              "17       69\n",
              "18       51\n",
              "19       44\n",
              "20       43\n",
              "21       35\n",
              "22       23\n",
              "25       22\n",
              "23       22\n",
              "24       20\n",
              "29       16\n",
              "28       16\n",
              "26       13\n",
              "31       12\n",
              "27       10\n",
              "32        9\n",
              "30        8\n",
              "33        6\n",
              "34        5\n",
              "36        4\n",
              "35        4\n",
              "43        3\n",
              "38        3\n",
              "37        2\n",
              "50        2\n",
              "41        2\n",
              "46        1\n",
              "58        1\n",
              "55        1\n",
              "63        1\n",
              "51        1\n",
              "39        1\n",
              "44        1\n",
              "Name: num_calls, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "df['num_calls'].value_counts()\n",
        "# the customers who had number of calls more than 10 are very less. It needs clipping"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2zaU9Y1Djxh4",
        "outputId": "f12299f0-41e3-406a-a0c5-a44d3bad4cfc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    45205.000000\n",
              "mean         2.763898\n",
              "std          3.098189\n",
              "min          1.000000\n",
              "25%          1.000000\n",
              "50%          2.000000\n",
              "75%          3.000000\n",
              "max         63.000000\n",
              "Name: num_calls, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "df['num_calls'].describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wUj0r0L8mAsX"
      },
      "outputs": [],
      "source": [
        "q1 = df['num_calls'].quantile(0.25)\n",
        "q3 = df['num_calls'].quantile(0.75)\n",
        "iqr = q3-q1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5cHC0xj2mr4m"
      },
      "outputs": [],
      "source": [
        "lower_threshold = round(q1-(1.5*iqr))\n",
        "upper_threshold  = round(q3+(1.5*iqr))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Yro0qm0ms9D",
        "outputId": "63ce23f6-4e67-40c9-9898-b612288a889a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-2, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "lower_threshold, upper_threshold"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L7LL-cTkmxFW"
      },
      "outputs": [],
      "source": [
        "df['num_calls'] = df['num_calls'].clip(lower_threshold, upper_threshold)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yROAafVGm6mk",
        "outputId": "0fbcda7f-bac6-41d3-ccbf-5b34d3202fa8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    45205.000000\n",
              "mean         2.392235\n",
              "std          1.600152\n",
              "min          1.000000\n",
              "25%          1.000000\n",
              "50%          2.000000\n",
              "75%          3.000000\n",
              "max          6.000000\n",
              "Name: num_calls, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "df['num_calls'].describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xgG1I6XCnJ2C"
      },
      "source": [
        "#### 'prev_outcome' Column"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7YaG90RSm9g5",
        "outputId": "231cd543-96b1-46a1-8fe9-f2f940751277"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "unknown    36953\n",
              "failure     4901\n",
              "other       1840\n",
              "success     1511\n",
              "Name: prev_outcome, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "df['prev_outcome'].value_counts()\n",
        "# the 'unknown' in this column are reasonable values in this column. They cannot be considered as null values\n",
        "# So, this column need no imputations or corrections"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fpQExEo3op73"
      },
      "source": [
        "#### 'y' Column"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6-sQ63yEos23",
        "outputId": "6e034b99-2a4f-43f1-9cf4-2bf04f2d0cea"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "no     39916\n",
              "yes     5289\n",
              "Name: y, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "df['y'].value_counts()\n",
        "# There are no unstructured data in this column. And\n",
        "# this column need no imputations or corrections"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZfLojegjoRAj"
      },
      "source": [
        "## Final Checks"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "79__xn0fnSKu",
        "outputId": "ccf9db06-582e-4e67-be5a-425ec9cce905"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age               0\n",
              "job               0\n",
              "marital           0\n",
              "education_qual    0\n",
              "call_type         0\n",
              "day               0\n",
              "mon               0\n",
              "dur               0\n",
              "num_calls         0\n",
              "prev_outcome      0\n",
              "y                 0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6i5HzhGQohjR",
        "outputId": "66ea5b9b-6c7f-4ab6-b944-acc63d2462d5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age                int64\n",
              "job               object\n",
              "marital           object\n",
              "education_qual    object\n",
              "call_type         object\n",
              "day                int64\n",
              "mon               object\n",
              "dur                int64\n",
              "num_calls          int64\n",
              "prev_outcome      object\n",
              "y                 object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ],
      "source": [
        "df.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ar1etgt6okeZ"
      },
      "outputs": [],
      "source": [
        "df = df.drop_duplicates()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A4-d9Gz0o6Dc",
        "outputId": "077a3178-6121-4e88-8e14-87efc7ae2505"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(45194, 11)"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-4v18jCP7qsg"
      },
      "source": [
        "## Countplot of the Distribution of Categorical Features in the Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "72X9Zgseo7M_",
        "outputId": "7902dcf3-ccc6-4a59-8bce-a01206f0b945"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['student', 'housemaid', 'unemployed', 'entrepreneur', 'self-employed',\n",
              "       'retired', 'services', 'admin.', 'technician', 'management',\n",
              "       'blue-collar'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ],
      "source": [
        "df['job'].value_counts().index[::-1]\n",
        "# to get the unique values in ascending order of the value_counts"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "roHp3cg08izr",
        "outputId": "ec4512a1-5dd0-4132-8d48-48bc508648c2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting seaborn==0.11\n",
            "  Downloading seaborn-0.11.0-py3-none-any.whl (283 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/283.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m283.1/283.1 kB\u001b[0m \u001b[31m9.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.10/dist-packages (from seaborn==0.11) (1.22.4)\n",
            "Requirement already satisfied: scipy>=1.0 in /usr/local/lib/python3.10/dist-packages (from seaborn==0.11) (1.10.1)\n",
            "Requirement already satisfied: pandas>=0.23 in /usr/local/lib/python3.10/dist-packages (from seaborn==0.11) (1.5.3)\n",
            "Requirement already satisfied: matplotlib>=2.2 in /usr/local/lib/python3.10/dist-packages (from seaborn==0.11) (3.7.1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.2->seaborn==0.11) (1.1.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.2->seaborn==0.11) (0.11.0)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.2->seaborn==0.11) (4.40.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.2->seaborn==0.11) (1.4.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.2->seaborn==0.11) (23.1)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.2->seaborn==0.11) (8.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.2->seaborn==0.11) (3.1.0)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.2->seaborn==0.11) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.23->seaborn==0.11) (2022.7.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib>=2.2->seaborn==0.11) (1.16.0)\n",
            "Installing collected packages: seaborn\n",
            "  Attempting uninstall: seaborn\n",
            "    Found existing installation: seaborn 0.12.2\n",
            "    Uninstalling seaborn-0.12.2:\n",
            "      Successfully uninstalled seaborn-0.12.2\n",
            "Successfully installed seaborn-0.11.0\n"
          ]
        }
      ],
      "source": [
        "!pip install seaborn==0.11"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VwCUHudtNTJN"
      },
      "outputs": [],
      "source": [
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 627
        },
        "id": "6qNxPkTR8YIa",
        "outputId": "dec57b85-dea0-4235-8fd1-1a1a0d092a37"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Customers according to job')"
            ]
          },
          "metadata": {},
          "execution_count": 52
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.countplot(df['job'], order = df['job'].value_counts().index[::-1])\n",
        "plt.xticks(rotation=90)\n",
        "plt.xlabel('Job')\n",
        "plt.ylabel('count')\n",
        "plt.title('Customers according to job')\n",
        "\n",
        "# Insights\n",
        "# Acc to the distribution of 'job' feature, most of the calls were made to blue-collar followed by management followed by technician\n",
        "# The least number of calls were made to students, house-maid, unemployed"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 590
        },
        "id": "VO5EenDbC3yl",
        "outputId": "ec16314d-3dcf-41d0-befb-010928dea6a7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Customers according to marital status')"
            ]
          },
          "metadata": {},
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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F1bhx47+0rNDQUIWGhurNN9/Uhg0bVLt2bc2cOVPvvPNOtv1///137du3T/PmzbO5UDgmJsamX/HixSXprsHWz89PXl5e9wy/xYoV0969e7O0m92vmdMzR+huld1yH1SxYsWUkZGhw4cP24xqHThwwNT8dzuupJu1NmzY0Gba3r17c+y4upOvv/5abm5uWrlypVxdXa3tc+bM+UvLvdP2fv/990pNTdV3331nMzqW3Wlns5ycnNSoUSM1atRIkyZN0nvvvac33nhDP/74oxo3bnzHWswe73fbnvv9/ClRooReffVVvfrqq9q/f78qV66siRMn6rPPPjOzqXgIGGnCI2fYsGHKnz+/evXqpVOnTmWZfvDgQU2dOlXSzZGpQoUKaf369TZ9PvroI5v3N27cyDLcXrhwYQUGBio1NdXalj9//myH5Zs1a6bExEQtWLDA2paenq5p06bJw8PDOqT/V0VERMjLy0vvvfeerl+/nmX6mTNn7rmMlJQUpaen27SFhobKycnJZltvlzkqcesIg2EY1n2dyc/PT/Xq1dPs2bOVkJBgMy1zXicnJ7Vs2VLff/+9Nm/enGVdmf2aNWumjRs3Kj4+3jrt8uXLmjVrloKDg+95/UyRIkVUuXJlzZs3z+bfLSYmJsv1UH9FRESEpKzH1bRp00zNn/nsrNt/uVarVk2FCxfWzJkzbf5tfvjhB+3evdvU3Xl/hbOzsywWi83IyJEjR7RkyZK/tNw7bW92x1hycvIDh7Tz589nact8gGXm/ryfWrI73u+2jGLFisnZ2fmenz9XrlzRtWvXbNpKlCghT0/Pu/4/iYePkSY8ckqUKKH58+erffv2Klu2rM0TwTds2GC91T9Tr1699M9//lO9evVStWrVtH79eu3bt89mmRcvXlTRokX14osvqlKlSvLw8NDq1au1adMmTZw40dqvatWqWrBggaKjo1W9enV5eHioRYsWioyM1L///W9169ZNW7ZsUXBwsL766ivFxcVpypQpOXbhupeXl2bMmKEuXbqoSpUq6tChg/z8/JSQkKBly5apdu3a+vDDD++6jDVr1qh///5q27atnnrqKaWnp+vTTz+Vs7Oz2rRpc8f5ypQpoxIlSmjIkCH6888/5eXlpa+//jrbC1c/+OAD1alTR1WqVFFkZKRCQkJ05MgRLVu2zPp1Fe+9955WrVql+vXrKzIyUmXLltXJkye1aNEi/fzzz/Lx8dFrr72mL774Qs8995wGDhwoX19fzZs3T4cPH9bXX39t6sGV48aNU/PmzVWnTh316NFD58+f17Rp01S+fHldunTpnvObUbVqVbVp00ZTpkzRuXPnrI8cyDzO7jQSkalEiRLy8fHRzJkz5enpqfz58yssLEwhISEaP368unfvrvr166tjx47WRw4EBwdbT1v9XZo3b65JkyapadOm6tSpk06fPq3p06erZMmS2rFjxwMvt2rVqpKkN954Qx06dFDevHnVokULNWnSRC4uLmrRooVefvllXbp0Sf/5z39UuHBhnTx58r7XM3bsWK1fv17NmzdXsWLFdPr0aX300UcqWrSo9eaCO+37+zneM7dn4MCBioiIkLOzszp06CBvb2+1bdtW06ZNk8ViUYkSJbR06dIs1x7u27dPjRo1Urt27VSuXDnlyZNHixcv1qlTp9ShQ4f73m78jexxyx6QE/bt22f07t3bCA4ONlxcXAxPT0+jdu3axrRp06y36BvGzduGe/bsaXh7exuenp5Gu3btjNOnT9vcBp2ammoMHTrUqFSpkuHp6Wnkz5/fqFSpUpaH6l26dMno1KmT4ePjk+3DLbt3724UKlTIcHFxMUJDQ7Pcxnzrwy1vlXlr8u2PAci8Zfz22/J//PFHIyIiwvD29jbc3NyMEiVKGN26dTM2b95s7ZP5cMvbHTp0yOjRo4dRokQJw83NzfD19TUaNGhgrF69+p77/I8//jAaN25seHh4GIUKFTJ69+5t/Pbbb9nesr1z506jVatWho+Pj+Hm5maULl3aeOutt2z6HD161HjppZcMPz8/w9XV1ShevLgRFRWV7cMtM5dTo0aNOz7c8k6PUfj666+NsmXLGq6urka5cuXu++GWt9+ynvnvcust85cvXzaioqIMX19fw8PDw2jZsqWxd+9eQ5Lxz3/+8x571jC+/fZb60Mhb9+fCxYsMJ5++mnD1dXV8PX1ve+HW95+/Nxpu7I7Zj755BOjVKlShqurq1GmTBljzpw51vlvpf97uGV2bt+vhmEYb7/9tvHEE08YTk5ONvvyu+++MypWrGi4ubkZwcHBxvjx443Zs2dn2d9mHjkQGxtr/OMf/zACAwMNFxcXIzAw0OjYsaOxb98+m3532vdmj/f09HRjwIABhp+fn2GxWGz2zZkzZ4w2bdoY+fLlMwoUKGC8/PLLxs6dO22WcfbsWSMqKsooU6aMkT9/fsPb29sICwszFi5ceNftw8NnMYxH5EpGAHjEbN++XU8//bQ+++wzde7c2d7lAPiLuKYJAHLA1atXs7RNmTJFTk5Oqlevnh0qApDTuKYJAHLAhAkTtGXLFjVo0EB58uTRDz/8oB9++EGRkZFZnocE4NHE6TkAyAExMTEaM2aM/vjjD126dElPPvmkunTpojfeeMPmKe8AHl2EJgAAABO4pgkAAMAEQhMAAIAJnGjPIRkZGTpx4oQ8PT3v+SA7AADgGAzD0MWLFxUYGHjPB+YSmnLIiRMnuEMGAIBH1LFjx1S0aNG79iE05ZDMr8k4duyYvLy87FwNAAAwIyUlRUFBQaa+7orQlEMyT8l5eXkRmgAAeMSYubSGC8EBAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABPy2LsAAADuR+1pte1dAhxM3IC4h7IeRpoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAAT7Bqaxo0bp+rVq8vT01OFCxdWy5YttXfvXps+zzzzjCwWi82rT58+Nn0SEhLUvHlz5cuXT4ULF9bQoUOVnp5u02ft2rWqUqWKXF1dVbJkSc2dOzdLPdOnT1dwcLDc3NwUFhamjRs35vg2AwCAR5NdQ9O6desUFRWlX375RTExMbp+/bqaNGmiy5cv2/Tr3bu3Tp48aX1NmDDBOu3GjRtq3ry50tLStGHDBs2bN09z587VyJEjrX0OHz6s5s2bq0GDBtq+fbsGDRqkXr16aeXKldY+CxYsUHR0tEaNGqWtW7eqUqVKioiI0OnTp//+HQEAAByexTAMw95FZDpz5owKFy6sdevWqV69epJujjRVrlxZU6ZMyXaeH374Qc8//7xOnDghf39/SdLMmTM1fPhwnTlzRi4uLho+fLiWLVumnTt3Wufr0KGDkpKStGLFCklSWFiYqlevrg8//FCSlJGRoaCgIA0YMECvvfbaPWtPSUmRt7e3kpOT5eXl9Vd2AwDgLmpPq23vEuBg4gbEPfC89/P726GuaUpOTpYk+fr62rR//vnnKlSokCpUqKARI0boypUr1mnx8fEKDQ21BiZJioiIUEpKinbt2mXt07hxY5tlRkREKD4+XpKUlpamLVu22PRxcnJS48aNrX1ul5qaqpSUFJsXAAB4fOWxdwGZMjIyNGjQINWuXVsVKlSwtnfq1EnFihVTYGCgduzYoeHDh2vv3r365ptvJEmJiYk2gUmS9X1iYuJd+6SkpOjq1au6cOGCbty4kW2fPXv2ZFvvuHHjNGbMmL+20QAA4JHhMKEpKipKO3fu1M8//2zTHhkZaf05NDRURYoUUaNGjXTw4EGVKFHiYZdpNWLECEVHR1vfp6SkKCgoyG71AACAv5dDhKb+/ftr6dKlWr9+vYoWLXrXvmFhYZKkAwcOqESJEgoICMhyl9upU6ckSQEBAdb/Zrbd2sfLy0vu7u5ydnaWs7Nztn0yl3E7V1dXubq6mt9IAADwSLPrNU2GYah///5avHix1qxZo5CQkHvOs337dklSkSJFJEnh4eH6/fffbe5yi4mJkZeXl8qVK2ftExsba7OcmJgYhYeHS5JcXFxUtWpVmz4ZGRmKjY219gEAALmbXUeaoqKiNH/+fH377bfy9PS0XoPk7e0td3d3HTx4UPPnz1ezZs1UsGBB7dixQ4MHD1a9evVUsWJFSVKTJk1Urlw5denSRRMmTFBiYqLefPNNRUVFWUeC+vTpow8//FDDhg1Tjx49tGbNGi1cuFDLli2z1hIdHa2uXbuqWrVqqlGjhqZMmaLLly+re/fuD3/HAAAAh2PX0DRjxgxJNx8rcKs5c+aoW7ducnFx0erVq60BJigoSG3atNGbb75p7evs7KylS5eqb9++Cg8PV/78+dW1a1eNHTvW2ickJETLli3T4MGDNXXqVBUtWlQff/yxIiIirH3at2+vM2fOaOTIkUpMTFTlypW1YsWKLBeHAwCA3MmhntP0KOM5TQDwcPCcJtwuVz6nCQAAwFERmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEu4amcePGqXr16vL09FThwoXVsmVL7d2716bPtWvXFBUVpYIFC8rDw0Nt2rTRqVOnbPokJCSoefPmypcvnwoXLqyhQ4cqPT3dps/atWtVpUoVubq6qmTJkpo7d26WeqZPn67g4GC5ubkpLCxMGzduzPFtBgAAjya7hqZ169YpKipKv/zyi2JiYnT9+nU1adJEly9ftvYZPHiwvv/+ey1atEjr1q3TiRMn1Lp1a+v0GzduqHnz5kpLS9OGDRs0b948zZ07VyNHjrT2OXz4sJo3b64GDRpo+/btGjRokHr16qWVK1da+yxYsEDR0dEaNWqUtm7dqkqVKikiIkKnT59+ODsDAAA4NIthGIa9i8h05swZFS5cWOvWrVO9evWUnJwsPz8/zZ8/Xy+++KIkac+ePSpbtqzi4+NVs2ZN/fDDD3r++ed14sQJ+fv7S5Jmzpyp4cOH68yZM3JxcdHw4cO1bNky7dy507quDh06KCkpSStWrJAkhYWFqXr16vrwww8lSRkZGQoKCtKAAQP02muv3bP2lJQUeXt7Kzk5WV5eXjm9awAA/6f2tNr2LgEOJm5A3APPez+/vx3qmqbk5GRJkq+vryRpy5Ytun79uho3bmztU6ZMGT355JOKj4+XJMXHxys0NNQamCQpIiJCKSkp2rVrl7XPrcvI7JO5jLS0NG3ZssWmj5OTkxo3bmztc7vU1FSlpKTYvAAAwOPLYUJTRkaGBg0apNq1a6tChQqSpMTERLm4uMjHx8emr7+/vxITE619bg1MmdMzp92tT0pKiq5evaqzZ8/qxo0b2fbJXMbtxo0bJ29vb+srKCjowTYcAAA8EhwmNEVFRWnnzp368ssv7V2KKSNGjFBycrL1dezYMXuXBAAA/kZ57F2AJPXv319Lly7V+vXrVbRoUWt7QECA0tLSlJSUZDPadOrUKQUEBFj73H6XW+bddbf2uf2Ou1OnTsnLy0vu7u5ydnaWs7Nztn0yl3E7V1dXubq6PtgGAwCAR45dR5oMw1D//v21ePFirVmzRiEhITbTq1atqrx58yo2NtbatnfvXiUkJCg8PFySFB4ert9//93mLreYmBh5eXmpXLly1j63LiOzT+YyXFxcVLVqVZs+GRkZio2NtfYBAAC5m11HmqKiojR//nx9++238vT0tF4/5O3tLXd3d3l7e6tnz56Kjo6Wr6+vvLy8NGDAAIWHh6tmzZqSpCZNmqhcuXLq0qWLJkyYoMTERL355puKioqyjgT16dNHH374oYYNG6YePXpozZo1WrhwoZYtW2atJTo6Wl27dlW1atVUo0YNTZkyRZcvX1b37t0f/o4BAAAOx66hacaMGZKkZ555xqZ9zpw56tatmyRp8uTJcnJyUps2bZSamqqIiAh99NFH1r7Ozs5aunSp+vbtq/DwcOXPn19du3bV2LFjrX1CQkK0bNkyDR48WFOnTlXRokX18ccfKyIiwtqnffv2OnPmjEaOHKnExERVrlxZK1asyHJxOAAAyJ0c6jlNjzKe0wQADwfPacLtcuVzmgAAABwVoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMCEBwpNDRs2VFJSUpb2lJQUNWzY8K/WBAAA4HAeKDStXbtWaWlpWdqvXbumn3766S8XBQAA4Gjy3E/nHTt2WH/+448/lJiYaH1/48YNrVixQk888UTOVQcAAOAg7mukqXLlynr66adlsVjUsGFDVa5c2fqqWrWq3nnnHY0cOdL08tavX68WLVooMDBQFotFS5YssZnerVs3WSwWm1fTpk1t+pw/f16dO3eWl5eXfHx81LNnT126dMmmz44dO1S3bl25ubkpKChIEyZMyFLLokWLVKZMGbm5uSk0NFTLly83v2MAAMBj775Gmg4fPizDMFS8eHFt3LhRfn5+1mkuLi4qXLiwnJ2dTS/v8uXLqlSpknr06KHWrVtn26dp06aaM2eO9b2rq6vN9M6dO+vkyZOKiYnR9evX1b17d0VGRmr+/PmSbl5n1aRJEzVu3FgzZ87U77//rh49esjHx0eRkZGSpA0bNqhjx44aN26cnn/+ec2fP18tW7bU1q1bVaFCBdPbAwAAHl8WwzAMexchSRaLRYsXL1bLli2tbd26dVNSUlKWEahMu3fvVrly5bRp0yZVq1ZNkrRixQo1a9ZMx48fV2BgoGbMmKE33nhDiYmJcnFxkSS99tprWrJkifbs2SNJat++vS5fvqylS5dal12zZk1VrlxZM2fONFV/SkqKvL29lZycLC8vrwfYAwAAM2pPq23vEuBg4gbEPfC89/P7+75Gmm61f/9+/fjjjzp9+rQyMjJspt3PKbp7Wbt2rQoXLqwCBQqoYcOGeuedd1SwYEFJUnx8vHx8fKyBSZIaN24sJycn/frrr2rVqpXi4+NVr149a2CSpIiICI0fP14XLlxQgQIFFB8fr+joaJv1RkRE3DGsAQCA3OeBQtN//vMf9e3bV4UKFVJAQIAsFot1msViybHQ1LRpU7Vu3VohISE6ePCgXn/9dT333HOKj4+Xs7OzEhMTVbhwYZt58uTJI19fX+tF6omJiQoJCbHp4+/vb51WoEABJSYmWttu7XPrhe63S01NVWpqqvV9SkrKX9pWAADg2B4oNL3zzjt69913NXz48Jyux0aHDh2sP4eGhqpixYoqUaKE1q5dq0aNGv2t676XcePGacyYMXatAQAAPDwP9JymCxcuqG3btjldyz0VL15chQoV0oEDByRJAQEBOn36tE2f9PR0nT9/XgEBAdY+p06dsumT+f5efTKnZ2fEiBFKTk62vo4dO/bXNg4AADi0BwpNbdu21apVq3K6lns6fvy4zp07pyJFikiSwsPDlZSUpC1btlj7rFmzRhkZGQoLC7P2Wb9+va5fv27tExMTo9KlS6tAgQLWPrGxsTbriomJUXh4+B1rcXV1lZeXl80LAAA8vh7o9FzJkiX11ltv6ZdfflFoaKjy5s1rM33gwIGmlnPp0iXrqJF085EG27dvl6+vr3x9fTVmzBi1adNGAQEBOnjwoIYNG6aSJUsqIiJCklS2bFk1bdpUvXv31syZM3X9+nX1799fHTp0UGBgoCSpU6dOGjNmjHr27Knhw4dr586dmjp1qiZPnmxd7yuvvKL69etr4sSJat68ub788ktt3rxZs2bNepDdAwAAHkMP9MiB2y+stlmgxaJDhw6ZWs7atWvVoEGDLO1du3bVjBkz1LJlS23btk1JSUkKDAxUkyZN9Pbbb9tctH3+/Hn1799f33//vZycnNSmTRt98MEH8vDwsPbZsWOHoqKitGnTJhUqVEgDBgzIcj3WokWL9Oabb+rIkSMqVaqUJkyYoGbNmpnaDolHDgDAw8IjB3C7h/XIAYd5TtOjjtAEAA8HoQm3e1ih6YGuaQIAAMhtHuiaph49etx1+uzZsx+oGAAAAEf1QKHpwoULNu+vX7+unTt3KikpSQ0bNsyRwgAAABzJA4WmxYsXZ2nLyMhQ3759VaJEib9cFAAAgKPJsWuanJycFB0dbXMrPwAAwOMiRy8EP3jwoNLT03NykQAAAA7hgU7PRUdH27w3DEMnT57UsmXL1LVr1xwpDAAAwJE8UGjatm2bzXsnJyf5+flp4sSJ97yzDgAA4FH0QKHpxx9/zOk6AAAAHNoDhaZMZ86c0d69eyVJpUuXlp+fX44UBQAA4Gge6ELwy5cvq0ePHipSpIjq1aunevXqKTAwUD179tSVK1dyukYAAAC7e6DQFB0drXXr1un7779XUlKSkpKS9O2332rdunV69dVXc7pGAAAAu3ug03Nff/21vvrqKz3zzDPWtmbNmsnd3V3t2rXTjBkzcqo+AAAAh/BAI01XrlyRv79/lvbChQtzeg4AADyWHig0hYeHa9SoUbp27Zq17erVqxozZozCw8NzrDgAAABH8UCn56ZMmaKmTZuqaNGiqlSpkiTpt99+k6urq1atWpWjBQIAADiCBwpNoaGh2r9/vz7//HPt2bNHktSxY0d17txZ7u7uOVogAACAI3ig0DRu3Dj5+/urd+/eNu2zZ8/WmTNnNHz48BwpDgAAwFE80DVN//73v1WmTJks7eXLl9fMmTP/clEAAACO5oFCU2JioooUKZKl3c/PTydPnvzLRQEAADiaBwpNQUFBiouLy9IeFxenwMDAv1wUAACAo3mga5p69+6tQYMG6fr162rYsKEkKTY2VsOGDeOJ4AAA4LH0QKFp6NChOnfunPr166e0tDRJkpubm4YPH64RI0bkaIEA7CthbKi9S4ADeXLk7/YuAbCbBwpNFotF48eP11tvvaXdu3fL3d1dpUqVkqura07XBwAA4BAeKDRl8vDwUPXq1XOqFgAAAIf1QBeCAwAA5DaEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhg19C0fv16tWjRQoGBgbJYLFqyZInNdMMwNHLkSBUpUkTu7u5q3Lix9u/fb9Pn/Pnz6ty5s7y8vOTj46OePXvq0qVLNn127NihunXrys3NTUFBQZowYUKWWhYtWqQyZcrIzc1NoaGhWr58eY5vLwAAeHTZNTRdvnxZlSpV0vTp07OdPmHCBH3wwQeaOXOmfv31V+XPn18RERG6du2atU/nzp21a9cuxcTEaOnSpVq/fr0iIyOt01NSUtSkSRMVK1ZMW7Zs0fvvv6/Ro0dr1qxZ1j4bNmxQx44d1bNnT23btk0tW7ZUy5YttXPnzr9v4wEAwCPFYhiGYe8iJMlisWjx4sVq2bKlpJujTIGBgXr11Vc1ZMgQSVJycrL8/f01d+5cdejQQbt371a5cuW0adMmVatWTZK0YsUKNWvWTMePH1dgYKBmzJihN954Q4mJiXJxcZEkvfbaa1qyZIn27NkjSWrfvr0uX76spUuXWuupWbOmKleurJkzZ5qqPyUlRd7e3kpOTpaXl1dO7RbA7hLGhtq7BDiQJ0f+bu8SVHtabXuXAAcTNyDugee9n9/fDntN0+HDh5WYmKjGjRtb27y9vRUWFqb4+HhJUnx8vHx8fKyBSZIaN24sJycn/frrr9Y+9erVswYmSYqIiNDevXt14cIFa59b15PZJ3M92UlNTVVKSorNCwAAPL4cNjQlJiZKkvz9/W3a/f39rdMSExNVuHBhm+l58uSRr6+vTZ/slnHrOu7UJ3N6dsaNGydvb2/rKygo6H43EQAAPEIcNjQ5uhEjRig5Odn6OnbsmL1LAgAAfyOHDU0BAQGSpFOnTtm0nzp1yjotICBAp0+ftpmenp6u8+fP2/TJbhm3ruNOfTKnZ8fV1VVeXl42LwAA8Phy2NAUEhKigIAAxcbGWttSUlL066+/Kjw8XJIUHh6upKQkbdmyxdpnzZo1ysjIUFhYmLXP+vXrdf36dWufmJgYlS5dWgUKFLD2uXU9mX0y1wMAAGDX0HTp0iVt375d27dvl3Tz4u/t27crISFBFotFgwYN0jvvvKPvvvtOv//+u1566SUFBgZa77ArW7asmjZtqt69e2vjxo2Ki4tT//791aFDBwUGBkqSOnXqJBcXF/Xs2VO7du3SggULNHXqVEVHR1vreOWVV7RixQpNnDhRe/bs0ejRo7V582b179//Ye8SAADgoPLYc+WbN29WgwYNrO8zg0zXrl01d+5cDRs2TJcvX1ZkZKSSkpJUp04drVixQm5ubtZ5Pv/8c/Xv31+NGjWSk5OT2rRpow8++MA63dvbW6tWrVJUVJSqVq2qQoUKaeTIkTbPcqpVq5bmz5+vN998U6+//rpKlSqlJUuWqEKFCg9hLwAAgEeBwzyn6VHHc5rwuOI5TbgVz2mCI8r1z2kCAABwJIQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMMGhQ9Po0aNlsVhsXmXKlLFOv3btmqKiolSwYEF5eHioTZs2OnXqlM0yEhIS1Lx5c+XLl0+FCxfW0KFDlZ6ebtNn7dq1qlKlilxdXVWyZEnNnTv3YWweAAB4hOSxdwH3Ur58ea1evdr6Pk+e/y958ODBWrZsmRYtWiRvb2/1799frVu3VlxcnCTpxo0bat68uQICArRhwwadPHlSL730kvLmzav33ntPknT48GE1b95cffr00eeff67Y2Fj16tVLRYoUUURExMPdWElVh/73oa8TjmvL+y/ZuwQAwP9x+NCUJ08eBQQEZGlPTk7WJ598ovnz56thw4aSpDlz5qhs2bL65ZdfVLNmTa1atUp//PGHVq9eLX9/f1WuXFlvv/22hg8frtGjR8vFxUUzZ85USEiIJk6cKEkqW7asfv75Z02ePNkuoQkAADgmhz49J0n79+9XYGCgihcvrs6dOyshIUGStGXLFl2/fl2NGze29i1TpoyefPJJxcfHS5Li4+MVGhoqf39/a5+IiAilpKRo165d1j63LiOzT+Yy7iQ1NVUpKSk2LwAA8Phy6NAUFhamuXPnasWKFZoxY4YOHz6sunXr6uLFi0pMTJSLi4t8fHxs5vH391diYqIkKTEx0SYwZU7PnHa3PikpKbp69eodaxs3bpy8vb2tr6CgoL+6uQAAwIE59Om55557zvpzxYoVFRYWpmLFimnhwoVyd3e3Y2XSiBEjFB0dbX2fkpJCcAIA4DHm0CNNt/Px8dFTTz2lAwcOKCAgQGlpaUpKSrLpc+rUKes1UAEBAVnupst8f68+Xl5edw1mrq6u8vLysnkBAIDH1yMVmi5duqSDBw+qSJEiqlq1qvLmzavY2Fjr9L179yohIUHh4eGSpPDwcP3+++86ffq0tU9MTIy8vLxUrlw5a59bl5HZJ3MZAAAAkoOHpiFDhmjdunU6cuSINmzYoFatWsnZ2VkdO3aUt7e3evbsqejoaP3444/asmWLunfvrvDwcNWsWVOS1KRJE5UrV05dunTRb7/9ppUrV+rNN99UVFSUXF1dJUl9+vTRoUOHNGzYMO3Zs0cfffSRFi5cqMGDB9tz0wEAgINx6Guajh8/ro4dO+rcuXPy8/NTnTp19Msvv8jPz0+SNHnyZDk5OalNmzZKTU1VRESEPvroI+v8zs7OWrp0qfr27avw8HDlz59fXbt21dixY619QkJCtGzZMg0ePFhTp05V0aJF9fHHH/O4AQAAYMOhQ9OXX3551+lubm6aPn26pk+ffsc+xYoV0/Lly++6nGeeeUbbtm17oBoBAEDu4NCn5wAAABwFoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJpuM336dAUHB8vNzU1hYWHauHGjvUsCAAAOgNB0iwULFig6OlqjRo3S1q1bValSJUVEROj06dP2Lg0AANgZoekWkyZNUu/evdW9e3eVK1dOM2fOVL58+TR79mx7lwYAAOyM0PR/0tLStGXLFjVu3Nja5uTkpMaNGys+Pt6OlQEAAEeQx94FOIqzZ8/qxo0b8vf3t2n39/fXnj17svRPTU1Vamqq9X1ycrIkKSUl5S/VcSP16l+aH4+Xv3o85YSL127YuwQ4EEc4JtOvptu7BDiYv3JcZs5rGMY9+xKaHtC4ceM0ZsyYLO1BQUF2qAaPK+9pfexdAmBrnLe9KwCy8B7+14/Lixcvytv77sshNP2fQoUKydnZWadOnbJpP3XqlAICArL0HzFihKKjo63vMzIydP78eRUsWFAWi+Vvr/dxlpKSoqCgIB07dkxeXl72LgfgmITD4ZjMOYZh6OLFiwoMDLxnX0LT/3FxcVHVqlUVGxurli1bSroZhGJjY9W/f/8s/V1dXeXq6mrT5uPj8xAqzT28vLz4MIBD4ZiEo+GYzBn3GmHKRGi6RXR0tLp27apq1aqpRo0amjJlii5fvqzu3bvbuzQAAGBnhKZbtG/fXmfOnNHIkSOVmJioypUra8WKFVkuDgcAALkPoek2/fv3z/Z0HB4eV1dXjRo1KsvpT8BeOCbhaDgm7cNimLnHDgAAIJfj4ZYAAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAO7iwIEDWrlypa5evfll2txwDORePKcJdtO6dWvTfb/55pu/sRIgq3Pnzql9+/Zas2aNLBaL9u/fr+LFi6tnz54qUKCAJk6caO8SkUt89913pvu+8MILf2MlIDTBbm79rh/DMLR48WJ5e3urWrVqkqQtW7YoKSnpvsIVkFMGDx6sPHnyKCEhQWXLlrW2t2/fXtHR0YQmPDSZ34eayWKx2Ix43vol8Tdu3HhYZeVKhCbYzZw5c6w/Dx8+XO3atdPMmTPl7Ows6eb//P369ePLKGEXq1at0sqVK1W0aFGb9lKlSuno0aN2qgq5UUZGhvXn1atXa/jw4XrvvfcUHh4uSYqPj9ebb76p9957z14l5ho8ERwOwc/PTz///LNKly5t0753717VqlVL586ds1NlyK08PT21detWlSpVSp6envrtt99UvHhxbd68WRERERyTsIsKFSpo5syZqlOnjk37Tz/9pMjISO3evdtOleUOXAgOh5Cenq49e/Zkad+zZ4/NX1nAw1K3bl3997//tb63WCzKyMjQhAkT1KBBAztWhtzs4MGD8vHxydLu7e2tI0eOPPR6chtOz8EhdO/eXT179tTBgwdVo0YNSdKvv/6qf/7zn+revbudq0NuNGHCBDVq1EibN29WWlqahg0bpl27dun8+fOKi4uzd3nIpapXr67o6Gh9+umn8vf3lySdOnVKQ4cOtX524u/D6Tk4hIyMDP3rX//S1KlTdfLkSUlSkSJF9Morr+jVV1+1XucEPEzJycn68MMP9dtvv+nSpUuqUqWKoqKiVKRIEXuXhlzqwIEDatWqlfbt26egoCBJ0rFjx1SqVCktWbJEJUuWtHOFjzdCExxOSkqKJHEBOABkwzAMxcTEWC9pKFu2rBo3bmxzFx3+HoQmOIz09HStXbtWBw8eVKdOneTp6akTJ07Iy8tLHh4e9i4PucCOHTtM961YseLfWAlwb9euXZOrqyth6SEiNMEhHD16VE2bNlVCQoJSU1O1b98+FS9eXK+88opSU1M1c+ZMe5eIXMDJySnLM3CyY7FYeB4O7CIjI0PvvvuuZs6cqVOnTlk/K9966y0FBwerZ8+e9i7xscaF4HAIr7zyiqpVq6bffvtNBQsWtLa3atVKvXv3tmNlyE0OHz5s7xKAu3rnnXc0b948TZgwweazsUKFCpoyZQqh6W9GaIJD+Omnn7Rhwwa5uLjYtAcHB+vPP/+0U1XIbYoVK2bvEoC7+u9//6tZs2apUaNG6tOnj7W9UqVK2T62BTmL0ASHkJGRke3pjuPHj8vT09MOFSG3u9P3fVksFrm5ualkyZIKCQl5yFUht/vzzz+zvUMuIyND169ft0NFuQuhCQ6hSZMmmjJlimbNmiXp5i+mS5cuadSoUWrWrJmdq0Nu1LJly2yvb8pss1gsqlOnjpYsWaICBQrYqUrkNuXKldNPP/2UZVT0q6++0tNPP22nqnIPnggOhzBx4kTFxcWpXLlyunbtmjp16mQ9NTd+/Hh7l4dcKCYmRtWrV1dMTIySk5OVnJysmJgYhYWFaenSpVq/fr3OnTunIUOG2LtU5CIjR45U//79NX78eGVkZOibb75R79699e6772rkyJH2Lu+xx91zcBjp6elasGCBzYMEO3fuLHd3d3uXhlyoQoUKmjVrlmrVqmXTHhcXp8jISO3atUurV69Wjx49lJCQYKcqkRv99NNPGjt2rM1n5ciRI9WkSRN7l/bYIzQBQDbc3d21adMmVahQwab9999/V40aNXT16lUdPXpUZcuW1ZUrV+xUJYCHidNzcAjjxo3T7Nmzs7TPnj2b03Owi6pVq2ro0KE6c+aMte3MmTMaNmyYqlevLknav3+/9assADz+CE1wCP/+979VpkyZLO3ly5fnwZawi08++USHDx9W0aJFVbJkSZUsWVJFixbVkSNH9PHHH0uSLl26pDfffNPOleJx5+vrq7Nnz0qSChQoIF9f3zu+8Pfi7jk4hMTExGy/BNXPz8/6Bb7Aw1S6dGn98ccfWrVqlfbt22dte/bZZ+XkdPPvzZYtW9qxQuQWkydPtj56ZcqUKfYtJpcjNMEhBAUFKS4uLstzb+Li4hQYGGinqpDbOTk5qWnTpmratKm9S0Eu1rVrV0k3b5axWCyKiIiQv7+/navKnQhNcAi9e/fWoEGDdP36dTVs2FCSFBsbq2HDhunVV1+1c3XIrWJjYxUbG6vTp08rIyPDZlp21+ABf6c8efKoT58+2r17t71LybUITXAIQ4cO1blz59SvXz+lpaVJktzc3DR8+HCNGDHCztUhNxozZozGjh2ratWqqUiRInyTPBxCjRo1tG3bNr7yx0545ADs7saNG4qLi1NoaKjy5s2r3bt3y93dXaVKlZKrq6u9y0MuVaRIEU2YMEFdunSxdymA1cKFCzVixAgNHjxYVatWVf78+W2mV6xY0U6V5Q6EJjgENzc37d69m+/ygsMoWLCgNm7cqBIlSti7FMAq8yaEW9361T7ZfYcncg6n5+AQKlSooEOHDhGa4DB69eql+fPn66233rJ3KYDV4cOH7V1CrkZogkN45513NGTIEL399tvZDjl7eXnZqTLkVteuXdOsWbO0evVqVaxYUXnz5rWZPmnSJDtVhtwq80aZpUuXqmzZsvYuJ1fi9Bwcwq1DzrdecMuQM+ylQYMGd5xmsVi0Zs2ah1gNcNMTTzyh1atXE5rshNAEh7Bu3bq7Tq9fv/5DqgQAHNd7772nffv26eOPP1aePJwsetgITQAAPCJatWql2NhYeXh4KDQ0NMulDN98842dKssdiKlwGElJSfrkk0+sD24rX768evToIW9vbztXhtyidevWmjt3rry8vNS6deu79uWXE+zBx8dHbdq0sXcZuRahCQ5h8+bNioiIkLu7u2rUqCHp5oW27777rlatWqUqVarYuULkBt7e3tZr6gjrcERz5syxdwm5Gqfn4BDq1q2rkiVL6j//+Y/1PH16erp69eqlQ4cOaf369XauELnN1atXlZGRYT39ceTIES1ZskRly5ZVRESEnasDYA+EJjgEd3d3bdu2TWXKlLFp/+OPP1StWjVduXLFTpUht2rSpIlat26tPn36KCkpSWXKlFHevHl19uxZTZo0SX379rV3icilvvrqKy1cuFAJCQnWr53KtHXrVjtVlTtkfbQoYAdeXl5KSEjI0n7s2DF5enraoSLkdlu3blXdunUl3fwl5e/vr6NHj+q///2vPvjgAztXh9zqgw8+UPfu3eXv769t27apRo0aKliwoA4dOqTnnnvO3uU99ghNcAjt27dXz549tWDBAh07dkzHjh3Tl19+qV69eqljx472Lg+50JUrV6yBfdWqVWrdurWcnJxUs2ZNHT161M7VIbf66KOPNGvWLE2bNk0uLi4aNmyYYmJiNHDgQCUnJ9u7vMceF4LDIfzrX/+SxWLRSy+9pPT0dElS3rx51bdvX/3zn/+0c3XIjUqWLKklS5aoVatWWrlypQYPHixJOn36NE+oh90kJCSoVq1akm5e1nDx4kVJUpcuXVSzZk19+OGH9izvscdIExyCi4uLpk6dqgsXLmj79u3avn27zp8/r8mTJ8vV1dXe5SEXGjlypIYMGaLg4GCFhYUpPDxc0s1Rp6efftrO1SG3CggI0Pnz5yVJTz75pH755RdJN7+TjkuU/35cCA6H8Nlnn6l169bKly+fvUsBrBITE3Xy5ElVqlTJ+lU/GzdulJeXV5abFoCHoVevXgoKCtKoUaM0ffp0DR06VLVr19bmzZvVunVrffLJJ/Yu8bFGaIJD8PPz09WrV/XCCy/of/7nfxQRESFnZ2d7lwUADiUjI0MZGRnWR7MsWLBAcXFxKlWqlPr06ZPli6WRswhNcAjp6elasWKFvvjiC3377bfKly+f2rZtq86dO1vP3wMApGvXrmnHjh06ffq0MjIyrO0Wi0UtWrSwY2WPP0ITHM6VK1e0ePFizZ8/X6tXr1bRokV18OBBe5cFAHa3YsUKdenSRefOncsyzWKx6MaNG3aoKvfgQnA4nHz58ikiIkLPPfecSpUqpSNHjti7JABwCAMGDFC7du108uRJ66m6zBeB6e/HSBMcRuYI0+eff67Y2FgFBQWpY8eO6ty5MxfdAoBuPgh427ZtKlGihL1LyZV4ThMcQocOHbR06VLly5dP7dq101tvvWW9xRsAcNOLL76otWvXEprshJEmOITOnTurc+fO3DUHAHdx5coVtW3bVn5+fgoNDc1yt9zAgQPtVFnuQGgCAOAR8cknn6hPnz5yc3NTwYIFZbFYrNMsFosOHTpkx+oef4Qm2M0HH3ygyMhIubm53fMLUPnrCQBuPhF84MCBeu2116wPXMXDQ2iC3YSEhGjz5s0qWLCgQkJC7tiPv54A4CZfX19t2rSJa5rshNAEAMAjYvDgwfLz89Prr79u71JyJe6eg91ER0eb6mexWDRx4sS/uRoAcHw3btzQhAkTtHLlSlWsWDHLheCTJk2yU2W5A6EJdrNt2zab91u3blV6erpKly4tSdq3b5+cnZ1VtWpVe5QHAA7n999/19NPPy1J2rlzp820Wy8Kx9+D0AS7+fHHH60/T5o0SZ6enpo3b54KFCggSbpw4YK6d++uunXr2qtEAHAot35u4uHjmiY4hCeeeEKrVq1S+fLlbdp37typJk2a6MSJE3aqDACAm7hfEQ4hJSVFZ86cydJ+5swZXbx40Q4VAQBgi9AEh9CqVSt1795d33zzjY4fP67jx4/r66+/Vs+ePdW6dWt7lwcAAKfn4BiuXLmiIUOGaPbs2bp+/bokKU+ePOrZs6fef/995c+f384VAgByO0ITHMrly5d18OBBSVKJEiUISwAAh0FoAgAAMIFrmgAAAEwgNAEAAJhAaAIAADCB0ATgsRUcHKwpU6Y4zHIAPNoITQAeW5s2bVJkZKT1vcVi0ZIlSx5qDaNHj1blypXve765c+fKx8cnx+sB8OD47jkAj520tDS5uLjIz8/P3qUAeIww0gTgoXnmmWc0YMAADRo0SAUKFJC/v7/+85//6PLly+revbs8PT1VsmRJ/fDDD9Z5bty4oZ49eyokJETu7u4qXbq0pk6darPcbt26qWXLlnr33XcVGBio0qVLS7I9rRYcHCzp5tPnLRaL9f3Bgwf1j3/8Q/7+/vLw8FD16tW1evXq+9qutWvXqkaNGsqfP798fHxUu3ZtHT16VHPnztWYMWP022+/yWKxyGKxaO7cuZJufkl1aGio8ufPr6CgIPXr10+XLl2yLq979+5KTk62zjd69GhJ2Y+W+fj4WJeblpam/v37q0iRInJzc1OxYsU0bty4+9oeANkjNAF4qObNm6dChQpp48aNGjBggPr27au2bduqVq1a2rp1q5o0aaIuXbroypUrkqSMjAwVLVpUixYt0h9//KGRI0fq9ddf18KFC22WGxsbq7179yomJkZLly7Nst5NmzZJkubMmaOTJ09a31+6dEnNmjVTbGystm3bpqZNm6pFixZKSEgwtT3p6elq2bKl6tevrx07dig+Pl6RkZGyWCxq3769Xn31VZUvX14nT57UyZMn1b59e0mSk5OTPvjgA+3atUvz5s3TmjVrNGzYMElSrVq1NGXKFHl5eVnnGzJkiKl6PvjgA3333XdauHCh9u7dq88//9waEAH8RQYAPCT169c36tSpY32fnp5u5M+f3+jSpYu17eTJk4YkIz4+/o7LiYqKMtq0aWN937VrV8Pf399ITU216VesWDFj8uTJ1veSjMWLF9+zzvLlyxvTpk2743Jude7cOUOSsXbt2mynjxo1yqhUqdI917lo0SKjYMGC1vdz5swxvL29s/TLbhu8vb2NOXPmGIZhGAMGDDAaNmxoZGRk3HOdAO4PI00AHqqKFStaf3Z2dlbBggUVGhpqbfP395cknT592to2ffp0Va1aVX5+fvLw8NCsWbOyjASFhobKxcXlvuu5dOmShgwZorJly8rHx0ceHh7avXu36ZEmX19fdevWTREREWrRooWmTp2qkydP3nO+1atXq1GjRnriiSfk6empLl266Ny5c9YRtgfVrVs3bd++XaVLl9bAgQO1atWqv7Q8AP+P0ATgocqbN6/Ne4vFYtNmsVgk3TwtJ0lffvmlhgwZop49e2rVqlXavn27unfvrrS0NJvlPOj3FA4ZMkSLFy/We++9p59++knbt29XaGholuXfzZw5cxQfH69atWppwYIFeuqpp/TLL7/csf+RI0f0/PPPq2LFivr666+1ZcsWTZ8+XZLuuV6LxSLjtm+/yvySa0mqUqWKDh8+rLfffltXr15Vu3bt9OKLL5reFgB3xt1zABxaXFycatWqpX79+lnbMr/U+X7lzZtXN27cyLL8bt26qVWrVpJujjwdOXLkvpf99NNP6+mnn9aIESMUHh6u+fPnq2bNmnJxccmyzi1btigjI0MTJ06Uk9PNv11vv0Yru/kkyc/Pz2Yka//+/VlGp7y8vNS+fXu1b99eL774opo2barz58/L19f3vrcLwP9jpAmAQytVqpQ2b96slStXat++fXrrrbesF3Hfr+DgYMXGxioxMVEXLlywLv+bb77R9u3b9dtvv6lTp07WUS4zDh8+rBEjRig+Pl5Hjx7VqlWrtH//fpUtW9a6zsOHD2v79u06e/asUlNTVbJkSV2/fl3Tpk3ToUOH9Omnn2rmzJlZar106ZJiY2N19uxZazBq2LChPvzwQ23btk2bN29Wnz59bEbqJk2apC+++EJ79uzRvn37tGjRIgUEBPDMJyAHEJoAOLSXX35ZrVu3Vvv27RUWFqZz587ZjDrdj4kTJyomJkZBQUF6+umnJd0MGQUKFFCtWrXUokULRUREqEqVKqaXmS9fPu3Zs0dt2rTRU089pcjISEVFRenll1+WJLVp00ZNmzZVgwYN5Ofnpy+++EKVKlXSpEmTNH78eFWoUEGff/55lscC1KpVS3369FH79u3l5+enCRMmWLchKChIdevWVadOnTRkyBDly5fPOp+np6cmTJigatWqqXr16jpy5IiWL19uHdEC8OAsxu0nxwEAAJAFf3oAAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwIT/BRuB5V2mFSKtAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.countplot(df['marital'], order = df['marital'].value_counts().index[::-1])\n",
        "plt.xticks(rotation=90)\n",
        "plt.xlabel('marital status')\n",
        "plt.ylabel('count')\n",
        "plt.title('Customers according to marital status')\n",
        "\n",
        "# Insights\n",
        "# Acc to the distribution of 'marital' feature, most calls were made to married followed by single\n",
        "# Least calls were made to divorced"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "id": "Holjo1OVHPR3",
        "outputId": "23bfa0df-1f0b-4d7c-b94a-1d3bcdca6384"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Customers according to education qualification')"
            ]
          },
          "metadata": {},
          "execution_count": 54
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.countplot(df['education_qual'], order = df['education_qual'].value_counts().index[::-1])\n",
        "plt.xticks(rotation=15)\n",
        "plt.xlabel('education qualification')\n",
        "plt.ylabel('count')\n",
        "plt.title('Customers according to education qualification')\n",
        "\n",
        "# Insights\n",
        "# Acc to the distribution of 'education_qual' feature, most calls were made to secondary followed by tertiary\n",
        "# Least calls were made to primary"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 561
        },
        "id": "XpDgi8E1KxP-",
        "outputId": "71589219-bc45-48f3-cb2d-dc8a23b8cc46"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Customers according to call type')"
            ]
          },
          "metadata": {},
          "execution_count": 55
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.countplot(df['call_type'], order = df['call_type'].value_counts().index[::-1])\n",
        "plt.xticks(rotation=15)\n",
        "plt.xlabel('call type')\n",
        "plt.ylabel('count')\n",
        "plt.title('Customers according to call type')\n",
        "\n",
        "# Insights\n",
        "# Acc to the distribution of 'call_type' feature, most calls were made to cellular followed by unknown\n",
        "# Least calls were made to telephone"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        },
        "id": "mr1aAwBCLEJ0",
        "outputId": "2f3a5ff6-917c-4cea-a2d6-5f9603c7d66a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Customers according to day of the month')"
            ]
          },
          "metadata": {},
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Here day is considered as categorical feature since it have unique values from 1 to 31 and we can see the distribution of it\n",
        "sns.countplot(df['day'], order = df['day'].value_counts().index[::-1])\n",
        "plt.xticks(rotation=90)\n",
        "plt.xlabel('day of the month')\n",
        "plt.ylabel('count')\n",
        "plt.title('Customers according to day of the month')\n",
        "\n",
        "# Insights\n",
        "# Acc to the distribution of 'day' feature, most calls were made on 20th day followed by 18th day followed by 21st day\n",
        "# Least calls were made on 1st, 24th, 10th days"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        },
        "id": "zHB2I0EELYUm",
        "outputId": "4de992a1-0a72-4331-d210-73deea7254d1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Customers according to month')"
            ]
          },
          "metadata": {},
          "execution_count": 57
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.countplot(df['mon'], order = df['mon'].value_counts().index[::-1])\n",
        "plt.xticks(rotation=90)\n",
        "plt.xlabel('month')\n",
        "plt.ylabel('count')\n",
        "plt.title('Customers according to month')\n",
        "\n",
        "# Insights\n",
        "# Acc to the distribution of 'mon' feature, most of the calls were made on May followed by July followed by August\n",
        "# The least number of calls were made on Dec, Mar, Sept"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        },
        "id": "I9kTxmJ8L_Lg",
        "outputId": "4d75f847-99b1-42fc-8d24-add23a0fb60b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Customers according to previous outcome')"
            ]
          },
          "metadata": {},
          "execution_count": 58
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.countplot(df['prev_outcome'], order = df['prev_outcome'].value_counts().index[::-1])\n",
        "plt.xticks(rotation=15)\n",
        "plt.xlabel('previous outcome')\n",
        "plt.ylabel('count')\n",
        "plt.title('Customers according to previous outcome')\n",
        "\n",
        "# Insights\n",
        "# Acc to the distribution of 'prev_outcome' feature, the status of most of the calls are unknown followed by failure followed by other\n",
        "# The least number of calls status are success here"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K1UDtDceNJCx"
      },
      "source": [
        "## Boxplot of the Distribution of Continuous Features in the Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "v7-no8GHMXZw",
        "outputId": "5ab2a0ae-6799-4abf-9dca-9d2b3afa610a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Customers according to age')"
            ]
          },
          "metadata": {},
          "execution_count": 59
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.boxplot(x = df['age'])\n",
        "plt.title('Customers according to age')\n",
        "\n",
        "# Insights\n",
        "# Acc to the distribution of 'age' feature, most calls were made to the people of age between 32 to 48 approximately\n",
        "# The median lies around 39 approximately\n",
        "# Since clipping of 'age' column is already done, there are no outliers found in this plot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "WYyUQfBBQbLI",
        "outputId": "37f7ff80-f4a5-441a-8640-3f3f40aa886b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Customers according to duration of a call')"
            ]
          },
          "metadata": {},
          "execution_count": 60
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.boxplot(x = df['dur'])\n",
        "plt.title('Customers according to duration of a call')\n",
        "\n",
        "# Insights\n",
        "# Acc to the distribution of 'dur' feature, most calls lasted in the range of 100 to 320 seconds approximately\n",
        "# The median lies around 180 approximately\n",
        "# Since clipping of 'dur' column is already done, there are no outliers found in this plot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "qBRxRCd9RY2q",
        "outputId": "87444e03-1c49-4d93-8566-eb5fb84d954e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Customers according to number of calls made to a customer')"
            ]
          },
          "metadata": {},
          "execution_count": 61
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.boxplot(x = df['num_calls'])\n",
        "plt.title('Customers according to number of calls made to a customer')\n",
        "\n",
        "# Insights\n",
        "# Acc to the distribution of 'num_calls' feature, the highest number of calls made to a customer is in the range of 1 to 3\n",
        "# The median lies around 2 approximately\n",
        "# Since clipping of 'num_calls' column is already done, there are no outliers found in this plot"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hf2fYXCUSWX1"
      },
      "source": [
        "## Countplot of the Distribution of Target Variable in the Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 544
        },
        "id": "aUgcbOjwO4ad",
        "outputId": "03ffa01b-b9ec-4c4f-bbd9-65611e20dd62"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Customers according to target variable')"
            ]
          },
          "metadata": {},
          "execution_count": 62
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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ZWVlu55qd7/v2bJ+ZM3355Ze6++671b59e7366quqVauWKleurHfeeUdz5swp1/ac6Xw/77j0+A6gwrvzzjv1xhtvKD09XbGxsX9aW7JHIC8vz2362f6HXqtWLQ0dOlRDhw5Vbm6uWrZsqaefftoMTWcLYXXr1tXGjRtVXFzs9gO35DBH3bp1LW3buTRo0ECSFBwcrLi4uL80VlRUlKKiojR+/HitXbtWN954o2bNmqV//etfZdZv2rRJP/74o2bPnq3evXub09PS0tzq6tevL0l/Gmxr1qwph8NxzvBbt25dbd++vdR0q69ryfyS/7Gfrqxxy6tu3boqLi7Wrl273PYo7Nixw9Lyf/a+kv7otWPHjm7ztm/ffs7tP9u4K1eu1IEDB/Txxx+rffv25vRdu3ZZ6rekty+++KLU7QfO3OaS90PlypXP+Z49W79/pnv37ho6dKi2b9+uuXPnqkqVKrrrrrvM+Vbft+fro48+kq+vr5YuXSq73W5Of+edd8qsL+s9+OOPP6pKlSqqWbNmmctcyM87Lg4Oz6HCGzNmjPz9/TVgwADl5OSUmr9z505Nnz5d0h97pmrUqFHq0t5XX33V7XlRUVGpXerBwcEKCwtTQUGBOc3f37/MXe933HGHsrOzzcME0h9X4bzyyisKCAgwDwX8VU6nUw6HQ88884xOnjxZav7ZdvOfzuVy6dSpU27ToqKi5OXl5batZyrZ23L63hXDMMzXukTNmjXVvn17vf3228rKynKbV7Ksl5eXunTpok8//VQbNmwota6SujvuuEPr1q1Tenq6Oe/o0aN64403VK9evXOel1SrVi21aNFCs2fPdvu+paWllTof6q8o2YNy5vvqlVdesbR8yb2zzgz3rVq1UnBwsGbNmuX2vVm8eLF++OGHc16dd7Zxy/peFhYWlur/zzidTp08eVJvvvmmOa24uFgzZ850qwsODlaHDh30+uuva9++faXGOf09e7Z+/0zXrl3l7e2t//73v5o/f77uvPNOt3uRWX3fni9vb2/ZbDa3vda7d+/WggULyqxPT093O9dpz549WrhwoTp16nTWPZkX4vOOi4s9TajwGjRooDlz5qh79+5q0qSJ2x3B165da17qX2LAgAF69tlnNWDAALVq1UqrV6/Wjz/+6Dbm4cOHVbt2bd1///1q3ry5AgIC9Pnnn2v9+vV64YUXzLro6GjNnTtXSUlJat26tQICAnTXXXdp0KBBev3119WnTx9lZGSoXr16+vDDD7VmzRpNmzbtgp247nA49Nprr6lXr15q2bKlevTooZo1ayorK0upqam68cYbNWPGjD8dY8WKFRo2bJi6deuma6+9VqdOndJ//vMfeXt7q2vXrmddrnHjxmrQoIEeffRR/fbbb3I4HProo4/M83pO9/LLL+umm25Sy5YtNWjQIEVERGj37t1KTU01/1TIM888o2XLlunmm2/WoEGD1KRJE+3bt0/z58/XV199paCgID322GP673//q86dO2vEiBGqVq2aZs+erV27dumjjz6ydBhl8uTJio+P10033aR+/frp4MGDeuWVV3TdddfpyJEj51zeiujoaHXt2lXTpk3TgQMHzFsOlLzPzrUHpUGDBgoKCtKsWbNUtWpV+fv7KyYmRhEREXruuefUt29f3XzzzXrwwQfNWw7Uq1dPo0aN+tNxW7RoIW9vbz333HPKz8+X3W5Xx44ddcMNN+iqq65SQkKCRowYIZvNpv/85z/ndbixS5cuatOmjR555BHt2LFDjRs31v/+9z/zvmKnb/PMmTN10003KSoqSgMHDlT9+vWVk5Oj9PR0/frrr/r+++//tN/g4OCz9hEcHKxbbrlFL774og4fPqzu3bu7zT+f9+35iI+P14svvqjbb79dDz30kHJzczVz5kw1bNhQGzduLFXftGlTOZ1Ot1sOSNLEiRPPuo4L8XnHReaJS/aA8vjxxx+NgQMHGvXq1TN8fHyMqlWrGjfeeKPxyiuvuF3afOzYMaN///5GYGCgUbVqVeOBBx4wcnNz3S4FLygoMEaPHm00b97cqFq1quHv7280b97cePXVV93WeeTIEeOhhx4ygoKCSl2ynpOTY/Tt29eoUaOG4ePjY0RFRZW6hLzklgNTp051m15yyfyZtwEouQT5zMvyv/jiC8PpdBqBgYGGr6+v0aBBA6NPnz5ulzQnJCQY/v7+pV63n3/+2ejXr5/RoEEDw9fX16hWrZpxyy23GJ9//vk5X/OtW7cacXFxRkBAgFGjRg1j4MCBxvfff1/m5fKbN2827r33XiMoKMjw9fU1GjVqZDz55JNuNb/88ovRu3dvo2bNmobdbjfq169vJCYmGgUFBWbNzp07jfvvv98cp02bNsaiRYssvX4lPvroI6NJkyaG3W43IiMjjY8//rjMS8ilsm85sH//fre6My8NNwzDOHr0qJGYmGhUq1bNCAgIMLp06WJs377dkGQ8++yz53hlDWPhwoVGZGSkUalSpVKv59y5c43rr7/esNvtRrVq1YyePXsav/766znHNAzDePPNN4369esb3t7ebpfzr1mzxmjbtq3h5+dnhIWFGWPGjDGWLl1a6pL/m2++2bjuuuvKHHv//v3GQw89ZFStWtUIDAw0+vTpY6xZs8aQZHzwwQdutTt37jR69+5thIaGGpUrVzauvvpq48477zQ+/PBDS/2eaxslGVWrVnW7NUMJq+/bs31mSuad+X7597//bVxzzTWG3W43GjdubLzzzjvme+Z0kozExETjvffeM+uvv/76UttW1vvKMKx93uEZNsO4TM9sBIAKJjMzU9dff73ee+89887SV7oFCxbo3nvv1VdffaUbb7zR0+0AFxXnNAFAOZT1p2SmTZsmLy8vt5OtryRnbnNRUZFeeeUVORwOtWzZ0kNdAZcO5zQBQDlMmTJFGRkZuuWWW1SpUiUtXrxYixcv1qBBg0rdf+tKMXz4cB0/flyxsbEqKCjQxx9/rLVr1+qZZ565aLd/ACoSDs8BQDmkpaVp4sSJ2rp1q44cOaI6deqoV69eeuKJJ67Y++nMmTNHL7zwgnbs2KETJ06oYcOGGjJkiIYNG+bp1oBLgtAEAABgAec0AQAAWEBoAgAAsODKPPDuAcXFxdq7d6+qVq1arj8NAAAALj3DMHT48GGFhYWd8wa6hKYLZO/evVfsFTMAAFzp9uzZc9Y/YF6C0HSBlPzZjD179sjhcHi4GwAAYIXL5VJ4eLilP39FaLpASg7JORwOQhMAAJcZK6fWcCI4AACABYQmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsqTGh69tlnZbPZNHLkSHPaiRMnlJiYqOrVqysgIEBdu3ZVTk6O23JZWVmKj49XlSpVFBwcrNGjR+vUqVNuNStXrlTLli1lt9vVsGFDpaSklFr/zJkzVa9ePfn6+iomJkbr1q27GJsJAAAuUxUiNK1fv16vv/66mjVr5jZ91KhR+vTTTzV//nytWrVKe/fu1X333WfOLyoqUnx8vAoLC7V27VrNnj1bKSkpSk5ONmt27dql+Ph43XLLLcrMzNTIkSM1YMAALV261KyZO3eukpKS9NRTT+nbb79V8+bN5XQ6lZube/E3HgAAXB4MDzt8+LBxzTXXGGlpacbNN99sPPzww4ZhGEZeXp5RuXJlY/78+WbtDz/8YEgy0tPTDcMwjM8++8zw8vIysrOzzZrXXnvNcDgcRkFBgWEYhjFmzBjjuuuuc1tn9+7dDafTaT5v06aNkZiYaD4vKioywsLCjMmTJ1vejvz8fEOSkZ+fb33jAQCAR53P72+P72lKTExUfHy84uLi3KZnZGTo5MmTbtMbN26sOnXqKD09XZKUnp6uqKgohYSEmDVOp1Mul0tbtmwxa84c2+l0mmMUFhYqIyPDrcbLy0txcXFmTVkKCgrkcrncHgAA4MpVyZMr/+CDD/Ttt99q/fr1peZlZ2fLx8dHQUFBbtNDQkKUnZ1t1pwemErml8z7sxqXy6Xjx4/r0KFDKioqKrNm27ZtZ+198uTJmjhxorUNBQAAlz2PhaY9e/bo4YcfVlpamnx9fT3VRrmNGzdOSUlJ5nOXy6Xw8HAPdgTgcpc1KcrTLQAVTp3kTZ5uweSxw3MZGRnKzc1Vy5YtValSJVWqVEmrVq3Syy+/rEqVKikkJESFhYXKy8tzWy4nJ0ehoaGSpNDQ0FJX05U8P1eNw+GQn5+fatSoIW9v7zJrSsYoi91ul8PhcHsAAIArl8dC06233qpNmzYpMzPTfLRq1Uo9e/Y0v65cubKWL19uLrN9+3ZlZWUpNjZWkhQbG6tNmza5XeWWlpYmh8OhyMhIs+b0MUpqSsbw8fFRdHS0W01xcbGWL19u1gAAAHjs8FzVqlXVtGlTt2n+/v6qXr26Ob1///5KSkpStWrV5HA4NHz4cMXGxqpt27aSpE6dOikyMlK9evXSlClTlJ2drfHjxysxMVF2u12SNHjwYM2YMUNjxoxRv379tGLFCs2bN0+pqanmepOSkpSQkKBWrVqpTZs2mjZtmo4ePaq+ffteolcDAABUdB49EfxcXnrpJXl5ealr164qKCiQ0+nUq6++as739vbWokWLNGTIEMXGxsrf318JCQmaNGmSWRMREaHU1FSNGjVK06dPV+3atfXWW2/J6XSaNd27d9f+/fuVnJys7OxstWjRQkuWLCl1cjgAAPj7shmGYXi6iSuBy+VSYGCg8vPzOb8JQLlwIjhQ2sU+Efx8fn97/D5NAAAAlwNCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABYQmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACj4am1157Tc2aNZPD4ZDD4VBsbKwWL15szu/QoYNsNpvbY/DgwW5jZGVlKT4+XlWqVFFwcLBGjx6tU6dOudWsXLlSLVu2lN1uV8OGDZWSklKql5kzZ6pevXry9fVVTEyM1q1bd1G2GQAAXJ48Gppq166tZ599VhkZGdqwYYM6duyoe+65R1u2bDFrBg4cqH379pmPKVOmmPOKiooUHx+vwsJCrV27VrNnz1ZKSoqSk5PNml27dik+Pl633HKLMjMzNXLkSA0YMEBLly41a+bOnaukpCQ99dRT+vbbb9W8eXM5nU7l5uZemhcCAABUeDbDMAxPN3G6atWqaerUqerfv786dOigFi1aaNq0aWXWLl68WHfeeaf27t2rkJAQSdKsWbM0duxY7d+/Xz4+Pho7dqxSU1O1efNmc7kePXooLy9PS5YskSTFxMSodevWmjFjhiSpuLhY4eHhGj58uB577DFLfbtcLgUGBio/P18Oh+MvvAIA/q6yJkV5ugWgwqmTvOmijn8+v78rzDlNRUVF+uCDD3T06FHFxsaa099//33VqFFDTZs21bhx43Ts2DFzXnp6uqKioszAJElOp1Mul8vcW5Wenq64uDi3dTmdTqWnp0uSCgsLlZGR4Vbj5eWluLg4s6YsBQUFcrlcbg8AAHDlquTpBjZt2qTY2FidOHFCAQEB+uSTTxQZGSlJeuihh1S3bl2FhYVp48aNGjt2rLZv366PP/5YkpSdne0WmCSZz7Ozs/+0xuVy6fjx4zp06JCKiorKrNm2bdtZ+548ebImTpz41zYeAABcNjwemho1aqTMzEzl5+frww8/VEJCglatWqXIyEgNGjTIrIuKilKtWrV06623aufOnWrQoIEHu5bGjRunpKQk87nL5VJ4eLgHOwIAABeTx0OTj4+PGjZsKEmKjo7W+vXrNX36dL3++uulamNiYiRJO3bsUIMGDRQaGlrqKrecnBxJUmhoqPlvybTTaxwOh/z8/OTt7S1vb+8ya0rGKIvdbpfdbj/PrQUAAJerCnNOU4ni4mIVFBSUOS8zM1OSVKtWLUlSbGysNm3a5HaVW1pamhwOh3mILzY2VsuXL3cbJy0tzTxvysfHR9HR0W41xcXFWr58udu5VQAA4O/No3uaxo0bp86dO6tOnTo6fPiw5syZo5UrV2rp0qXauXOn5syZozvuuEPVq1fXxo0bNWrUKLVv317NmjWTJHXq1EmRkZHq1auXpkyZouzsbI0fP16JiYnmXqDBgwdrxowZGjNmjPr166cVK1Zo3rx5Sk1NNftISkpSQkKCWrVqpTZt2mjatGk6evSo+vbt65HXBQAAVDweDU25ubnq3bu39u3bp8DAQDVr1kxLly7Vbbfdpj179ujzzz83A0x4eLi6du2q8ePHm8t7e3tr0aJFGjJkiGJjY+Xv76+EhARNmjTJrImIiFBqaqpGjRql6dOnq3bt2nrrrbfkdDrNmu7du2v//v1KTk5Wdna2WrRooSVLlpQ6ORwAAPx9Vbj7NF2uuE8TgL+K+zQBpXGfJgAAgMsMoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABYQmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABggUdD02uvvaZmzZrJ4XDI4XAoNjZWixcvNuefOHFCiYmJql69ugICAtS1a1fl5OS4jZGVlaX4+HhVqVJFwcHBGj16tE6dOuVWs3LlSrVs2VJ2u10NGzZUSkpKqV5mzpypevXqydfXVzExMVq3bt1F2WYAAHB58mhoql27tp599lllZGRow4YN6tixo+655x5t2bJFkjRq1Ch9+umnmj9/vlatWqW9e/fqvvvuM5cvKipSfHy8CgsLtXbtWs2ePVspKSlKTk42a3bt2qX4+HjdcsstyszM1MiRIzVgwAAtXbrUrJk7d66SkpL01FNP6dtvv1Xz5s3ldDqVm5t76V4MAABQodkMwzA83cTpqlWrpqlTp+r+++9XzZo1NWfOHN1///2SpG3btqlJkyZKT09X27ZttXjxYt15553au3evQkJCJEmzZs3S2LFjtX//fvn4+Gjs2LFKTU3V5s2bzXX06NFDeXl5WrJkiSQpJiZGrVu31owZMyRJxcXFCg8P1/Dhw/XYY49Z6tvlcikwMFD5+flyOBwX8iUB8DeRNSnK0y0AFU6d5E0Xdfzz+f1dYc5pKioq0gcffKCjR48qNjZWGRkZOnnypOLi4syaxo0bq06dOkpPT5ckpaenKyoqygxMkuR0OuVyucy9Venp6W5jlNSUjFFYWKiMjAy3Gi8vL8XFxZk1ZSkoKJDL5XJ7AACAK5fHQ9OmTZsUEBAgu92uwYMH65NPPlFkZKSys7Pl4+OjoKAgt/qQkBBlZ2dLkrKzs90CU8n8knl/VuNyuXT8+HH9/vvvKioqKrOmZIyyTJ48WYGBgeYjPDy8XNsPAAAuDx4PTY0aNVJmZqa++eYbDRkyRAkJCdq6daun2zqncePGKT8/33zs2bPH0y0BAICLqJKnG/Dx8VHDhg0lSdHR0Vq/fr2mT5+u7t27q7CwUHl5eW57m3JychQaGipJCg0NLXWVW8nVdafXnHnFXU5OjhwOh/z8/OTt7S1vb+8ya0rGKIvdbpfdbi/fRgMAgMuOx/c0nam4uFgFBQWKjo5W5cqVtXz5cnPe9u3blZWVpdjYWElSbGysNm3a5HaVW1pamhwOhyIjI82a08coqSkZw8fHR9HR0W41xcXFWr58uVkDAADg0T1N48aNU+fOnVWnTh0dPnxYc+bM0cqVK7V06VIFBgaqf//+SkpKUrVq1eRwODR8+HDFxsaqbdu2kqROnTopMjJSvXr10pQpU5Sdna3x48crMTHR3As0ePBgzZgxQ2PGjFG/fv20YsUKzZs3T6mpqWYfSUlJSkhIUKtWrdSmTRtNmzZNR48eVd++fT3yugAAgIrHo6EpNzdXvXv31r59+xQYGKhmzZpp6dKluu222yRJL730kry8vNS1a1cVFBTI6XTq1VdfNZf39vbWokWLNGTIEMXGxsrf318JCQmaNGmSWRMREaHU1FSNGjVK06dPV+3atfXWW2/J6XSaNd27d9f+/fuVnJys7OxstWjRQkuWLCl1cjgAAPj7qnD3abpccZ8mAH8V92kCSuM+TQAAAJcZQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABYQmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAAo+GpsmTJ6t169aqWrWqgoOD1aVLF23fvt2tpkOHDrLZbG6PwYMHu9VkZWUpPj5eVapUUXBwsEaPHq1Tp0651axcuVItW7aU3W5Xw4YNlZKSUqqfmTNnql69evL19VVMTIzWrVt3wbcZAABcnjwamlatWqXExER9/fXXSktL08mTJ9WpUycdPXrUrW7gwIHat2+f+ZgyZYo5r6ioSPHx8SosLNTatWs1e/ZspaSkKDk52azZtWuX4uPjdcsttygzM1MjR47UgAEDtHTpUrNm7ty5SkpK0lNPPaVvv/1WzZs3l9PpVG5u7sV/IQAAQIVnMwzD8HQTJfbv36/g4GCtWrVK7du3l/THnqYWLVpo2rRpZS6zePFi3Xnnndq7d69CQkIkSbNmzdLYsWO1f/9++fj4aOzYsUpNTdXmzZvN5Xr06KG8vDwtWbJEkhQTE6PWrVtrxowZkqTi4mKFh4dr+PDheuyxx87Zu8vlUmBgoPLz8+VwOP7KywDgbyprUpSnWwAqnDrJmy7q+Ofz+7tCndOUn58vSapWrZrb9Pfff181atRQ06ZNNW7cOB07dsycl56erqioKDMwSZLT6ZTL5dKWLVvMmri4OLcxnU6n0tPTJUmFhYXKyMhwq/Hy8lJcXJxZc6aCggK5XC63BwAAuHJV8nQDJYqLizVy5EjdeOONatq0qTn9oYceUt26dRUWFqaNGzdq7Nix2r59uz7++GNJUnZ2tltgkmQ+z87O/tMal8ul48eP69ChQyoqKiqzZtu2bWX2O3nyZE2cOPGvbTQAALhsVJjQlJiYqM2bN+urr75ymz5o0CDz66ioKNWqVUu33nqrdu7cqQYNGlzqNk3jxo1TUlKS+dzlcik8PNxj/QAAgIurQoSmYcOGadGiRVq9erVq1679p7UxMTGSpB07dqhBgwYKDQ0tdZVbTk6OJCk0NNT8t2Ta6TUOh0N+fn7y9vaWt7d3mTUlY5zJbrfLbrdb30gAAHBZ8+g5TYZhaNiwYfrkk0+0YsUKRUREnHOZzMxMSVKtWrUkSbGxsdq0aZPbVW5paWlyOByKjIw0a5YvX+42TlpammJjYyVJPj4+io6OdqspLi7W8uXLzRoAAPD35tE9TYmJiZozZ44WLlyoqlWrmucgBQYGys/PTzt37tScOXN0xx13qHr16tq4caNGjRql9u3bq1mzZpKkTp06KTIyUr169dKUKVOUnZ2t8ePHKzEx0dwTNHjwYM2YMUNjxoxRv379tGLFCs2bN0+pqalmL0lJSUpISFCrVq3Upk0bTZs2TUePHlXfvn0v/QsDAAAqHI+Gptdee03SH7cVON0777yjPn36yMfHR59//rkZYMLDw9W1a1eNHz/erPX29taiRYs0ZMgQxcbGyt/fXwkJCZo0aZJZExERodTUVI0aNUrTp09X7dq19dZbb8npdJo13bt31/79+5WcnKzs7Gy1aNFCS5YsKXVyOAAA+HuqUPdpupxxnyYAfxX3aQJK4z5NAAAAlxlCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWlCs0dezYUXl5eaWmu1wudezY8a/2BAAAUOGUKzStXLlShYWFpaafOHFCX3755V9uCgAAoKKpdD7FGzduNL/eunWrsrOzzedFRUVasmSJrr766gvXHQAAQAVxXqGpRYsWstlsstlsZR6G8/Pz0yuvvHLBmgMAAKgozis07dq1S4ZhqH79+lq3bp1q1qxpzvPx8VFwcLC8vb0veJMAAACedl6hqW7dupKk4uLii9IMAABARXVeoel0P/30k7744gvl5uaWClHJycl/uTEAAICKpFyh6c0339SQIUNUo0YNhYaGymazmfNsNhuhCQAAXHHKFZr+9a9/6emnn9bYsWMvdD8AAAAVUrnu03To0CF169btQvcCAABQYZUrNHXr1k3Lli270L0AAABUWOU6PNewYUM9+eST+vrrrxUVFaXKlSu7zR8xYsQFaQ4AAKCisBmGYZzvQhEREWcf0GbTzz///Jeauhy5XC4FBgYqPz9fDofD0+0AuAxlTYrydAtAhVMnedNFHf98fn+Xa0/Trl27ytUYAADA5apc5zQBAAD83ZRrT1O/fv3+dP7bb79drmYAAAAqqnKFpkOHDrk9P3nypDZv3qy8vLwy/5AvAADA5a5coemTTz4pNa24uFhDhgxRgwYN/nJTAAAAFc0FO6fJy8tLSUlJeumlly7UkAAAABXGBT0RfOfOnTp16tSFHBIAAKBCKNfhuaSkJLfnhmFo3759Sk1NVUJCwgVpDAAAoCIp156m7777zu2xceNGSdILL7ygadOmWR5n8uTJat26tapWrarg4GB16dJF27dvd6s5ceKEEhMTVb16dQUEBKhr167Kyclxq8nKylJ8fLyqVKmi4OBgjR49utQer5UrV6ply5ay2+1q2LChUlJSSvUzc+ZM1atXT76+voqJidG6dessbwsAALiylWtP0xdffHFBVr5q1SolJiaqdevWOnXqlB5//HF16tRJW7dulb+/vyRp1KhRSk1N1fz58xUYGKhhw4bpvvvu05o1ayRJRUVFio+PV2hoqNauXat9+/apd+/eqly5sp555hlJf9yMMz4+XoMHD9b777+v5cuXa8CAAapVq5acTqckae7cuUpKStKsWbMUExOjadOmyel0avv27QoODr4g2wsAAC5f5fozKiX2799v7hlq1KiRatas+Zea2b9/v4KDg7Vq1Sq1b99e+fn5qlmzpubMmaP7779fkrRt2zY1adJE6enpatu2rRYvXqw777xTe/fuVUhIiCRp1qxZGjt2rPbv3y8fHx+NHTtWqamp2rx5s7muHj16KC8vT0uWLJEkxcTEqHXr1poxY4akP64GDA8P1/Dhw/XYY4+ds3f+jAqAv4o/owKUVpH+jEq5Ds8dPXpU/fr1U61atdS+fXu1b99eYWFh6t+/v44dO1aupiUpPz9fklStWjVJUkZGhk6ePKm4uDizpnHjxqpTp47S09MlSenp6YqKijIDkyQ5nU65XC5t2bLFrDl9jJKakjEKCwuVkZHhVuPl5aW4uDiz5kwFBQVyuVxuDwAAcOUqV2hKSkrSqlWr9OmnnyovL095eXlauHChVq1apUceeaRcjRQXF2vkyJG68cYb1bRpU0lSdna2fHx8FBQU5FYbEhKi7Oxss+b0wFQyv2Ten9W4XC4dP35cv//+u4qKisqsKRnjTJMnT1ZgYKD5CA8PL9d2AwCAy0O5QtNHH32kf//73+rcubMcDoccDofuuOMOvfnmm/rwww/L1UhiYqI2b96sDz74oFzLX2rjxo1Tfn6++dizZ4+nWwIAABdRuU4EP3bsWKm9MpIUHBxcrsNzw4YN06JFi7R69WrVrl3bnB4aGqrCwkLl5eW57W3KyclRaGioWXPmVW4lV9edXnPmFXc5OTlyOBzy8/OTt7e3vL29y6wpGeNMdrtddrv9vLcVAABcnsq1pyk2NlZPPfWUTpw4YU47fvy4Jk6cqNjYWMvjGIahYcOG6ZNPPtGKFSsUERHhNj86OlqVK1fW8uXLzWnbt29XVlaWuZ7Y2Fht2rRJubm5Zk1aWpocDociIyPNmtPHKKkpGcPHx0fR0dFuNcXFxVq+fPl5bQ8AALhylWtP07Rp03T77berdu3aat68uSTp+++/l91u17JlyyyPk5iYqDlz5mjhwoWqWrWqef5QYGCg/Pz8FBgYqP79+yspKUnVqlWTw+HQ8OHDFRsbq7Zt20qSOnXqpMjISPXq1UtTpkxRdna2xo8fr8TERHNP0ODBgzVjxgyNGTNG/fr104oVKzRv3jylpqaavSQlJSkhIUGtWrVSmzZtNG3aNB09elR9+/Ytz0sEAACuMOW+5cCxY8f0/vvva9u2bZKkJk2aqGfPnvLz87O+cputzOnvvPOO+vTpI+mPm1s+8sgj+u9//6uCggI5nU69+uqrbofNfvnlFw0ZMkQrV66Uv7+/EhIS9Oyzz6pSpf8/E65cuVKjRo3S1q1bVbt2bT355JPmOkrMmDFDU6dOVXZ2tlq0aKGXX35ZMTExlraFWw4A+Ku45QBQWkW65UC5QtPkyZMVEhKifv36uU1/++23tX//fo0dO/Z8h7zsEZoA/FWEJqC0ihSaynVO0+uvv67GjRuXmn7ddddp1qxZ5RkSAACgQitXaMrOzlatWrVKTa9Zs6b27dv3l5sCAACoaMoVmsLDw82//Xa6NWvWKCws7C83BQAAUNGU6+q5gQMHauTIkTp58qQ6duwoSVq+fLnGjBlT7juCAwAAVGTlCk2jR4/WgQMHNHToUBUWFkqSfH19NXbsWI0bN+6CNggAAFARlCs02Ww2Pffcc3ryySf1ww8/yM/PT9dccw13yAYAAFescoWmEgEBAWrduvWF6gUAAKDCKteJ4AAAAH83hCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABYQmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALPBqaVq9erbvuukthYWGy2WxasGCB2/w+ffrIZrO5PW6//Xa3moMHD6pnz55yOBwKCgpS//79deTIEbeajRs3ql27dvL19VV4eLimTJlSqpf58+ercePG8vX1VVRUlD777LMLvr0AAODy5dHQdPToUTVv3lwzZ848a83tt9+uffv2mY///ve/bvN79uypLVu2KC0tTYsWLdLq1as1aNAgc77L5VKnTp1Ut25dZWRkaOrUqZowYYLeeOMNs2bt2rV68MEH1b9/f3333Xfq0qWLunTpos2bN1/4jQYAAJclm2EYhqebkCSbzaZPPvlEXbp0Maf16dNHeXl5pfZAlfjhhx8UGRmp9evXq1WrVpKkJUuW6I477tCvv/6qsLAwvfbaa3riiSeUnZ0tHx8fSdJjjz2mBQsWaNu2bZKk7t276+jRo1q0aJE5dtu2bdWiRQvNmjXLUv8ul0uBgYHKz8+Xw+EoxysA4O8ua1KUp1sAKpw6yZsu6vjn8/u7wp/TtHLlSgUHB6tRo0YaMmSIDhw4YM5LT09XUFCQGZgkKS4uTl5eXvrmm2/Mmvbt25uBSZKcTqe2b9+uQ4cOmTVxcXFu63U6nUpPTz9rXwUFBXK5XG4PAABw5arQoen222/Xu+++q+XLl+u5557TqlWr1LlzZxUVFUmSsrOzFRwc7LZMpUqVVK1aNWVnZ5s1ISEhbjUlz89VUzK/LJMnT1ZgYKD5CA8P/2sbCwAAKrRKnm7gz/To0cP8OioqSs2aNVODBg20cuVK3XrrrR7sTBo3bpySkpLM5y6Xi+AEAMAVrELvaTpT/fr1VaNGDe3YsUOSFBoaqtzcXLeaU6dO6eDBgwoNDTVrcnJy3GpKnp+rpmR+Wex2uxwOh9sDAABcuS6r0PTrr7/qwIEDqlWrliQpNjZWeXl5ysjIMGtWrFih4uJixcTEmDWrV6/WyZMnzZq0tDQ1atRIV111lVmzfPlyt3WlpaUpNjb2Ym8SAAC4THg0NB05ckSZmZnKzMyUJO3atUuZmZnKysrSkSNHNHr0aH399dfavXu3li9frnvuuUcNGzaU0+mUJDVp0kS33367Bg4cqHXr1mnNmjUaNmyYevToobCwMEnSQw89JB8fH/Xv319btmzR3LlzNX36dLdDaw8//LCWLFmiF154Qdu2bdOECRO0YcMGDRs27JK/JgAAoGLyaGjasGGDrr/+el1//fWSpKSkJF1//fVKTk6Wt7e3Nm7cqLvvvlvXXnut+vfvr+joaH355Zey2+3mGO+//74aN26sW2+9VXfccYduuukmt3swBQYGatmyZdq1a5eio6P1yCOPKDk52e1eTjfccIPmzJmjN954Q82bN9eHH36oBQsWqGnTppfuxQAAABVahblP0+WO+zQB+Ku4TxNQGvdpAgAAuMwQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABYQmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWeDQ0rV69WnfddZfCwsJks9m0YMECt/mGYSg5OVm1atWSn5+f4uLi9NNPP7nVHDx4UD179pTD4VBQUJD69++vI0eOuNVs3LhR7dq1k6+vr8LDwzVlypRSvcyfP1+NGzeWr6+voqKi9Nlnn13w7QUAAJcvj4amo0ePqnnz5po5c2aZ86dMmaKXX35Zs2bN0jfffCN/f385nU6dOHHCrOnZs6e2bNmitLQ0LVq0SKtXr9agQYPM+S6XS506dVLdunWVkZGhqVOnasKECXrjjTfMmrVr1+rBBx9U//799d1336lLly7q0qWLNm/efPE2HgAAXFZshmEYnm5Ckmw2mz755BN16dJF0h97mcLCwvTII4/o0UcflSTl5+crJCREKSkp6tGjh3744QdFRkZq/fr1atWqlSRpyZIluuOOO/Trr78qLCxMr732mp544gllZ2fLx8dHkvTYY49pwYIF2rZtmySpe/fuOnr0qBYtWmT207ZtW7Vo0UKzZs2y1L/L5VJgYKDy8/PlcDgu1MsC4G8ka1KUp1sAKpw6yZsu6vjn8/u7wp7TtGvXLmVnZysuLs6cFhgYqJiYGKWnp0uS0tPTFRQUZAYmSYqLi5OXl5e++eYbs6Z9+/ZmYJIkp9Op7du369ChQ2bN6espqSlZDwAAQCVPN3A22dnZkqSQkBC36SEhIea87OxsBQcHu82vVKmSqlWr5lYTERFRaoySeVdddZWys7P/dD1lKSgoUEFBgfnc5XKdz+YBAIDLTIXd01TRTZ48WYGBgeYjPDzc0y0BAICLqMKGptDQUElSTk6O2/ScnBxzXmhoqHJzc93mnzp1SgcPHnSrKWuM09dxtpqS+WUZN26c8vPzzceePXvOdxMBAMBlpMKGpoiICIWGhmr58uXmNJfLpW+++UaxsbGSpNjYWOXl5SkjI8OsWbFihYqLixUTE2PWrF69WidPnjRr0tLS1KhRI1111VVmzenrKakpWU9Z7Ha7HA6H2wMAAFy5PBqajhw5oszMTGVmZkr64+TvzMxMZWVlyWazaeTIkfrXv/6l//3vf9q0aZN69+6tsLAw8wq7Jk2a6Pbbb9fAgQO1bt06rVmzRsOGDVOPHj0UFhYmSXrooYfk4+Oj/v37a8uWLZo7d66mT5+upKQks4+HH35YS5Ys0QsvvKBt27ZpwoQJ2rBhg4YNG3apXxIAAFBBefRE8A0bNuiWW24xn5cEmYSEBKWkpGjMmDE6evSoBg0apLy8PN10001asmSJfH19zWXef/99DRs2TLfeequ8vLzUtWtXvfzyy+b8wMBALVu2TImJiYqOjlaNGjWUnJzsdi+nG264QXPmzNH48eP1+OOP65prrtGCBQvUtGnTS/AqAACAy0GFuU/T5Y77NAH4q7hPE1Aa92kCAAC4zBCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABYQmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABZU6NA0YcIE2Ww2t0fjxo3N+SdOnFBiYqKqV6+ugIAAde3aVTk5OW5jZGVlKT4+XlWqVFFwcLBGjx6tU6dOudWsXLlSLVu2lN1uV8OGDZWSknIpNg8AAFxGKnRokqTrrrtO+/btMx9fffWVOW/UqFH69NNPNX/+fK1atUp79+7VfffdZ84vKipSfHy8CgsLtXbtWs2ePVspKSlKTk42a3bt2qX4+HjdcsstyszM1MiRIzVgwAAtXbr0km4nAACo2Cp5uoFzqVSpkkJDQ0tNz8/P17///W/NmTNHHTt2lCS98847atKkib7++mu1bdtWy5Yt09atW/X5558rJCRELVq00D//+U+NHTtWEyZMkI+Pj2bNmqWIiAi98MILkqQmTZroq6++0ksvvSSn03lJt9WK6NHveroFoMLJmNrb0y0A+Buo8HuafvrpJ4WFhal+/frq2bOnsrKyJEkZGRk6efKk4uLizNrGjRurTp06Sk9PlySlp6crKipKISEhZo3T6ZTL5dKWLVvMmtPHKKkpGQMAAECq4HuaYmJilJKSokaNGmnfvn2aOHGi2rVrp82bNys7O1s+Pj4KCgpyWyYkJETZ2dmSpOzsbLfAVDK/ZN6f1bhcLh0/flx+fn5l9lZQUKCCggLzucvl+kvbCgAAKrYKHZo6d+5sft2sWTPFxMSobt26mjdv3lnDzKUyefJkTZw40aM9AACAS6fCH547XVBQkK699lrt2LFDoaGhKiwsVF5enltNTk6OeQ5UaGhoqavpSp6fq8bhcPxpMBs3bpzy8/PNx549e/7q5gEAgArssgpNR44c0c6dO1WrVi1FR0ercuXKWr58uTl/+/btysrKUmxsrCQpNjZWmzZtUm5urlmTlpYmh8OhyMhIs+b0MUpqSsY4G7vdLofD4fYAAABXrgodmh599FGtWrVKu3fv1tq1a3XvvffK29tbDz74oAIDA9W/f38lJSXpiy++UEZGhvr27avY2Fi1bdtWktSpUydFRkaqV69e+v7777V06VKNHz9eiYmJstvtkqTBgwfr559/1pgxY7Rt2za9+uqrmjdvnkaNGuXJTQcAABVMhT6n6ddff9WDDz6oAwcOqGbNmrrpppv09ddfq2bNmpKkl156SV5eXuratasKCgrkdDr16quvmst7e3tr0aJFGjJkiGJjY+Xv76+EhARNmjTJrImIiFBqaqpGjRql6dOnq3bt2nrrrbcq5O0GAACA59gMwzA83cSVwOVyKTAwUPn5+Rf1UB33aQJKu1Lu05Q1KcrTLQAVTp3kTRd1/PP5/V2hD88BAABUFIQmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABYQmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEpjPMnDlT9erVk6+vr2JiYrRu3TpPtwQAACoAQtNp5s6dq6SkJD311FP69ttv1bx5czmdTuXm5nq6NQAA4GGEptO8+OKLGjhwoPr27avIyEjNmjVLVapU0dtvv+3p1gAAgIcRmv6fwsJCZWRkKC4uzpzm5eWluLg4paene7AzAABQEVTydAMVxe+//66ioiKFhIS4TQ8JCdG2bdtK1RcUFKigoMB8np+fL0lyuVwXtc+iguMXdXzgcnSxP3eXyuETRZ5uAahwLvbnu2R8wzDOWUtoKqfJkydr4sSJpaaHh4d7oBvg7y3wlcGebgHAxTI58JKs5vDhwwoM/PN1EZr+nxo1asjb21s5OTlu03NychQaGlqqfty4cUpKSjKfFxcX6+DBg6pevbpsNttF7xee5XK5FB4erj179sjhcHi6HQAXEJ/vvxfDMHT48GGFhYWds5bQ9P/4+PgoOjpay5cvV5cuXST9EYSWL1+uYcOGlaq32+2y2+1u04KCgi5Bp6hIHA4HP1SBKxSf77+Pc+1hKkFoOk1SUpISEhLUqlUrtWnTRtOmTdPRo0fVt29fT7cGAAA8jNB0mu7du2v//v1KTk5Wdna2WrRooSVLlpQ6ORwAAPz9EJrOMGzYsDIPxwGns9vteuqpp0odogVw+ePzjbOxGVausQMAAPib4+aWAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCE3Cad999V9WrV1dBQYHb9C5duqhXr16SpIULF6ply5by9fVV/fr1NXHiRJ06dUrSH3/DaMKECapTp47sdrvCwsI0YsSIS74dAM6tQ4cOGjFihMaMGaNq1aopNDRUEyZMMOdnZWXpnnvuUUBAgBwOhx544IFSf58Ufy+EJuA03bp1U1FRkf73v/+Z03Jzc5Wamqp+/frpyy+/VO/evfXwww9r69atev3115WSkqKnn35akvTRRx/ppZde0uuvv66ffvpJCxYsUFRUlKc2B8A5zJ49W/7+/vrmm280ZcoUTZo0SWlpaSouLtY999yjgwcPatWqVUpLS9PPP/+s7t27e7pleBA3twTOMHToUO3evVufffaZJOnFF1/UzJkztWPHDt1222269dZbNW7cOLP+vffe05gxY7R37169+OKLev3117V582ZVrlzZU5sAwIIOHTqoqKhIX375pTmtTZs26tixo2699VZ17txZu3btUnh4uCRp69atuu6667Ru3Tq1bt3aU23Dg9jTBJxh4MCBWrZsmX777TdJUkpKivr06SObzabvv/9ekyZNUkBAgPkYOHCg9u3bp2PHjqlbt246fvy46tevr4EDB+qTTz4xD90BqHiaNWvm9rxWrVrKzc3VDz/8oPDwcDMwSVJkZKSCgoL0ww8/XOo2UUHwt+eAM1x//fVq3ry53n33XXXq1ElbtmxRamqqJOnIkSOaOHGi7rvvvlLL+fr6Kjw8XNu3b9fnn3+utLQ0DR06VFOnTtWqVavY8wRUQGd+Lm02m4qLiz3UDSo6QhNQhgEDBmjatGn67bffFBcXZ/5vs2XLltq+fbsaNmx41mX9/Px011136a677lJiYqIaN26sTZs2qWXLlpeqfQB/UZMmTbRnzx7t2bPH7fBcXl6eIiMjPdwdPIXQBJThoYce0qOPPqo333xT7777rjk9OTlZd955p+rUqaP7779fXl5e+v7777V582b961//UkpKioqKihQTE6MqVarovffek5+fn+rWrevBrQFwvuLi4hQVFaWePXtq2rRpOnXqlIYOHaqbb75ZrVq18nR78BDOaQLKEBgYqK5duyogIEBdunQxpzudTi1atEjLli1T69at1bZtW7300ktmKAoKCtKbb76pG2+8Uc2aNdPnn3+uTz/9VNWrV/fQlgAoD5vNpoULF+qqq65S+/btFRcXp/r162vu3Lmebg0exNVzwFnceuutuu666/Tyyy97uhUAQAVAaALOcOjQIa1cuVL333+/tm7dqkaNGnm6JQBABcA5TcAZrr/+eh06dEjPPfccgQkAYGJPEwAAgAWcCA4AAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAXJE6dOigkSNHeroNU0XrB8D5IzQBwFkUFhZ6ugUAFQihCcAVp0+fPlq1apWmT58um80mm82mnTt3qn///oqIiJCfn58aNWqk6dOnl1quS5cuevrppxUWFmbe3HTt2rVq0aKFfH191apVKy1YsEA2m02ZmZnmsps3b1bnzp0VEBCgkJAQ9erVS7///vtZ+9m9e/elejkAXCDcERzAFWf69On68ccf1bRpU02aNEmSdNVVV6l27dqaP3++qlevrrVr12rQoEGqVauWHnjgAXPZ5cuXy+FwKC0tTZLkcrl011136Y477tCcOXP0yy+/lDrMlpeXp44dO2rAgAF66aWXdPz4cY0dO1YPPPCAVqxYUWY/NWvWvDQvBoALhtAE4IoTGBgoHx8fValSRaGhoeb0iRMnml9HREQoPT1d8+bNcwtN/v7+euutt+Tj4yNJmjVrlmw2m9588035+voqMjJSv/32mwYOHGguM2PGDF1//fV65plnzGlvv/22wsPD9eOPP+raa68tsx8AlxdCE4C/jZkzZ+rtt99WVlaWjh8/rsLCQrVo0cKtJioqygxMkrR9+3Y1a9ZMvr6+5rQ2bdq4LfP999/riy++UEBAQKl17ty5U9dee+2F3RAAHkFoAvC38MEHH+jRRx/VCy+8oNjYWFWtWlVTp07VN99841bn7+9/3mMfOXJEd911l5577rlS82rVqlXungFULIQmAFckHx8fFRUVmc/XrFmjG264QUOHDjWn7dy585zjNGrUSO+9954KCgpkt9slSevXr3eradmypT766CPVq1dPlSqV/WP1zH4AXH64eg7AFalevXr65ptvtHv3bv3++++65pprtGHDBi1dulQ//vijnnzyyVLhpywPPfSQiouLNWjQIP3www9aunSpnn/+eUmSzWaTJCUmJurgwYN68MEHtX79eu3cuVNLly5V3759zaB0Zj/FxcUXb+MBXBSEJgBXpEcffVTe3t6KjIxUzZo15XQ6dd9996l79+6KiYnRgQMH3PY6nY3D4dCnn36qzMxMtWjRQk888YSSk5MlyTzPKSwsTGvWrFFRUZE6deqkqKgojRw5UkFBQfLy8iqzn6ysrIu38QAuCpthGIanmwCAy8n777+vvn37Kj8/X35+fp5uB8AlwjlNAHAO7777rurXr6+rr75a33//vXkPJgIT8PdCaAKAc8jOzlZycrKys7NVq1YtdevWTU8//bSn2wJwiXF4DgAAwAJOBAcAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACw4P8DxcVVXQA3NZoAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.countplot(df['y'], order = df['y'].value_counts().index[::-1])\n",
        "plt.xlabel('target')\n",
        "plt.ylabel('count')\n",
        "plt.title('Customers according to target variable')\n",
        "\n",
        "# Insights\n",
        "# Acc to the distribution of the target variable, Most of the calls made to customers lead to the people not taking the insurance\n",
        "# Very less number of customers atlast are opting for insurance\n",
        "# From this plot, it is clearly seen that it is an imbalanced target data.\n",
        "# We need to balance it before applying any ML techniques to get the perfect result"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w0tM4GGPezDg"
      },
      "source": [
        "## Feature v/s Target Plot for Categorical Columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7-p4MQn4eY0N"
      },
      "outputs": [],
      "source": [
        "# Encoding the target variable to 1 and 0\n",
        "df['y'] = df['y'].map({'yes' : 1, 'no' : 0})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "HAnxEqKLfvEJ",
        "outputId": "5883b439-165b-496f-e59f-2bd65d702ad2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Distribution of job with respect to customers who opted for insurance')"
            ]
          },
          "metadata": {},
          "execution_count": 64
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "df.groupby('job')['y'].mean().sort_values().plot(kind = 'barh')\n",
        "plt.xlabel('Distribution of job with respect to target')\n",
        "plt.title('Distribution of job with respect to customers who opted for insurance')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aCEFXwkPjYu9"
      },
      "source": [
        "### Insights\n",
        "From the 'job' v/s 'y' barplot, the most who are opting for insurance are student followed by retired followed by unemployed.\n",
        "\n",
        "The least in this plot are blue-collar, entreprenuer, house-maid\n",
        "\n",
        "With respect to target variable, 'job' column have linearly increasing pattern. So, Label Encoding shall be done to this column\n",
        "\n",
        "### Suggestion to the company\n",
        "\n",
        "When comparing Distribution of the feature 'job' and 'job' v/s 'y' barplot, More number of customers from student, retired and unemployed are opting for insurance. But these types of customers were called the least.\n",
        "\n",
        "Meanwhile, least customers from blue-collar opted for insurance. But the insurance company called those categories the most.\n",
        "\n",
        "So, the insurance company have to make more calls on student, retired and unemployed rather than blue-collar."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "PKXv7UeekXMo",
        "outputId": "9d40be90-29d4-4954-d238-3ab8e4e5bd54"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Distribution of marital status with respect to customers who opted for insurance')"
            ]
          },
          "metadata": {},
          "execution_count": 65
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "df.groupby('marital')['y'].mean().sort_values().plot(kind='barh')\n",
        "plt.xlabel('Distribution of marital status with respect to target')\n",
        "plt.title('Distribution of marital status with respect to customers who opted for insurance')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f_DuU00xmTgh"
      },
      "source": [
        "### Insights\n",
        "From the 'marital' v/s 'y' barplot, the most who are opting for insurance are single followed by divorced\n",
        "\n",
        "The least in this plot are married\n",
        "\n",
        "With respect to target variable, 'marital' column follows linearly increasing pattern. So, Label Encoding shall be done to this column\n",
        "\n",
        "### Suggestion to the company\n",
        "\n",
        "When comparing Distribution of the feature 'marital' and 'marital' v/s 'y' barplot, More number of customers from single and divorced are opting for insurance. But these types of customers were called lesser than married ones\n",
        "\n",
        "Meanwhile, least customers from married opted for insurance. But the insurance company called those people the most.\n",
        "\n",
        "So, the insurance company have to make more calls on single and divorced rather than married."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "O5276KHikkx_",
        "outputId": "a510175f-dbf3-4b7e-8a60-de268c734778"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Distribution of education qualification with respect to customers who opted for insurance')"
            ]
          },
          "metadata": {},
          "execution_count": 66
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "df.groupby('education_qual')['y'].mean().sort_values().plot(kind='barh')\n",
        "plt.xlabel('Distribution of education qualification with respect to target')\n",
        "plt.title('Distribution of education qualification with respect to customers who opted for insurance')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W9X2wkDgoQLQ"
      },
      "source": [
        "### Insights\n",
        "From the 'education_qual' v/s 'y' barplot, the most who are opting for insurance are tertiary followed by secondary\n",
        "\n",
        "The least in this plot are primary\n",
        "\n",
        "With respect to target variable, 'education_qual' column follows linearly increasing pattern. So, Label Encoding shall be done to this column\n",
        "\n",
        "### Suggestion to the company\n",
        "\n",
        "When comparing Distribution of the feature 'education_qual' and 'education_qual' v/s 'y' barplot, More number of customers from tertiary than secondary are opting for insurance. But these types of customers were called lesser than the secondary category.\n",
        "\n",
        "So, the insurance company have to make more calls on tertiary rather than secondary."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "ajGmQ8E4n2-J",
        "outputId": "fca20e56-0032-4f72-eb33-4e1bdfb03c9c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Distribution of call type with respect to customers who opted for insurance')"
            ]
          },
          "metadata": {},
          "execution_count": 67
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "df.groupby('call_type')['y'].mean().sort_values().plot(kind='barh')\n",
        "plt.xlabel('Distribution of call type with respect to target')\n",
        "plt.title('Distribution of call type with respect to customers who opted for insurance')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tp1vHk140Vt8"
      },
      "source": [
        "### Insights\n",
        "From the 'call_type' v/s 'y' barplot, the most who are opting for insurance are cellular followed by telephone\n",
        "\n",
        "The least in this plot are unknown\n",
        "\n",
        "With respect to target variable, 'call_type' column follows linearly increasing pattern. So, Label Encoding shall be done to this column\n",
        "\n",
        "### Suggestion to the company\n",
        "\n",
        "When comparing Distribution of the feature 'call_type' and 'call_type' v/s 'y' barplot, More number of customers from telephone than unknown are opting for insurance. But these types of customers were called lesser than the unknown category.\n",
        "\n",
        "So, the insurance company have to make more calls on telephone rather than unknown."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "fTu6AsKx0Bc4",
        "outputId": "da54f299-33e3-4b1c-92e3-69cf9bce9676"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Distribution of day of the month with respect to customers who opted for insurance')"
            ]
          },
          "metadata": {},
          "execution_count": 68
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "df.groupby('day')['y'].mean().sort_values().plot(kind='barh')\n",
        "plt.xlabel('Distribution of day of the month with respect to target')\n",
        "plt.title('Distribution of day of the month with respect to customers who opted for insurance')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N2-b5Fpf1Rgm"
      },
      "source": [
        "### Insights\n",
        "From the 'day' v/s 'y' barplot, the most who are opting for insurance were called on the dates 1st followed by 10th followed by 30th.\n",
        "\n",
        "The least who are opting for insurance were called on the dates 19th, 20th and 31st.\n",
        "\n",
        "### Suggestion to the company\n",
        "\n",
        "When comparing Distribution of the feature 'day' and 'day' v/s 'y' barplot, More number of customers opted for insurance on the dates 1st, 10th and 30th. But On those dates, less number of customers were called.\n",
        "\n",
        "Meanwhile, least customers have taken insurance on the dates 19th, 20th and 31st. But the insurance company called more number of people on those dates.\n",
        "\n",
        "So, the insurance company have to make more calls on start, mid and last day of the month to gain more customers from leads."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "aYdgyLFo4HhQ",
        "outputId": "a802336c-68ed-4f0d-a575-7ca987918ea5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Distribution of month with respect to customers who opted for insurance')"
            ]
          },
          "metadata": {},
          "execution_count": 69
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "df.groupby('mon')['y'].mean().sort_values().plot(kind='barh')\n",
        "plt.xlabel('Distribution of month with respect to target')\n",
        "plt.title('Distribution of month with respect to customers who opted for insurance')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qwtd9Jti4nQn"
      },
      "source": [
        "### Insights\n",
        "From the 'mon' v/s 'y' barplot, the most who are opting for insurance were called on the months Mar followed by Dec followed by Sep\n",
        "\n",
        "The least who are opting for insurance were called on the months May, July and Jan\n",
        "\n",
        "With respect to target variable, 'mon' column follows linearly increasing pattern. So, Label Encoding shall be done to this column\n",
        "\n",
        "### Suggestion to the company\n",
        "\n",
        "When comparing Distribution of the feature 'mon' and 'mon' v/s 'y' barplot, More number of customers opted for insurance on the months Mar, Dec and Sept. But On those months, less number of customers were called.\n",
        "\n",
        "Meanwhile, least customers have taken insurance on the months May, July and Jan. But the insurance company called more number of people on those months.\n",
        "\n",
        "So, the insurance company have to make more calls on Mar, Dec and Sept rather than calling on the months May, July and Jan to gain more customers.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "YfDT6myr4Rpz",
        "outputId": "84ed0293-3d0e-411e-f1c2-dc34ac9d593f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Distribution of prev_outcome with respect to customers who opted for insurance')"
            ]
          },
          "metadata": {},
          "execution_count": 70
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "df.groupby('prev_outcome')['y'].mean().sort_values().plot(kind='barh')\n",
        "plt.xlabel('Distribution of prev_outcome with respect to target')\n",
        "plt.title('Distribution of prev_outcome with respect to customers who opted for insurance')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N92PrfXz7C9b"
      },
      "source": [
        "### Insights\n",
        "From the 'prev_outcome' v/s 'y' barplot, the most who are opting for insurance are from the category success followed by other followed by failure.\n",
        "\n",
        "The least in this plot are unknown\n",
        "\n",
        "With respect to target variable, 'prev_outcome' column have linearly increasing pattern. So, Label Encoding shall be done to this column\n",
        "\n",
        "### Suggestion to the company\n",
        "\n",
        "When comparing Distribution of the feature 'prev_outcome' and 'prev_outcome' v/s 'y' barplot, More number of customers from success, other and failure category are opting for insurance. But these types of customers were called the least\n",
        "\n",
        "Meanwhile, least customers from unknown category opted for insurance. But the insurance company called them the most\n",
        "\n",
        "So, the insurance company should try to convert the previous outcome of the customers to success to gain more customers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IUWPZokN-S_s"
      },
      "source": [
        "## Feature v/s Target Plot for Continuous Columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 544
        },
        "id": "QdyY2n4Y-p4g",
        "outputId": "0fb95f4b-4602-4bb4-b360-9c33a926f9e8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Age v/s Target Line Plot')"
            ]
          },
          "metadata": {},
          "execution_count": 71
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.lineplot(df['age'], df['y'])\n",
        "plt.title('Age v/s Target Line Plot')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-ck-Or5OAFUv"
      },
      "source": [
        "### Insights\n",
        "From the 'age' v/s 'y' lineplot, the most who are opting for insurance are in the age of 18-25 and 60-70\n",
        "\n",
        "The least in this plot are of age 25-59\n",
        "\n",
        "With respect to target variable, 'age' column have varying patterns which are increasing as well as decreasing\n",
        "\n",
        "### Suggestion to the company\n",
        "\n",
        "When comparing Distribution of the feature 'age' and 'age' v/s 'y' line plot, More number of customers in the age of 18-25 and 60-70 are opting for insurance. But the customers of this age were called the least\n",
        "\n",
        "Meanwhile, least customers of age 25-59 opted for insurance. But the insurance company called them the most\n",
        "\n",
        "So, the insurance company need to concentrate more on youngsters and older people rather than calling middle aged people"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 544
        },
        "id": "tS8aOuKSBzh4",
        "outputId": "a263b88e-fcf5-4a43-a598-a79b72b8d5a1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Duration (in seconds) v/s Target Line Plot')"
            ]
          },
          "metadata": {},
          "execution_count": 72
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.lineplot(df['dur'], df['y'])\n",
        "plt.title('Duration (in seconds) v/s Target Line Plot')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cupi0gdPCe_a"
      },
      "source": [
        "### Insights\n",
        "From the 'dur' v/s 'y' lineplot, the most who are opting for insurance lasted in call for longer duration from 450-640 seconds\n",
        "\n",
        "The least in this plot are of duration 0-200\n",
        "\n",
        "With respect to target variable, 'dur' column have clear linearly increasing relationship with the target\n",
        "\n",
        "### Suggestion to the company\n",
        "\n",
        "When comparing Distribution of the feature 'dur' and 'dur' v/s 'y' line plot, More number of customers who lasted in the call from 450-640 seconds are opting for insurance. But the number of calls in this duration are the least\n",
        "\n",
        "Meanwhile, minimal customers opted for insurance who lasted in the call from 0-200 seconds. But the insurance company made calls in this duration the most\n",
        "\n",
        "So, the insurance company employees have to try to talk for longer duration to customers so that most of the customers will take the insurance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 544
        },
        "id": "0B84EJTyEMkj",
        "outputId": "6143f656-6543-40a4-f094-512e9221e330"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Number of Calls v/s Target Line Plot')"
            ]
          },
          "metadata": {},
          "execution_count": 73
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.lineplot(df['num_calls'], df['y'])\n",
        "plt.title('Number of Calls v/s Target Line Plot')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QK3JYDjgEmdk"
      },
      "outputs": [],
      "source": [
        "# Acc to the distribution of 'num_calls' feature, the highest number of calls made to a customer is in the range of 1 to 3\n",
        "# The median lies around 2 approximately"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tv0AV6tXFLgH"
      },
      "source": [
        "### Insights\n",
        "From the 'num_calls' v/s 'y' lineplot, the most who are opting for insurance have got the calls 1 to 3 times\n",
        "\n",
        "The least in this plot are 4 to 6 times\n",
        "\n",
        "With respect to target variable, 'dur' column have clear linearly decreasing relationship with the target\n",
        "\n",
        "### Suggestion to the company\n",
        "\n",
        "When comparing Distribution of the feature 'dur' and 'dur' v/s 'y' line plot, the most who are opting for insurance have got the calls 1 to 3 times. And the highest most number of calls made to a customer is 1 to 3 times\n",
        "\n",
        "So, the insurance company is in the right track with respect to num_calls column. It can continue in this same trend to gain more customers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kQ9u7U41Ug9L"
      },
      "source": [
        "## Encoding Categorical Columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eQYJNkb2G2xq"
      },
      "outputs": [],
      "source": [
        "# Since all the categorical feature follow linearly increasing relationship with target (Refer Feature v/s Target Plot),\n",
        "# Label encoding is done since the feature is behaving ordinal with respect to target variable.\n",
        "\n",
        "df['job'] = df['job'].map({'blue-collar':0, 'entrepreneur':1, 'housemaid':2, 'services':3, 'technician':4, 'self-employed':5, 'admin.':6, 'management':7, 'unemployed':8, 'retired':9, 'student':10})\n",
        "df['marital'] = df['marital'].map({'married':0,'divorced':1,'single':2})\n",
        "df['education_qual'] = df['education_qual'].map({'primary':0,'secondary':1,'tertiary':2})\n",
        "df['call_type'] = df['call_type'].map({'unknown':0,'telephone':1,'cellular':2})\n",
        "df['mon'] = df['mon'].map({'may':0, 'jul':1,  'jan':2, 'nov':3, 'jun':4, 'aug':5, 'feb':6, 'apr':7, 'oct':8, 'sep':9, 'dec':10, 'mar':11})\n",
        "df['prev_outcome'] = df['prev_outcome'].map({'unknown':0,'failure':1,'other':2,'success':3})\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "A0Q7JEp6ZmTU",
        "outputId": "cff21190-1a20-44e7-e07e-2c8f226c5e23"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       age  job  marital  education_qual  call_type  day  mon  dur  num_calls  \\\n",
              "0       58    7        0               2          0    5    0  261          1   \n",
              "1       44    4        2               1          0    5    0  151          1   \n",
              "2       33    1        0               1          0    5    0   76          1   \n",
              "3       47    0        0               1          0    5    0   92          1   \n",
              "4       33    0        2               1          0    5    0  198          1   \n",
              "...    ...  ...      ...             ...        ...  ...  ...  ...        ...   \n",
              "45206   51    4        0               2          2   17    3  643          3   \n",
              "45207   70    9        1               0          2   17    3  456          2   \n",
              "45208   70    9        0               1          2   17    3  643          5   \n",
              "45209   57    0        0               1          1   17    3  508          4   \n",
              "45210   37    1        0               1          2   17    3  361          2   \n",
              "\n",
              "       prev_outcome  y  \n",
              "0                 0  0  \n",
              "1                 0  0  \n",
              "2                 0  0  \n",
              "3                 0  0  \n",
              "4                 0  0  \n",
              "...             ... ..  \n",
              "45206             0  1  \n",
              "45207             0  1  \n",
              "45208             3  1  \n",
              "45209             0  0  \n",
              "45210             2  0  \n",
              "\n",
              "[45194 rows x 11 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-30df31fb-826e-40d8-8bc9-21f84853f201\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
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              "        text-align: right;\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>age</th>\n",
              "      <th>job</th>\n",
              "      <th>marital</th>\n",
              "      <th>education_qual</th>\n",
              "      <th>call_type</th>\n",
              "      <th>day</th>\n",
              "      <th>mon</th>\n",
              "      <th>dur</th>\n",
              "      <th>num_calls</th>\n",
              "      <th>prev_outcome</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>58</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>261</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>44</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>151</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>76</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>47</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>92</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>33</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>198</td>\n",
              "      <td>1</td>\n",
              "      <td>0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45206</th>\n",
              "      <td>51</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>17</td>\n",
              "      <td>3</td>\n",
              "      <td>643</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45207</th>\n",
              "      <td>70</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>17</td>\n",
              "      <td>3</td>\n",
              "      <td>456</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45208</th>\n",
              "      <td>70</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>17</td>\n",
              "      <td>3</td>\n",
              "      <td>643</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45209</th>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>17</td>\n",
              "      <td>3</td>\n",
              "      <td>508</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45210</th>\n",
              "      <td>37</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>17</td>\n",
              "      <td>3</td>\n",
              "      <td>361</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>45194 rows × 11 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-30df31fb-826e-40d8-8bc9-21f84853f201')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-30df31fb-826e-40d8-8bc9-21f84853f201 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-30df31fb-826e-40d8-8bc9-21f84853f201');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZUdHJkQzoHZ7"
      },
      "source": [
        "## Splitting the Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GiLPXSz6mNoj"
      },
      "outputs": [],
      "source": [
        "x = df.drop('y', axis=1)\n",
        "y = df['y']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Q3IAAg7Ip9XG"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_tr, x_te, y_tr, y_te = train_test_split(x,y,test_size=0.25,random_state=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B9jLUqO8p-pE",
        "outputId": "45a8e366-313a-45e6-94bb-d586ec896c29"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((33895, 10), (11299, 10), (33895,), (11299,))"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ],
      "source": [
        "x_tr.shape, x_te.shape, y_tr.shape, y_te.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qHo-nBXHrnLW"
      },
      "source": [
        "## Balancing the Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hcLycJ_EqZ1R",
        "outputId": "f5107c7f-5b5b-4388-8ef6-1649ced0a068"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting imblearn\n",
            "  Downloading imblearn-0.0-py2.py3-none-any.whl (1.9 kB)\n",
            "Requirement already satisfied: imbalanced-learn in /usr/local/lib/python3.10/dist-packages (from imblearn) (0.10.1)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn->imblearn) (1.22.4)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn->imblearn) (1.10.1)\n",
            "Requirement already satisfied: scikit-learn>=1.0.2 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn->imblearn) (1.2.2)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn->imblearn) (1.2.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn->imblearn) (3.1.0)\n",
            "Installing collected packages: imblearn\n",
            "Successfully installed imblearn-0.0\n"
          ]
        }
      ],
      "source": [
        "!pip install imblearn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Qn4aaw0wrw4d"
      },
      "outputs": [],
      "source": [
        "import imblearn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wpXNZ9MOtEBR"
      },
      "outputs": [],
      "source": [
        "from imblearn.combine import SMOTEENN\n",
        "smt = SMOTEENN(sampling_strategy='all')\n",
        "x_smt_tr,y_smt_tr = smt.fit_resample(x_tr,y_tr)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1agoEuniE_It"
      },
      "source": [
        "## Fit ML Models"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-nJ9nY18FF0r"
      },
      "source": [
        "#### Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sy0um5VBE2wC"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "log_model = LogisticRegression(max_iter=1000)\n",
        "log_model.fit(x_smt_tr,y_smt_tr)\n",
        "y_pred_log = log_model.predict(x_te)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "cross_val_log = cross_val_score(log_model, x_tr, y_tr, cv=10).mean()\n",
        "cross_val_log"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pd51SSMA3wd4",
        "outputId": "e23dcf85-641b-4d19-d07f-ce3f9a535206"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8924028372201928"
            ]
          },
          "metadata": {},
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_auc_score\n",
        "auroc_log = round(roc_auc_score(y_te, log_model.predict_proba(x_te)[:,1]),2)\n",
        "auroc_log"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aKx10hJw_bN9",
        "outputId": "198ba48d-13fe-4374-d6dd-746967fa1d50"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.88"
            ]
          },
          "metadata": {},
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "confusion_matrix(y_te,y_pred_log)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ii51I0LPajvn",
        "outputId": "05e112b2-7f72-46cb-bc83-da14c89f159c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[7407, 2623],\n",
              "       [ 160, 1109]])"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FB-Mkwl4E403"
      },
      "outputs": [],
      "source": [
        "# Interpretations\n",
        "# 0.88 is the AUROC score for Logistic Regression model. This is a good fit model with a descent AUROC score"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DR3i5rOcEpem"
      },
      "source": [
        "#### KNN ALGORITHM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6r2KEETaEkux",
        "outputId": "c3d07511-4b6f-4de3-ae2f-2ec18093cb33"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "K value:  1 cross_val_score:  0.859683994112481\n",
            "K value:  2 cross_val_score:  0.8814868684125546\n",
            "K value:  3 cross_val_score:  0.8797461333778989\n",
            "K value:  4 cross_val_score:  0.8846436806221064\n",
            "K value:  5 cross_val_score:  0.8833161077266289\n",
            "K value:  6 cross_val_score:  0.8842896983212214\n",
            "K value:  7 cross_val_score:  0.8841717738545058\n",
            "K value:  8 cross_val_score:  0.8846732661891543\n",
            "K value:  9 cross_val_score:  0.8836112583571175\n",
            "K value:  10 cross_val_score:  0.8843782026006402\n",
            "K value:  11 cross_val_score:  0.8839948001124582\n",
            "K value:  12 cross_val_score:  0.8856175062300293\n",
            "K value:  13 cross_val_score:  0.8846143039557965\n",
            "K value:  14 cross_val_score:  0.8858238218215971\n",
            "K value:  15 cross_val_score:  0.8854402887704538\n",
            "K value:  16 cross_val_score:  0.8858828449843369\n",
            "K value:  17 cross_val_score:  0.8854992858206014\n",
            "K value:  18 cross_val_score:  0.8862072765349636\n",
            "K value:  19 cross_val_score:  0.8860006562964859\n",
            "K value:  20 cross_val_score:  0.8863548562023065\n"
          ]
        }
      ],
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "k_values = np.arange(1,21)\n",
        "cross_val_knn_list = []\n",
        "\n",
        "for k in k_values:\n",
        "  knn_model = KNeighborsClassifier(n_neighbors=k)\n",
        "  knn_model.fit(x_smt_tr,y_smt_tr)\n",
        "  print('K value: ', k, 'cross_val_score: ', cross_val_score(knn_model, x_tr,y_tr,cv=10).mean())\n",
        "  cross_val_knn_list.append(cross_val_score(knn_model, x_tr,y_tr,cv=10).mean())\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "k_values = np.arange(21,41)\n",
        "cross_val_knn = []\n",
        "\n",
        "for k in k_values:\n",
        "  knn_model = KNeighborsClassifier(n_neighbors=k)\n",
        "  knn_model.fit(x_smt_tr,y_smt_tr)\n",
        "  print('K value: ', k, 'cross_val_score: ', cross_val_score(knn_model, x_tr,y_tr,cv=10).mean())\n",
        "  cross_val_knn.append(cross_val_score(knn_model, x_tr,y_tr,cv=10).mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EJi0S951IgWC",
        "outputId": "201025a4-3175-49ee-e04a-fc57a9be8c9b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "K value:  21 cross_val_score:  0.8854697872955276\n",
            "K value:  22 cross_val_score:  0.8856467001081931\n",
            "K value:  23 cross_val_score:  0.8855876160160715\n",
            "K value:  24 cross_val_score:  0.8855581348993926\n",
            "K value:  25 cross_val_score:  0.8860006824090781\n",
            "K value:  26 cross_val_score:  0.8862662300641236\n",
            "K value:  27 cross_val_score:  0.8859417549925099\n",
            "K value:  28 cross_val_score:  0.8862367924684321\n",
            "K value:  29 cross_val_score:  0.8857350738246504\n",
            "K value:  30 cross_val_score:  0.8863251922974815\n",
            "K value:  31 cross_val_score:  0.886266169134742\n",
            "K value:  32 cross_val_score:  0.8862956850682104\n",
            "K value:  33 cross_val_score:  0.8863546473015683\n",
            "K value:  34 cross_val_score:  0.8862956937724078\n",
            "K value:  35 cross_val_score:  0.8862071894929893\n",
            "K value:  36 cross_val_score:  0.8856171319495401\n",
            "K value:  37 cross_val_score:  0.8860006911132757\n",
            "K value:  38 cross_val_score:  0.8859417027673256\n",
            "K value:  39 cross_val_score:  0.8861187635513474\n",
            "K value:  40 cross_val_score:  0.8859712709259787\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0QKujvxUG5yI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a032240a-2abe-4f87-9557-b6841ef2bf6c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8863546473015683"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ],
      "source": [
        "cross_val_knn_max = max(cross_val_knn)\n",
        "cross_val_knn_max"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NrqAc01QMVLd",
        "outputId": "c032af86-68d1-49c1-9d2c-4a343748cd76"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The best value of k is 33 and cross val score is  0.8863546473015683\n"
          ]
        }
      ],
      "source": [
        "print('The best value of k is 33 and cross val score is ', cross_val_knn_max)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qG_YrpDCM-DM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "outputId": "c676e171-860c-4f44-c7f3-4def5e64d0ea"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(n_neighbors=33)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=33)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=33)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ],
      "source": [
        "# building knn model with best value of k\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.model_selection import cross_val_score\n",
        "knn_model = KNeighborsClassifier(n_neighbors=33)\n",
        "knn_model.fit(x_smt_tr,y_smt_tr)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PvOO7avHNSvz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7fbc669d-8250-419b-87d9-7e55a9134535"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8863546473015683"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ],
      "source": [
        "cross_val_score_knn_best = cross_val_score(knn_model, x_tr,y_tr,cv=10).mean()\n",
        "cross_val_score_knn_best"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "auroc_knn = round(roc_auc_score(y_te, knn_model.predict_proba(x_te)[:,1]),2)\n",
        "auroc_knn"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EMFGs7yOTHVE",
        "outputId": "d9a84b82-91e6-49c8-9e83-ff81949c32ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.84"
            ]
          },
          "metadata": {},
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SilpdWgLNfYO"
      },
      "outputs": [],
      "source": [
        "# Interpretation\n",
        "# 0.84 is the AUROC value of KNN model which is less than logistic regression model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LuVRlnBeOyyD"
      },
      "source": [
        "#### Decision Tree"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_wReZTBQOlip",
        "outputId": "b0cae220-32b4-4ad4-8e32-4f018865490b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Depth:  1 cross_val_score:  0.8814574482252577\n",
            "Depth:  5 cross_val_score:  0.9008406687957133\n",
            "Depth:  6 cross_val_score:  0.9003686401693489\n",
            "Depth:  7 cross_val_score:  0.899188411927884\n",
            "Depth:  8 cross_val_score:  0.8997783911335564\n",
            "Depth:  9 cross_val_score:  0.8992769423198951\n",
            "Depth:  10 cross_val_score:  0.8960903704593466\n",
            "Depth:  12 cross_val_score:  0.8927270685742785\n",
            "Depth:  15 cross_val_score:  0.8881541791898308\n"
          ]
        }
      ],
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "cross_val_dt_list = []\n",
        "\n",
        "for d in [1,5,6,7,8,9,10,12,15]:\n",
        "  dt_model = DecisionTreeClassifier(max_depth = d, random_state = 0)\n",
        "  dt_model.fit(x_smt_tr,y_smt_tr)\n",
        "  print('Depth: ', d, 'cross_val_score: ', cross_val_score(dt_model, x_tr, y_tr, cv=10).mean())\n",
        "  cross_val_dt_list.append(cross_val_score(dt_model, x_tr, y_tr, cv=10).mean())\n",
        "\n",
        "# depth = 5 is the best max_depth with high cross val score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "l6TFFYrnQRf4"
      },
      "outputs": [],
      "source": [
        "cross_val_dt_max = max(cross_val_dt_list)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L9lTOl8BTnDW",
        "outputId": "da8873b3-2f42-464e-b061-fe546481b901"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The best depth is 5 and cross val score is  0.9008406687957133\n"
          ]
        }
      ],
      "source": [
        "print('The best depth is 5 and cross val score is ', cross_val_dt_max)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XhOqK7OKTtZv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "outputId": "ce4192d1-fbb0-41d2-f32a-4d8fff0cb0f6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(max_depth=5, random_state=0)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=5, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=5, random_state=0)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ],
      "source": [
        "# Building decision tree model with best max_depth value\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "dt_model = DecisionTreeClassifier(max_depth = 5, random_state = 0)\n",
        "dt_model.fit(x_smt_tr,y_smt_tr)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4Wlf12DdT6g0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e8aff84a-ec5b-4b33-d6e6-b6d6afbe4a3b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9008406687957133"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ],
      "source": [
        "cross_val_dt_best = cross_val_score(dt_model, x_tr, y_tr, cv=10).mean()\n",
        "cross_val_dt_best"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "auroc_dt = round(roc_auc_score(y_te, dt_model.predict_proba(x_te)[:,1]),2)\n",
        "auroc_dt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hpjxf-AaWjsx",
        "outputId": "0abf4dba-20be-48bd-940d-2d3e904a2140"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.87"
            ]
          },
          "metadata": {},
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vsL285omUKB2"
      },
      "outputs": [],
      "source": [
        "# Interpretations\n",
        "# 0.87 is the AUROC value of Decision Tree Model which is still less than logistic regression model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ATeIZSOWEO1"
      },
      "source": [
        "#### Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v6tcKg2rUZ3w",
        "outputId": "3e3af137-ac36-49cd-9db1-bc2559624daf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Depth:  1 cross_val_score:  0.8814574482252577\n",
            "Depth:  5 cross_val_score:  0.8960614028903157\n",
            "Depth:  10 cross_val_score:  0.9041449910390286\n",
            "Depth:  15 cross_val_score:  0.9039384578425255\n",
            "Depth:  20 cross_val_score:  0.9034663943993714\n",
            "Depth:  22 cross_val_score:  0.9024633923216794\n",
            "Depth:  24 cross_val_score:  0.9029647105723795\n",
            "Depth:  26 cross_val_score:  0.9020796503697979\n",
            "Depth:  28 cross_val_score:  0.9025221891752861\n",
            "Depth:  30 cross_val_score:  0.9021387518703146\n",
            "Depth:  32 cross_val_score:  0.9034959103328397\n",
            "Depth:  34 cross_val_score:  0.9034958842202476\n",
            "Depth:  36 cross_val_score:  0.9034073886450263\n"
          ]
        }
      ],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "cross_val_rf = []\n",
        "\n",
        "for d in [1,5,10,15,20,22,24,26,28,30,32,34,36]:\n",
        "  rf_model = RandomForestClassifier(max_depth = d, random_state = 1)\n",
        "  rf_model.fit(x_smt_tr,y_smt_tr)\n",
        "  print('Depth: ', d, 'cross_val_score: ', cross_val_score(rf_model, x_tr, y_tr, cv=10).mean())\n",
        "  cross_val_rf.append(cross_val_score(rf_model, x_tr, y_tr, cv=10).mean())\n",
        "\n",
        "# depth = 10 is the best depth."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1EteZqdiY1Az"
      },
      "outputs": [],
      "source": [
        "cross_val_rf_max = max(cross_val_rf)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"The best value of max_depth is 10 and cross val score is \", cross_val_rf_max)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lUg-8AXLDhKB",
        "outputId": "07fbf772-699b-41c6-cb89-6e38b959f838"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The best value of max_depth is 10 and cross val score is  0.9041449910390286\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Building the random forest model mwith best max_depth model\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "rf_model = RandomForestClassifier(max_depth = 10, random_state = 0)\n",
        "rf_model.fit(x_smt_tr,y_smt_tr)"
      ],
      "metadata": {
        "id": "FqgAK9htGBeW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "outputId": "3e34cc32-9f86-4969-8b4e-7998cfae5729"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(max_depth=10, random_state=0)"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=10, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=10, random_state=0)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cross_val_rf_best = cross_val_score(rf_model, x_tr, y_tr, cv=10).mean()"
      ],
      "metadata": {
        "id": "U5vOKOjUGqZY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cross_val_rf_best"
      ],
      "metadata": {
        "id": "2-s6tUTRGzZq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e09c1e49-418f-4632-daa7-095073ad7649"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9035844755416405"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "auroc_rf = roc_auc_score(y_te, rf_model.predict_proba(x_te)[:,1])\n",
        "auroc_rf = round(auroc_rf, 2)\n",
        "auroc_rf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dscomjZ8Sz4V",
        "outputId": "8b4056c3-a5c2-449b-a851-f4c74b9c51be"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9"
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Interpretations\n",
        "# 0.9 is the auroc score of random forest model. Out of all the model trained so far, random forest gives the best result with\n",
        "# good auroc value."
      ],
      "metadata": {
        "id": "-fyJkQSBHpM4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Gradient Boosting"
      ],
      "metadata": {
        "id": "GSjE4rSAHP6h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "\n",
        "cross_val_gb = []\n",
        "for lr in [0.01,0.05,0.08,0.1,0.15,0.2,0.3,0.5,0.7,0.9,1]:\n",
        "  gb_model = GradientBoostingClassifier(learning_rate = lr)\n",
        "  gb_model.fit(x_smt_tr, y_smt_tr)\n",
        "  print('Learning rate: ', lr, 'cross_val_score: ', cross_val_score(gb_model,x_tr,y_tr,cv=10).mean())\n",
        "  cross_val_gb.append(cross_val_score(gb_model,x_tr,y_tr,cv=10).mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IVyEeDx1HNSB",
        "outputId": "e2313a75-e04e-4a2f-a6e0-fece83e6d783"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Learning rate:  0.01 cross_val_score:  0.8856469090089314\n",
            "Learning rate:  0.05 cross_val_score:  0.9032895425160875\n",
            "Learning rate:  0.08 cross_val_score:  0.9043810575773955\n",
            "Learning rate:  0.1 cross_val_score:  0.9047940456326253\n",
            "Learning rate:  0.15 cross_val_score:  0.9069771976140053\n",
            "Learning rate:  0.2 cross_val_score:  0.9067412268218102\n",
            "Learning rate:  0.3 cross_val_score:  0.9067116847757493\n",
            "Learning rate:  0.5 cross_val_score:  0.9051775699795712\n",
            "Learning rate:  0.7 cross_val_score:  0.9044399153603843\n",
            "Learning rate:  0.9 cross_val_score:  0.9022860268907478\n",
            "Learning rate:  1 cross_val_score:  0.901696126022852\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cross_val_gb_max = max(cross_val_gb)"
      ],
      "metadata": {
        "id": "T0gaaMwYTCNq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('The best learning rate is 0.15 and cross val score is ', cross_val_gb_max)"
      ],
      "metadata": {
        "id": "PcdtBfkaVgbN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8602758e-12f7-4655-c276-121ebf379d24"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The best learning rate is 0.15 and cross val score is  0.9069771976140053\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Buiding gradient boosting model with best learning rate\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "gb_model = GradientBoostingClassifier(learning_rate = 0.15)\n",
        "gb_model.fit(x_smt_tr, y_smt_tr)"
      ],
      "metadata": {
        "id": "YCiv4hBeVpPO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "outputId": "26488ca6-9bb7-402e-fe44-afccefe7a55c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GradientBoostingClassifier(learning_rate=0.15)"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier(learning_rate=0.15)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier(learning_rate=0.15)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cross_val_gb_best = cross_val_score(gb_model,x_tr,y_tr,cv=10).mean()\n",
        "cross_val_gb_best"
      ],
      "metadata": {
        "id": "xGwgYSEWWoeV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b7d2efd7-fa0a-4bf8-d04e-3d2516817775"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9069771976140053"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "auroc_gb = roc_auc_score(y_te, gb_model.predict_proba(x_te)[:,1])\n",
        "auroc_gb = round(auroc_gb,2)\n",
        "auroc_gb"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W4Y4e1z0U431",
        "outputId": "73847e87-5366-417f-b13d-4ad6e217d114"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9"
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Interpretations\n",
        "# 0.9 is the auroc score of gradient boosting model. Out of all the models trained so far, random forest and gradient boosting\n",
        "# gives the best result with good auroc value of 0.9"
      ],
      "metadata": {
        "id": "ArpqWA9ZWxt0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### XGBoost"
      ],
      "metadata": {
        "id": "FjFOsk49XM7n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import xgboost as xgb\n",
        "\n",
        "cross_val_xgb = []\n",
        "for lr in [0.01,0.05,0.08,0.1,0.15,0.2,0.3,0.5,0.7,0.9,1]:\n",
        "  xgb_model = xgb.XGBClassifier(colsample_bytree = 0.4, learning_rate = lr, n_estimators = 100)\n",
        "  xgb_model.fit(x_smt_tr,y_smt_tr)\n",
        "  print('Learning rate: ', lr, 'cross val score: ', cross_val_score(xgb_model, x_tr, y_tr, cv=10).mean())\n",
        "  cross_val_xgb.append(cross_val_score(xgb_model, x_tr, y_tr, cv=10).mean())\n",
        "\n",
        "# learning rate of 0.2 is the best learning rate"
      ],
      "metadata": {
        "id": "k44dAqyfXLWy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3a106af0-c05d-4177-922d-100eecf27520"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Learning rate:  0.01 cross val score:  0.8899837666718022\n",
            "Learning rate:  0.05 cross val score:  0.9034959538538269\n",
            "Learning rate:  0.08 cross val score:  0.9055907321187495\n",
            "Learning rate:  0.1 cross val score:  0.9057676536356128\n",
            "Learning rate:  0.15 cross val score:  0.9057677145649945\n",
            "Learning rate:  0.2 cross val score:  0.9073018380653701\n",
            "Learning rate:  0.3 cross val score:  0.9044991996490467\n",
            "Learning rate:  0.5 cross val score:  0.9027878412807008\n",
            "Learning rate:  0.7 cross val score:  0.9011063034927332\n",
            "Learning rate:  0.9 cross val score:  0.8972119324101661\n",
            "Learning rate:  1 cross val score:  0.8946451429272738\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cross_val_xgb_max = max(cross_val_xgb)"
      ],
      "metadata": {
        "id": "GlevYswSYpbo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('the best learning rate is 0.2 and cross val score is ', cross_val_xgb_max)"
      ],
      "metadata": {
        "id": "mhlMwQdEfut5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d77a5177-cae8-4c9c-c166-d6958a52bb73"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "the best learning rate is 0.2 and cross val score is  0.9073018380653701\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# building the xgboost model with best learning rate value\n",
        "import xgboost as xgb\n",
        "xgb_model = xgb.XGBClassifier(colsample_bytree = 0.4, learning_rate = 0.2, n_estimators = 100)\n",
        "xgb_model.fit(x_smt_tr,y_smt_tr)"
      ],
      "metadata": {
        "id": "JvtlejbCgLBC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "outputId": "2aeeef07-4ae1-498d-b98f-64bdf6bb0351"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=0.4, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.2, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
              "              predictor=None, random_state=None, ...)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=0.4, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.2, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
              "              predictor=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=0.4, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.2, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
              "              predictor=None, random_state=None, ...)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cross_val_xgb_best = cross_val_score(xgb_model, x_tr, y_tr, cv=10).mean()\n",
        "cross_val_xgb_best"
      ],
      "metadata": {
        "id": "dv7sAjPOgbGP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5e8c396e-3a96-4355-f7ba-ad26fec91dfd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9073018380653701"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "auroc_xgb = roc_auc_score(y_te, xgb_model.predict_proba(x_te)[:,1])\n",
        "auroc_xgb = round(auroc_xgb, 2)\n",
        "auroc_xgb"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZMwT-lEAUatu",
        "outputId": "5dabe857-2281-4448-c0ef-a68ca8e9c79f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.91"
            ]
          },
          "metadata": {},
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Interpretations\n",
        "The data is trained with 6 ML models respectively\n",
        "\n",
        "The 6 models are Logistic Regression, KNN, Decision Tree, Random Forest, Gradient Boosting, XGBoost\n",
        "\n",
        "Out of the 6 Models, with the highest cross validation score of 0.91, XGBoost is the best fit model"
      ],
      "metadata": {
        "id": "5_gDm_BEiA9t"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Cross Validation Score for ML Models"
      ],
      "metadata": {
        "id": "Ebc86HGhiMxQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('Cross Validation Score for Logistic Regression Model: ', cross_val_log)\n",
        "print('Cross Validation Score for K-Nearest Neighbors Classification Model: ', cross_val_score_knn_best)\n",
        "print('Cross Validation Score for Decision Tree Classification Model: ', cross_val_dt_best)\n",
        "print('Cross Validation Score for Random Forest Classification Model: ', cross_val_rf_best)\n",
        "print('Cross Validation Score for Gradient Boosting Classification Model: ', cross_val_gb_best)\n",
        "print('Cross Validation Score for Extreme-Gradient Boosting Classification Model: ', cross_val_xgb_best)"
      ],
      "metadata": {
        "id": "h29q6zq9h1qt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6e2495ed-2888-48a7-d999-4051f67d6245"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cross Validation Score for Logistic Regression Model:  0.8924028372201928\n",
            "Cross Validation Score for K-Nearest Neighbors Classification Model:  0.8863546473015683\n",
            "Cross Validation Score for Decision Tree Classification Model:  0.9008406687957133\n",
            "Cross Validation Score for Random Forest Classification Model:  0.9035844755416405\n",
            "Cross Validation Score for Gradient Boosting Classification Model:  0.9069771976140053\n",
            "Cross Validation Score for Extreme-Gradient Boosting Classification Model:  0.9073018380653701\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## AUROC Score for ML Models"
      ],
      "metadata": {
        "id": "OH01k0JB4hUA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('AUROC Score for Logistic Regression Model: ', auroc_log)\n",
        "print('AUROC Score for K-Nearest Neighbors Classification Model: ', auroc_knn)\n",
        "print('AUROC Score for Decision Tree Classification Model: ', auroc_dt)\n",
        "print('AUROC Score for Random Forest Classification Model: ', auroc_rf)\n",
        "print('AUROC Score for Gradient Boosting Classification Model: ', auroc_gb)\n",
        "print('AUROC Score for Extreme-Gradient Boosting Classification Model: ', auroc_xgb)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SCWfYpnl4UG6",
        "outputId": "52bf53df-9863-4314-c65c-eb9acfb840fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AUROC Score for Logistic Regression Model:  0.88\n",
            "AUROC Score for K-Nearest Neighbors Classification Model:  0.84\n",
            "AUROC Score for Decision Tree Classification Model:  0.87\n",
            "AUROC Score for Random Forest Classification Model:  0.9\n",
            "AUROC Score for Gradient Boosting Classification Model:  0.9\n",
            "AUROC Score for Extreme-Gradient Boosting Classification Model:  0.91\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Feature Importance: XGBoost Model"
      ],
      "metadata": {
        "id": "kSluSoUa5wHi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ft_imp_xgb = list(zip(xgb_model.feature_importances_,df.columns[:-1]))\n",
        "imp = pd.DataFrame(ft_imp_xgb, columns = ['Importance', 'Feature'])\n",
        "imp.sort_values('Importance', ascending = False, inplace = True)\n",
        "imp"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "myOw556S5F0G",
        "outputId": "cf672f4f-a39f-4a9a-ed3a-fd0ad0cf9c71"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Importance         Feature\n",
              "7    0.299045             dur\n",
              "4    0.212240       call_type\n",
              "6    0.150209             mon\n",
              "8    0.092908       num_calls\n",
              "9    0.072255    prev_outcome\n",
              "5    0.046567             day\n",
              "1    0.038353             job\n",
              "0    0.036010             age\n",
              "3    0.027120  education_qual\n",
              "2    0.025294         marital"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-25782140-1c5e-4ba7-b87c-315bafefdbff\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Importance</th>\n",
              "      <th>Feature</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.299045</td>\n",
              "      <td>dur</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.212240</td>\n",
              "      <td>call_type</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.150209</td>\n",
              "      <td>mon</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.092908</td>\n",
              "      <td>num_calls</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.072255</td>\n",
              "      <td>prev_outcome</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.046567</td>\n",
              "      <td>day</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.038353</td>\n",
              "      <td>job</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.036010</td>\n",
              "      <td>age</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.027120</td>\n",
              "      <td>education_qual</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.025294</td>\n",
              "      <td>marital</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-25782140-1c5e-4ba7-b87c-315bafefdbff')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-25782140-1c5e-4ba7-b87c-315bafefdbff button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-25782140-1c5e-4ba7-b87c-315bafefdbff');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sorted_idx = xgb_model.feature_importances_.argsort()\n",
        "plt.figure(figsize=(10,5))\n",
        "plt.barh(df.columns[sorted_idx], xgb_model.feature_importances_[sorted_idx])\n",
        "plt.xlabel(\"XGBoost Feature Importance\")\n",
        "plt.title(\"Feature Importance\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "IR87m6rnBcbo",
        "outputId": "c6e24772-1890-417c-ccfc-152512990f6c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Top 3 Important Features\n",
        "1. Duration of the call in seconds\n",
        "\n",
        "2. call_type\n",
        "\n",
        "3. month"
      ],
      "metadata": {
        "id": "uHGOXOMoO9w0"
      }
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}