Bank_Marketing_Analysis.ipynb

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  "metadata": {
    "colab": {
      "name": "Bank Marketing Analysis.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNIKhC53xjjEss4H/IxbCJg",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/DataMinati/Super-Notebooks-1K/blob/main/Bank_Marketing_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NK6OL4ZibrGf"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn import datasets\n",
        "from io import StringIO\n",
        "from sklearn.tree import export_graphviz\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import tree\n",
        "from sklearn import metrics\n",
        "%matplotlib inline"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "X2rA_sEibyBZ",
        "outputId": "5ae3ccbe-24da-499a-dfe6-f5d066c7cf7a"
      },
      "source": [
        "url = 'https://raw.githubusercontent.com/MainakRepositor/Datasets-/master/bank.csv'\n",
        "bank = pd.read_csv(url,error_bad_lines=False)\n",
        "bank.head()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<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</th>\n",
              "      <th>default</th>\n",
              "      <th>balance</th>\n",
              "      <th>housing</th>\n",
              "      <th>loan</th>\n",
              "      <th>contact</th>\n",
              "      <th>day</th>\n",
              "      <th>month</th>\n",
              "      <th>duration</th>\n",
              "      <th>campaign</th>\n",
              "      <th>pdays</th>\n",
              "      <th>previous</th>\n",
              "      <th>poutcome</th>\n",
              "      <th>deposit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>59</td>\n",
              "      <td>admin.</td>\n",
              "      <td>married</td>\n",
              "      <td>secondary</td>\n",
              "      <td>no</td>\n",
              "      <td>2343</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>unknown</td>\n",
              "      <td>5</td>\n",
              "      <td>may</td>\n",
              "      <td>1042</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>56</td>\n",
              "      <td>admin.</td>\n",
              "      <td>married</td>\n",
              "      <td>secondary</td>\n",
              "      <td>no</td>\n",
              "      <td>45</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>unknown</td>\n",
              "      <td>5</td>\n",
              "      <td>may</td>\n",
              "      <td>1467</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>41</td>\n",
              "      <td>technician</td>\n",
              "      <td>married</td>\n",
              "      <td>secondary</td>\n",
              "      <td>no</td>\n",
              "      <td>1270</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>unknown</td>\n",
              "      <td>5</td>\n",
              "      <td>may</td>\n",
              "      <td>1389</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>55</td>\n",
              "      <td>services</td>\n",
              "      <td>married</td>\n",
              "      <td>secondary</td>\n",
              "      <td>no</td>\n",
              "      <td>2476</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>unknown</td>\n",
              "      <td>5</td>\n",
              "      <td>may</td>\n",
              "      <td>579</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>54</td>\n",
              "      <td>admin.</td>\n",
              "      <td>married</td>\n",
              "      <td>tertiary</td>\n",
              "      <td>no</td>\n",
              "      <td>184</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>unknown</td>\n",
              "      <td>5</td>\n",
              "      <td>may</td>\n",
              "      <td>673</td>\n",
              "      <td>2</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age         job  marital  education  ... pdays  previous poutcome deposit\n",
              "0   59      admin.  married  secondary  ...    -1         0  unknown     yes\n",
              "1   56      admin.  married  secondary  ...    -1         0  unknown     yes\n",
              "2   41  technician  married  secondary  ...    -1         0  unknown     yes\n",
              "3   55    services  married  secondary  ...    -1         0  unknown     yes\n",
              "4   54      admin.  married   tertiary  ...    -1         0  unknown     yes\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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        },
        "id": "2NKAiDeqcHRf",
        "outputId": "4d7fca72-5924-4325-9e73-c2778524e96a"
      },
      "source": [
        "bank[bank.isnull().any(axis=1)].count()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age          0\n",
              "job          0\n",
              "marital      0\n",
              "education    0\n",
              "default      0\n",
              "balance      0\n",
              "housing      0\n",
              "loan         0\n",
              "contact      0\n",
              "day          0\n",
              "month        0\n",
              "duration     0\n",
              "campaign     0\n",
              "pdays        0\n",
              "previous     0\n",
              "poutcome     0\n",
              "deposit      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "P79z93DrcLb1",
        "outputId": "7a2b1ea0-16c2-4f71-861c-dea3956116a7"
      },
      "source": [
        "bank.describe()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>balance</th>\n",
              "      <th>day</th>\n",
              "      <th>duration</th>\n",
              "      <th>campaign</th>\n",
              "      <th>pdays</th>\n",
              "      <th>previous</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>11162.000000</td>\n",
              "      <td>11162.000000</td>\n",
              "      <td>11162.000000</td>\n",
              "      <td>11162.000000</td>\n",
              "      <td>11162.000000</td>\n",
              "      <td>11162.000000</td>\n",
              "      <td>11162.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>41.231948</td>\n",
              "      <td>1528.538524</td>\n",
              "      <td>15.658036</td>\n",
              "      <td>371.993818</td>\n",
              "      <td>2.508421</td>\n",
              "      <td>51.330407</td>\n",
              "      <td>0.832557</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>11.913369</td>\n",
              "      <td>3225.413326</td>\n",
              "      <td>8.420740</td>\n",
              "      <td>347.128386</td>\n",
              "      <td>2.722077</td>\n",
              "      <td>108.758282</td>\n",
              "      <td>2.292007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>18.000000</td>\n",
              "      <td>-6847.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>32.000000</td>\n",
              "      <td>122.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>138.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>39.000000</td>\n",
              "      <td>550.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>255.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>49.000000</td>\n",
              "      <td>1708.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>496.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>20.750000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>95.000000</td>\n",
              "      <td>81204.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>3881.000000</td>\n",
              "      <td>63.000000</td>\n",
              "      <td>854.000000</td>\n",
              "      <td>58.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                age       balance  ...         pdays      previous\n",
              "count  11162.000000  11162.000000  ...  11162.000000  11162.000000\n",
              "mean      41.231948   1528.538524  ...     51.330407      0.832557\n",
              "std       11.913369   3225.413326  ...    108.758282      2.292007\n",
              "min       18.000000  -6847.000000  ...     -1.000000      0.000000\n",
              "25%       32.000000    122.000000  ...     -1.000000      0.000000\n",
              "50%       39.000000    550.000000  ...     -1.000000      0.000000\n",
              "75%       49.000000   1708.000000  ...     20.750000      1.000000\n",
              "max       95.000000  81204.000000  ...    854.000000     58.000000\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "Gk9mxSkBcNki",
        "outputId": "50e40ab9-0d3d-4962-b989-b17762afb573"
      },
      "source": [
        "# Boxplot for 'age'\n",
        "g = sns.boxplot(x=bank[\"age\"])"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RX4mIb1Yfn8ydXto1fxBDw0JEjC2lvF79ePg3af06xGdp/T6HnwGrgOdLKX8REV8C/qGU8p/Vj6Z+rZTy22nDS7vRtfdNpFpYEBHnV5+fCPwJ8O+llNcBIuLLwG9V66cDp7R+bQEAH4iII0sp2zo5sLQ3Bli1FxENWlE9o5SyPSKeofV7CXZ3VHsYcHop5Z3OTCi9O54D1nBwFPBGFd+Taf0pq9HA70fEmOpXIl4wYPuVwPy+hYiY1NFppX1kgDUcrAC6ImId8LfAf9H63bM30fptXM/S+lNIb1bbLwCmRMR3IuIl4M86PrG0D/xHOA1bfed1qyPgh4F7SikPZ88l7SuPgDWc/U31t/vW0vqDm48kzyMNiUfAkpTEI2BJSmKAJSmJAZakJAZYkpIYYElK8v8dGVqg7XegoAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "cs500ZhQcPpx",
        "outputId": "ad20afa4-b90f-4a1b-9e20-e61baa85b842"
      },
      "source": [
        "# Distribution of Age\n",
        "sns.distplot(bank.age, bins=70)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3b31db4ad0>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "HlMCBgUxcRhh",
        "outputId": "634e9b84-8324-498e-d5c5-20907f0d8073"
      },
      "source": [
        "# Boxplot for 'duration'\n",
        "g = sns.boxplot(x=bank[\"duration\"])"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "_VfPciFPcYjk",
        "outputId": "c54010a6-d895-4cbf-f206-13d8b47af548"
      },
      "source": [
        "sns.distplot(bank.duration, bins=100)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3b25a92b50>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9aBzVR1IcaIm"
      },
      "source": [
        "bank_data = bank.copy()"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-nk8Ogkgccam",
        "outputId": "b97d16b3-0e77-4431-f6de-f1fd93233dde"
      },
      "source": [
        "# Explore People who made a deposit Vs Job category\n",
        "jobs = ['management','blue-collar','technician','admin.','services','retired','self-employed','student',\\\n",
        "        'unemployed','entrepreneur','housemaid','unknown']\n",
        "\n",
        "for j in jobs:\n",
        "    print(\"{:15} : {:5}\". format(j, len(bank_data[(bank_data.deposit == \"yes\") & (bank_data.job ==j)])))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "management      :  1301\n",
            "blue-collar     :   708\n",
            "technician      :   840\n",
            "admin.          :   631\n",
            "services        :   369\n",
            "retired         :   516\n",
            "self-employed   :   187\n",
            "student         :   269\n",
            "unemployed      :   202\n",
            "entrepreneur    :   123\n",
            "housemaid       :   109\n",
            "unknown         :    34\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ORQ7Hjftcdum",
        "outputId": "4cf622e6-4402-4410-ea4e-afab4abd0001"
      },
      "source": [
        "# Different types of job categories and their counts\n",
        "bank_data.job.value_counts()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "management       2566\n",
              "blue-collar      1944\n",
              "technician       1823\n",
              "admin.           1334\n",
              "services          923\n",
              "retired           778\n",
              "self-employed     405\n",
              "student           360\n",
              "unemployed        357\n",
              "entrepreneur      328\n",
              "housemaid         274\n",
              "unknown            70\n",
              "Name: job, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7p-vK-S-cfly"
      },
      "source": [
        "# Combine similar jobs into categiroes\n",
        "bank_data['job'] = bank_data['job'].replace(['management', 'admin.'], 'white-collar')\n",
        "bank_data['job'] = bank_data['job'].replace(['services','housemaid'], 'pink-collar')\n",
        "bank_data['job'] = bank_data['job'].replace(['retired', 'student', 'unemployed', 'unknown'], 'other')"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nUSc0ASNchCE",
        "outputId": "abfbae0c-1037-4465-9719-3e20a8d6e933"
      },
      "source": [
        "# New value counts\n",
        "bank_data.job.value_counts()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "white-collar     3900\n",
              "blue-collar      1944\n",
              "technician       1823\n",
              "other            1565\n",
              "pink-collar      1197\n",
              "self-employed     405\n",
              "entrepreneur      328\n",
              "Name: job, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pHhJLO1Gci8y",
        "outputId": "1688c7c4-3807-4b44-809d-81df5db5d05b"
      },
      "source": [
        "bank_data.poutcome.value_counts()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "unknown    8326\n",
              "failure    1228\n",
              "success    1071\n",
              "other       537\n",
              "Name: poutcome, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LgCUIjIFcoV9",
        "outputId": "e1705315-2806-423a-a576-177121ccd975"
      },
      "source": [
        "# Combine 'unknown' and 'other' as 'other' isn't really match with either 'success' or 'failure'\n",
        "bank_data['poutcome'] = bank_data['poutcome'].replace(['other'] , 'unknown')\n",
        "bank_data.poutcome.value_counts()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "unknown    8863\n",
              "failure    1228\n",
              "success    1071\n",
              "Name: poutcome, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U9qrQI04cqPS"
      },
      "source": [
        "# Drop 'contact', as every participant has been contacted. \n",
        "bank_data.drop('contact', axis=1, inplace=True)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wBlDAk9ocr-j"
      },
      "source": [
        "# values for \"default\" : yes/no\n",
        "bank_data[\"default\"]\n",
        "bank_data['default_cat'] = bank_data['default'].map( {'yes':1, 'no':0} )\n",
        "bank_data.drop('default', axis=1,inplace = True)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CsXIYijBctj_"
      },
      "source": [
        "# values for \"housing\" : yes/no\n",
        "bank_data[\"housing_cat\"]=bank_data['housing'].map({'yes':1, 'no':0})\n",
        "bank_data.drop('housing', axis=1,inplace = True)"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "POukE4j2cvHC"
      },
      "source": [
        "# values for \"loan\" : yes/no\n",
        "bank_data[\"loan_cat\"] = bank_data['loan'].map({'yes':1, 'no':0})\n",
        "bank_data.drop('loan', axis=1, inplace=True)"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nYYXFP3OcxQZ"
      },
      "source": [
        "# day  : last contact day of the month\n",
        "# month: last contact month of year\n",
        "# Drop 'month' and 'day' as they don't have any intrinsic meaning\n",
        "bank_data.drop('month', axis=1, inplace=True)\n",
        "bank_data.drop('day', axis=1, inplace=True)"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sJznpPzQcy8r"
      },
      "source": [
        "# values for \"deposit\" : yes/no\n",
        "bank_data[\"deposit_cat\"] = bank_data['deposit'].map({'yes':1, 'no':0})\n",
        "bank_data.drop('deposit', axis=1, inplace=True)"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IFq4s_l5c0XC",
        "outputId": "9c1e01bc-6a5c-404e-8531-c8a1485d8b4d"
      },
      "source": [
        "# pdays: number of days that passed by after the client was last contacted from a previous campaign\n",
        "#       -1 means client was not previously contacted\n",
        "\n",
        "print(\"Customers that have not been contacted before:\", len(bank_data[bank_data.pdays==-1]))\n",
        "print(\"Maximum values on padys    :\", bank_data['pdays'].max())"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Customers that have not been contacted before: 8324\n",
            "Maximum values on padys    : 854\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9PWl3HUmc1ya"
      },
      "source": [
        "# Map padys=-1 into a large value (10000 is used) to indicate that it is so far in the past that it has no effect\n",
        "bank_data.loc[bank_data['pdays'] == -1, 'pdays'] = 10000"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jSt0n8g8c3Y8"
      },
      "source": [
        "# Create a new column: recent_pdays \n",
        "bank_data['recent_pdays'] = np.where(bank_data['pdays'], 1/bank_data.pdays, 1/bank_data.pdays)\n",
        "\n",
        "# Drop 'pdays'\n",
        "bank_data.drop('pdays', axis=1, inplace = True)"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Zm8rLZNSc4xc",
        "outputId": "db3c5efa-f2e8-4c4c-c811-3242a4a3a337"
      },
      "source": [
        "bank_data.tail()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>job</th>\n",
              "      <th>marital</th>\n",
              "      <th>education</th>\n",
              "      <th>balance</th>\n",
              "      <th>duration</th>\n",
              "      <th>campaign</th>\n",
              "      <th>previous</th>\n",
              "      <th>poutcome</th>\n",
              "      <th>default_cat</th>\n",
              "      <th>housing_cat</th>\n",
              "      <th>loan_cat</th>\n",
              "      <th>deposit_cat</th>\n",
              "      <th>recent_pdays</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>11157</th>\n",
              "      <td>33</td>\n",
              "      <td>blue-collar</td>\n",
              "      <td>single</td>\n",
              "      <td>primary</td>\n",
              "      <td>1</td>\n",
              "      <td>257</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11158</th>\n",
              "      <td>39</td>\n",
              "      <td>pink-collar</td>\n",
              "      <td>married</td>\n",
              "      <td>secondary</td>\n",
              "      <td>733</td>\n",
              "      <td>83</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11159</th>\n",
              "      <td>32</td>\n",
              "      <td>technician</td>\n",
              "      <td>single</td>\n",
              "      <td>secondary</td>\n",
              "      <td>29</td>\n",
              "      <td>156</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11160</th>\n",
              "      <td>43</td>\n",
              "      <td>technician</td>\n",
              "      <td>married</td>\n",
              "      <td>secondary</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>failure</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.005814</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11161</th>\n",
              "      <td>34</td>\n",
              "      <td>technician</td>\n",
              "      <td>married</td>\n",
              "      <td>secondary</td>\n",
              "      <td>0</td>\n",
              "      <td>628</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000100</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       age          job  marital  ... loan_cat  deposit_cat  recent_pdays\n",
              "11157   33  blue-collar   single  ...        0            0      0.000100\n",
              "11158   39  pink-collar  married  ...        0            0      0.000100\n",
              "11159   32   technician   single  ...        0            0      0.000100\n",
              "11160   43   technician  married  ...        1            0      0.005814\n",
              "11161   34   technician  married  ...        0            0      0.000100\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "id": "At6GzqUoc6E6",
        "outputId": "90e92b66-4f06-4223-d28b-5547b78fa4a3"
      },
      "source": [
        "# Convert categorical variables to dummies\n",
        "bank_with_dummies = pd.get_dummies(data=bank_data, columns = ['job', 'marital', 'education', 'poutcome'], \\\n",
        "                                   prefix = ['job', 'marital', 'education', 'poutcome'])\n",
        "bank_with_dummies.head()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>balance</th>\n",
              "      <th>duration</th>\n",
              "      <th>campaign</th>\n",
              "      <th>previous</th>\n",
              "      <th>default_cat</th>\n",
              "      <th>housing_cat</th>\n",
              "      <th>loan_cat</th>\n",
              "      <th>deposit_cat</th>\n",
              "      <th>recent_pdays</th>\n",
              "      <th>job_blue-collar</th>\n",
              "      <th>job_entrepreneur</th>\n",
              "      <th>job_other</th>\n",
              "      <th>job_pink-collar</th>\n",
              "      <th>job_self-employed</th>\n",
              "      <th>job_technician</th>\n",
              "      <th>job_white-collar</th>\n",
              "      <th>marital_divorced</th>\n",
              "      <th>marital_married</th>\n",
              "      <th>marital_single</th>\n",
              "      <th>education_primary</th>\n",
              "      <th>education_secondary</th>\n",
              "      <th>education_tertiary</th>\n",
              "      <th>education_unknown</th>\n",
              "      <th>poutcome_failure</th>\n",
              "      <th>poutcome_success</th>\n",
              "      <th>poutcome_unknown</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>59</td>\n",
              "      <td>2343</td>\n",
              "      <td>1042</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0001</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>56</td>\n",
              "      <td>45</td>\n",
              "      <td>1467</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0001</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>41</td>\n",
              "      <td>1270</td>\n",
              "      <td>1389</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0001</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>55</td>\n",
              "      <td>2476</td>\n",
              "      <td>579</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0001</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>54</td>\n",
              "      <td>184</td>\n",
              "      <td>673</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0001</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age  balance  duration  ...  poutcome_failure  poutcome_success  poutcome_unknown\n",
              "0   59     2343      1042  ...                 0                 0                 1\n",
              "1   56       45      1467  ...                 0                 0                 1\n",
              "2   41     1270      1389  ...                 0                 0                 1\n",
              "3   55     2476       579  ...                 0                 0                 1\n",
              "4   54      184       673  ...                 0                 0                 1\n",
              "\n",
              "[5 rows x 27 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3rH3mtOFc7ZA",
        "outputId": "a83fe0c3-e041-49ff-9580-b0c63faea67f"
      },
      "source": [
        "bank_with_dummies.shape"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(11162, 27)"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "s1I0Iunqc9YP",
        "outputId": "269c9c66-9e19-478a-c3cd-dedec1eba975"
      },
      "source": [
        "bank_with_dummies.describe()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>balance</th>\n",
              "      <th>duration</th>\n",
              "      <th>campaign</th>\n",
              "      <th>previous</th>\n",
              "      <th>default_cat</th>\n",
              "      <th>housing_cat</th>\n",
              "      <th>loan_cat</th>\n",
              "      <th>deposit_cat</th>\n",
              "      <th>recent_pdays</th>\n",
              "      <th>job_blue-collar</th>\n",
              "      <th>job_entrepreneur</th>\n",
              "      <th>job_other</th>\n",
              "      <th>job_pink-collar</th>\n",
              "      <th>job_self-employed</th>\n",
              "      <th>job_technician</th>\n",
              "      <th>job_white-collar</th>\n",
              "      <th>marital_divorced</th>\n",
              "      <th>marital_married</th>\n",
              "      <th>marital_single</th>\n",
              "      <th>education_primary</th>\n",
              "      <th>education_secondary</th>\n",
              "      <th>education_tertiary</th>\n",
              "      <th>education_unknown</th>\n",
              "      <th>poutcome_failure</th>\n",
              "      <th>poutcome_success</th>\n",
              "      <th>poutcome_unknown</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>11162.000000</td>\n",
              "      <td>11162.000000</td>\n",
              "      <td>11162.000000</td>\n",
              "      <td>11162.000000</td>\n",
              "      <td>11162.000000</td>\n",
              "      <td>11162.000000</td>\n",
              "      <td>11162.000000</td>\n",
              "      <td>11162.000000</td>\n",
              "      <td>11162.000000</td>\n",
              "      <td>11162.000000</td>\n",
              "      <td>11162.000000</td>\n",
              "      <td>11162.000000</td>\n",
              "      <td>11162.000000</td>\n",
              "      <td>11162.000000</td>\n",
              "      <td>11162.000000</td>\n",
              "      <td>11162.000000</td>\n",
              "      <td>11162.000000</td>\n",
              "      <td>11162.000000</td>\n",
              "      <td>11162.000000</td>\n",
              "      <td>11162.000000</td>\n",
              "      <td>11162.000000</td>\n",
              "      <td>11162.000000</td>\n",
              "      <td>11162.000000</td>\n",
              "      <td>11162.000000</td>\n",
              "      <td>11162.000000</td>\n",
              "      <td>11162.000000</td>\n",
              "      <td>11162.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>41.231948</td>\n",
              "      <td>1528.538524</td>\n",
              "      <td>371.993818</td>\n",
              "      <td>2.508421</td>\n",
              "      <td>0.832557</td>\n",
              "      <td>0.015051</td>\n",
              "      <td>0.473123</td>\n",
              "      <td>0.130801</td>\n",
              "      <td>0.473840</td>\n",
              "      <td>0.003124</td>\n",
              "      <td>0.174162</td>\n",
              "      <td>0.029385</td>\n",
              "      <td>0.140208</td>\n",
              "      <td>0.107239</td>\n",
              "      <td>0.036284</td>\n",
              "      <td>0.163322</td>\n",
              "      <td>0.349400</td>\n",
              "      <td>0.115839</td>\n",
              "      <td>0.568984</td>\n",
              "      <td>0.315176</td>\n",
              "      <td>0.134385</td>\n",
              "      <td>0.490593</td>\n",
              "      <td>0.330496</td>\n",
              "      <td>0.044526</td>\n",
              "      <td>0.110016</td>\n",
              "      <td>0.095951</td>\n",
              "      <td>0.794033</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>11.913369</td>\n",
              "      <td>3225.413326</td>\n",
              "      <td>347.128386</td>\n",
              "      <td>2.722077</td>\n",
              "      <td>2.292007</td>\n",
              "      <td>0.121761</td>\n",
              "      <td>0.499299</td>\n",
              "      <td>0.337198</td>\n",
              "      <td>0.499338</td>\n",
              "      <td>0.030686</td>\n",
              "      <td>0.379266</td>\n",
              "      <td>0.168892</td>\n",
              "      <td>0.347218</td>\n",
              "      <td>0.309431</td>\n",
              "      <td>0.187004</td>\n",
              "      <td>0.369676</td>\n",
              "      <td>0.476802</td>\n",
              "      <td>0.320047</td>\n",
              "      <td>0.495241</td>\n",
              "      <td>0.464607</td>\n",
              "      <td>0.341080</td>\n",
              "      <td>0.499934</td>\n",
              "      <td>0.470413</td>\n",
              "      <td>0.206270</td>\n",
              "      <td>0.312924</td>\n",
              "      <td>0.294537</td>\n",
              "      <td>0.404424</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>18.000000</td>\n",
              "      <td>-6847.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000100</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>32.000000</td>\n",
              "      <td>122.000000</td>\n",
              "      <td>138.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000100</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>39.000000</td>\n",
              "      <td>550.000000</td>\n",
              "      <td>255.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000100</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>49.000000</td>\n",
              "      <td>1708.000000</td>\n",
              "      <td>496.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.001919</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>95.000000</td>\n",
              "      <td>81204.000000</td>\n",
              "      <td>3881.000000</td>\n",
              "      <td>63.000000</td>\n",
              "      <td>58.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                age       balance  ...  poutcome_success  poutcome_unknown\n",
              "count  11162.000000  11162.000000  ...      11162.000000      11162.000000\n",
              "mean      41.231948   1528.538524  ...          0.095951          0.794033\n",
              "std       11.913369   3225.413326  ...          0.294537          0.404424\n",
              "min       18.000000  -6847.000000  ...          0.000000          0.000000\n",
              "25%       32.000000    122.000000  ...          0.000000          1.000000\n",
              "50%       39.000000    550.000000  ...          0.000000          1.000000\n",
              "75%       49.000000   1708.000000  ...          0.000000          1.000000\n",
              "max       95.000000  81204.000000  ...          1.000000          1.000000\n",
              "\n",
              "[8 rows x 27 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "oAeOFLkSc-11",
        "outputId": "9a532976-bffa-4084-fabc-974fe9a170f9"
      },
      "source": [
        "# Scatterplot showing age and balance\n",
        "bank_with_dummies.plot(kind='scatter', x='age', y='balance');\n",
        "\n",
        "# Across all ages, majority of people have savings of less than 20000."
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "5d_dpawhdA53",
        "outputId": "8573a628-6913-41e8-9ab4-b7f87fa813f7"
      },
      "source": [
        "bank_with_dummies.plot(kind='hist', x='poutcome_success', y='duration')"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3b25801b10>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "u1duo69idCu_",
        "outputId": "b5686b9b-8b1b-4808-c60f-e59dda94b554"
      },
      "source": [
        "# People who sign up to a term deposite\n",
        "bank_with_dummies[bank_data.deposit_cat == 1].describe()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>balance</th>\n",
              "      <th>duration</th>\n",
              "      <th>campaign</th>\n",
              "      <th>previous</th>\n",
              "      <th>default_cat</th>\n",
              "      <th>housing_cat</th>\n",
              "      <th>loan_cat</th>\n",
              "      <th>deposit_cat</th>\n",
              "      <th>recent_pdays</th>\n",
              "      <th>job_blue-collar</th>\n",
              "      <th>job_entrepreneur</th>\n",
              "      <th>job_other</th>\n",
              "      <th>job_pink-collar</th>\n",
              "      <th>job_self-employed</th>\n",
              "      <th>job_technician</th>\n",
              "      <th>job_white-collar</th>\n",
              "      <th>marital_divorced</th>\n",
              "      <th>marital_married</th>\n",
              "      <th>marital_single</th>\n",
              "      <th>education_primary</th>\n",
              "      <th>education_secondary</th>\n",
              "      <th>education_tertiary</th>\n",
              "      <th>education_unknown</th>\n",
              "      <th>poutcome_failure</th>\n",
              "      <th>poutcome_success</th>\n",
              "      <th>poutcome_unknown</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>5289.000000</td>\n",
              "      <td>5289.000000</td>\n",
              "      <td>5289.000000</td>\n",
              "      <td>5289.000000</td>\n",
              "      <td>5289.000000</td>\n",
              "      <td>5289.000000</td>\n",
              "      <td>5289.000000</td>\n",
              "      <td>5289.000000</td>\n",
              "      <td>5289.0</td>\n",
              "      <td>5289.000000</td>\n",
              "      <td>5289.000000</td>\n",
              "      <td>5289.000000</td>\n",
              "      <td>5289.000000</td>\n",
              "      <td>5289.000000</td>\n",
              "      <td>5289.000000</td>\n",
              "      <td>5289.000000</td>\n",
              "      <td>5289.000000</td>\n",
              "      <td>5289.000000</td>\n",
              "      <td>5289.000000</td>\n",
              "      <td>5289.000000</td>\n",
              "      <td>5289.000000</td>\n",
              "      <td>5289.000000</td>\n",
              "      <td>5289.000000</td>\n",
              "      <td>5289.000000</td>\n",
              "      <td>5289.000000</td>\n",
              "      <td>5289.000000</td>\n",
              "      <td>5289.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>41.670070</td>\n",
              "      <td>1804.267915</td>\n",
              "      <td>537.294574</td>\n",
              "      <td>2.141047</td>\n",
              "      <td>1.170354</td>\n",
              "      <td>0.009832</td>\n",
              "      <td>0.365854</td>\n",
              "      <td>0.091511</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.004238</td>\n",
              "      <td>0.133863</td>\n",
              "      <td>0.023256</td>\n",
              "      <td>0.193042</td>\n",
              "      <td>0.090376</td>\n",
              "      <td>0.035356</td>\n",
              "      <td>0.158820</td>\n",
              "      <td>0.365286</td>\n",
              "      <td>0.117603</td>\n",
              "      <td>0.520892</td>\n",
              "      <td>0.361505</td>\n",
              "      <td>0.111741</td>\n",
              "      <td>0.463226</td>\n",
              "      <td>0.377387</td>\n",
              "      <td>0.047646</td>\n",
              "      <td>0.116846</td>\n",
              "      <td>0.184912</td>\n",
              "      <td>0.698242</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>13.497781</td>\n",
              "      <td>3501.104777</td>\n",
              "      <td>392.525262</td>\n",
              "      <td>1.921826</td>\n",
              "      <td>2.553272</td>\n",
              "      <td>0.098676</td>\n",
              "      <td>0.481714</td>\n",
              "      <td>0.288361</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.035686</td>\n",
              "      <td>0.340537</td>\n",
              "      <td>0.150729</td>\n",
              "      <td>0.394723</td>\n",
              "      <td>0.286747</td>\n",
              "      <td>0.184696</td>\n",
              "      <td>0.365543</td>\n",
              "      <td>0.481556</td>\n",
              "      <td>0.322167</td>\n",
              "      <td>0.499611</td>\n",
              "      <td>0.480482</td>\n",
              "      <td>0.315078</td>\n",
              "      <td>0.498693</td>\n",
              "      <td>0.484779</td>\n",
              "      <td>0.213036</td>\n",
              "      <td>0.321267</td>\n",
              "      <td>0.388263</td>\n",
              "      <td>0.459064</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>18.000000</td>\n",
              "      <td>-3058.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000100</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>31.000000</td>\n",
              "      <td>210.000000</td>\n",
              "      <td>244.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000100</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>38.000000</td>\n",
              "      <td>733.000000</td>\n",
              "      <td>426.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000100</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>50.000000</td>\n",
              "      <td>2159.000000</td>\n",
              "      <td>725.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.005128</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>95.000000</td>\n",
              "      <td>81204.000000</td>\n",
              "      <td>3881.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>58.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               age       balance  ...  poutcome_success  poutcome_unknown\n",
              "count  5289.000000   5289.000000  ...       5289.000000       5289.000000\n",
              "mean     41.670070   1804.267915  ...          0.184912          0.698242\n",
              "std      13.497781   3501.104777  ...          0.388263          0.459064\n",
              "min      18.000000  -3058.000000  ...          0.000000          0.000000\n",
              "25%      31.000000    210.000000  ...          0.000000          0.000000\n",
              "50%      38.000000    733.000000  ...          0.000000          1.000000\n",
              "75%      50.000000   2159.000000  ...          0.000000          1.000000\n",
              "max      95.000000  81204.000000  ...          1.000000          1.000000\n",
              "\n",
              "[8 rows x 27 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yAOfsAJzdERN",
        "outputId": "436285a9-7132-4aee-8f75-73c496c6b7cd"
      },
      "source": [
        "# People signed up to a term deposite having a personal loan (loan_cat) and housing loan (housing_cat)\n",
        "len(bank_with_dummies[(bank_with_dummies.deposit_cat == 1) & (bank_with_dummies.loan_cat) & (bank_with_dummies.housing_cat)])"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "265"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0prUO9cfdFgD",
        "outputId": "faa57fa1-ebfc-4fd4-be9d-ff83111d97d5"
      },
      "source": [
        "# People signed up to a term deposite with a credit default \n",
        "len(bank_with_dummies[(bank_with_dummies.deposit_cat == 1) & (bank_with_dummies.default_cat ==1)])"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "52"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "BOChk5rsdG-I",
        "outputId": "ea1ec31c-0949-49fa-b701-a84acff40085"
      },
      "source": [
        "# Bar chart of job Vs deposite\n",
        "plt.figure(figsize = (10,6))\n",
        "sns.barplot(x='job', y = 'deposit_cat', data = bank_data)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3b324e8c50>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "hfrHqmoFdPpf",
        "outputId": "7881d85a-88fd-4191-c102-ecca5437055a"
      },
      "source": [
        "# Bar chart of \"previous outcome\" Vs \"call duration\"\n",
        "\n",
        "plt.figure(figsize = (10,6))\n",
        "sns.barplot(x='poutcome', y = 'duration', data = bank_data)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3b257c1290>"
            ]
          },
          "metadata": {},
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 933
        },
        "id": "xz21HpuMdRNs",
        "outputId": "b8987270-f24f-40ca-c064-dd4be4f36696"
      },
      "source": [
        "# make a copy\n",
        "bankcl = bank_with_dummies\n",
        "# The Correltion matrix\n",
        "corr = bankcl.corr()\n",
        "corr"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>balance</th>\n",
              "      <th>duration</th>\n",
              "      <th>campaign</th>\n",
              "      <th>previous</th>\n",
              "      <th>default_cat</th>\n",
              "      <th>housing_cat</th>\n",
              "      <th>loan_cat</th>\n",
              "      <th>deposit_cat</th>\n",
              "      <th>recent_pdays</th>\n",
              "      <th>job_blue-collar</th>\n",
              "      <th>job_entrepreneur</th>\n",
              "      <th>job_other</th>\n",
              "      <th>job_pink-collar</th>\n",
              "      <th>job_self-employed</th>\n",
              "      <th>job_technician</th>\n",
              "      <th>job_white-collar</th>\n",
              "      <th>marital_divorced</th>\n",
              "      <th>marital_married</th>\n",
              "      <th>marital_single</th>\n",
              "      <th>education_primary</th>\n",
              "      <th>education_secondary</th>\n",
              "      <th>education_tertiary</th>\n",
              "      <th>education_unknown</th>\n",
              "      <th>poutcome_failure</th>\n",
              "      <th>poutcome_success</th>\n",
              "      <th>poutcome_unknown</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.112300</td>\n",
              "      <td>0.000189</td>\n",
              "      <td>-0.005278</td>\n",
              "      <td>0.020169</td>\n",
              "      <td>-0.011425</td>\n",
              "      <td>-0.168700</td>\n",
              "      <td>-0.031418</td>\n",
              "      <td>0.034901</td>\n",
              "      <td>0.019102</td>\n",
              "      <td>-0.066567</td>\n",
              "      <td>0.024176</td>\n",
              "      <td>0.296418</td>\n",
              "      <td>-0.027942</td>\n",
              "      <td>-0.023163</td>\n",
              "      <td>-0.082716</td>\n",
              "      <td>-0.080122</td>\n",
              "      <td>0.186349</td>\n",
              "      <td>0.318436</td>\n",
              "      <td>-0.467799</td>\n",
              "      <td>0.231150</td>\n",
              "      <td>-0.094400</td>\n",
              "      <td>-0.101372</td>\n",
              "      <td>0.077761</td>\n",
              "      <td>-0.008071</td>\n",
              "      <td>0.062114</td>\n",
              "      <td>-0.038992</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>balance</th>\n",
              "      <td>0.112300</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.022436</td>\n",
              "      <td>-0.013894</td>\n",
              "      <td>0.030805</td>\n",
              "      <td>-0.060954</td>\n",
              "      <td>-0.077092</td>\n",
              "      <td>-0.084589</td>\n",
              "      <td>0.081129</td>\n",
              "      <td>-0.004379</td>\n",
              "      <td>-0.046220</td>\n",
              "      <td>0.005039</td>\n",
              "      <td>0.050744</td>\n",
              "      <td>-0.041063</td>\n",
              "      <td>0.020264</td>\n",
              "      <td>0.003802</td>\n",
              "      <td>0.013780</td>\n",
              "      <td>-0.017586</td>\n",
              "      <td>0.025431</td>\n",
              "      <td>-0.014994</td>\n",
              "      <td>-0.000673</td>\n",
              "      <td>-0.070609</td>\n",
              "      <td>0.069128</td>\n",
              "      <td>0.014596</td>\n",
              "      <td>0.001695</td>\n",
              "      <td>0.045603</td>\n",
              "      <td>-0.034524</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>duration</th>\n",
              "      <td>0.000189</td>\n",
              "      <td>0.022436</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.041557</td>\n",
              "      <td>-0.026716</td>\n",
              "      <td>-0.009760</td>\n",
              "      <td>0.035051</td>\n",
              "      <td>-0.001914</td>\n",
              "      <td>0.451919</td>\n",
              "      <td>-0.014868</td>\n",
              "      <td>0.029986</td>\n",
              "      <td>-0.000908</td>\n",
              "      <td>0.010680</td>\n",
              "      <td>0.005345</td>\n",
              "      <td>0.013506</td>\n",
              "      <td>-0.010440</td>\n",
              "      <td>-0.031980</td>\n",
              "      <td>0.021364</td>\n",
              "      <td>-0.036179</td>\n",
              "      <td>0.023847</td>\n",
              "      <td>0.013405</td>\n",
              "      <td>0.003820</td>\n",
              "      <td>-0.006813</td>\n",
              "      <td>-0.015887</td>\n",
              "      <td>-0.033966</td>\n",
              "      <td>-0.022578</td>\n",
              "      <td>0.042725</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>campaign</th>\n",
              "      <td>-0.005278</td>\n",
              "      <td>-0.013894</td>\n",
              "      <td>-0.041557</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.049699</td>\n",
              "      <td>0.030975</td>\n",
              "      <td>0.006660</td>\n",
              "      <td>0.034722</td>\n",
              "      <td>-0.128081</td>\n",
              "      <td>-0.026296</td>\n",
              "      <td>0.005522</td>\n",
              "      <td>0.013883</td>\n",
              "      <td>-0.050212</td>\n",
              "      <td>0.011958</td>\n",
              "      <td>0.001776</td>\n",
              "      <td>0.021738</td>\n",
              "      <td>0.001944</td>\n",
              "      <td>-0.006828</td>\n",
              "      <td>0.047722</td>\n",
              "      <td>-0.046165</td>\n",
              "      <td>0.019915</td>\n",
              "      <td>-0.013834</td>\n",
              "      <td>-0.005427</td>\n",
              "      <td>0.012976</td>\n",
              "      <td>-0.080188</td>\n",
              "      <td>-0.091807</td>\n",
              "      <td>0.128907</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>previous</th>\n",
              "      <td>0.020169</td>\n",
              "      <td>0.030805</td>\n",
              "      <td>-0.026716</td>\n",
              "      <td>-0.049699</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.035273</td>\n",
              "      <td>-0.000840</td>\n",
              "      <td>-0.022668</td>\n",
              "      <td>0.139867</td>\n",
              "      <td>0.122076</td>\n",
              "      <td>-0.039939</td>\n",
              "      <td>-0.022470</td>\n",
              "      <td>0.031191</td>\n",
              "      <td>-0.028623</td>\n",
              "      <td>-0.002338</td>\n",
              "      <td>0.002035</td>\n",
              "      <td>0.034929</td>\n",
              "      <td>-0.026566</td>\n",
              "      <td>-0.005176</td>\n",
              "      <td>0.023817</td>\n",
              "      <td>-0.024852</td>\n",
              "      <td>-0.004620</td>\n",
              "      <td>0.028146</td>\n",
              "      <td>-0.011898</td>\n",
              "      <td>0.335870</td>\n",
              "      <td>0.325477</td>\n",
              "      <td>-0.496921</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>default_cat</th>\n",
              "      <td>-0.011425</td>\n",
              "      <td>-0.060954</td>\n",
              "      <td>-0.009760</td>\n",
              "      <td>0.030975</td>\n",
              "      <td>-0.035273</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.011076</td>\n",
              "      <td>0.076434</td>\n",
              "      <td>-0.040680</td>\n",
              "      <td>-0.011290</td>\n",
              "      <td>0.022779</td>\n",
              "      <td>0.022060</td>\n",
              "      <td>-0.018130</td>\n",
              "      <td>-0.007173</td>\n",
              "      <td>0.007493</td>\n",
              "      <td>0.003109</td>\n",
              "      <td>-0.013425</td>\n",
              "      <td>0.019633</td>\n",
              "      <td>-0.006819</td>\n",
              "      <td>-0.006255</td>\n",
              "      <td>0.013858</td>\n",
              "      <td>-0.000618</td>\n",
              "      <td>-0.011768</td>\n",
              "      <td>0.005421</td>\n",
              "      <td>-0.024650</td>\n",
              "      <td>-0.040272</td>\n",
              "      <td>0.048403</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>housing_cat</th>\n",
              "      <td>-0.168700</td>\n",
              "      <td>-0.077092</td>\n",
              "      <td>0.035051</td>\n",
              "      <td>0.006660</td>\n",
              "      <td>-0.000840</td>\n",
              "      <td>0.011076</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.076761</td>\n",
              "      <td>-0.203888</td>\n",
              "      <td>-0.029350</td>\n",
              "      <td>0.189848</td>\n",
              "      <td>0.011492</td>\n",
              "      <td>-0.233309</td>\n",
              "      <td>0.043884</td>\n",
              "      <td>-0.016903</td>\n",
              "      <td>0.006551</td>\n",
              "      <td>-0.012111</td>\n",
              "      <td>0.007430</td>\n",
              "      <td>0.036305</td>\n",
              "      <td>-0.043817</td>\n",
              "      <td>0.017002</td>\n",
              "      <td>0.118514</td>\n",
              "      <td>-0.114955</td>\n",
              "      <td>-0.053191</td>\n",
              "      <td>0.087741</td>\n",
              "      <td>-0.136299</td>\n",
              "      <td>0.031375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loan_cat</th>\n",
              "      <td>-0.031418</td>\n",
              "      <td>-0.084589</td>\n",
              "      <td>-0.001914</td>\n",
              "      <td>0.034722</td>\n",
              "      <td>-0.022668</td>\n",
              "      <td>0.076434</td>\n",
              "      <td>0.076761</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.110580</td>\n",
              "      <td>-0.012697</td>\n",
              "      <td>0.057956</td>\n",
              "      <td>0.042631</td>\n",
              "      <td>-0.096196</td>\n",
              "      <td>0.014969</td>\n",
              "      <td>0.004299</td>\n",
              "      <td>0.006864</td>\n",
              "      <td>-0.007871</td>\n",
              "      <td>0.026463</td>\n",
              "      <td>0.044148</td>\n",
              "      <td>-0.065288</td>\n",
              "      <td>0.006854</td>\n",
              "      <td>0.079583</td>\n",
              "      <td>-0.067513</td>\n",
              "      <td>-0.050249</td>\n",
              "      <td>0.006264</td>\n",
              "      <td>-0.080370</td>\n",
              "      <td>0.053686</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>deposit_cat</th>\n",
              "      <td>0.034901</td>\n",
              "      <td>0.081129</td>\n",
              "      <td>0.451919</td>\n",
              "      <td>-0.128081</td>\n",
              "      <td>0.139867</td>\n",
              "      <td>-0.040680</td>\n",
              "      <td>-0.203888</td>\n",
              "      <td>-0.110580</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.034457</td>\n",
              "      <td>-0.100840</td>\n",
              "      <td>-0.034443</td>\n",
              "      <td>0.144408</td>\n",
              "      <td>-0.051717</td>\n",
              "      <td>-0.004707</td>\n",
              "      <td>-0.011557</td>\n",
              "      <td>0.031621</td>\n",
              "      <td>0.005228</td>\n",
              "      <td>-0.092157</td>\n",
              "      <td>0.094632</td>\n",
              "      <td>-0.063002</td>\n",
              "      <td>-0.051952</td>\n",
              "      <td>0.094598</td>\n",
              "      <td>0.014355</td>\n",
              "      <td>0.020714</td>\n",
              "      <td>0.286642</td>\n",
              "      <td>-0.224785</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>recent_pdays</th>\n",
              "      <td>0.019102</td>\n",
              "      <td>-0.004379</td>\n",
              "      <td>-0.014868</td>\n",
              "      <td>-0.026296</td>\n",
              "      <td>0.122076</td>\n",
              "      <td>-0.011290</td>\n",
              "      <td>-0.029350</td>\n",
              "      <td>-0.012697</td>\n",
              "      <td>0.034457</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.018514</td>\n",
              "      <td>0.006251</td>\n",
              "      <td>0.024356</td>\n",
              "      <td>-0.001183</td>\n",
              "      <td>-0.008226</td>\n",
              "      <td>-0.007412</td>\n",
              "      <td>0.004516</td>\n",
              "      <td>-0.020253</td>\n",
              "      <td>0.009583</td>\n",
              "      <td>0.003736</td>\n",
              "      <td>-0.007034</td>\n",
              "      <td>-0.017129</td>\n",
              "      <td>0.017346</td>\n",
              "      <td>0.013590</td>\n",
              "      <td>0.051422</td>\n",
              "      <td>0.119598</td>\n",
              "      <td>-0.126890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>job_blue-collar</th>\n",
              "      <td>-0.066567</td>\n",
              "      <td>-0.046220</td>\n",
              "      <td>0.029986</td>\n",
              "      <td>0.005522</td>\n",
              "      <td>-0.039939</td>\n",
              "      <td>0.022779</td>\n",
              "      <td>0.189848</td>\n",
              "      <td>0.057956</td>\n",
              "      <td>-0.100840</td>\n",
              "      <td>-0.018514</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.079905</td>\n",
              "      <td>-0.185447</td>\n",
              "      <td>-0.159162</td>\n",
              "      <td>-0.089107</td>\n",
              "      <td>-0.202896</td>\n",
              "      <td>-0.336538</td>\n",
              "      <td>-0.056240</td>\n",
              "      <td>0.109188</td>\n",
              "      <td>-0.077645</td>\n",
              "      <td>0.299737</td>\n",
              "      <td>0.076687</td>\n",
              "      <td>-0.298548</td>\n",
              "      <td>-0.000640</td>\n",
              "      <td>-0.018022</td>\n",
              "      <td>-0.077422</td>\n",
              "      <td>0.070330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>job_entrepreneur</th>\n",
              "      <td>0.024176</td>\n",
              "      <td>0.005039</td>\n",
              "      <td>-0.000908</td>\n",
              "      <td>0.013883</td>\n",
              "      <td>-0.022470</td>\n",
              "      <td>0.022060</td>\n",
              "      <td>0.011492</td>\n",
              "      <td>0.042631</td>\n",
              "      <td>-0.034443</td>\n",
              "      <td>0.006251</td>\n",
              "      <td>-0.079905</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.070264</td>\n",
              "      <td>-0.060305</td>\n",
              "      <td>-0.033762</td>\n",
              "      <td>-0.076875</td>\n",
              "      <td>-0.127511</td>\n",
              "      <td>0.006638</td>\n",
              "      <td>0.050746</td>\n",
              "      <td>-0.058665</td>\n",
              "      <td>-0.004788</td>\n",
              "      <td>-0.021132</td>\n",
              "      <td>0.026612</td>\n",
              "      <td>-0.001555</td>\n",
              "      <td>-0.001840</td>\n",
              "      <td>-0.035072</td>\n",
              "      <td>0.026966</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>job_other</th>\n",
              "      <td>0.296418</td>\n",
              "      <td>0.050744</td>\n",
              "      <td>0.010680</td>\n",
              "      <td>-0.050212</td>\n",
              "      <td>0.031191</td>\n",
              "      <td>-0.018130</td>\n",
              "      <td>-0.233309</td>\n",
              "      <td>-0.096196</td>\n",
              "      <td>0.144408</td>\n",
              "      <td>0.024356</td>\n",
              "      <td>-0.185447</td>\n",
              "      <td>-0.070264</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.139958</td>\n",
              "      <td>-0.078356</td>\n",
              "      <td>-0.178415</td>\n",
              "      <td>-0.295933</td>\n",
              "      <td>0.032824</td>\n",
              "      <td>-0.030982</td>\n",
              "      <td>0.010413</td>\n",
              "      <td>0.114003</td>\n",
              "      <td>-0.020532</td>\n",
              "      <td>-0.110383</td>\n",
              "      <td>0.112986</td>\n",
              "      <td>-0.010865</td>\n",
              "      <td>0.099733</td>\n",
              "      <td>-0.064228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>job_pink-collar</th>\n",
              "      <td>-0.027942</td>\n",
              "      <td>-0.041063</td>\n",
              "      <td>0.005345</td>\n",
              "      <td>0.011958</td>\n",
              "      <td>-0.028623</td>\n",
              "      <td>-0.007173</td>\n",
              "      <td>0.043884</td>\n",
              "      <td>0.014969</td>\n",
              "      <td>-0.051717</td>\n",
              "      <td>-0.001183</td>\n",
              "      <td>-0.159162</td>\n",
              "      <td>-0.060305</td>\n",
              "      <td>-0.139958</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.067250</td>\n",
              "      <td>-0.153127</td>\n",
              "      <td>-0.253988</td>\n",
              "      <td>0.025640</td>\n",
              "      <td>0.007558</td>\n",
              "      <td>-0.025718</td>\n",
              "      <td>0.056150</td>\n",
              "      <td>0.137129</td>\n",
              "      <td>-0.184418</td>\n",
              "      <td>-0.004629</td>\n",
              "      <td>-0.010816</td>\n",
              "      <td>-0.030331</td>\n",
              "      <td>0.030459</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>job_self-employed</th>\n",
              "      <td>-0.023163</td>\n",
              "      <td>0.020264</td>\n",
              "      <td>0.013506</td>\n",
              "      <td>0.001776</td>\n",
              "      <td>-0.002338</td>\n",
              "      <td>0.007493</td>\n",
              "      <td>-0.016903</td>\n",
              "      <td>0.004299</td>\n",
              "      <td>-0.004707</td>\n",
              "      <td>-0.008226</td>\n",
              "      <td>-0.089107</td>\n",
              "      <td>-0.033762</td>\n",
              "      <td>-0.078356</td>\n",
              "      <td>-0.067250</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.085728</td>\n",
              "      <td>-0.142196</td>\n",
              "      <td>-0.011849</td>\n",
              "      <td>-0.008164</td>\n",
              "      <td>0.016864</td>\n",
              "      <td>-0.037121</td>\n",
              "      <td>-0.060080</td>\n",
              "      <td>0.097929</td>\n",
              "      <td>-0.016336</td>\n",
              "      <td>-0.010039</td>\n",
              "      <td>-0.001399</td>\n",
              "      <td>0.008786</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>job_technician</th>\n",
              "      <td>-0.082716</td>\n",
              "      <td>0.003802</td>\n",
              "      <td>-0.010440</td>\n",
              "      <td>0.021738</td>\n",
              "      <td>0.002035</td>\n",
              "      <td>0.003109</td>\n",
              "      <td>0.006551</td>\n",
              "      <td>0.006864</td>\n",
              "      <td>-0.011557</td>\n",
              "      <td>-0.007412</td>\n",
              "      <td>-0.202896</td>\n",
              "      <td>-0.076875</td>\n",
              "      <td>-0.178415</td>\n",
              "      <td>-0.153127</td>\n",
              "      <td>-0.085728</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.323778</td>\n",
              "      <td>-0.005434</td>\n",
              "      <td>-0.052492</td>\n",
              "      <td>0.059696</td>\n",
              "      <td>-0.144948</td>\n",
              "      <td>0.152542</td>\n",
              "      <td>-0.041988</td>\n",
              "      <td>-0.034276</td>\n",
              "      <td>0.005763</td>\n",
              "      <td>-0.014744</td>\n",
              "      <td>0.006279</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>job_white-collar</th>\n",
              "      <td>-0.080122</td>\n",
              "      <td>0.013780</td>\n",
              "      <td>-0.031980</td>\n",
              "      <td>0.001944</td>\n",
              "      <td>0.034929</td>\n",
              "      <td>-0.013425</td>\n",
              "      <td>-0.012111</td>\n",
              "      <td>-0.007871</td>\n",
              "      <td>0.031621</td>\n",
              "      <td>0.004516</td>\n",
              "      <td>-0.336538</td>\n",
              "      <td>-0.127511</td>\n",
              "      <td>-0.295933</td>\n",
              "      <td>-0.253988</td>\n",
              "      <td>-0.142196</td>\n",
              "      <td>-0.323778</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.010701</td>\n",
              "      <td>-0.043270</td>\n",
              "      <td>0.038752</td>\n",
              "      <td>-0.229245</td>\n",
              "      <td>-0.222261</td>\n",
              "      <td>0.422261</td>\n",
              "      <td>-0.045233</td>\n",
              "      <td>0.029387</td>\n",
              "      <td>0.033044</td>\n",
              "      <td>-0.046804</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>marital_divorced</th>\n",
              "      <td>0.186349</td>\n",
              "      <td>-0.017586</td>\n",
              "      <td>0.021364</td>\n",
              "      <td>-0.006828</td>\n",
              "      <td>-0.026566</td>\n",
              "      <td>0.019633</td>\n",
              "      <td>0.007430</td>\n",
              "      <td>0.026463</td>\n",
              "      <td>0.005228</td>\n",
              "      <td>-0.020253</td>\n",
              "      <td>-0.056240</td>\n",
              "      <td>0.006638</td>\n",
              "      <td>0.032824</td>\n",
              "      <td>0.025640</td>\n",
              "      <td>-0.011849</td>\n",
              "      <td>-0.005434</td>\n",
              "      <td>0.010701</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.415878</td>\n",
              "      <td>-0.245556</td>\n",
              "      <td>0.024821</td>\n",
              "      <td>0.009891</td>\n",
              "      <td>-0.024597</td>\n",
              "      <td>-0.008920</td>\n",
              "      <td>-0.026169</td>\n",
              "      <td>-0.018120</td>\n",
              "      <td>0.033445</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>marital_married</th>\n",
              "      <td>0.318436</td>\n",
              "      <td>0.025431</td>\n",
              "      <td>-0.036179</td>\n",
              "      <td>0.047722</td>\n",
              "      <td>-0.005176</td>\n",
              "      <td>-0.006819</td>\n",
              "      <td>0.036305</td>\n",
              "      <td>0.044148</td>\n",
              "      <td>-0.092157</td>\n",
              "      <td>0.009583</td>\n",
              "      <td>0.109188</td>\n",
              "      <td>0.050746</td>\n",
              "      <td>-0.030982</td>\n",
              "      <td>0.007558</td>\n",
              "      <td>-0.008164</td>\n",
              "      <td>-0.052492</td>\n",
              "      <td>-0.043270</td>\n",
              "      <td>-0.415878</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.779455</td>\n",
              "      <td>0.130232</td>\n",
              "      <td>0.001536</td>\n",
              "      <td>-0.098449</td>\n",
              "      <td>0.005451</td>\n",
              "      <td>0.007682</td>\n",
              "      <td>-0.010063</td>\n",
              "      <td>0.001384</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>marital_single</th>\n",
              "      <td>-0.467799</td>\n",
              "      <td>-0.014994</td>\n",
              "      <td>0.023847</td>\n",
              "      <td>-0.046165</td>\n",
              "      <td>0.023817</td>\n",
              "      <td>-0.006255</td>\n",
              "      <td>-0.043817</td>\n",
              "      <td>-0.065288</td>\n",
              "      <td>0.094632</td>\n",
              "      <td>0.003736</td>\n",
              "      <td>-0.077645</td>\n",
              "      <td>-0.058665</td>\n",
              "      <td>0.010413</td>\n",
              "      <td>-0.025718</td>\n",
              "      <td>0.016864</td>\n",
              "      <td>0.059696</td>\n",
              "      <td>0.038752</td>\n",
              "      <td>-0.245556</td>\n",
              "      <td>-0.779455</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.155917</td>\n",
              "      <td>-0.008450</td>\n",
              "      <td>0.121884</td>\n",
              "      <td>0.000334</td>\n",
              "      <td>0.009838</td>\n",
              "      <td>0.023208</td>\n",
              "      <td>-0.024514</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>education_primary</th>\n",
              "      <td>0.231150</td>\n",
              "      <td>-0.000673</td>\n",
              "      <td>0.013405</td>\n",
              "      <td>0.019915</td>\n",
              "      <td>-0.024852</td>\n",
              "      <td>0.013858</td>\n",
              "      <td>0.017002</td>\n",
              "      <td>0.006854</td>\n",
              "      <td>-0.063002</td>\n",
              "      <td>-0.007034</td>\n",
              "      <td>0.299737</td>\n",
              "      <td>-0.004788</td>\n",
              "      <td>0.114003</td>\n",
              "      <td>0.056150</td>\n",
              "      <td>-0.037121</td>\n",
              "      <td>-0.144948</td>\n",
              "      <td>-0.229245</td>\n",
              "      <td>0.024821</td>\n",
              "      <td>0.130232</td>\n",
              "      <td>-0.155917</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.386670</td>\n",
              "      <td>-0.276834</td>\n",
              "      <td>-0.085057</td>\n",
              "      <td>-0.026044</td>\n",
              "      <td>-0.049879</td>\n",
              "      <td>0.056477</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>education_secondary</th>\n",
              "      <td>-0.094400</td>\n",
              "      <td>-0.070609</td>\n",
              "      <td>0.003820</td>\n",
              "      <td>-0.013834</td>\n",
              "      <td>-0.004620</td>\n",
              "      <td>-0.000618</td>\n",
              "      <td>0.118514</td>\n",
              "      <td>0.079583</td>\n",
              "      <td>-0.051952</td>\n",
              "      <td>-0.017129</td>\n",
              "      <td>0.076687</td>\n",
              "      <td>-0.021132</td>\n",
              "      <td>-0.020532</td>\n",
              "      <td>0.137129</td>\n",
              "      <td>-0.060080</td>\n",
              "      <td>0.152542</td>\n",
              "      <td>-0.222261</td>\n",
              "      <td>0.009891</td>\n",
              "      <td>0.001536</td>\n",
              "      <td>-0.008450</td>\n",
              "      <td>-0.386670</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.689501</td>\n",
              "      <td>-0.211849</td>\n",
              "      <td>0.010625</td>\n",
              "      <td>-0.029466</td>\n",
              "      <td>0.013238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>education_tertiary</th>\n",
              "      <td>-0.101372</td>\n",
              "      <td>0.069128</td>\n",
              "      <td>-0.006813</td>\n",
              "      <td>-0.005427</td>\n",
              "      <td>0.028146</td>\n",
              "      <td>-0.011768</td>\n",
              "      <td>-0.114955</td>\n",
              "      <td>-0.067513</td>\n",
              "      <td>0.094598</td>\n",
              "      <td>0.017346</td>\n",
              "      <td>-0.298548</td>\n",
              "      <td>0.026612</td>\n",
              "      <td>-0.110383</td>\n",
              "      <td>-0.184418</td>\n",
              "      <td>0.097929</td>\n",
              "      <td>-0.041988</td>\n",
              "      <td>0.422261</td>\n",
              "      <td>-0.024597</td>\n",
              "      <td>-0.098449</td>\n",
              "      <td>0.121884</td>\n",
              "      <td>-0.276834</td>\n",
              "      <td>-0.689501</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.151672</td>\n",
              "      <td>0.012265</td>\n",
              "      <td>0.059518</td>\n",
              "      <td>-0.052836</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>education_unknown</th>\n",
              "      <td>0.077761</td>\n",
              "      <td>0.014596</td>\n",
              "      <td>-0.015887</td>\n",
              "      <td>0.012976</td>\n",
              "      <td>-0.011898</td>\n",
              "      <td>0.005421</td>\n",
              "      <td>-0.053191</td>\n",
              "      <td>-0.050249</td>\n",
              "      <td>0.014355</td>\n",
              "      <td>0.013590</td>\n",
              "      <td>-0.000640</td>\n",
              "      <td>-0.001555</td>\n",
              "      <td>0.112986</td>\n",
              "      <td>-0.004629</td>\n",
              "      <td>-0.016336</td>\n",
              "      <td>-0.034276</td>\n",
              "      <td>-0.045233</td>\n",
              "      <td>-0.008920</td>\n",
              "      <td>0.005451</td>\n",
              "      <td>0.000334</td>\n",
              "      <td>-0.085057</td>\n",
              "      <td>-0.211849</td>\n",
              "      <td>-0.151672</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.010658</td>\n",
              "      <td>0.018158</td>\n",
              "      <td>-0.004978</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>poutcome_failure</th>\n",
              "      <td>-0.008071</td>\n",
              "      <td>0.001695</td>\n",
              "      <td>-0.033966</td>\n",
              "      <td>-0.080188</td>\n",
              "      <td>0.335870</td>\n",
              "      <td>-0.024650</td>\n",
              "      <td>0.087741</td>\n",
              "      <td>0.006264</td>\n",
              "      <td>0.020714</td>\n",
              "      <td>0.051422</td>\n",
              "      <td>-0.018022</td>\n",
              "      <td>-0.001840</td>\n",
              "      <td>-0.010865</td>\n",
              "      <td>-0.010816</td>\n",
              "      <td>-0.010039</td>\n",
              "      <td>0.005763</td>\n",
              "      <td>0.029387</td>\n",
              "      <td>-0.026169</td>\n",
              "      <td>0.007682</td>\n",
              "      <td>0.009838</td>\n",
              "      <td>-0.026044</td>\n",
              "      <td>0.010625</td>\n",
              "      <td>0.012265</td>\n",
              "      <td>-0.010658</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.114542</td>\n",
              "      <td>-0.690332</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>poutcome_success</th>\n",
              "      <td>0.062114</td>\n",
              "      <td>0.045603</td>\n",
              "      <td>-0.022578</td>\n",
              "      <td>-0.091807</td>\n",
              "      <td>0.325477</td>\n",
              "      <td>-0.040272</td>\n",
              "      <td>-0.136299</td>\n",
              "      <td>-0.080370</td>\n",
              "      <td>0.286642</td>\n",
              "      <td>0.119598</td>\n",
              "      <td>-0.077422</td>\n",
              "      <td>-0.035072</td>\n",
              "      <td>0.099733</td>\n",
              "      <td>-0.030331</td>\n",
              "      <td>-0.001399</td>\n",
              "      <td>-0.014744</td>\n",
              "      <td>0.033044</td>\n",
              "      <td>-0.018120</td>\n",
              "      <td>-0.010063</td>\n",
              "      <td>0.023208</td>\n",
              "      <td>-0.049879</td>\n",
              "      <td>-0.029466</td>\n",
              "      <td>0.059518</td>\n",
              "      <td>0.018158</td>\n",
              "      <td>-0.114542</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.639659</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>poutcome_unknown</th>\n",
              "      <td>-0.038992</td>\n",
              "      <td>-0.034524</td>\n",
              "      <td>0.042725</td>\n",
              "      <td>0.128907</td>\n",
              "      <td>-0.496921</td>\n",
              "      <td>0.048403</td>\n",
              "      <td>0.031375</td>\n",
              "      <td>0.053686</td>\n",
              "      <td>-0.224785</td>\n",
              "      <td>-0.126890</td>\n",
              "      <td>0.070330</td>\n",
              "      <td>0.026966</td>\n",
              "      <td>-0.064228</td>\n",
              "      <td>0.030459</td>\n",
              "      <td>0.008786</td>\n",
              "      <td>0.006279</td>\n",
              "      <td>-0.046804</td>\n",
              "      <td>0.033445</td>\n",
              "      <td>0.001384</td>\n",
              "      <td>-0.024514</td>\n",
              "      <td>0.056477</td>\n",
              "      <td>0.013238</td>\n",
              "      <td>-0.052836</td>\n",
              "      <td>-0.004978</td>\n",
              "      <td>-0.690332</td>\n",
              "      <td>-0.639659</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                          age   balance  ...  poutcome_success  poutcome_unknown\n",
              "age                  1.000000  0.112300  ...          0.062114         -0.038992\n",
              "balance              0.112300  1.000000  ...          0.045603         -0.034524\n",
              "duration             0.000189  0.022436  ...         -0.022578          0.042725\n",
              "campaign            -0.005278 -0.013894  ...         -0.091807          0.128907\n",
              "previous             0.020169  0.030805  ...          0.325477         -0.496921\n",
              "default_cat         -0.011425 -0.060954  ...         -0.040272          0.048403\n",
              "housing_cat         -0.168700 -0.077092  ...         -0.136299          0.031375\n",
              "loan_cat            -0.031418 -0.084589  ...         -0.080370          0.053686\n",
              "deposit_cat          0.034901  0.081129  ...          0.286642         -0.224785\n",
              "recent_pdays         0.019102 -0.004379  ...          0.119598         -0.126890\n",
              "job_blue-collar     -0.066567 -0.046220  ...         -0.077422          0.070330\n",
              "job_entrepreneur     0.024176  0.005039  ...         -0.035072          0.026966\n",
              "job_other            0.296418  0.050744  ...          0.099733         -0.064228\n",
              "job_pink-collar     -0.027942 -0.041063  ...         -0.030331          0.030459\n",
              "job_self-employed   -0.023163  0.020264  ...         -0.001399          0.008786\n",
              "job_technician      -0.082716  0.003802  ...         -0.014744          0.006279\n",
              "job_white-collar    -0.080122  0.013780  ...          0.033044         -0.046804\n",
              "marital_divorced     0.186349 -0.017586  ...         -0.018120          0.033445\n",
              "marital_married      0.318436  0.025431  ...         -0.010063          0.001384\n",
              "marital_single      -0.467799 -0.014994  ...          0.023208         -0.024514\n",
              "education_primary    0.231150 -0.000673  ...         -0.049879          0.056477\n",
              "education_secondary -0.094400 -0.070609  ...         -0.029466          0.013238\n",
              "education_tertiary  -0.101372  0.069128  ...          0.059518         -0.052836\n",
              "education_unknown    0.077761  0.014596  ...          0.018158         -0.004978\n",
              "poutcome_failure    -0.008071  0.001695  ...         -0.114542         -0.690332\n",
              "poutcome_success     0.062114  0.045603  ...          1.000000         -0.639659\n",
              "poutcome_unknown    -0.038992 -0.034524  ...         -0.639659          1.000000\n",
              "\n",
              "[27 rows x 27 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624
        },
        "id": "3tmxBM4sdSty",
        "outputId": "a93217ac-f147-4e1b-908d-91c7a1a0d69a"
      },
      "source": [
        "# Heatmap\n",
        "plt.figure(figsize = (10,10))\n",
        "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
        "sns.heatmap(corr, xticklabels=corr.columns.values, yticklabels=corr.columns.values, cmap=cmap, vmax=.3, center=0, square=True, linewidths=.5, cbar_kws={\"shrink\": .82})\n",
        "plt.title('Heatmap of Correlation Matrix')"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Heatmap of Correlation Matrix')"
            ]
          },
          "metadata": {},
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 865
        },
        "id": "blkD6na5dU9R",
        "outputId": "376fe3d8-3ce6-4959-fae4-0ee7b61ad61f"
      },
      "source": [
        "# Extract the deposte_cat column (the dependent variable)\n",
        "corr_deposite = pd.DataFrame(corr['deposit_cat'].drop('deposit_cat'))\n",
        "corr_deposite.sort_values(by = 'deposit_cat', ascending = False)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>deposit_cat</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>duration</th>\n",
              "      <td>0.451919</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>poutcome_success</th>\n",
              "      <td>0.286642</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>job_other</th>\n",
              "      <td>0.144408</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>previous</th>\n",
              "      <td>0.139867</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>marital_single</th>\n",
              "      <td>0.094632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>education_tertiary</th>\n",
              "      <td>0.094598</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>balance</th>\n",
              "      <td>0.081129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>0.034901</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>recent_pdays</th>\n",
              "      <td>0.034457</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>job_white-collar</th>\n",
              "      <td>0.031621</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>poutcome_failure</th>\n",
              "      <td>0.020714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>education_unknown</th>\n",
              "      <td>0.014355</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>marital_divorced</th>\n",
              "      <td>0.005228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>job_self-employed</th>\n",
              "      <td>-0.004707</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>job_technician</th>\n",
              "      <td>-0.011557</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>job_entrepreneur</th>\n",
              "      <td>-0.034443</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>default_cat</th>\n",
              "      <td>-0.040680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>job_pink-collar</th>\n",
              "      <td>-0.051717</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>education_secondary</th>\n",
              "      <td>-0.051952</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>education_primary</th>\n",
              "      <td>-0.063002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>marital_married</th>\n",
              "      <td>-0.092157</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>job_blue-collar</th>\n",
              "      <td>-0.100840</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loan_cat</th>\n",
              "      <td>-0.110580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>campaign</th>\n",
              "      <td>-0.128081</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>housing_cat</th>\n",
              "      <td>-0.203888</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>poutcome_unknown</th>\n",
              "      <td>-0.224785</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     deposit_cat\n",
              "duration                0.451919\n",
              "poutcome_success        0.286642\n",
              "job_other               0.144408\n",
              "previous                0.139867\n",
              "marital_single          0.094632\n",
              "education_tertiary      0.094598\n",
              "balance                 0.081129\n",
              "age                     0.034901\n",
              "recent_pdays            0.034457\n",
              "job_white-collar        0.031621\n",
              "poutcome_failure        0.020714\n",
              "education_unknown       0.014355\n",
              "marital_divorced        0.005228\n",
              "job_self-employed      -0.004707\n",
              "job_technician         -0.011557\n",
              "job_entrepreneur       -0.034443\n",
              "default_cat            -0.040680\n",
              "job_pink-collar        -0.051717\n",
              "education_secondary    -0.051952\n",
              "education_primary      -0.063002\n",
              "marital_married        -0.092157\n",
              "job_blue-collar        -0.100840\n",
              "loan_cat               -0.110580\n",
              "campaign               -0.128081\n",
              "housing_cat            -0.203888\n",
              "poutcome_unknown       -0.224785"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M23mhElUdWyJ",
        "outputId": "680fc684-a167-468f-b5e0-e6524b3d0a81"
      },
      "source": [
        "# Train-Test split: 20% test data\n",
        "data_drop_deposite = bankcl.drop('deposit_cat', 1)\n",
        "label = bankcl.deposit_cat\n",
        "data_train, data_test, label_train, label_test = train_test_split(data_drop_deposite, label, test_size = 0.2, random_state = 50)\n",
        "# Decision tree with depth = 2\n",
        "dt2 = tree.DecisionTreeClassifier(random_state=1, max_depth=2)\n",
        "dt2.fit(data_train, label_train)\n",
        "dt2_score_train = dt2.score(data_train, label_train)\n",
        "print(\"Training score: \",dt2_score_train)\n",
        "dt2_score_test = dt2.score(data_test, label_test)\n",
        "print(\"Testing score: \",dt2_score_test)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training score:  0.7285250307985217\n",
            "Testing score:  0.7268248992386923\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gcLrCD7BdZED",
        "outputId": "fb133186-303e-4828-a24c-ef7fa467e4ae"
      },
      "source": [
        "# Decision tree with depth = 3\n",
        "dt3 = tree.DecisionTreeClassifier(random_state=1, max_depth=3)\n",
        "dt3.fit(data_train, label_train)\n",
        "dt3_score_train = dt3.score(data_train, label_train)\n",
        "print(\"Training score: \",dt3_score_train)\n",
        "dt3_score_test = dt3.score(data_test, label_test)\n",
        "print(\"Testing score: \",dt3_score_test)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training score:  0.770411020271027\n",
            "Testing score:  0.7572772055530677\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GBWnKKKAdbHK",
        "outputId": "1c9e7b8b-4e28-48da-fd08-535615dbc826"
      },
      "source": [
        "# Decision tree with depth = 4\n",
        "dt4 = tree.DecisionTreeClassifier(random_state=1, max_depth=4)\n",
        "dt4.fit(data_train, label_train)\n",
        "dt4_score_train = dt4.score(data_train, label_train)\n",
        "print(\"Training score: \",dt4_score_train)\n",
        "dt4_score_test = dt4.score(data_test, label_test)\n",
        "print(\"Testing score: \",dt4_score_test)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training score:  0.7885541494008288\n",
            "Testing score:  0.774294670846395\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wdma-_Dfdcim",
        "outputId": "46a1948b-d26f-42de-ba80-fd957bcaf7d2"
      },
      "source": [
        "# Decision tree with depth = 6\n",
        "dt6 = tree.DecisionTreeClassifier(random_state=1, max_depth=6)\n",
        "dt6.fit(data_train, label_train)\n",
        "dt6_score_train = dt6.score(data_train, label_train)\n",
        "print(\"Training score: \",dt6_score_train)\n",
        "dt6_score_test = dt6.score(data_test, label_test)\n",
        "print(\"Testing score: \",dt6_score_test)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training score:  0.8080412140217269\n",
            "Testing score:  0.7796686072548141\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vxnZXYB2deDc",
        "outputId": "33da3415-dffd-490c-b13a-9dc3eed9449d"
      },
      "source": [
        "# Decision tree: To the full depth\n",
        "dt1 = tree.DecisionTreeClassifier()\n",
        "dt1.fit(data_train, label_train)\n",
        "dt1_score_train = dt1.score(data_train, label_train)\n",
        "print(\"Training score: \", dt1_score_train)\n",
        "dt1_score_test = dt1.score(data_test, label_test)\n",
        "print(\"Testing score: \", dt1_score_test)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training score:  1.0\n",
            "Testing score:  0.7380206000895656\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U6AUlZD8dfTW",
        "outputId": "3da165a2-3299-4937-cf82-90189477f20b"
      },
      "source": [
        "print('{:10} {:20} {:20}'.format('depth', 'Training score','Testing score'))\n",
        "print('{:10} {:20} {:20}'.format('-----', '--------------','-------------'))\n",
        "print('{:1} {:>25} {:>20}'.format(2, dt2_score_train, dt2_score_test))\n",
        "print('{:1} {:>25} {:>20}'.format(3, dt3_score_train, dt3_score_test))\n",
        "print('{:1} {:>25} {:>20}'.format(4, dt4_score_train, dt4_score_test))\n",
        "print('{:1} {:>25} {:>20}'.format(6, dt6_score_train, dt6_score_test))\n",
        "print('{:1} {:>23} {:>20}'.format(\"max\", dt1_score_train, dt1_score_test))"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "depth      Training score       Testing score       \n",
            "-----      --------------       -------------       \n",
            "2        0.7285250307985217   0.7268248992386923\n",
            "3         0.770411020271027   0.7572772055530677\n",
            "4        0.7885541494008288    0.774294670846395\n",
            "6        0.8080412140217269   0.7796686072548141\n",
            "max                     1.0   0.7380206000895656\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GuKeknsqdg9m"
      },
      "source": [
        "# Let's generate the decision tree for depth = 2\n",
        "# Create a feature vector\n",
        "features = bankcl.columns.tolist()"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1LCbxuJedjQJ",
        "outputId": "b2908bc9-63b4-4859-a7ba-847ad0a14bd1"
      },
      "source": [
        "# Two classes: 0 = not signed up,  1 = signed up\n",
        "dt2.classes_"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4turSlITdk8i",
        "outputId": "4f6874b9-0ff5-4aa5-fcc0-472518d682b3"
      },
      "source": [
        "# Create a feature vector\n",
        "features = data_drop_deposite.columns.tolist()\n",
        "features"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['age',\n",
              " 'balance',\n",
              " 'duration',\n",
              " 'campaign',\n",
              " 'previous',\n",
              " 'default_cat',\n",
              " 'housing_cat',\n",
              " 'loan_cat',\n",
              " 'recent_pdays',\n",
              " 'job_blue-collar',\n",
              " 'job_entrepreneur',\n",
              " 'job_other',\n",
              " 'job_pink-collar',\n",
              " 'job_self-employed',\n",
              " 'job_technician',\n",
              " 'job_white-collar',\n",
              " 'marital_divorced',\n",
              " 'marital_married',\n",
              " 'marital_single',\n",
              " 'education_primary',\n",
              " 'education_secondary',\n",
              " 'education_tertiary',\n",
              " 'education_unknown',\n",
              " 'poutcome_failure',\n",
              " 'poutcome_success',\n",
              " 'poutcome_unknown']"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8bGkEQnAdm6u",
        "outputId": "44ce65c4-26b4-40f9-8061-abc62d7b1727"
      },
      "source": [
        "# Investigate most important features with depth =2\n",
        "\n",
        "dt2 = tree.DecisionTreeClassifier(random_state=1, max_depth=2)\n",
        "\n",
        "# Fit the decision tree classifier\n",
        "dt2.fit(data_train, label_train)\n",
        "\n",
        "fi = dt2.feature_importances_\n",
        "\n",
        "l = len(features)\n",
        "for i in range(0,len(features)):\n",
        "    print('{:.<20} {:3}'.format(features[i],fi[i]))"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "age................. 0.0\n",
            "balance............. 0.0\n",
            "duration............ 0.849306123902405\n",
            "campaign............ 0.0\n",
            "previous............ 0.0\n",
            "default_cat......... 0.0\n",
            "housing_cat......... 0.0\n",
            "loan_cat............ 0.0\n",
            "recent_pdays........ 0.0\n",
            "job_blue-collar..... 0.0\n",
            "job_entrepreneur.... 0.0\n",
            "job_other........... 0.0\n",
            "job_pink-collar..... 0.0\n",
            "job_self-employed... 0.0\n",
            "job_technician...... 0.0\n",
            "job_white-collar.... 0.0\n",
            "marital_divorced.... 0.0\n",
            "marital_married..... 0.0\n",
            "marital_single...... 0.0\n",
            "education_primary... 0.0\n",
            "education_secondary. 0.0\n",
            "education_tertiary.. 0.0\n",
            "education_unknown... 0.0\n",
            "poutcome_failure.... 0.0\n",
            "poutcome_success.... 0.15069387609759496\n",
            "poutcome_unknown.... 0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YBvdLXFedogx",
        "outputId": "f806d44a-68d8-48cf-9313-5753e3829ff7"
      },
      "source": [
        "# According to feature importance results, most importtant feature is the \"Duration\"\n",
        "# Let's calculte statistics on Duration\n",
        "print(\"Mean duration   : \", data_drop_deposite.duration.mean())\n",
        "print(\"Maximun duration: \", data_drop_deposite.duration.max())\n",
        "print(\"Minimum duration: \", data_drop_deposite.duration.min())"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean duration   :  371.99381831213043\n",
            "Maximun duration:  3881\n",
            "Minimum duration:  2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ED_PC69RdqFe",
        "outputId": "002315ec-4d0a-42dd-a712-e4e57dde2833"
      },
      "source": [
        "# Predict: Successful deposite with a call duration = 371 sec\n",
        "\n",
        "print(dt2.predict_proba(np.array([0, 0, 371, 0, 0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0]).reshape(1, -1)))\n",
        "print(dt2.predict(np.array([0, 0, 371, 0, 0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0]).reshape(1, -1)))"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.48515568 0.51484432]]\n",
            "[1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NCzpaluudrpb",
        "outputId": "92ebd94b-0407-4a41-e755-67d2a8d9cf3d"
      },
      "source": [
        "# Predict: Successful deposite with a maximun call duration = 3881 sec\n",
        "\n",
        "print(dt2.predict_proba(np.array([0, 0, 3881, 0, 0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0]).reshape(1, -1)))\n",
        "print(dt2.predict(np.array([0, 0, 3881, 0, 0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0]).reshape(1, -1)))"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.19295499 0.80704501]]\n",
            "[1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4zPliq3bduCt",
        "outputId": "41a9dc84-6a42-45f8-da41-715fd50c24c0"
      },
      "source": [
        "# Get a row with poutcome_success = 1\n",
        "#bank_with_dummies[(bank_with_dummies.poutcome_success == 1)]\n",
        "data_drop_deposite.iloc[985]\n"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age                      46.000000\n",
              "balance                3354.000000\n",
              "duration                522.000000\n",
              "campaign                  1.000000\n",
              "previous                  1.000000\n",
              "default_cat               0.000000\n",
              "housing_cat               1.000000\n",
              "loan_cat                  0.000000\n",
              "recent_pdays              0.005747\n",
              "job_blue-collar           0.000000\n",
              "job_entrepreneur          0.000000\n",
              "job_other                 1.000000\n",
              "job_pink-collar           0.000000\n",
              "job_self-employed         0.000000\n",
              "job_technician            0.000000\n",
              "job_white-collar          0.000000\n",
              "marital_divorced          1.000000\n",
              "marital_married           0.000000\n",
              "marital_single            0.000000\n",
              "education_primary         0.000000\n",
              "education_secondary       1.000000\n",
              "education_tertiary        0.000000\n",
              "education_unknown         0.000000\n",
              "poutcome_failure          0.000000\n",
              "poutcome_success          1.000000\n",
              "poutcome_unknown          0.000000\n",
              "Name: 985, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GwKrRpBedv7b",
        "outputId": "72205269-4f31-4d9d-8456-3428cae3329b"
      },
      "source": [
        "# Predict: Probability for above\n",
        "\n",
        "print(dt2.predict_proba(np.array([46,3354,522,1,1,0,1,0,0.005747,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,1,0]).reshape(1, -1)))"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.19295499 0.80704501]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9v-ZlyE4dxyU",
        "outputId": "27c669e7-796b-4715-88a9-e7adc96dd6cd"
      },
      "source": [
        "# Make predictions on the test set\n",
        "preds = dt2.predict(data_test)\n",
        "\n",
        "# Calculate accuracy\n",
        "print(\"\\nAccuracy score: \\n{}\".format(metrics.accuracy_score(label_test, preds)))\n",
        "\n",
        "# Make predictions on the test set using predict_proba\n",
        "probs = dt2.predict_proba(data_test)[:,1]\n",
        "\n",
        "# Calculate the AUC metric\n",
        "print(\"\\nArea Under Curve: \\n{}\".format(metrics.roc_auc_score(label_test, probs)))"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Accuracy score: \n",
            "0.7268248992386923\n",
            "\n",
            "Area Under Curve: \n",
            "0.7880265888143609\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z1Pc926Jdzn7"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}