Bank_Marketing.ipynb

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    "colab": {
      "name": "Bank Marketing.ipynb",
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    "kernelspec": {
      "name": "python3",
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      "name": "python"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/addicted-ai/bank-marketing-classification/blob/main/Bank_Marketing.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LSeBg0NrG6jq"
      },
      "source": [
        "## Business Problem:\n",
        "\n",
        "\n",
        "- Term diposits are major source of income for banks. So bank tries various methods to reach potential customers, one of which is **Telemarketing**.\n",
        "- However Bank can't call everyone since it'll require huge investment as large call centers are hired to actually execute these campaigns.\n",
        "- Given dataset is collected from direct marketing campaigns (phone calls) of a Portuguese banking institution.\n",
        "- The classification goal is to predict if the client will subscribe to a term deposit or not.\n",
        "- Based on prediction Bank can assign more resources for those potential clients & target them accordingly."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ElzZUC6sGXz6"
      },
      "source": [
        "## Import Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DBr0OAhyGQLy"
      },
      "source": [
        "# supressing warnings\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w_zSp8xd6iSf"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# Viz libraries\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# ML libraries\n",
        "import statsmodels.api as sm # we'll use statsmodel for model building since it provides better statistics\n",
        "\n",
        "# preprocessing of data\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "#sklearn is only used for automated feature seletion\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.feature_selection import RFE\n",
        "\n",
        "# For model Evaluation\n",
        "from sklearn import metrics\n",
        "from sklearn.metrics import confusion_matrix\n"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ttrTYdut8eM-"
      },
      "source": [
        "url='https://raw.githubusercontent.com/addicted-ai/bank-marketing-classification/main/train.csv'\n",
        "df = pd.read_csv(url,delimiter=';')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xLDP_WNY8qkW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "outputId": "9352ed6b-2278-4074-c7fe-3812574e670c"
      },
      "source": [
        "df"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
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              "      <th>marital</th>\n",
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              "      <th>balance</th>\n",
              "      <th>housing</th>\n",
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              "      <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",
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              "  </thead>\n",
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              "      <th>0</th>\n",
              "      <td>58</td>\n",
              "      <td>management</td>\n",
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              "      <th>2</th>\n",
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              "      <td>entrepreneur</td>\n",
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              "      <td>secondary</td>\n",
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              "      <td>yes</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45206</th>\n",
              "      <td>51</td>\n",
              "      <td>technician</td>\n",
              "      <td>married</td>\n",
              "      <td>tertiary</td>\n",
              "      <td>no</td>\n",
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              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>cellular</td>\n",
              "      <td>17</td>\n",
              "      <td>nov</td>\n",
              "      <td>977</td>\n",
              "      <td>3</td>\n",
              "      <td>-1</td>\n",
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              "      <td>unknown</td>\n",
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              "      <td>cellular</td>\n",
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              "      <td>456</td>\n",
              "      <td>2</td>\n",
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              "      <td>yes</td>\n",
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              "      <th>45208</th>\n",
              "      <td>72</td>\n",
              "      <td>retired</td>\n",
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              "      <td>5715</td>\n",
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              "      <td>yes</td>\n",
              "    </tr>\n",
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              "      <th>45209</th>\n",
              "      <td>57</td>\n",
              "      <td>blue-collar</td>\n",
              "      <td>married</td>\n",
              "      <td>secondary</td>\n",
              "      <td>no</td>\n",
              "      <td>668</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>telephone</td>\n",
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              "      <td>nov</td>\n",
              "      <td>508</td>\n",
              "      <td>4</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>45210</th>\n",
              "      <td>37</td>\n",
              "      <td>entrepreneur</td>\n",
              "      <td>married</td>\n",
              "      <td>secondary</td>\n",
              "      <td>no</td>\n",
              "      <td>2971</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>cellular</td>\n",
              "      <td>17</td>\n",
              "      <td>nov</td>\n",
              "      <td>361</td>\n",
              "      <td>2</td>\n",
              "      <td>188</td>\n",
              "      <td>11</td>\n",
              "      <td>other</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>45211 rows × 17 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       age           job   marital  education  ... pdays  previous poutcome    y\n",
              "0       58    management   married   tertiary  ...    -1         0  unknown   no\n",
              "1       44    technician    single  secondary  ...    -1         0  unknown   no\n",
              "2       33  entrepreneur   married  secondary  ...    -1         0  unknown   no\n",
              "3       47   blue-collar   married    unknown  ...    -1         0  unknown   no\n",
              "4       33       unknown    single    unknown  ...    -1         0  unknown   no\n",
              "...    ...           ...       ...        ...  ...   ...       ...      ...  ...\n",
              "45206   51    technician   married   tertiary  ...    -1         0  unknown  yes\n",
              "45207   71       retired  divorced    primary  ...    -1         0  unknown  yes\n",
              "45208   72       retired   married  secondary  ...   184         3  success  yes\n",
              "45209   57   blue-collar   married  secondary  ...    -1         0  unknown   no\n",
              "45210   37  entrepreneur   married  secondary  ...   188        11    other   no\n",
              "\n",
              "[45211 rows x 17 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ca633c2a-b004-45a0-c17a-ac84f47d183e"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(45211, 17)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "rr9w1I78JHG5",
        "outputId": "f9c57dfa-b5ae-4043-d2c3-0a8ce337e7a2"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <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>45211.000000</td>\n",
              "      <td>45211.000000</td>\n",
              "      <td>45211.000000</td>\n",
              "      <td>45211.000000</td>\n",
              "      <td>45211.000000</td>\n",
              "      <td>45211.000000</td>\n",
              "      <td>45211.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>40.936210</td>\n",
              "      <td>1362.272058</td>\n",
              "      <td>15.806419</td>\n",
              "      <td>258.163080</td>\n",
              "      <td>2.763841</td>\n",
              "      <td>40.197828</td>\n",
              "      <td>0.580323</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>10.618762</td>\n",
              "      <td>3044.765829</td>\n",
              "      <td>8.322476</td>\n",
              "      <td>257.527812</td>\n",
              "      <td>3.098021</td>\n",
              "      <td>100.128746</td>\n",
              "      <td>2.303441</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>18.000000</td>\n",
              "      <td>-8019.000000</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>33.000000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>103.000000</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>448.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>180.000000</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>48.000000</td>\n",
              "      <td>1428.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>319.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>95.000000</td>\n",
              "      <td>102127.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>4918.000000</td>\n",
              "      <td>63.000000</td>\n",
              "      <td>871.000000</td>\n",
              "      <td>275.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                age        balance  ...         pdays      previous\n",
              "count  45211.000000   45211.000000  ...  45211.000000  45211.000000\n",
              "mean      40.936210    1362.272058  ...     40.197828      0.580323\n",
              "std       10.618762    3044.765829  ...    100.128746      2.303441\n",
              "min       18.000000   -8019.000000  ...     -1.000000      0.000000\n",
              "25%       33.000000      72.000000  ...     -1.000000      0.000000\n",
              "50%       39.000000     448.000000  ...     -1.000000      0.000000\n",
              "75%       48.000000    1428.000000  ...     -1.000000      0.000000\n",
              "max       95.000000  102127.000000  ...    871.000000    275.000000\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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        },
        "id": "1yBo1MlNJHik",
        "outputId": "d1d6f5fc-b322-4f87-a061-77fe071b6f84"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 45211 entries, 0 to 45210\n",
            "Data columns (total 17 columns):\n",
            " #   Column     Non-Null Count  Dtype \n",
            "---  ------     --------------  ----- \n",
            " 0   age        45211 non-null  int64 \n",
            " 1   job        45211 non-null  object\n",
            " 2   marital    45211 non-null  object\n",
            " 3   education  45211 non-null  object\n",
            " 4   default    45211 non-null  object\n",
            " 5   balance    45211 non-null  int64 \n",
            " 6   housing    45211 non-null  object\n",
            " 7   loan       45211 non-null  object\n",
            " 8   contact    45211 non-null  object\n",
            " 9   day        45211 non-null  int64 \n",
            " 10  month      45211 non-null  object\n",
            " 11  duration   45211 non-null  int64 \n",
            " 12  campaign   45211 non-null  int64 \n",
            " 13  pdays      45211 non-null  int64 \n",
            " 14  previous   45211 non-null  int64 \n",
            " 15  poutcome   45211 non-null  object\n",
            " 16  y          45211 non-null  object\n",
            "dtypes: int64(7), object(10)\n",
            "memory usage: 5.9+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BnLOnndgDSij"
      },
      "source": [
        "df.rename(columns={'y':'output'}, inplace=True)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TyzymqmtJUxo"
      },
      "source": [
        "We can see that all 16 columns have `45211` non-null values that's also total row no. So there is no need to check null values or imputation.\n",
        "\n",
        "## Data Analysis\n",
        "\n",
        "### Checking Object Columns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eTSNKZ2iJQMj"
      },
      "source": [
        "obj_cols = list(df.dtypes[df.dtypes==np.object].keys())"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4U5DKrLkJZKc",
        "outputId": "c37fea3f-52c6-413b-d75f-ddefe3aa6cea"
      },
      "source": [
        "for i in obj_cols:\n",
        "    print('----------', i, '----------\\n', df[i].value_counts(),'\\n-----------------------------', sep='')"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "----------job----------\n",
            "blue-collar      9732\n",
            "management       9458\n",
            "technician       7597\n",
            "admin.           5171\n",
            "services         4154\n",
            "retired          2264\n",
            "self-employed    1579\n",
            "entrepreneur     1487\n",
            "unemployed       1303\n",
            "housemaid        1240\n",
            "student           938\n",
            "unknown           288\n",
            "Name: job, dtype: int64\n",
            "-----------------------------\n",
            "----------marital----------\n",
            "married     27214\n",
            "single      12790\n",
            "divorced     5207\n",
            "Name: marital, dtype: int64\n",
            "-----------------------------\n",
            "----------education----------\n",
            "secondary    23202\n",
            "tertiary     13301\n",
            "primary       6851\n",
            "unknown       1857\n",
            "Name: education, dtype: int64\n",
            "-----------------------------\n",
            "----------default----------\n",
            "no     44396\n",
            "yes      815\n",
            "Name: default, dtype: int64\n",
            "-----------------------------\n",
            "----------housing----------\n",
            "yes    25130\n",
            "no     20081\n",
            "Name: housing, dtype: int64\n",
            "-----------------------------\n",
            "----------loan----------\n",
            "no     37967\n",
            "yes     7244\n",
            "Name: loan, dtype: int64\n",
            "-----------------------------\n",
            "----------contact----------\n",
            "cellular     29285\n",
            "unknown      13020\n",
            "telephone     2906\n",
            "Name: contact, dtype: int64\n",
            "-----------------------------\n",
            "----------month----------\n",
            "may    13766\n",
            "jul     6895\n",
            "aug     6247\n",
            "jun     5341\n",
            "nov     3970\n",
            "apr     2932\n",
            "feb     2649\n",
            "jan     1403\n",
            "oct      738\n",
            "sep      579\n",
            "mar      477\n",
            "dec      214\n",
            "Name: month, dtype: int64\n",
            "-----------------------------\n",
            "----------poutcome----------\n",
            "unknown    36959\n",
            "failure     4901\n",
            "other       1840\n",
            "success     1511\n",
            "Name: poutcome, dtype: int64\n",
            "-----------------------------\n",
            "----------output----------\n",
            "no     39922\n",
            "yes     5289\n",
            "Name: output, dtype: int64\n",
            "-----------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q9BCckSEJdMY"
      },
      "source": [
        "- We can see the column `output` i.e our output column as 2 levels yes & no.\n",
        "- `loan`, `housing` & `default` column has also 2 values yes & no.\n",
        "- `job` column has `288` unknown value. `education` column has `1857` unknow value. These can be imputed.\n",
        "- `poutcome` has `36959` unknown value, that's fairly large no. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tRkYWYrzM0Gz"
      },
      "source": [
        "df=df.drop(['contact','day','month','poutcome'],axis=1)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0KOjYrQWCPdr"
      },
      "source": [
        "## Creating Dummy Variables:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "on2bUTfbCExz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5c56bed0-3cea-400c-8da7-00e6d3b9e111"
      },
      "source": [
        "obj_cols"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['job',\n",
              " 'marital',\n",
              " 'education',\n",
              " 'default',\n",
              " 'housing',\n",
              " 'loan',\n",
              " 'contact',\n",
              " 'month',\n",
              " 'poutcome',\n",
              " 'output']"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-deYGnWuCEvt"
      },
      "source": [
        "#variable to map\n",
        "\n",
        "var =  ['default','housing','loan','output']\n",
        "\n",
        "# Defining the map function\n",
        "def binary_map(x):\n",
        "    return x.map({'yes': 1, \"no\": 0})\n",
        "\n",
        "# Applying the function \n",
        "df[var] = df[var].apply(binary_map)\n"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cUBXnQc9HxKO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "3b4aa0bf-aa05-4bc4-d0be-03f0063bfe54"
      },
      "source": [
        "df[var]"
      ],
      "execution_count": 14,
      "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>default</th>\n",
              "      <th>housing</th>\n",
              "      <th>loan</th>\n",
              "      <th>output</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45206</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45207</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45208</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45209</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45210</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>45211 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       default  housing  loan  output\n",
              "0            0        1     0       0\n",
              "1            0        1     0       0\n",
              "2            0        1     1       0\n",
              "3            0        1     0       0\n",
              "4            0        0     0       0\n",
              "...        ...      ...   ...     ...\n",
              "45206        0        0     0       1\n",
              "45207        0        0     0       1\n",
              "45208        0        0     0       1\n",
              "45209        0        0     0       0\n",
              "45210        0        0     0       0\n",
              "\n",
              "[45211 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xSQ1EATHCEtH"
      },
      "source": [
        "# Creating a dummy variable for some of the categorical variables and dropping the first one.\n",
        "dummy = pd.get_dummies(df[['job', 'marital', 'education']], drop_first=True)\n",
        "\n",
        "# Adding the results to the master dataframe\n",
        "df = pd.concat([df, dummy], axis=1)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PgMEd0xTCEqe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "outputId": "cc2b0729-fbdf-4672-9e9d-df6b4ab609f3"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 16,
      "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>default</th>\n",
              "      <th>balance</th>\n",
              "      <th>housing</th>\n",
              "      <th>loan</th>\n",
              "      <th>duration</th>\n",
              "      <th>campaign</th>\n",
              "      <th>pdays</th>\n",
              "      <th>previous</th>\n",
              "      <th>output</th>\n",
              "      <th>job_blue-collar</th>\n",
              "      <th>job_entrepreneur</th>\n",
              "      <th>job_housemaid</th>\n",
              "      <th>job_management</th>\n",
              "      <th>job_retired</th>\n",
              "      <th>job_self-employed</th>\n",
              "      <th>job_services</th>\n",
              "      <th>job_student</th>\n",
              "      <th>job_technician</th>\n",
              "      <th>job_unemployed</th>\n",
              "      <th>job_unknown</th>\n",
              "      <th>marital_married</th>\n",
              "      <th>marital_single</th>\n",
              "      <th>education_secondary</th>\n",
              "      <th>education_tertiary</th>\n",
              "      <th>education_unknown</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>58</td>\n",
              "      <td>management</td>\n",
              "      <td>married</td>\n",
              "      <td>tertiary</td>\n",
              "      <td>0</td>\n",
              "      <td>2143</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>261</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</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>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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>44</td>\n",
              "      <td>technician</td>\n",
              "      <td>single</td>\n",
              "      <td>secondary</td>\n",
              "      <td>0</td>\n",
              "      <td>29</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>151</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</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>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>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>33</td>\n",
              "      <td>entrepreneur</td>\n",
              "      <td>married</td>\n",
              "      <td>secondary</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>76</td>\n",
              "      <td>1</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>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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>47</td>\n",
              "      <td>blue-collar</td>\n",
              "      <td>married</td>\n",
              "      <td>unknown</td>\n",
              "      <td>0</td>\n",
              "      <td>1506</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>92</td>\n",
              "      <td>1</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>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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>33</td>\n",
              "      <td>unknown</td>\n",
              "      <td>single</td>\n",
              "      <td>unknown</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>198</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</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>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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age           job  ... education_tertiary education_unknown\n",
              "0   58    management  ...                  1                 0\n",
              "1   44    technician  ...                  0                 0\n",
              "2   33  entrepreneur  ...                  0                 0\n",
              "3   47   blue-collar  ...                  0                 1\n",
              "4   33       unknown  ...                  0                 1\n",
              "\n",
              "[5 rows x 29 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rhe4UXL2GaGK"
      },
      "source": [
        "- We can no drop those categorical columns since we already have created dummy variables for them."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v7L3WWRKCEmT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "outputId": "d5acef8f-a0ff-419f-ecef-9d2dcfa0e229"
      },
      "source": [
        "df.drop(['job', 'marital', 'education'], axis=1, inplace=True)\n",
        "df.head()"
      ],
      "execution_count": 17,
      "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>default</th>\n",
              "      <th>balance</th>\n",
              "      <th>housing</th>\n",
              "      <th>loan</th>\n",
              "      <th>duration</th>\n",
              "      <th>campaign</th>\n",
              "      <th>pdays</th>\n",
              "      <th>previous</th>\n",
              "      <th>output</th>\n",
              "      <th>job_blue-collar</th>\n",
              "      <th>job_entrepreneur</th>\n",
              "      <th>job_housemaid</th>\n",
              "      <th>job_management</th>\n",
              "      <th>job_retired</th>\n",
              "      <th>job_self-employed</th>\n",
              "      <th>job_services</th>\n",
              "      <th>job_student</th>\n",
              "      <th>job_technician</th>\n",
              "      <th>job_unemployed</th>\n",
              "      <th>job_unknown</th>\n",
              "      <th>marital_married</th>\n",
              "      <th>marital_single</th>\n",
              "      <th>education_secondary</th>\n",
              "      <th>education_tertiary</th>\n",
              "      <th>education_unknown</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>58</td>\n",
              "      <td>0</td>\n",
              "      <td>2143</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>261</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</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>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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>44</td>\n",
              "      <td>0</td>\n",
              "      <td>29</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>151</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</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>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>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>33</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>76</td>\n",
              "      <td>1</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>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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>47</td>\n",
              "      <td>0</td>\n",
              "      <td>1506</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>92</td>\n",
              "      <td>1</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>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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>33</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>198</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</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>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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age  default  ...  education_tertiary  education_unknown\n",
              "0   58        0  ...                   1                  0\n",
              "1   44        0  ...                   0                  0\n",
              "2   33        0  ...                   0                  0\n",
              "3   47        0  ...                   0                  1\n",
              "4   33        0  ...                   0                  1\n",
              "\n",
              "[5 rows x 26 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "51WqaqUCImQF"
      },
      "source": [
        "X = df.drop(['output'], axis=1)\n",
        "y = df['output']"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8N2BaK_cHGtq"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Specifying seed, so we always get same rows for train and test dataset respectively\n",
        "np.random.seed(0)\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size = 0.7, random_state = 100, stratify=y)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VoSKaesDJn7K",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "906a3297-d014-4917-9486-fbb0e248f4d8"
      },
      "source": [
        "y_train.value_counts(normalize=True)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.883022\n",
              "1    0.116978\n",
              "Name: output, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WnPw-t6SJr1H",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "96ec05ad-1678-4cc9-85df-0215de73fe7d"
      },
      "source": [
        "y_test.value_counts(normalize=True)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.882999\n",
              "1    0.117001\n",
              "Name: output, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g9anE2rJSwuC",
        "outputId": "e981afd1-0890-40bd-d054-74bbc37cc0fb"
      },
      "source": [
        "num_cols=X_train.select_dtypes(include=['float64', 'int64']).columns.values\n",
        "for i in num_cols:\n",
        "    print('----------', i, '----------\\n', df[i].value_counts(),'\\n-----------------------------', sep='')"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "----------age----------\n",
            "32    2085\n",
            "31    1996\n",
            "33    1972\n",
            "34    1930\n",
            "35    1894\n",
            "      ... \n",
            "90       2\n",
            "92       2\n",
            "93       2\n",
            "95       2\n",
            "94       1\n",
            "Name: age, Length: 77, dtype: int64\n",
            "-----------------------------\n",
            "----------default----------\n",
            "0    44396\n",
            "1      815\n",
            "Name: default, dtype: int64\n",
            "-----------------------------\n",
            "----------balance----------\n",
            "0        3514\n",
            "1         195\n",
            "2         156\n",
            "4         139\n",
            "3         134\n",
            "         ... \n",
            "4305        1\n",
            "6352        1\n",
            "18881       1\n",
            "14889       1\n",
            "7218        1\n",
            "Name: balance, Length: 7168, dtype: int64\n",
            "-----------------------------\n",
            "----------housing----------\n",
            "1    25130\n",
            "0    20081\n",
            "Name: housing, dtype: int64\n",
            "-----------------------------\n",
            "----------loan----------\n",
            "0    37967\n",
            "1     7244\n",
            "Name: loan, dtype: int64\n",
            "-----------------------------\n",
            "----------duration----------\n",
            "124     188\n",
            "90      184\n",
            "89      177\n",
            "122     175\n",
            "104     175\n",
            "       ... \n",
            "2150      1\n",
            "1970      1\n",
            "1906      1\n",
            "1842      1\n",
            "2015      1\n",
            "Name: duration, Length: 1573, dtype: int64\n",
            "-----------------------------\n",
            "----------campaign----------\n",
            "1     17544\n",
            "2     12505\n",
            "3      5521\n",
            "4      3522\n",
            "5      1764\n",
            "6      1291\n",
            "7       735\n",
            "8       540\n",
            "9       327\n",
            "10      266\n",
            "11      201\n",
            "12      155\n",
            "13      133\n",
            "14       93\n",
            "15       84\n",
            "16       79\n",
            "17       69\n",
            "18       51\n",
            "19       44\n",
            "20       43\n",
            "21       35\n",
            "22       23\n",
            "23       22\n",
            "25       22\n",
            "24       20\n",
            "28       16\n",
            "29       16\n",
            "26       13\n",
            "31       12\n",
            "27       10\n",
            "32        9\n",
            "30        8\n",
            "33        6\n",
            "34        5\n",
            "36        4\n",
            "35        4\n",
            "43        3\n",
            "38        3\n",
            "41        2\n",
            "50        2\n",
            "37        2\n",
            "51        1\n",
            "55        1\n",
            "46        1\n",
            "58        1\n",
            "44        1\n",
            "39        1\n",
            "63        1\n",
            "Name: campaign, dtype: int64\n",
            "-----------------------------\n",
            "----------pdays----------\n",
            "-1      36954\n",
            " 182      167\n",
            " 92       147\n",
            " 183      126\n",
            " 91       126\n",
            "        ...  \n",
            " 749        1\n",
            " 717        1\n",
            " 589        1\n",
            " 493        1\n",
            " 32         1\n",
            "Name: pdays, Length: 559, dtype: int64\n",
            "-----------------------------\n",
            "----------previous----------\n",
            "0      36954\n",
            "1       2772\n",
            "2       2106\n",
            "3       1142\n",
            "4        714\n",
            "5        459\n",
            "6        277\n",
            "7        205\n",
            "8        129\n",
            "9         92\n",
            "10        67\n",
            "11        65\n",
            "12        44\n",
            "13        38\n",
            "15        20\n",
            "14        19\n",
            "17        15\n",
            "16        13\n",
            "19        11\n",
            "23         8\n",
            "20         8\n",
            "22         6\n",
            "18         6\n",
            "24         5\n",
            "27         5\n",
            "29         4\n",
            "25         4\n",
            "21         4\n",
            "30         3\n",
            "28         2\n",
            "26         2\n",
            "37         2\n",
            "38         2\n",
            "55         1\n",
            "40         1\n",
            "35         1\n",
            "58         1\n",
            "51         1\n",
            "41         1\n",
            "32         1\n",
            "275        1\n",
            "Name: previous, dtype: int64\n",
            "-----------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QQWu0lNvTPzO"
      },
      "source": [
        "num_cols = list(num_cols)\n",
        "num_cols.remove('loan')\n",
        "num_cols.remove('housing')\n",
        "num_cols.remove('default')"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "e5dxB63uKQd1",
        "outputId": "aac63f61-5ca9-47b0-ecb7-0bff10d72824"
      },
      "source": [
        "# Initializing object\n",
        "scaler = StandardScaler()\n",
        "\n",
        "#scaling numeric columns\n",
        "X_train[num_cols] = scaler.fit_transform(X_train[num_cols])\n",
        "X_train"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>default</th>\n",
              "      <th>balance</th>\n",
              "      <th>housing</th>\n",
              "      <th>loan</th>\n",
              "      <th>duration</th>\n",
              "      <th>campaign</th>\n",
              "      <th>pdays</th>\n",
              "      <th>previous</th>\n",
              "      <th>job_blue-collar</th>\n",
              "      <th>job_entrepreneur</th>\n",
              "      <th>job_housemaid</th>\n",
              "      <th>job_management</th>\n",
              "      <th>job_retired</th>\n",
              "      <th>job_self-employed</th>\n",
              "      <th>job_services</th>\n",
              "      <th>job_student</th>\n",
              "      <th>job_technician</th>\n",
              "      <th>job_unemployed</th>\n",
              "      <th>job_unknown</th>\n",
              "      <th>marital_married</th>\n",
              "      <th>marital_single</th>\n",
              "      <th>education_secondary</th>\n",
              "      <th>education_tertiary</th>\n",
              "      <th>education_unknown</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>44481</th>\n",
              "      <td>1.047863</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.446361</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.441644</td>\n",
              "      <td>0.078624</td>\n",
              "      <td>0.536601</td>\n",
              "      <td>1.392249</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>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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25167</th>\n",
              "      <td>-0.462281</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.446361</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.265546</td>\n",
              "      <td>-0.243851</td>\n",
              "      <td>-0.410917</td>\n",
              "      <td>-0.238248</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>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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40209</th>\n",
              "      <td>-1.500505</td>\n",
              "      <td>0</td>\n",
              "      <td>1.603911</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.625439</td>\n",
              "      <td>0.078624</td>\n",
              "      <td>-0.410917</td>\n",
              "      <td>-0.238248</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",
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              "      <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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30954</th>\n",
              "      <td>-0.462281</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.408612</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.731023</td>\n",
              "      <td>-0.243851</td>\n",
              "      <td>-0.410917</td>\n",
              "      <td>-0.238248</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26429</th>\n",
              "      <td>1.047863</td>\n",
              "      <td>0</td>\n",
              "      <td>0.954954</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-0.836608</td>\n",
              "      <td>-0.566326</td>\n",
              "      <td>-0.410917</td>\n",
              "      <td>-0.238248</td>\n",
              "      <td>1</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>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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22013</th>\n",
              "      <td>1.519784</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.446361</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.023216</td>\n",
              "      <td>0.078624</td>\n",
              "      <td>-0.410917</td>\n",
              "      <td>-0.238248</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>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>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39259</th>\n",
              "      <td>1.425400</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.158811</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.644991</td>\n",
              "      <td>-0.243851</td>\n",
              "      <td>-0.410917</td>\n",
              "      <td>-0.238248</td>\n",
              "      <td>1</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>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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8859</th>\n",
              "      <td>-0.934201</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.329831</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-0.770129</td>\n",
              "      <td>1.046050</td>\n",
              "      <td>-0.410917</td>\n",
              "      <td>-0.238248</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>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>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36047</th>\n",
              "      <td>0.009639</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.517592</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.215327</td>\n",
              "      <td>-0.566326</td>\n",
              "      <td>3.079936</td>\n",
              "      <td>0.984625</td>\n",
              "      <td>1</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>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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43761</th>\n",
              "      <td>0.575943</td>\n",
              "      <td>0</td>\n",
              "      <td>0.068341</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.124890</td>\n",
              "      <td>0.078624</td>\n",
              "      <td>0.506679</td>\n",
              "      <td>3.022746</td>\n",
              "      <td>1</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>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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>31647 rows × 25 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            age  default  ...  education_tertiary  education_unknown\n",
              "44481  1.047863        0  ...                   1                  0\n",
              "25167 -0.462281        0  ...                   0                  0\n",
              "40209 -1.500505        0  ...                   1                  0\n",
              "30954 -0.462281        0  ...                   0                  0\n",
              "26429  1.047863        0  ...                   0                  0\n",
              "...         ...      ...  ...                 ...                ...\n",
              "22013  1.519784        0  ...                   0                  0\n",
              "39259  1.425400        0  ...                   0                  0\n",
              "8859  -0.934201        0  ...                   0                  0\n",
              "36047  0.009639        0  ...                   0                  0\n",
              "43761  0.575943        0  ...                   0                  0\n",
              "\n",
              "[31647 rows x 25 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kyASDl6LKp1H"
      },
      "source": [
        "# Model Building using Stats Model & RFE:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ffhTXbR9KkXb"
      },
      "source": [
        ""
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EIFvK3TWKoz7"
      },
      "source": [
        "# initializing sklearn LogisticRegression object\n",
        "logreg = LogisticRegression()\n",
        "rfe = RFE(logreg, 20)             # running RFE with 20 variables as output\n",
        "rfe = rfe.fit(X_train, y_train)"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cvYfTO8uK3Aa",
        "outputId": "4bb979b1-1648-4f93-e2d7-584f5604fa72"
      },
      "source": [
        "rfe.support_"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([False,  True, False,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True, False,  True,  True,  True,  True,  True,\n",
              "       False,  True, False,  True,  True,  True,  True])"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LjWBnTaNK9Km",
        "outputId": "a3392cca-0061-4075-f74f-28bc1e1ab88f"
      },
      "source": [
        "list(zip(X_train.columns, rfe.support_, rfe.ranking_))"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('age', False, 4),\n",
              " ('default', True, 1),\n",
              " ('balance', False, 5),\n",
              " ('housing', True, 1),\n",
              " ('loan', True, 1),\n",
              " ('duration', True, 1),\n",
              " ('campaign', True, 1),\n",
              " ('pdays', True, 1),\n",
              " ('previous', True, 1),\n",
              " ('job_blue-collar', True, 1),\n",
              " ('job_entrepreneur', True, 1),\n",
              " ('job_housemaid', True, 1),\n",
              " ('job_management', False, 2),\n",
              " ('job_retired', True, 1),\n",
              " ('job_self-employed', True, 1),\n",
              " ('job_services', True, 1),\n",
              " ('job_student', True, 1),\n",
              " ('job_technician', True, 1),\n",
              " ('job_unemployed', False, 3),\n",
              " ('job_unknown', True, 1),\n",
              " ('marital_married', False, 6),\n",
              " ('marital_single', True, 1),\n",
              " ('education_secondary', True, 1),\n",
              " ('education_tertiary', True, 1),\n",
              " ('education_unknown', True, 1)]"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YetNVXOALLQb",
        "outputId": "17950e70-cf4e-442a-9530-8aa3e670d104"
      },
      "source": [
        "#list of RFE supported columns\n",
        "cols = X_train.columns[rfe.support_]\n",
        "cols"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['default', 'housing', 'loan', 'duration', 'campaign', 'pdays',\n",
              "       'previous', 'job_blue-collar', 'job_entrepreneur', 'job_housemaid',\n",
              "       'job_retired', 'job_self-employed', 'job_services', 'job_student',\n",
              "       'job_technician', 'job_unknown', 'marital_single',\n",
              "       'education_secondary', 'education_tertiary', 'education_unknown'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 701
        },
        "id": "rZhfFkNaLP9a",
        "outputId": "a88f1f5d-08b3-4650-db86-d1ebef9a2a8c"
      },
      "source": [
        "#BUILDING MODEL #1\n",
        "X_train_sm = sm.add_constant(X_train[cols])\n",
        "logm1 = sm.GLM(y_train,X_train_sm, family=sm.families.Binomial()).fit()\n",
        "logm1.summary()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>Generalized Linear Model Regression Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>        <td>output</td>      <th>  No. Observations:  </th>  <td> 31647</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>                  <td>GLM</td>       <th>  Df Residuals:      </th>  <td> 31626</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model Family:</th>        <td>Binomial</td>     <th>  Df Model:          </th>  <td>    20</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Link Function:</th>         <td>logit</td>      <th>  Scale:             </th> <td>  1.0000</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>                <td>IRLS</td>       <th>  Log-Likelihood:    </th> <td> -8595.6</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>            <td>Thu, 26 Aug 2021</td> <th>  Deviance:          </th> <td>  17191.</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                <td>11:11:35</td>     <th>  Pearson chi2:      </th> <td>3.59e+08</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>No. Iterations:</th>          <td>7</td>        <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "           <td></td>              <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>const</th>               <td>   -2.1066</td> <td>    0.080</td> <td>  -26.448</td> <td> 0.000</td> <td>   -2.263</td> <td>   -1.950</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>default</th>             <td>   -0.3451</td> <td>    0.191</td> <td>   -1.811</td> <td> 0.070</td> <td>   -0.719</td> <td>    0.028</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>housing</th>             <td>   -1.0863</td> <td>    0.044</td> <td>  -24.559</td> <td> 0.000</td> <td>   -1.173</td> <td>   -1.000</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>loan</th>                <td>   -0.6631</td> <td>    0.068</td> <td>   -9.793</td> <td> 0.000</td> <td>   -0.796</td> <td>   -0.530</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>duration</th>            <td>    1.0004</td> <td>    0.018</td> <td>   54.304</td> <td> 0.000</td> <td>    0.964</td> <td>    1.037</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>campaign</th>            <td>   -0.4220</td> <td>    0.037</td> <td>  -11.353</td> <td> 0.000</td> <td>   -0.495</td> <td>   -0.349</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>pdays</th>               <td>    0.2866</td> <td>    0.020</td> <td>   14.511</td> <td> 0.000</td> <td>    0.248</td> <td>    0.325</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>previous</th>            <td>    0.1907</td> <td>    0.023</td> <td>    8.266</td> <td> 0.000</td> <td>    0.146</td> <td>    0.236</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>job_blue-collar</th>     <td>   -0.4230</td> <td>    0.070</td> <td>   -6.044</td> <td> 0.000</td> <td>   -0.560</td> <td>   -0.286</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>job_entrepreneur</th>    <td>   -0.3944</td> <td>    0.126</td> <td>   -3.124</td> <td> 0.002</td> <td>   -0.642</td> <td>   -0.147</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>job_housemaid</th>       <td>   -0.6732</td> <td>    0.156</td> <td>   -4.327</td> <td> 0.000</td> <td>   -0.978</td> <td>   -0.368</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>job_retired</th>         <td>    0.5586</td> <td>    0.081</td> <td>    6.859</td> <td> 0.000</td> <td>    0.399</td> <td>    0.718</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>job_self-employed</th>   <td>   -0.3097</td> <td>    0.111</td> <td>   -2.780</td> <td> 0.005</td> <td>   -0.528</td> <td>   -0.091</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>job_services</th>        <td>   -0.3013</td> <td>    0.086</td> <td>   -3.512</td> <td> 0.000</td> <td>   -0.470</td> <td>   -0.133</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>job_student</th>         <td>    0.5978</td> <td>    0.108</td> <td>    5.521</td> <td> 0.000</td> <td>    0.386</td> <td>    0.810</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>job_technician</th>      <td>   -0.1439</td> <td>    0.060</td> <td>   -2.379</td> <td> 0.017</td> <td>   -0.263</td> <td>   -0.025</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>job_unknown</th>         <td>   -0.1714</td> <td>    0.245</td> <td>   -0.700</td> <td> 0.484</td> <td>   -0.651</td> <td>    0.308</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>marital_single</th>      <td>    0.2701</td> <td>    0.045</td> <td>    6.006</td> <td> 0.000</td> <td>    0.182</td> <td>    0.358</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>education_secondary</th> <td>    0.2381</td> <td>    0.072</td> <td>    3.307</td> <td> 0.001</td> <td>    0.097</td> <td>    0.379</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>education_tertiary</th>  <td>    0.4611</td> <td>    0.079</td> <td>    5.820</td> <td> 0.000</td> <td>    0.306</td> <td>    0.616</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>education_unknown</th>   <td>    0.2899</td> <td>    0.114</td> <td>    2.532</td> <td> 0.011</td> <td>    0.066</td> <td>    0.514</td>\n",
              "</tr>\n",
              "</table>"
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                 Generalized Linear Model Regression Results                  \n",
              "==============================================================================\n",
              "Dep. Variable:                 output   No. Observations:                31647\n",
              "Model:                            GLM   Df Residuals:                    31626\n",
              "Model Family:                Binomial   Df Model:                           20\n",
              "Link Function:                  logit   Scale:                          1.0000\n",
              "Method:                          IRLS   Log-Likelihood:                -8595.6\n",
              "Date:                Thu, 26 Aug 2021   Deviance:                       17191.\n",
              "Time:                        11:11:35   Pearson chi2:                 3.59e+08\n",
              "No. Iterations:                     7                                         \n",
              "Covariance Type:            nonrobust                                         \n",
              "=======================================================================================\n",
              "                          coef    std err          z      P>|z|      [0.025      0.975]\n",
              "---------------------------------------------------------------------------------------\n",
              "const                  -2.1066      0.080    -26.448      0.000      -2.263      -1.950\n",
              "default                -0.3451      0.191     -1.811      0.070      -0.719       0.028\n",
              "housing                -1.0863      0.044    -24.559      0.000      -1.173      -1.000\n",
              "loan                   -0.6631      0.068     -9.793      0.000      -0.796      -0.530\n",
              "duration                1.0004      0.018     54.304      0.000       0.964       1.037\n",
              "campaign               -0.4220      0.037    -11.353      0.000      -0.495      -0.349\n",
              "pdays                   0.2866      0.020     14.511      0.000       0.248       0.325\n",
              "previous                0.1907      0.023      8.266      0.000       0.146       0.236\n",
              "job_blue-collar        -0.4230      0.070     -6.044      0.000      -0.560      -0.286\n",
              "job_entrepreneur       -0.3944      0.126     -3.124      0.002      -0.642      -0.147\n",
              "job_housemaid          -0.6732      0.156     -4.327      0.000      -0.978      -0.368\n",
              "job_retired             0.5586      0.081      6.859      0.000       0.399       0.718\n",
              "job_self-employed      -0.3097      0.111     -2.780      0.005      -0.528      -0.091\n",
              "job_services           -0.3013      0.086     -3.512      0.000      -0.470      -0.133\n",
              "job_student             0.5978      0.108      5.521      0.000       0.386       0.810\n",
              "job_technician         -0.1439      0.060     -2.379      0.017      -0.263      -0.025\n",
              "job_unknown            -0.1714      0.245     -0.700      0.484      -0.651       0.308\n",
              "marital_single          0.2701      0.045      6.006      0.000       0.182       0.358\n",
              "education_secondary     0.2381      0.072      3.307      0.001       0.097       0.379\n",
              "education_tertiary      0.4611      0.079      5.820      0.000       0.306       0.616\n",
              "education_unknown       0.2899      0.114      2.532      0.011       0.066       0.514\n",
              "=======================================================================================\n",
              "\"\"\""
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 677
        },
        "id": "SwJNuNUIN9Hj",
        "outputId": "5d6cc604-c99d-433e-845c-12398f1a51a7"
      },
      "source": [
        "# Creating a function to print VIF for selected features\n",
        "def print_vif(df, cols):\n",
        "    from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
        "    vif = pd.DataFrame()\n",
        "    vif['Features'] = cols\n",
        "    vif['VIF'] = [variance_inflation_factor(df[cols].values, i) for i in range(df[cols].shape[1])]\n",
        "    vif['VIF'] = round(vif['VIF'], 2)\n",
        "    vif = vif.sort_values(by = \"VIF\", ascending = False)\n",
        "    return vif\n",
        "print_vif(X_train, cols)"
      ],
      "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>Features</th>\n",
              "      <th>VIF</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>education_secondary</td>\n",
              "      <td>2.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>housing</td>\n",
              "      <td>2.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>education_tertiary</td>\n",
              "      <td>1.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>marital_single</td>\n",
              "      <td>1.52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>job_blue-collar</td>\n",
              "      <td>1.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>job_technician</td>\n",
              "      <td>1.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>job_services</td>\n",
              "      <td>1.31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>pdays</td>\n",
              "      <td>1.26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>previous</td>\n",
              "      <td>1.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>loan</td>\n",
              "      <td>1.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>education_unknown</td>\n",
              "      <td>1.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>job_student</td>\n",
              "      <td>1.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>job_entrepreneur</td>\n",
              "      <td>1.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>job_self-employed</td>\n",
              "      <td>1.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>job_retired</td>\n",
              "      <td>1.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>job_unknown</td>\n",
              "      <td>1.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>default</td>\n",
              "      <td>1.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>job_housemaid</td>\n",
              "      <td>1.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>campaign</td>\n",
              "      <td>1.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>duration</td>\n",
              "      <td>1.01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Features   VIF\n",
              "17  education_secondary  2.57\n",
              "1               housing  2.25\n",
              "18   education_tertiary  1.60\n",
              "16       marital_single  1.52\n",
              "7       job_blue-collar  1.51\n",
              "14       job_technician  1.45\n",
              "12         job_services  1.31\n",
              "5                 pdays  1.26\n",
              "6              previous  1.23\n",
              "2                  loan  1.21\n",
              "19    education_unknown  1.14\n",
              "13          job_student  1.12\n",
              "8      job_entrepreneur  1.08\n",
              "11    job_self-employed  1.07\n",
              "10          job_retired  1.06\n",
              "15          job_unknown  1.04\n",
              "0               default  1.03\n",
              "9         job_housemaid  1.02\n",
              "4              campaign  1.02\n",
              "3              duration  1.01"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "18bDo7fBOAvi"
      },
      "source": [
        "#dropping column with high p-value\n",
        "cols = cols.drop('job_unknown',1)"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 679
        },
        "id": "Gvww3P-7OoHC",
        "outputId": "379c397f-c1da-493b-ba56-a73bcba1a181"
      },
      "source": [
        "#BUILDING MODEL #2\n",
        "X_train_sm = sm.add_constant(X_train[cols])\n",
        "logm2 = sm.GLM(y_train,X_train_sm, family = sm.families.Binomial()).fit()\n",
        "logm2.summary()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>Generalized Linear Model Regression Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>        <td>output</td>      <th>  No. Observations:  </th>  <td> 31647</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>                  <td>GLM</td>       <th>  Df Residuals:      </th>  <td> 31627</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model Family:</th>        <td>Binomial</td>     <th>  Df Model:          </th>  <td>    19</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Link Function:</th>         <td>logit</td>      <th>  Scale:             </th> <td>  1.0000</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>                <td>IRLS</td>       <th>  Log-Likelihood:    </th> <td> -8595.9</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>            <td>Thu, 26 Aug 2021</td> <th>  Deviance:          </th> <td>  17192.</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                <td>11:11:36</td>     <th>  Pearson chi2:      </th> <td>3.64e+08</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>No. Iterations:</th>          <td>7</td>        <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "           <td></td>              <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>const</th>               <td>   -2.1128</td> <td>    0.079</td> <td>  -26.692</td> <td> 0.000</td> <td>   -2.268</td> <td>   -1.958</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>default</th>             <td>   -0.3445</td> <td>    0.191</td> <td>   -1.808</td> <td> 0.071</td> <td>   -0.718</td> <td>    0.029</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>housing</th>             <td>   -1.0847</td> <td>    0.044</td> <td>  -24.551</td> <td> 0.000</td> <td>   -1.171</td> <td>   -0.998</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>loan</th>                <td>   -0.6624</td> <td>    0.068</td> <td>   -9.783</td> <td> 0.000</td> <td>   -0.795</td> <td>   -0.530</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>duration</th>            <td>    1.0005</td> <td>    0.018</td> <td>   54.308</td> <td> 0.000</td> <td>    0.964</td> <td>    1.037</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>campaign</th>            <td>   -0.4223</td> <td>    0.037</td> <td>  -11.366</td> <td> 0.000</td> <td>   -0.495</td> <td>   -0.350</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>pdays</th>               <td>    0.2867</td> <td>    0.020</td> <td>   14.512</td> <td> 0.000</td> <td>    0.248</td> <td>    0.325</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>previous</th>            <td>    0.1909</td> <td>    0.023</td> <td>    8.273</td> <td> 0.000</td> <td>    0.146</td> <td>    0.236</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>job_blue-collar</th>     <td>   -0.4185</td> <td>    0.070</td> <td>   -6.003</td> <td> 0.000</td> <td>   -0.555</td> <td>   -0.282</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>job_entrepreneur</th>    <td>   -0.3908</td> <td>    0.126</td> <td>   -3.098</td> <td> 0.002</td> <td>   -0.638</td> <td>   -0.144</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>job_housemaid</th>       <td>   -0.6681</td> <td>    0.155</td> <td>   -4.299</td> <td> 0.000</td> <td>   -0.973</td> <td>   -0.364</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>job_retired</th>         <td>    0.5636</td> <td>    0.081</td> <td>    6.946</td> <td> 0.000</td> <td>    0.405</td> <td>    0.723</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>job_self-employed</th>   <td>   -0.3065</td> <td>    0.111</td> <td>   -2.754</td> <td> 0.006</td> <td>   -0.525</td> <td>   -0.088</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>job_services</th>        <td>   -0.2975</td> <td>    0.086</td> <td>   -3.473</td> <td> 0.001</td> <td>   -0.465</td> <td>   -0.130</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>job_student</th>         <td>    0.6034</td> <td>    0.108</td> <td>    5.588</td> <td> 0.000</td> <td>    0.392</td> <td>    0.815</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>job_technician</th>      <td>   -0.1404</td> <td>    0.060</td> <td>   -2.329</td> <td> 0.020</td> <td>   -0.259</td> <td>   -0.022</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>marital_single</th>      <td>    0.2704</td> <td>    0.045</td> <td>    6.013</td> <td> 0.000</td> <td>    0.182</td> <td>    0.359</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>education_secondary</th> <td>    0.2398</td> <td>    0.072</td> <td>    3.333</td> <td> 0.001</td> <td>    0.099</td> <td>    0.381</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>education_tertiary</th>  <td>    0.4645</td> <td>    0.079</td> <td>    5.876</td> <td> 0.000</td> <td>    0.310</td> <td>    0.619</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>education_unknown</th>   <td>    0.2795</td> <td>    0.114</td> <td>    2.461</td> <td> 0.014</td> <td>    0.057</td> <td>    0.502</td>\n",
              "</tr>\n",
              "</table>"
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                 Generalized Linear Model Regression Results                  \n",
              "==============================================================================\n",
              "Dep. Variable:                 output   No. Observations:                31647\n",
              "Model:                            GLM   Df Residuals:                    31627\n",
              "Model Family:                Binomial   Df Model:                           19\n",
              "Link Function:                  logit   Scale:                          1.0000\n",
              "Method:                          IRLS   Log-Likelihood:                -8595.9\n",
              "Date:                Thu, 26 Aug 2021   Deviance:                       17192.\n",
              "Time:                        11:11:36   Pearson chi2:                 3.64e+08\n",
              "No. Iterations:                     7                                         \n",
              "Covariance Type:            nonrobust                                         \n",
              "=======================================================================================\n",
              "                          coef    std err          z      P>|z|      [0.025      0.975]\n",
              "---------------------------------------------------------------------------------------\n",
              "const                  -2.1128      0.079    -26.692      0.000      -2.268      -1.958\n",
              "default                -0.3445      0.191     -1.808      0.071      -0.718       0.029\n",
              "housing                -1.0847      0.044    -24.551      0.000      -1.171      -0.998\n",
              "loan                   -0.6624      0.068     -9.783      0.000      -0.795      -0.530\n",
              "duration                1.0005      0.018     54.308      0.000       0.964       1.037\n",
              "campaign               -0.4223      0.037    -11.366      0.000      -0.495      -0.350\n",
              "pdays                   0.2867      0.020     14.512      0.000       0.248       0.325\n",
              "previous                0.1909      0.023      8.273      0.000       0.146       0.236\n",
              "job_blue-collar        -0.4185      0.070     -6.003      0.000      -0.555      -0.282\n",
              "job_entrepreneur       -0.3908      0.126     -3.098      0.002      -0.638      -0.144\n",
              "job_housemaid          -0.6681      0.155     -4.299      0.000      -0.973      -0.364\n",
              "job_retired             0.5636      0.081      6.946      0.000       0.405       0.723\n",
              "job_self-employed      -0.3065      0.111     -2.754      0.006      -0.525      -0.088\n",
              "job_services           -0.2975      0.086     -3.473      0.001      -0.465      -0.130\n",
              "job_student             0.6034      0.108      5.588      0.000       0.392       0.815\n",
              "job_technician         -0.1404      0.060     -2.329      0.020      -0.259      -0.022\n",
              "marital_single          0.2704      0.045      6.013      0.000       0.182       0.359\n",
              "education_secondary     0.2398      0.072      3.333      0.001       0.099       0.381\n",
              "education_tertiary      0.4645      0.079      5.876      0.000       0.310       0.619\n",
              "education_unknown       0.2795      0.114      2.461      0.014       0.057       0.502\n",
              "=======================================================================================\n",
              "\"\"\""
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645
        },
        "id": "2lkSaqOROuCh",
        "outputId": "2d673776-3c86-4c36-8459-ee52d9b7714c"
      },
      "source": [
        "# checking VIF value\n",
        "print_vif(X_train, cols)"
      ],
      "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>Features</th>\n",
              "      <th>VIF</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>education_secondary</td>\n",
              "      <td>2.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>housing</td>\n",
              "      <td>2.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>education_tertiary</td>\n",
              "      <td>1.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>marital_single</td>\n",
              "      <td>1.52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>job_blue-collar</td>\n",
              "      <td>1.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>job_technician</td>\n",
              "      <td>1.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>job_services</td>\n",
              "      <td>1.31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>pdays</td>\n",
              "      <td>1.26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>previous</td>\n",
              "      <td>1.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>loan</td>\n",
              "      <td>1.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>job_student</td>\n",
              "      <td>1.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>education_unknown</td>\n",
              "      <td>1.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>job_entrepreneur</td>\n",
              "      <td>1.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>job_self-employed</td>\n",
              "      <td>1.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>job_retired</td>\n",
              "      <td>1.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>default</td>\n",
              "      <td>1.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>campaign</td>\n",
              "      <td>1.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>job_housemaid</td>\n",
              "      <td>1.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>duration</td>\n",
              "      <td>1.01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Features   VIF\n",
              "16  education_secondary  2.55\n",
              "1               housing  2.25\n",
              "17   education_tertiary  1.60\n",
              "15       marital_single  1.52\n",
              "7       job_blue-collar  1.51\n",
              "14       job_technician  1.45\n",
              "12         job_services  1.31\n",
              "5                 pdays  1.26\n",
              "6              previous  1.23\n",
              "2                  loan  1.21\n",
              "13          job_student  1.11\n",
              "18    education_unknown  1.10\n",
              "8      job_entrepreneur  1.08\n",
              "11    job_self-employed  1.07\n",
              "10          job_retired  1.06\n",
              "0               default  1.03\n",
              "4              campaign  1.02\n",
              "9         job_housemaid  1.02\n",
              "3              duration  1.01"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BJn0ihaVO-rK"
      },
      "source": [
        "#dropping column with high p-value\n",
        "cols = cols.drop('default',1)"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 658
        },
        "id": "bE-BFIb5PIJK",
        "outputId": "27080265-0caa-4c3a-c5b8-bec031b52e6c"
      },
      "source": [
        "#BUILDING MODEL #3\n",
        "\n",
        "X_train_sm = sm.add_constant(X_train[cols])\n",
        "logm3 = sm.GLM(y_train,X_train_sm, family = sm.families.Binomial()).fit()\n",
        "logm3.summary()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>Generalized Linear Model Regression Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>        <td>output</td>      <th>  No. Observations:  </th>  <td> 31647</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>                  <td>GLM</td>       <th>  Df Residuals:      </th>  <td> 31628</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model Family:</th>        <td>Binomial</td>     <th>  Df Model:          </th>  <td>    18</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Link Function:</th>         <td>logit</td>      <th>  Scale:             </th> <td>  1.0000</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>                <td>IRLS</td>       <th>  Log-Likelihood:    </th> <td> -8597.7</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>            <td>Thu, 26 Aug 2021</td> <th>  Deviance:          </th> <td>  17195.</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                <td>11:11:38</td>     <th>  Pearson chi2:      </th> <td>3.85e+08</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>No. Iterations:</th>          <td>7</td>        <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "           <td></td>              <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>const</th>               <td>   -2.1157</td> <td>    0.079</td> <td>  -26.741</td> <td> 0.000</td> <td>   -2.271</td> <td>   -1.961</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>housing</th>             <td>   -1.0850</td> <td>    0.044</td> <td>  -24.561</td> <td> 0.000</td> <td>   -1.172</td> <td>   -0.998</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>loan</th>                <td>   -0.6710</td> <td>    0.068</td> <td>   -9.931</td> <td> 0.000</td> <td>   -0.803</td> <td>   -0.539</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>duration</th>            <td>    1.0007</td> <td>    0.018</td> <td>   54.325</td> <td> 0.000</td> <td>    0.965</td> <td>    1.037</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>campaign</th>            <td>   -0.4229</td> <td>    0.037</td> <td>  -11.381</td> <td> 0.000</td> <td>   -0.496</td> <td>   -0.350</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>pdays</th>               <td>    0.2875</td> <td>    0.020</td> <td>   14.560</td> <td> 0.000</td> <td>    0.249</td> <td>    0.326</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>previous</th>            <td>    0.1914</td> <td>    0.023</td> <td>    8.297</td> <td> 0.000</td> <td>    0.146</td> <td>    0.237</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>job_blue-collar</th>     <td>   -0.4198</td> <td>    0.070</td> <td>   -6.023</td> <td> 0.000</td> <td>   -0.556</td> <td>   -0.283</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>job_entrepreneur</th>    <td>   -0.3989</td> <td>    0.126</td> <td>   -3.163</td> <td> 0.002</td> <td>   -0.646</td> <td>   -0.152</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>job_housemaid</th>       <td>   -0.6685</td> <td>    0.155</td> <td>   -4.304</td> <td> 0.000</td> <td>   -0.973</td> <td>   -0.364</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>job_retired</th>         <td>    0.5651</td> <td>    0.081</td> <td>    6.966</td> <td> 0.000</td> <td>    0.406</td> <td>    0.724</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>job_self-employed</th>   <td>   -0.3087</td> <td>    0.111</td> <td>   -2.775</td> <td> 0.006</td> <td>   -0.527</td> <td>   -0.091</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>job_services</th>        <td>   -0.2959</td> <td>    0.086</td> <td>   -3.455</td> <td> 0.001</td> <td>   -0.464</td> <td>   -0.128</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>job_student</th>         <td>    0.6067</td> <td>    0.108</td> <td>    5.620</td> <td> 0.000</td> <td>    0.395</td> <td>    0.818</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>job_technician</th>      <td>   -0.1409</td> <td>    0.060</td> <td>   -2.336</td> <td> 0.019</td> <td>   -0.259</td> <td>   -0.023</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>marital_single</th>      <td>    0.2693</td> <td>    0.045</td> <td>    5.990</td> <td> 0.000</td> <td>    0.181</td> <td>    0.357</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>education_secondary</th> <td>    0.2386</td> <td>    0.072</td> <td>    3.318</td> <td> 0.001</td> <td>    0.098</td> <td>    0.380</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>education_tertiary</th>  <td>    0.4651</td> <td>    0.079</td> <td>    5.885</td> <td> 0.000</td> <td>    0.310</td> <td>    0.620</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>education_unknown</th>   <td>    0.2785</td> <td>    0.114</td> <td>    2.452</td> <td> 0.014</td> <td>    0.056</td> <td>    0.501</td>\n",
              "</tr>\n",
              "</table>"
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                 Generalized Linear Model Regression Results                  \n",
              "==============================================================================\n",
              "Dep. Variable:                 output   No. Observations:                31647\n",
              "Model:                            GLM   Df Residuals:                    31628\n",
              "Model Family:                Binomial   Df Model:                           18\n",
              "Link Function:                  logit   Scale:                          1.0000\n",
              "Method:                          IRLS   Log-Likelihood:                -8597.7\n",
              "Date:                Thu, 26 Aug 2021   Deviance:                       17195.\n",
              "Time:                        11:11:38   Pearson chi2:                 3.85e+08\n",
              "No. Iterations:                     7                                         \n",
              "Covariance Type:            nonrobust                                         \n",
              "=======================================================================================\n",
              "                          coef    std err          z      P>|z|      [0.025      0.975]\n",
              "---------------------------------------------------------------------------------------\n",
              "const                  -2.1157      0.079    -26.741      0.000      -2.271      -1.961\n",
              "housing                -1.0850      0.044    -24.561      0.000      -1.172      -0.998\n",
              "loan                   -0.6710      0.068     -9.931      0.000      -0.803      -0.539\n",
              "duration                1.0007      0.018     54.325      0.000       0.965       1.037\n",
              "campaign               -0.4229      0.037    -11.381      0.000      -0.496      -0.350\n",
              "pdays                   0.2875      0.020     14.560      0.000       0.249       0.326\n",
              "previous                0.1914      0.023      8.297      0.000       0.146       0.237\n",
              "job_blue-collar        -0.4198      0.070     -6.023      0.000      -0.556      -0.283\n",
              "job_entrepreneur       -0.3989      0.126     -3.163      0.002      -0.646      -0.152\n",
              "job_housemaid          -0.6685      0.155     -4.304      0.000      -0.973      -0.364\n",
              "job_retired             0.5651      0.081      6.966      0.000       0.406       0.724\n",
              "job_self-employed      -0.3087      0.111     -2.775      0.006      -0.527      -0.091\n",
              "job_services           -0.2959      0.086     -3.455      0.001      -0.464      -0.128\n",
              "job_student             0.6067      0.108      5.620      0.000       0.395       0.818\n",
              "job_technician         -0.1409      0.060     -2.336      0.019      -0.259      -0.023\n",
              "marital_single          0.2693      0.045      5.990      0.000       0.181       0.357\n",
              "education_secondary     0.2386      0.072      3.318      0.001       0.098       0.380\n",
              "education_tertiary      0.4651      0.079      5.885      0.000       0.310       0.620\n",
              "education_unknown       0.2785      0.114      2.452      0.014       0.056       0.501\n",
              "=======================================================================================\n",
              "\"\"\""
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NnJzt9tcPKyK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 614
        },
        "outputId": "7566f995-d920-4da6-fca9-b88624de0425"
      },
      "source": [
        "# Printing VIF\n",
        "print_vif(X_train, cols)"
      ],
      "execution_count": 36,
      "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>Features</th>\n",
              "      <th>VIF</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>education_secondary</td>\n",
              "      <td>2.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>housing</td>\n",
              "      <td>2.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>education_tertiary</td>\n",
              "      <td>1.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>job_blue-collar</td>\n",
              "      <td>1.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>marital_single</td>\n",
              "      <td>1.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>job_technician</td>\n",
              "      <td>1.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>job_services</td>\n",
              "      <td>1.31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>pdays</td>\n",
              "      <td>1.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>previous</td>\n",
              "      <td>1.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>loan</td>\n",
              "      <td>1.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>job_student</td>\n",
              "      <td>1.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>education_unknown</td>\n",
              "      <td>1.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>job_self-employed</td>\n",
              "      <td>1.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>job_entrepreneur</td>\n",
              "      <td>1.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>job_retired</td>\n",
              "      <td>1.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>job_housemaid</td>\n",
              "      <td>1.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>campaign</td>\n",
              "      <td>1.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>duration</td>\n",
              "      <td>1.01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Features   VIF\n",
              "15  education_secondary  2.55\n",
              "0               housing  2.25\n",
              "16   education_tertiary  1.60\n",
              "6       job_blue-collar  1.51\n",
              "14       marital_single  1.51\n",
              "13       job_technician  1.45\n",
              "11         job_services  1.31\n",
              "4                 pdays  1.25\n",
              "5              previous  1.23\n",
              "1                  loan  1.20\n",
              "12          job_student  1.11\n",
              "17    education_unknown  1.10\n",
              "10    job_self-employed  1.07\n",
              "7      job_entrepreneur  1.07\n",
              "9           job_retired  1.06\n",
              "8         job_housemaid  1.02\n",
              "3              campaign  1.02\n",
              "2              duration  1.01"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x2EhkUgPQlIT"
      },
      "source": [
        "`We have achieved our desired model as it is suggested by the 'smaller' or less than'0.05' p values and vif lower than '5'.`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 692
        },
        "id": "qJ942jAgQoVB",
        "outputId": "cadc3261-7f42-4967-dee9-444114c157c6"
      },
      "source": [
        "# Let's see the correlation matrix \n",
        "plt.figure(figsize = (20,10))        # Size of the figure\n",
        "sns.heatmap(X_train[cols].corr(),annot = True)\n",
        "plt.show()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kfZ-km1uQs1C",
        "outputId": "e4d6c5bd-e4ce-475f-de55-2a9f01960015"
      },
      "source": [
        "# Getting the Predicted values on the train set\n",
        "y_train_pred = logm3.predict(X_train_sm)\n",
        "y_train_pred[:10]"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "44481    0.136347\n",
              "25167    0.487267\n",
              "40209    0.099378\n",
              "30954    0.083180\n",
              "26429    0.008048\n",
              "9926     0.075558\n",
              "5350     0.021337\n",
              "27903    0.140808\n",
              "19437    0.017542\n",
              "21459    0.376723\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6b-nH9j1QzNq"
      },
      "source": [
        "# y_train_pred = y_train_pred.values.reshape(-1)\n",
        "# y_train_pred[:10]"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cEqFfGZB7EHJ"
      },
      "source": [
        "## Creating an table to analyze Actual & Predicted value"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "RahHzJMKRHb7",
        "outputId": "3dc3c907-1b56-4c9e-980a-e1f8f974776f"
      },
      "source": [
        "train_pred_table = pd.DataFrame()\n",
        "train_pred_table['Actual'] = y_train\n",
        "train_pred_table['Predict_Prob'] = y_train_pred\n",
        "train_pred_table.head()"
      ],
      "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>Actual</th>\n",
              "      <th>Predict_Prob</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>44481</th>\n",
              "      <td>0</td>\n",
              "      <td>0.136347</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25167</th>\n",
              "      <td>0</td>\n",
              "      <td>0.487267</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40209</th>\n",
              "      <td>0</td>\n",
              "      <td>0.099378</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30954</th>\n",
              "      <td>0</td>\n",
              "      <td>0.083180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26429</th>\n",
              "      <td>0</td>\n",
              "      <td>0.008048</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Actual  Predict_Prob\n",
              "44481       0      0.136347\n",
              "25167       0      0.487267\n",
              "40209       0      0.099378\n",
              "30954       0      0.083180\n",
              "26429       0      0.008048"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "id": "iRO5lAe3RKHQ",
        "outputId": "68814d5c-b6b0-40de-cd1f-c168d4845099"
      },
      "source": [
        "train_pred_table['Predicted'] = train_pred_table.Predict_Prob.map(lambda x: 1 if x > 0.5 else 0)\n",
        "\n",
        "# Let's see the head\n",
        "train_pred_table.head(15)"
      ],
      "execution_count": 41,
      "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>Actual</th>\n",
              "      <th>Predict_Prob</th>\n",
              "      <th>Predicted</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>44481</th>\n",
              "      <td>0</td>\n",
              "      <td>0.136347</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25167</th>\n",
              "      <td>0</td>\n",
              "      <td>0.487267</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40209</th>\n",
              "      <td>0</td>\n",
              "      <td>0.099378</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30954</th>\n",
              "      <td>0</td>\n",
              "      <td>0.083180</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26429</th>\n",
              "      <td>0</td>\n",
              "      <td>0.008048</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9926</th>\n",
              "      <td>0</td>\n",
              "      <td>0.075558</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5350</th>\n",
              "      <td>0</td>\n",
              "      <td>0.021337</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27903</th>\n",
              "      <td>0</td>\n",
              "      <td>0.140808</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19437</th>\n",
              "      <td>0</td>\n",
              "      <td>0.017542</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21459</th>\n",
              "      <td>0</td>\n",
              "      <td>0.376723</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33194</th>\n",
              "      <td>0</td>\n",
              "      <td>0.083951</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27644</th>\n",
              "      <td>0</td>\n",
              "      <td>0.055809</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12906</th>\n",
              "      <td>0</td>\n",
              "      <td>0.158567</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34419</th>\n",
              "      <td>0</td>\n",
              "      <td>0.176488</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34719</th>\n",
              "      <td>0</td>\n",
              "      <td>0.118229</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Actual  Predict_Prob  Predicted\n",
              "44481       0      0.136347          0\n",
              "25167       0      0.487267          0\n",
              "40209       0      0.099378          0\n",
              "30954       0      0.083180          0\n",
              "26429       0      0.008048          0\n",
              "9926        0      0.075558          0\n",
              "5350        0      0.021337          0\n",
              "27903       0      0.140808          0\n",
              "19437       0      0.017542          0\n",
              "21459       0      0.376723          0\n",
              "33194       0      0.083951          0\n",
              "27644       0      0.055809          0\n",
              "12906       0      0.158567          0\n",
              "34419       0      0.176488          0\n",
              "34719       0      0.118229          0"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6w2sfRlHRg37",
        "outputId": "808a17ac-acce-491a-a4f4-39e6475e55f2"
      },
      "source": [
        "# Confusion matrix \n",
        "confusion = metrics.confusion_matrix(train_pred_table.Actual, train_pred_table.Predicted)\n",
        "print(confusion)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[27412   533]\n",
            " [ 2935   767]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mbdKEIreRrbb",
        "outputId": "c7d4d255-e2b5-4735-aebd-cb9b46b6aa6f"
      },
      "source": [
        "# Let's check the overall accuracy.\n",
        "print(metrics.accuracy_score(train_pred_table.Actual, train_pred_table.Predicted))"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8904161531898758\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pSEZ_8TNRwBR"
      },
      "source": [
        "TP = confusion[1,1] # true positive \n",
        "TN = confusion[0,0] # true negatives\n",
        "FP = confusion[0,1] # false positives\n",
        "FN = confusion[1,0] # false negatives"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xM71jK75Rynz",
        "outputId": "aae38cfa-9183-467d-ebb2-043c1861eccb"
      },
      "source": [
        "# Let's check the sensitivity of our logistic regression model\n",
        "TP / float(TP+FN)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.2071853052404106"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1w4n1-2rSSp4"
      },
      "source": [
        "`It indicates that 20.71% of true positives or here in our case converted values  are predicted correctly by our model.which is very low . we need to modify the cutoff value .`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gCj17V-iR2QC",
        "outputId": "27050f4d-b0bf-4d12-dda3-b1180580144b"
      },
      "source": [
        "# Let us calculate specificity\n",
        "TN / float(TN+FP)"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9809268205403471"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yZPYUPa3SKgT",
        "outputId": "46efce2c-72af-4026-944f-1062485c582f"
      },
      "source": [
        "# Calculate False Postive Rate - predicting conversion when customer does not have convert\n",
        "print(FP/ float(TN+FP))"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.01907317945965289\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mc5slQfBVtCU",
        "outputId": "a0d40ae4-8614-4681-c0d1-56303437d081"
      },
      "source": [
        "# positive predictive value \n",
        "print (TP / float(TP+FP))"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.59\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7q9S_H9gVxdC",
        "outputId": "da996e31-ef23-4afc-bf70-f7f917df48c7"
      },
      "source": [
        "# Negative predictive value\n",
        "print (TN / float(TN+ FN))"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9032853329818433\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZkJeAA_6Vz5q"
      },
      "source": [
        "# Using a function to plot ROC curve\n",
        "def draw_roc( actual, probs ):\n",
        "    fpr, tpr, thresholds = metrics.roc_curve( actual, probs,\n",
        "                                              drop_intermediate = False )\n",
        "    auc_score = metrics.roc_auc_score( actual, probs )\n",
        "    plt.figure(figsize=(5, 5))\n",
        "    plt.plot( fpr, tpr, label='ROC curve (area = %0.2f)' % auc_score )\n",
        "    plt.plot([0, 1], [0, 1], 'k--')\n",
        "    plt.xlim([0.0, 1.0])\n",
        "    plt.ylim([0.0, 1.05])\n",
        "    plt.xlabel('False Positive Rate or [1 - True Negative Rate]')\n",
        "    plt.ylabel('True Positive Rate')\n",
        "    plt.title('Receiver operating characteristic example')\n",
        "    plt.legend(loc=\"lower right\")\n",
        "    plt.show()\n",
        "\n",
        "    return None"
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "-BdTSoGEV2oJ",
        "outputId": "57b7648b-e55f-4aff-b43e-8953b9e87688"
      },
      "source": [
        "fpr, tpr, thresholds = metrics.roc_curve(train_pred_table.Actual, train_pred_table.Predict_Prob, drop_intermediate = False )\n",
        "draw_roc(train_pred_table.Actual, train_pred_table.Predict_Prob)"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "BWJGEmh3WIdS",
        "outputId": "732ea8c7-f19c-4ed0-f046-88794a924490"
      },
      "source": [
        "# Let's create columns with different probability cutoffs \n",
        "numbers = [float(x)/10 for x in range(10)]\n",
        "for i in numbers:\n",
        "    train_pred_table[i]= train_pred_table.Predict_Prob.map(lambda x: 1 if x > i else 0)\n",
        "train_pred_table.head(10)"
      ],
      "execution_count": 52,
      "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>Actual</th>\n",
              "      <th>Predict_Prob</th>\n",
              "      <th>Predicted</th>\n",
              "      <th>0.0</th>\n",
              "      <th>0.1</th>\n",
              "      <th>0.2</th>\n",
              "      <th>0.3</th>\n",
              "      <th>0.4</th>\n",
              "      <th>0.5</th>\n",
              "      <th>0.6</th>\n",
              "      <th>0.7</th>\n",
              "      <th>0.8</th>\n",
              "      <th>0.9</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>44481</th>\n",
              "      <td>0</td>\n",
              "      <td>0.136347</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</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>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25167</th>\n",
              "      <td>0</td>\n",
              "      <td>0.487267</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40209</th>\n",
              "      <td>0</td>\n",
              "      <td>0.099378</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30954</th>\n",
              "      <td>0</td>\n",
              "      <td>0.083180</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26429</th>\n",
              "      <td>0</td>\n",
              "      <td>0.008048</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9926</th>\n",
              "      <td>0</td>\n",
              "      <td>0.075558</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5350</th>\n",
              "      <td>0</td>\n",
              "      <td>0.021337</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27903</th>\n",
              "      <td>0</td>\n",
              "      <td>0.140808</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</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>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19437</th>\n",
              "      <td>0</td>\n",
              "      <td>0.017542</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21459</th>\n",
              "      <td>0</td>\n",
              "      <td>0.376723</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Actual  Predict_Prob  Predicted  0.0  0.1  ...  0.5  0.6  0.7  0.8  0.9\n",
              "44481       0      0.136347          0    1    1  ...    0    0    0    0    0\n",
              "25167       0      0.487267          0    1    1  ...    0    0    0    0    0\n",
              "40209       0      0.099378          0    1    0  ...    0    0    0    0    0\n",
              "30954       0      0.083180          0    1    0  ...    0    0    0    0    0\n",
              "26429       0      0.008048          0    1    0  ...    0    0    0    0    0\n",
              "9926        0      0.075558          0    1    0  ...    0    0    0    0    0\n",
              "5350        0      0.021337          0    1    0  ...    0    0    0    0    0\n",
              "27903       0      0.140808          0    1    1  ...    0    0    0    0    0\n",
              "19437       0      0.017542          0    1    0  ...    0    0    0    0    0\n",
              "21459       0      0.376723          0    1    1  ...    0    0    0    0    0\n",
              "\n",
              "[10 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tQJ2XMgvWSF5",
        "outputId": "30e88eb6-ff5e-4c21-ff41-428bd0806f91"
      },
      "source": [
        "# Now let's calculate accuracy sensitivity and specificity for various probability cutoffs.\n",
        "cutoff_df = pd.DataFrame( columns = ['cutoff','accuracy','sensitivity','specificity'])\n",
        "\n",
        "# TP = confusion[1,1] # true positive \n",
        "# TN = confusion[0,0] # true negatives\n",
        "# FP = confusion[0,1] # false positives\n",
        "# FN = confusion[1,0] # false negatives\n",
        "\n",
        "num = [0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]\n",
        "for i in num:\n",
        "    cm1 = metrics.confusion_matrix(train_pred_table.Actual, train_pred_table[i] )\n",
        "    total1=sum(sum(cm1))\n",
        "    accuracy = (cm1[0,0]+cm1[1,1])/total1\n",
        "    \n",
        "    speci = cm1[0,0]/(cm1[0,0]+cm1[0,1])\n",
        "    sensi = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n",
        "    cutoff_df.loc[i] =[ i ,accuracy,sensi,speci]\n",
        "print(cutoff_df)"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     cutoff  accuracy  sensitivity  specificity\n",
            "0.0     0.0  0.116978     1.000000     0.000000\n",
            "0.1     0.1  0.752204     0.840086     0.740562\n",
            "0.2     0.2  0.864853     0.588061     0.901521\n",
            "0.3     0.3  0.886024     0.410589     0.949007\n",
            "0.4     0.4  0.888963     0.284441     0.969046\n",
            "0.5     0.5  0.890416     0.207185     0.980927\n",
            "0.6     0.6  0.889563     0.151270     0.987368\n",
            "0.7     0.7  0.887920     0.104268     0.991734\n",
            "0.8     0.8  0.886245     0.068341     0.994597\n",
            "0.9     0.9  0.884570     0.035386     0.997066\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "35b-BxwfWg_k",
        "outputId": "0f02175d-1121-460c-d417-fd34627faf7d"
      },
      "source": [
        "# Let's plot accuracy sensitivity and specificity for various probabilities.\n",
        "cutoff_df.plot.line(x='cutoff', y=['accuracy','sensitivity','specificity'])\n",
        "plt.show()"
      ],
      "execution_count": 54,
      "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": 363
        },
        "id": "gIN0ywpoXAGp",
        "outputId": "f5ac7ab2-9502-42f4-f50c-ca9362e3f90c"
      },
      "source": [
        "#### From the curve above, 0.3 is the optimum point to take it as a cutoff probability.\n",
        "cutoff = 0.1\n",
        "train_pred_table['Predicted'] = train_pred_table.Predict_Prob.map( lambda x: 1 if x > cutoff else 0)\n",
        "\n",
        "train_pred_table.head(10)"
      ],
      "execution_count": 55,
      "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>Actual</th>\n",
              "      <th>Predict_Prob</th>\n",
              "      <th>Predicted</th>\n",
              "      <th>0.0</th>\n",
              "      <th>0.1</th>\n",
              "      <th>0.2</th>\n",
              "      <th>0.3</th>\n",
              "      <th>0.4</th>\n",
              "      <th>0.5</th>\n",
              "      <th>0.6</th>\n",
              "      <th>0.7</th>\n",
              "      <th>0.8</th>\n",
              "      <th>0.9</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>44481</th>\n",
              "      <td>0</td>\n",
              "      <td>0.136347</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</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>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25167</th>\n",
              "      <td>0</td>\n",
              "      <td>0.487267</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40209</th>\n",
              "      <td>0</td>\n",
              "      <td>0.099378</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30954</th>\n",
              "      <td>0</td>\n",
              "      <td>0.083180</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26429</th>\n",
              "      <td>0</td>\n",
              "      <td>0.008048</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9926</th>\n",
              "      <td>0</td>\n",
              "      <td>0.075558</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5350</th>\n",
              "      <td>0</td>\n",
              "      <td>0.021337</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27903</th>\n",
              "      <td>0</td>\n",
              "      <td>0.140808</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</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>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19437</th>\n",
              "      <td>0</td>\n",
              "      <td>0.017542</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21459</th>\n",
              "      <td>0</td>\n",
              "      <td>0.376723</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Actual  Predict_Prob  Predicted  0.0  0.1  ...  0.5  0.6  0.7  0.8  0.9\n",
              "44481       0      0.136347          1    1    1  ...    0    0    0    0    0\n",
              "25167       0      0.487267          1    1    1  ...    0    0    0    0    0\n",
              "40209       0      0.099378          0    1    0  ...    0    0    0    0    0\n",
              "30954       0      0.083180          0    1    0  ...    0    0    0    0    0\n",
              "26429       0      0.008048          0    1    0  ...    0    0    0    0    0\n",
              "9926        0      0.075558          0    1    0  ...    0    0    0    0    0\n",
              "5350        0      0.021337          0    1    0  ...    0    0    0    0    0\n",
              "27903       0      0.140808          1    1    1  ...    0    0    0    0    0\n",
              "19437       0      0.017542          0    1    0  ...    0    0    0    0    0\n",
              "21459       0      0.376723          1    1    1  ...    0    0    0    0    0\n",
              "\n",
              "[10 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4xkChfvOXSPK",
        "outputId": "e7bdae64-0bfc-4c2c-d16b-dc701b0e277b"
      },
      "source": [
        "# Let's check the overall accuracy.\n",
        "metrics.accuracy_score(train_pred_table.Actual, train_pred_table.Predicted)"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7522040003791829"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i3XmCI2nXcyr",
        "outputId": "129b00c5-e757-4e2f-8f43-cfc5c30be667"
      },
      "source": [
        "confusion2 = metrics.confusion_matrix(train_pred_table.Actual, train_pred_table.Predicted )\n",
        "confusion2"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[20695,  7250],\n",
              "       [  592,  3110]])"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gl65SBW6XfM5"
      },
      "source": [
        "TP = confusion2[1,1] # true positive\n",
        "TN = confusion2[0,0] # true negatives\n",
        "FP = confusion2[0,1] # false positives\n",
        "FN = confusion2[1,0] # false negatives"
      ],
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o2RN3FzTXmOQ",
        "outputId": "a1d4b172-4769-4b33-b56f-7e0ca1532a4e"
      },
      "source": [
        "# Let's see the sensitivity of our logistic regression model\n",
        "TP / float(TP+FN)"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8400864397622907"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ddPS7w1JXoUa",
        "outputId": "f7f2dbd0-dd5f-425c-9996-21a7de955b59"
      },
      "source": [
        "# Let us calculate specificity\n",
        "TN / float(TN+FP)"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7405618178565039"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QfO7xemNXrd5",
        "outputId": "d1059f13-a302-47d9-acd1-34ed19fe59aa"
      },
      "source": [
        "# Calculate False Postive Rate - predicting conversion when customer does not convert\n",
        "print(FP/ float(TN+FP))"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.2594381821434962\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wjaSrYiSYfkC",
        "outputId": "8b8b80eb-ed9a-43a9-e85e-50cffd21f69f"
      },
      "source": [
        "# Positive predictive value \n",
        "print (TP / float(TP+FP))"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.3001930501930502\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p9I0vglNYiAx",
        "outputId": "4ab0317b-ec62-4fa7-b2fd-e2fb718eabd6"
      },
      "source": [
        "# Negative predictive value\n",
        "print (TN / float(TN+ FN))"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9721895992859492\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6NhmRFGq5l_D"
      },
      "source": [
        "# Making Prediction on Test Data set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "id": "5zeL1Jg55o-v",
        "outputId": "a2187190-dc17-4610-d64f-82040831f5e3"
      },
      "source": [
        "X_test[num_cols] = scaler.transform(X_test[num_cols])\n",
        "X_test = X_test[cols]\n",
        "X_test.head(10)"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "\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>housing</th>\n",
              "      <th>loan</th>\n",
              "      <th>duration</th>\n",
              "      <th>campaign</th>\n",
              "      <th>pdays</th>\n",
              "      <th>previous</th>\n",
              "      <th>job_blue-collar</th>\n",
              "      <th>job_entrepreneur</th>\n",
              "      <th>job_housemaid</th>\n",
              "      <th>job_retired</th>\n",
              "      <th>job_self-employed</th>\n",
              "      <th>job_services</th>\n",
              "      <th>job_student</th>\n",
              "      <th>job_technician</th>\n",
              "      <th>marital_single</th>\n",
              "      <th>education_secondary</th>\n",
              "      <th>education_tertiary</th>\n",
              "      <th>education_unknown</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>-0.566326</td>\n",
              "      <td>-0.410917</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>9180</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2.921418</td>\n",
              "      <td>-0.243851</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6311</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.871803</td>\n",
              "      <td>-0.566326</td>\n",
              "      <td>-0.410917</td>\n",
              "      <td>-0.238248</td>\n",
              "      <td>1</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35131</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.672365</td>\n",
              "      <td>-0.243851</td>\n",
              "      <td>-0.410917</td>\n",
              "      <td>-0.238248</td>\n",
              "      <td>1</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25392</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-0.359522</td>\n",
              "      <td>0.078624</td>\n",
              "      <td>0.556548</td>\n",
              "      <td>0.577001</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       housing  loan  ...  education_tertiary  education_unknown\n",
              "34601        1     0  ...                   0                  0\n",
              "17030        0     0  ...                   0                  0\n",
              "27798        0     0  ...                   1                  0\n",
              "41138        0     0  ...                   0                  0\n",
              "29144        1     0  ...                   1                  0\n",
              "37314        1     0  ...                   0                  0\n",
              "9180         0     0  ...                   0                  0\n",
              "6311         1     0  ...                   0                  0\n",
              "35131        1     0  ...                   0                  0\n",
              "25392        1     1  ...                   0                  0\n",
              "\n",
              "[10 rows x 18 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "os0TikpL6i9w",
        "outputId": "2eaf042a-4fb9-41e8-f6b5-41bba4fa12e1"
      },
      "source": [
        "# Predicting Y Probability\n",
        "X_test_sm = sm.add_constant(X_test)\n",
        "y_test_pred = logm3.predict(X_test_sm)\n",
        "y_test_pred[:10]"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "34601    0.040052\n",
              "17030    0.056812\n",
              "27798    0.178339\n",
              "41138    0.082059\n",
              "29144    0.047245\n",
              "37314    0.140981\n",
              "9180     0.722929\n",
              "6311     0.011924\n",
              "35131    0.012693\n",
              "25392    0.022852\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "V8d494FB63QE",
        "outputId": "fa03d7a7-e182-4219-baaf-3f4165aa7728"
      },
      "source": [
        "test_pred_table = pd.DataFrame()\n",
        "test_pred_table['Actual'] = y_test\n",
        "test_pred_table['Predict_Prob'] = y_test_pred\n",
        "test_pred_table['Predicted'] = test_pred_table['Predict_Prob'].map(lambda x: 1 if x > cutoff else 0)\n",
        "test_pred_table.head()"
      ],
      "execution_count": 66,
      "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>Actual</th>\n",
              "      <th>Predict_Prob</th>\n",
              "      <th>Predicted</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>34601</th>\n",
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              "      <td>1</td>\n",
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              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Actual  Predict_Prob  Predicted\n",
              "34601       0      0.040052          0\n",
              "17030       0      0.056812          0\n",
              "27798       0      0.178339          1\n",
              "41138       0      0.082059          0\n",
              "29144       0      0.047245          0"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KywC8qG38Neh",
        "outputId": "aa104147-792a-49c0-c772-a57ae2cf9bf3"
      },
      "source": [
        "metrics.accuracy_score(test_pred_table['Actual'], test_pred_table['Predicted'])\n",
        "confusion3 = metrics.confusion_matrix(test_pred_table['Actual'], test_pred_table['Predicted'])\n",
        "confusion3"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[8926, 3051],\n",
              "       [ 290, 1297]])"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a25XpNnt8cOJ"
      },
      "source": [
        "TP2 = confusion3[1,1] # true positive \n",
        "TN2 = confusion3[0,0] # true negatives\n",
        "FP2 = confusion3[0,1] # false positives\n",
        "FN2 = confusion3[1,0] # false negatives"
      ],
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mnIhJbHr8j64",
        "outputId": "40f909ad-4d23-41f6-bbf1-1cef2e602a8e"
      },
      "source": [
        "# Let's see the sensitivity/specificity of our logistic regression model\n",
        "print('Sensitivity in test dataset is: ', (TP2 / float(TP2+FN2)))\n",
        "print('Specificity in test dataset is: ', (TN2 / float(TN2+FP2)))\n",
        "print('___________________________________________________')\n",
        "print('Sensitivity in train dataset is: ', (TP / float(TP+FN)))\n",
        "print('Specificity in train dataset is: ', (TN / float(TN+FP)))"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sensitivity in test dataset is:  0.8172652804032766\n",
            "Specificity in test dataset is:  0.7452617516907406\n",
            "___________________________________________________\n",
            "Sensitivity in train dataset is:  0.8400864397622907\n",
            "Specificity in train dataset is:  0.7405618178565039\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DGuzdaOCjHfD"
      },
      "source": [
        "# Let's Export the final Model for Deployment"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DEExgmmNjGQq",
        "outputId": "5b53e7e0-7e38-44ea-ae56-af2070e49c0c"
      },
      "source": [
        "# Exporting Model for implementation\n",
        "from joblib import dump\n",
        "dump(logm3,'model.pkl')\n",
        "dump(scaler, 'scaler.pkl')"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['scaler.pkl']"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    }
  ]
}