solution.ipynb

{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# Banking marketing campaign solution"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Express EDA\n",
                "\n",
                "This solution does not go deep into EDA, but includes the minimum code to process the data before modeling."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "metadata": {},
            "outputs": [
                {
                    "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>housing</th>\n",
                            "      <th>loan</th>\n",
                            "      <th>contact</th>\n",
                            "      <th>month</th>\n",
                            "      <th>day_of_week</th>\n",
                            "      <th>...</th>\n",
                            "      <th>campaign</th>\n",
                            "      <th>pdays</th>\n",
                            "      <th>previous</th>\n",
                            "      <th>poutcome</th>\n",
                            "      <th>emp.var.rate</th>\n",
                            "      <th>cons.price.idx</th>\n",
                            "      <th>cons.conf.idx</th>\n",
                            "      <th>euribor3m</th>\n",
                            "      <th>nr.employed</th>\n",
                            "      <th>y</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>56</td>\n",
                            "      <td>housemaid</td>\n",
                            "      <td>married</td>\n",
                            "      <td>basic.4y</td>\n",
                            "      <td>no</td>\n",
                            "      <td>no</td>\n",
                            "      <td>no</td>\n",
                            "      <td>telephone</td>\n",
                            "      <td>may</td>\n",
                            "      <td>mon</td>\n",
                            "      <td>...</td>\n",
                            "      <td>1</td>\n",
                            "      <td>999</td>\n",
                            "      <td>0</td>\n",
                            "      <td>nonexistent</td>\n",
                            "      <td>1.1</td>\n",
                            "      <td>93.994</td>\n",
                            "      <td>-36.4</td>\n",
                            "      <td>4.857</td>\n",
                            "      <td>5191.0</td>\n",
                            "      <td>no</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>57</td>\n",
                            "      <td>services</td>\n",
                            "      <td>married</td>\n",
                            "      <td>high.school</td>\n",
                            "      <td>unknown</td>\n",
                            "      <td>no</td>\n",
                            "      <td>no</td>\n",
                            "      <td>telephone</td>\n",
                            "      <td>may</td>\n",
                            "      <td>mon</td>\n",
                            "      <td>...</td>\n",
                            "      <td>1</td>\n",
                            "      <td>999</td>\n",
                            "      <td>0</td>\n",
                            "      <td>nonexistent</td>\n",
                            "      <td>1.1</td>\n",
                            "      <td>93.994</td>\n",
                            "      <td>-36.4</td>\n",
                            "      <td>4.857</td>\n",
                            "      <td>5191.0</td>\n",
                            "      <td>no</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>37</td>\n",
                            "      <td>services</td>\n",
                            "      <td>married</td>\n",
                            "      <td>high.school</td>\n",
                            "      <td>no</td>\n",
                            "      <td>yes</td>\n",
                            "      <td>no</td>\n",
                            "      <td>telephone</td>\n",
                            "      <td>may</td>\n",
                            "      <td>mon</td>\n",
                            "      <td>...</td>\n",
                            "      <td>1</td>\n",
                            "      <td>999</td>\n",
                            "      <td>0</td>\n",
                            "      <td>nonexistent</td>\n",
                            "      <td>1.1</td>\n",
                            "      <td>93.994</td>\n",
                            "      <td>-36.4</td>\n",
                            "      <td>4.857</td>\n",
                            "      <td>5191.0</td>\n",
                            "      <td>no</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>3</th>\n",
                            "      <td>40</td>\n",
                            "      <td>admin.</td>\n",
                            "      <td>married</td>\n",
                            "      <td>basic.6y</td>\n",
                            "      <td>no</td>\n",
                            "      <td>no</td>\n",
                            "      <td>no</td>\n",
                            "      <td>telephone</td>\n",
                            "      <td>may</td>\n",
                            "      <td>mon</td>\n",
                            "      <td>...</td>\n",
                            "      <td>1</td>\n",
                            "      <td>999</td>\n",
                            "      <td>0</td>\n",
                            "      <td>nonexistent</td>\n",
                            "      <td>1.1</td>\n",
                            "      <td>93.994</td>\n",
                            "      <td>-36.4</td>\n",
                            "      <td>4.857</td>\n",
                            "      <td>5191.0</td>\n",
                            "      <td>no</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>4</th>\n",
                            "      <td>56</td>\n",
                            "      <td>services</td>\n",
                            "      <td>married</td>\n",
                            "      <td>high.school</td>\n",
                            "      <td>no</td>\n",
                            "      <td>no</td>\n",
                            "      <td>yes</td>\n",
                            "      <td>telephone</td>\n",
                            "      <td>may</td>\n",
                            "      <td>mon</td>\n",
                            "      <td>...</td>\n",
                            "      <td>1</td>\n",
                            "      <td>999</td>\n",
                            "      <td>0</td>\n",
                            "      <td>nonexistent</td>\n",
                            "      <td>1.1</td>\n",
                            "      <td>93.994</td>\n",
                            "      <td>-36.4</td>\n",
                            "      <td>4.857</td>\n",
                            "      <td>5191.0</td>\n",
                            "      <td>no</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "<p>5 rows × 21 columns</p>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "   age        job  marital    education  default housing loan    contact  \\\n",
                            "0   56  housemaid  married     basic.4y       no      no   no  telephone   \n",
                            "1   57   services  married  high.school  unknown      no   no  telephone   \n",
                            "2   37   services  married  high.school       no     yes   no  telephone   \n",
                            "3   40     admin.  married     basic.6y       no      no   no  telephone   \n",
                            "4   56   services  married  high.school       no      no  yes  telephone   \n",
                            "\n",
                            "  month day_of_week  ...  campaign  pdays  previous     poutcome emp.var.rate  \\\n",
                            "0   may         mon  ...         1    999         0  nonexistent          1.1   \n",
                            "1   may         mon  ...         1    999         0  nonexistent          1.1   \n",
                            "2   may         mon  ...         1    999         0  nonexistent          1.1   \n",
                            "3   may         mon  ...         1    999         0  nonexistent          1.1   \n",
                            "4   may         mon  ...         1    999         0  nonexistent          1.1   \n",
                            "\n",
                            "   cons.price.idx  cons.conf.idx  euribor3m  nr.employed   y  \n",
                            "0          93.994          -36.4      4.857       5191.0  no  \n",
                            "1          93.994          -36.4      4.857       5191.0  no  \n",
                            "2          93.994          -36.4      4.857       5191.0  no  \n",
                            "3          93.994          -36.4      4.857       5191.0  no  \n",
                            "4          93.994          -36.4      4.857       5191.0  no  \n",
                            "\n",
                            "[5 rows x 21 columns]"
                        ]
                    },
                    "execution_count": 1,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "import pandas as pd\n",
                "\n",
                "total_data = pd.read_csv(\"https://raw.githubusercontent.com/4GeeksAcademy/logistic-regression-project-tutorial/main/bank-marketing-campaign-data.csv\", sep = \";\")\n",
                "total_data.head()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "metadata": {},
            "outputs": [
                {
                    "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>housing</th>\n",
                            "      <th>loan</th>\n",
                            "      <th>contact</th>\n",
                            "      <th>month</th>\n",
                            "      <th>day_of_week</th>\n",
                            "      <th>...</th>\n",
                            "      <th>campaign</th>\n",
                            "      <th>pdays</th>\n",
                            "      <th>previous</th>\n",
                            "      <th>poutcome</th>\n",
                            "      <th>emp.var.rate</th>\n",
                            "      <th>cons.price.idx</th>\n",
                            "      <th>cons.conf.idx</th>\n",
                            "      <th>euribor3m</th>\n",
                            "      <th>nr.employed</th>\n",
                            "      <th>y</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>56</td>\n",
                            "      <td>housemaid</td>\n",
                            "      <td>married</td>\n",
                            "      <td>basic.4y</td>\n",
                            "      <td>no</td>\n",
                            "      <td>no</td>\n",
                            "      <td>no</td>\n",
                            "      <td>telephone</td>\n",
                            "      <td>may</td>\n",
                            "      <td>mon</td>\n",
                            "      <td>...</td>\n",
                            "      <td>1</td>\n",
                            "      <td>999</td>\n",
                            "      <td>0</td>\n",
                            "      <td>nonexistent</td>\n",
                            "      <td>1.1</td>\n",
                            "      <td>93.994</td>\n",
                            "      <td>-36.4</td>\n",
                            "      <td>4.857</td>\n",
                            "      <td>5191.0</td>\n",
                            "      <td>no</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>57</td>\n",
                            "      <td>services</td>\n",
                            "      <td>married</td>\n",
                            "      <td>high.school</td>\n",
                            "      <td>unknown</td>\n",
                            "      <td>no</td>\n",
                            "      <td>no</td>\n",
                            "      <td>telephone</td>\n",
                            "      <td>may</td>\n",
                            "      <td>mon</td>\n",
                            "      <td>...</td>\n",
                            "      <td>1</td>\n",
                            "      <td>999</td>\n",
                            "      <td>0</td>\n",
                            "      <td>nonexistent</td>\n",
                            "      <td>1.1</td>\n",
                            "      <td>93.994</td>\n",
                            "      <td>-36.4</td>\n",
                            "      <td>4.857</td>\n",
                            "      <td>5191.0</td>\n",
                            "      <td>no</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>37</td>\n",
                            "      <td>services</td>\n",
                            "      <td>married</td>\n",
                            "      <td>high.school</td>\n",
                            "      <td>no</td>\n",
                            "      <td>yes</td>\n",
                            "      <td>no</td>\n",
                            "      <td>telephone</td>\n",
                            "      <td>may</td>\n",
                            "      <td>mon</td>\n",
                            "      <td>...</td>\n",
                            "      <td>1</td>\n",
                            "      <td>999</td>\n",
                            "      <td>0</td>\n",
                            "      <td>nonexistent</td>\n",
                            "      <td>1.1</td>\n",
                            "      <td>93.994</td>\n",
                            "      <td>-36.4</td>\n",
                            "      <td>4.857</td>\n",
                            "      <td>5191.0</td>\n",
                            "      <td>no</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>3</th>\n",
                            "      <td>40</td>\n",
                            "      <td>admin.</td>\n",
                            "      <td>married</td>\n",
                            "      <td>basic.6y</td>\n",
                            "      <td>no</td>\n",
                            "      <td>no</td>\n",
                            "      <td>no</td>\n",
                            "      <td>telephone</td>\n",
                            "      <td>may</td>\n",
                            "      <td>mon</td>\n",
                            "      <td>...</td>\n",
                            "      <td>1</td>\n",
                            "      <td>999</td>\n",
                            "      <td>0</td>\n",
                            "      <td>nonexistent</td>\n",
                            "      <td>1.1</td>\n",
                            "      <td>93.994</td>\n",
                            "      <td>-36.4</td>\n",
                            "      <td>4.857</td>\n",
                            "      <td>5191.0</td>\n",
                            "      <td>no</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>4</th>\n",
                            "      <td>56</td>\n",
                            "      <td>services</td>\n",
                            "      <td>married</td>\n",
                            "      <td>high.school</td>\n",
                            "      <td>no</td>\n",
                            "      <td>no</td>\n",
                            "      <td>yes</td>\n",
                            "      <td>telephone</td>\n",
                            "      <td>may</td>\n",
                            "      <td>mon</td>\n",
                            "      <td>...</td>\n",
                            "      <td>1</td>\n",
                            "      <td>999</td>\n",
                            "      <td>0</td>\n",
                            "      <td>nonexistent</td>\n",
                            "      <td>1.1</td>\n",
                            "      <td>93.994</td>\n",
                            "      <td>-36.4</td>\n",
                            "      <td>4.857</td>\n",
                            "      <td>5191.0</td>\n",
                            "      <td>no</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "<p>5 rows × 21 columns</p>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "   age        job  marital    education  default housing loan    contact  \\\n",
                            "0   56  housemaid  married     basic.4y       no      no   no  telephone   \n",
                            "1   57   services  married  high.school  unknown      no   no  telephone   \n",
                            "2   37   services  married  high.school       no     yes   no  telephone   \n",
                            "3   40     admin.  married     basic.6y       no      no   no  telephone   \n",
                            "4   56   services  married  high.school       no      no  yes  telephone   \n",
                            "\n",
                            "  month day_of_week  ...  campaign  pdays  previous     poutcome emp.var.rate  \\\n",
                            "0   may         mon  ...         1    999         0  nonexistent          1.1   \n",
                            "1   may         mon  ...         1    999         0  nonexistent          1.1   \n",
                            "2   may         mon  ...         1    999         0  nonexistent          1.1   \n",
                            "3   may         mon  ...         1    999         0  nonexistent          1.1   \n",
                            "4   may         mon  ...         1    999         0  nonexistent          1.1   \n",
                            "\n",
                            "   cons.price.idx  cons.conf.idx  euribor3m  nr.employed   y  \n",
                            "0          93.994          -36.4      4.857       5191.0  no  \n",
                            "1          93.994          -36.4      4.857       5191.0  no  \n",
                            "2          93.994          -36.4      4.857       5191.0  no  \n",
                            "3          93.994          -36.4      4.857       5191.0  no  \n",
                            "4          93.994          -36.4      4.857       5191.0  no  \n",
                            "\n",
                            "[5 rows x 21 columns]"
                        ]
                    },
                    "execution_count": 2,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "total_data = total_data.drop_duplicates().reset_index(drop = True)\n",
                "total_data.head()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "age               0\n",
                            "job               0\n",
                            "marital           0\n",
                            "education         0\n",
                            "default           0\n",
                            "housing           0\n",
                            "loan              0\n",
                            "contact           0\n",
                            "month             0\n",
                            "day_of_week       0\n",
                            "duration          0\n",
                            "campaign          0\n",
                            "pdays             0\n",
                            "previous          0\n",
                            "poutcome          0\n",
                            "emp.var.rate      0\n",
                            "cons.price.idx    0\n",
                            "cons.conf.idx     0\n",
                            "euribor3m         0\n",
                            "nr.employed       0\n",
                            "y                 0\n",
                            "dtype: int64"
                        ]
                    },
                    "execution_count": 3,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "total_data.isnull().sum()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "metadata": {},
            "outputs": [
                {
                    "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>job_n</th>\n",
                            "      <th>marital_n</th>\n",
                            "      <th>education_n</th>\n",
                            "      <th>default_n</th>\n",
                            "      <th>housing_n</th>\n",
                            "      <th>loan_n</th>\n",
                            "      <th>contact_n</th>\n",
                            "      <th>month_n</th>\n",
                            "      <th>day_of_week_n</th>\n",
                            "      <th>poutcome_n</th>\n",
                            "      <th>...</th>\n",
                            "      <th>duration</th>\n",
                            "      <th>campaign</th>\n",
                            "      <th>pdays</th>\n",
                            "      <th>previous</th>\n",
                            "      <th>emp.var.rate</th>\n",
                            "      <th>cons.price.idx</th>\n",
                            "      <th>cons.conf.idx</th>\n",
                            "      <th>euribor3m</th>\n",
                            "      <th>nr.employed</th>\n",
                            "      <th>y_n</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>0.000000</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.000000</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>...</td>\n",
                            "      <td>0.053070</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>1.0</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.9375</td>\n",
                            "      <td>0.698753</td>\n",
                            "      <td>0.60251</td>\n",
                            "      <td>0.957379</td>\n",
                            "      <td>0.859735</td>\n",
                            "      <td>0.0</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>0.090909</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.142857</td>\n",
                            "      <td>0.5</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>...</td>\n",
                            "      <td>0.030297</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>1.0</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.9375</td>\n",
                            "      <td>0.698753</td>\n",
                            "      <td>0.60251</td>\n",
                            "      <td>0.957379</td>\n",
                            "      <td>0.859735</td>\n",
                            "      <td>0.0</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>0.090909</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.142857</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.5</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>...</td>\n",
                            "      <td>0.045954</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>1.0</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.9375</td>\n",
                            "      <td>0.698753</td>\n",
                            "      <td>0.60251</td>\n",
                            "      <td>0.957379</td>\n",
                            "      <td>0.859735</td>\n",
                            "      <td>0.0</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>3</th>\n",
                            "      <td>0.181818</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.285714</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>...</td>\n",
                            "      <td>0.030704</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>1.0</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.9375</td>\n",
                            "      <td>0.698753</td>\n",
                            "      <td>0.60251</td>\n",
                            "      <td>0.957379</td>\n",
                            "      <td>0.859735</td>\n",
                            "      <td>0.0</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>4</th>\n",
                            "      <td>0.090909</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.142857</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.5</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>...</td>\n",
                            "      <td>0.062424</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>1.0</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.9375</td>\n",
                            "      <td>0.698753</td>\n",
                            "      <td>0.60251</td>\n",
                            "      <td>0.957379</td>\n",
                            "      <td>0.859735</td>\n",
                            "      <td>0.0</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "<p>5 rows × 21 columns</p>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "      job_n  marital_n  education_n  default_n  housing_n  loan_n  contact_n  \\\n",
                            "0  0.000000        0.0     0.000000        0.0        0.0     0.0        0.0   \n",
                            "1  0.090909        0.0     0.142857        0.5        0.0     0.0        0.0   \n",
                            "2  0.090909        0.0     0.142857        0.0        0.5     0.0        0.0   \n",
                            "3  0.181818        0.0     0.285714        0.0        0.0     0.0        0.0   \n",
                            "4  0.090909        0.0     0.142857        0.0        0.0     0.5        0.0   \n",
                            "\n",
                            "   month_n  day_of_week_n  poutcome_n  ...  duration  campaign  pdays  \\\n",
                            "0      0.0            0.0         0.0  ...  0.053070       0.0    1.0   \n",
                            "1      0.0            0.0         0.0  ...  0.030297       0.0    1.0   \n",
                            "2      0.0            0.0         0.0  ...  0.045954       0.0    1.0   \n",
                            "3      0.0            0.0         0.0  ...  0.030704       0.0    1.0   \n",
                            "4      0.0            0.0         0.0  ...  0.062424       0.0    1.0   \n",
                            "\n",
                            "   previous  emp.var.rate  cons.price.idx  cons.conf.idx  euribor3m  \\\n",
                            "0       0.0        0.9375        0.698753        0.60251   0.957379   \n",
                            "1       0.0        0.9375        0.698753        0.60251   0.957379   \n",
                            "2       0.0        0.9375        0.698753        0.60251   0.957379   \n",
                            "3       0.0        0.9375        0.698753        0.60251   0.957379   \n",
                            "4       0.0        0.9375        0.698753        0.60251   0.957379   \n",
                            "\n",
                            "   nr.employed  y_n  \n",
                            "0     0.859735  0.0  \n",
                            "1     0.859735  0.0  \n",
                            "2     0.859735  0.0  \n",
                            "3     0.859735  0.0  \n",
                            "4     0.859735  0.0  \n",
                            "\n",
                            "[5 rows x 21 columns]"
                        ]
                    },
                    "execution_count": 4,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "# Min-Max scaler\n",
                "\n",
                "from sklearn.preprocessing import MinMaxScaler\n",
                "\n",
                "total_data[\"job_n\"] = pd.factorize(total_data[\"job\"])[0]\n",
                "total_data[\"marital_n\"] = pd.factorize(total_data[\"marital\"])[0]\n",
                "total_data[\"education_n\"] = pd.factorize(total_data[\"education\"])[0]\n",
                "total_data[\"default_n\"] = pd.factorize(total_data[\"default\"])[0]\n",
                "total_data[\"housing_n\"] = pd.factorize(total_data[\"housing\"])[0]\n",
                "total_data[\"loan_n\"] = pd.factorize(total_data[\"loan\"])[0]\n",
                "total_data[\"contact_n\"] = pd.factorize(total_data[\"contact\"])[0]\n",
                "total_data[\"month_n\"] = pd.factorize(total_data[\"month\"])[0]\n",
                "total_data[\"day_of_week_n\"] = pd.factorize(total_data[\"day_of_week\"])[0]\n",
                "total_data[\"poutcome_n\"] = pd.factorize(total_data[\"poutcome\"])[0]\n",
                "total_data[\"y_n\"] = pd.factorize(total_data[\"y\"])[0]\n",
                "num_variables = [\"job_n\", \"marital_n\", \"education_n\", \"default_n\", \"housing_n\", \"loan_n\", \"contact_n\", \"month_n\", \"day_of_week_n\", \"poutcome_n\",\n",
                "                 \"age\", \"duration\", \"campaign\", \"pdays\", \"previous\", \"emp.var.rate\", \"cons.price.idx\", \"cons.conf.idx\", \"euribor3m\", \"nr.employed\", \"y_n\"]\n",
                "\n",
                "scaler = MinMaxScaler()\n",
                "scal_features = scaler.fit_transform(total_data[num_variables])\n",
                "total_data_scal = pd.DataFrame(scal_features, index = total_data.index, columns = num_variables)\n",
                "total_data_scal.head()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "metadata": {},
            "outputs": [
                {
                    "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>poutcome_n</th>\n",
                            "      <th>previous</th>\n",
                            "      <th>emp.var.rate</th>\n",
                            "      <th>euribor3m</th>\n",
                            "      <th>nr.employed</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>1.000000</td>\n",
                            "      <td>0.980730</td>\n",
                            "      <td>1.000000</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.333333</td>\n",
                            "      <td>0.138291</td>\n",
                            "      <td>0.512287</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.937500</td>\n",
                            "      <td>0.956926</td>\n",
                            "      <td>0.859735</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>3</th>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.937500</td>\n",
                            "      <td>0.957379</td>\n",
                            "      <td>0.859735</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>4</th>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.333333</td>\n",
                            "      <td>0.175924</td>\n",
                            "      <td>0.512287</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "   poutcome_n  previous  emp.var.rate  euribor3m  nr.employed\n",
                            "0         0.0       0.0      1.000000   0.980730     1.000000\n",
                            "1         0.0       0.0      0.333333   0.138291     0.512287\n",
                            "2         0.0       0.0      0.937500   0.956926     0.859735\n",
                            "3         0.0       0.0      0.937500   0.957379     0.859735\n",
                            "4         0.0       0.0      0.333333   0.175924     0.512287"
                        ]
                    },
                    "execution_count": 5,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "# Feature selection\n",
                "\n",
                "from sklearn.feature_selection import chi2, SelectKBest\n",
                "from sklearn.model_selection import train_test_split\n",
                "\n",
                "X = total_data_scal.drop(\"y_n\", axis = 1)\n",
                "y = total_data_scal[\"y_n\"]\n",
                "\n",
                "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.1, random_state = 42)\n",
                "\n",
                "selection_model = SelectKBest(chi2, k = 5)\n",
                "selection_model.fit(X_train, y_train)\n",
                "ix = selection_model.get_support()\n",
                "X_train_sel = pd.DataFrame(selection_model.transform(X_train), columns = X_train.columns.values[ix])\n",
                "X_test_sel = pd.DataFrame(selection_model.transform(X_test), columns = X_test.columns.values[ix])\n",
                "\n",
                "X_train_sel.head()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "metadata": {},
            "outputs": [
                {
                    "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>poutcome_n</th>\n",
                            "      <th>previous</th>\n",
                            "      <th>emp.var.rate</th>\n",
                            "      <th>euribor3m</th>\n",
                            "      <th>nr.employed</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.000000</td>\n",
                            "      <td>0.104167</td>\n",
                            "      <td>0.143278</td>\n",
                            "      <td>0.425709</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>0.5</td>\n",
                            "      <td>0.142857</td>\n",
                            "      <td>0.333333</td>\n",
                            "      <td>0.150759</td>\n",
                            "      <td>0.512287</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.000000</td>\n",
                            "      <td>0.479167</td>\n",
                            "      <td>0.073679</td>\n",
                            "      <td>0.000000</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>3</th>\n",
                            "      <td>0.5</td>\n",
                            "      <td>0.142857</td>\n",
                            "      <td>0.333333</td>\n",
                            "      <td>0.150759</td>\n",
                            "      <td>0.512287</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>4</th>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.000000</td>\n",
                            "      <td>1.000000</td>\n",
                            "      <td>0.981410</td>\n",
                            "      <td>1.000000</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "   poutcome_n  previous  emp.var.rate  euribor3m  nr.employed\n",
                            "0         0.0  0.000000      0.104167   0.143278     0.425709\n",
                            "1         0.5  0.142857      0.333333   0.150759     0.512287\n",
                            "2         0.0  0.000000      0.479167   0.073679     0.000000\n",
                            "3         0.5  0.142857      0.333333   0.150759     0.512287\n",
                            "4         0.0  0.000000      1.000000   0.981410     1.000000"
                        ]
                    },
                    "execution_count": 6,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "X_test_sel.head()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "metadata": {},
            "outputs": [],
            "source": [
                "X_train_sel[\"y_n\"] = list(y_train)\n",
                "X_test_sel[\"y_n\"] = list(y_test)\n",
                "X_train_sel.to_csv(\"../data/processed/clean_train.csv\", index = False)\n",
                "X_test_sel.to_csv(\"../data/processed/clean_test.csv\", index = False)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Logistic Regression model"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "metadata": {},
            "outputs": [
                {
                    "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>poutcome_n</th>\n",
                            "      <th>previous</th>\n",
                            "      <th>emp.var.rate</th>\n",
                            "      <th>euribor3m</th>\n",
                            "      <th>nr.employed</th>\n",
                            "      <th>y_n</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>1.000000</td>\n",
                            "      <td>0.980730</td>\n",
                            "      <td>1.000000</td>\n",
                            "      <td>0.0</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.333333</td>\n",
                            "      <td>0.138291</td>\n",
                            "      <td>0.512287</td>\n",
                            "      <td>0.0</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.937500</td>\n",
                            "      <td>0.956926</td>\n",
                            "      <td>0.859735</td>\n",
                            "      <td>0.0</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>3</th>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.937500</td>\n",
                            "      <td>0.957379</td>\n",
                            "      <td>0.859735</td>\n",
                            "      <td>0.0</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>4</th>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>0.333333</td>\n",
                            "      <td>0.175924</td>\n",
                            "      <td>0.512287</td>\n",
                            "      <td>0.0</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "   poutcome_n  previous  emp.var.rate  euribor3m  nr.employed  y_n\n",
                            "0         0.0       0.0      1.000000   0.980730     1.000000  0.0\n",
                            "1         0.0       0.0      0.333333   0.138291     0.512287  0.0\n",
                            "2         0.0       0.0      0.937500   0.956926     0.859735  0.0\n",
                            "3         0.0       0.0      0.937500   0.957379     0.859735  0.0\n",
                            "4         0.0       0.0      0.333333   0.175924     0.512287  0.0"
                        ]
                    },
                    "execution_count": 8,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "train_data = pd.read_csv(\"../data/processed/clean_train.csv\")\n",
                "test_data = pd.read_csv(\"../data/processed/clean_test.csv\")\n",
                "\n",
                "train_data.head()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 9,
            "metadata": {},
            "outputs": [],
            "source": [
                "X_train = train_data.drop([\"y_n\"], axis = 1)\n",
                "y_train = train_data[\"y_n\"]\n",
                "X_test = test_data.drop([\"y_n\"], axis = 1)\n",
                "y_test = test_data[\"y_n\"]"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 10,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": [
                            "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
                        ],
                        "text/plain": [
                            "LogisticRegression()"
                        ]
                    },
                    "execution_count": 10,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "from sklearn.linear_model import LogisticRegression\n",
                "\n",
                "model = LogisticRegression()\n",
                "model.fit(X_train, y_train)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 11,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "array([0., 0., 0., ..., 0., 0., 0.])"
                        ]
                    },
                    "execution_count": 11,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "y_pred = model.predict(X_test)\n",
                "y_pred"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 12,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "0.8921806702282662"
                        ]
                    },
                    "execution_count": 12,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "from sklearn.metrics import accuracy_score\n",
                "\n",
                "accuracy_score(y_test, y_pred)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Model optimization"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 13,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": [
                            "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=LogisticRegression(),\n",
                            "             param_grid={&#x27;C&#x27;: [0.001, 0.01, 0.1, 1, 10, 100, 1000],\n",
                            "                         &#x27;penalty&#x27;: [&#x27;l1&#x27;, &#x27;l2&#x27;, &#x27;elasticnet&#x27;, None],\n",
                            "                         &#x27;solver&#x27;: [&#x27;newton-cg&#x27;, &#x27;lbfgs&#x27;, &#x27;liblinear&#x27;, &#x27;sag&#x27;,\n",
                            "                                    &#x27;saga&#x27;]},\n",
                            "             scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, estimator=LogisticRegression(),\n",
                            "             param_grid={&#x27;C&#x27;: [0.001, 0.01, 0.1, 1, 10, 100, 1000],\n",
                            "                         &#x27;penalty&#x27;: [&#x27;l1&#x27;, &#x27;l2&#x27;, &#x27;elasticnet&#x27;, None],\n",
                            "                         &#x27;solver&#x27;: [&#x27;newton-cg&#x27;, &#x27;lbfgs&#x27;, &#x27;liblinear&#x27;, &#x27;sag&#x27;,\n",
                            "                                    &#x27;saga&#x27;]},\n",
                            "             scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div></div></div></div>"
                        ],
                        "text/plain": [
                            "GridSearchCV(cv=10, estimator=LogisticRegression(),\n",
                            "             param_grid={'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],\n",
                            "                         'penalty': ['l1', 'l2', 'elasticnet', None],\n",
                            "                         'solver': ['newton-cg', 'lbfgs', 'liblinear', 'sag',\n",
                            "                                    'saga']},\n",
                            "             scoring='accuracy')"
                        ]
                    },
                    "execution_count": 13,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "from sklearn.model_selection import GridSearchCV\n",
                "\n",
                "hyperparams = {\n",
                "    \"C\": [0.001, 0.01, 0.1, 1, 10, 100, 1000],\n",
                "    \"penalty\": [\"l1\", \"l2\", \"elasticnet\", None],\n",
                "    \"solver\": [\"newton-cg\", \"lbfgs\", \"liblinear\", \"sag\", \"saga\"]\n",
                "}\n",
                "\n",
                "grid = GridSearchCV(model, hyperparams, scoring = \"accuracy\", cv = 10)\n",
                "grid"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 14,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Best hyperparameters: {'C': 0.1, 'penalty': 'l2', 'solver': 'liblinear'}\n"
                    ]
                }
            ],
            "source": [
                "def warn(*args, **kwargs):\n",
                "    pass\n",
                "import warnings\n",
                "warnings.warn = warn\n",
                "\n",
                "grid.fit(X_train, y_train)\n",
                "\n",
                "print(f\"Best hyperparameters: {grid.best_params_}\")"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 15,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": [
                            "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(C=0.1, solver=&#x27;liblinear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=0.1, solver=&#x27;liblinear&#x27;)</pre></div></div></div></div></div>"
                        ],
                        "text/plain": [
                            "LogisticRegression(C=0.1, solver='liblinear')"
                        ]
                    },
                    "execution_count": 15,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "model = LogisticRegression(C = 0.1, penalty = \"l2\", solver = \"liblinear\")\n",
                "model.fit(X_train, y_train)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 16,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "array([0., 0., 0., ..., 0., 0., 0.])"
                        ]
                    },
                    "execution_count": 16,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "y_pred = model.predict(X_test)\n",
                "y_pred"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 17,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "0.8938805245264692"
                        ]
                    },
                    "execution_count": 17,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "accuracy_score(y_test, y_pred)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 18,
            "metadata": {},
            "outputs": [],
            "source": [
                "from pickle import dump\n",
                "\n",
                "dump(model, open(\"../models/logistic_regression_C-0.1_penalty-l2_solver-liblinear_42.sav\", \"wb\"))"
            ]
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3.8.13 64-bit ('3.8.13')",
            "language": "python",
            "name": "python3"
        },
        "language_info": {
            "codemirror_mode": {
                "name": "ipython",
                "version": 3
            },
            "file_extension": ".py",
            "mimetype": "text/x-python",
            "name": "python",
            "nbconvert_exporter": "python",
            "pygments_lexer": "ipython3",
            "version": "3.11.4"
        },
        "orig_nbformat": 4,
        "vscode": {
            "interpreter": {
                "hash": "110cc1dee26208153f2972f08a2ad52b6a56238dc66d48e87fb757ef2996db56"
            }
        }
    },
    "nbformat": 4,
    "nbformat_minor": 2
}