3. SKLearn_Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "225f6aad",
   "metadata": {},
   "source": [
    "### SKLEARN - Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9226d137",
   "metadata": {},
   "source": [
    ">  The goal is to predict the categorical class labels which are discrete and unordered. The goal is to predict the categorical class labels which are discrete and unordered."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec364275",
   "metadata": {},
   "source": [
    "### Library Importation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0ad10655",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Import the Libraries\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ff639b2",
   "metadata": {},
   "source": [
    "### Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2390f294",
   "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>checking_acc</th>\n",
       "      <th>duration_mnth</th>\n",
       "      <th>credit_hist</th>\n",
       "      <th>purpose</th>\n",
       "      <th>credit_amt</th>\n",
       "      <th>savings_acc</th>\n",
       "      <th>emp_status</th>\n",
       "      <th>installment_rate</th>\n",
       "      <th>pstatus_sex</th>\n",
       "      <th>guarantor</th>\n",
       "      <th>...</th>\n",
       "      <th>property</th>\n",
       "      <th>age</th>\n",
       "      <th>installment_plan</th>\n",
       "      <th>housing</th>\n",
       "      <th>existing_credit</th>\n",
       "      <th>job</th>\n",
       "      <th>dependents</th>\n",
       "      <th>telephone</th>\n",
       "      <th>foreign_worker</th>\n",
       "      <th>credit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Less than 0 DM</td>\n",
       "      <td>6</td>\n",
       "      <td>Critical account</td>\n",
       "      <td>radio/television</td>\n",
       "      <td>1169</td>\n",
       "      <td>none</td>\n",
       "      <td>Greater than 7 years</td>\n",
       "      <td>4</td>\n",
       "      <td>male_single</td>\n",
       "      <td>none</td>\n",
       "      <td>...</td>\n",
       "      <td>real estate</td>\n",
       "      <td>67</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>2</td>\n",
       "      <td>skilled employee</td>\n",
       "      <td>1</td>\n",
       "      <td>registered</td>\n",
       "      <td>yes</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Btw 0 to 199 DM</td>\n",
       "      <td>48</td>\n",
       "      <td>Existing credits paid back duly</td>\n",
       "      <td>radio/television</td>\n",
       "      <td>5951</td>\n",
       "      <td>Less than 100 DM</td>\n",
       "      <td>Btw 1 to 4 years</td>\n",
       "      <td>2</td>\n",
       "      <td>female_divorced/separated/married</td>\n",
       "      <td>none</td>\n",
       "      <td>...</td>\n",
       "      <td>real estate</td>\n",
       "      <td>22</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>1</td>\n",
       "      <td>skilled employee</td>\n",
       "      <td>1</td>\n",
       "      <td>none</td>\n",
       "      <td>yes</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>none</td>\n",
       "      <td>12</td>\n",
       "      <td>Critical account</td>\n",
       "      <td>education</td>\n",
       "      <td>2096</td>\n",
       "      <td>Less than 100 DM</td>\n",
       "      <td>Btw 4 to 7 years</td>\n",
       "      <td>2</td>\n",
       "      <td>male_single</td>\n",
       "      <td>none</td>\n",
       "      <td>...</td>\n",
       "      <td>real estate</td>\n",
       "      <td>49</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>1</td>\n",
       "      <td>unskilled - resident</td>\n",
       "      <td>2</td>\n",
       "      <td>none</td>\n",
       "      <td>yes</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Less than 0 DM</td>\n",
       "      <td>42</td>\n",
       "      <td>Existing credits paid back duly</td>\n",
       "      <td>furniture/equipment</td>\n",
       "      <td>7882</td>\n",
       "      <td>Less than 100 DM</td>\n",
       "      <td>Btw 4 to 7 years</td>\n",
       "      <td>2</td>\n",
       "      <td>male_single</td>\n",
       "      <td>guarantor</td>\n",
       "      <td>...</td>\n",
       "      <td>BSSA or life insurance</td>\n",
       "      <td>45</td>\n",
       "      <td>none</td>\n",
       "      <td>free</td>\n",
       "      <td>1</td>\n",
       "      <td>skilled employee</td>\n",
       "      <td>2</td>\n",
       "      <td>none</td>\n",
       "      <td>yes</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Less than 0 DM</td>\n",
       "      <td>24</td>\n",
       "      <td>Delays in paying off</td>\n",
       "      <td>new car</td>\n",
       "      <td>4870</td>\n",
       "      <td>Less than 100 DM</td>\n",
       "      <td>Btw 1 to 4 years</td>\n",
       "      <td>3</td>\n",
       "      <td>male_single</td>\n",
       "      <td>none</td>\n",
       "      <td>...</td>\n",
       "      <td>none</td>\n",
       "      <td>53</td>\n",
       "      <td>none</td>\n",
       "      <td>free</td>\n",
       "      <td>2</td>\n",
       "      <td>skilled employee</td>\n",
       "      <td>2</td>\n",
       "      <td>none</td>\n",
       "      <td>yes</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      checking_acc  duration_mnth                      credit_hist  \\\n",
       "0   Less than 0 DM              6                 Critical account   \n",
       "1  Btw 0 to 199 DM             48  Existing credits paid back duly   \n",
       "2             none             12                 Critical account   \n",
       "3   Less than 0 DM             42  Existing credits paid back duly   \n",
       "4   Less than 0 DM             24             Delays in paying off   \n",
       "\n",
       "               purpose  credit_amt       savings_acc            emp_status  \\\n",
       "0     radio/television        1169              none  Greater than 7 years   \n",
       "1     radio/television        5951  Less than 100 DM      Btw 1 to 4 years   \n",
       "2            education        2096  Less than 100 DM      Btw 4 to 7 years   \n",
       "3  furniture/equipment        7882  Less than 100 DM      Btw 4 to 7 years   \n",
       "4              new car        4870  Less than 100 DM      Btw 1 to 4 years   \n",
       "\n",
       "   installment_rate                        pstatus_sex  guarantor  ...  \\\n",
       "0                 4                        male_single       none  ...   \n",
       "1                 2  female_divorced/separated/married       none  ...   \n",
       "2                 2                        male_single       none  ...   \n",
       "3                 2                        male_single  guarantor  ...   \n",
       "4                 3                        male_single       none  ...   \n",
       "\n",
       "                 property age  installment_plan housing existing_credit  \\\n",
       "0             real estate  67              none     own               2   \n",
       "1             real estate  22              none     own               1   \n",
       "2             real estate  49              none     own               1   \n",
       "3  BSSA or life insurance  45              none    free               1   \n",
       "4                    none  53              none    free               2   \n",
       "\n",
       "                    job dependents   telephone foreign_worker credit  \n",
       "0      skilled employee          1  registered            yes    bad  \n",
       "1      skilled employee          1        none            yes   good  \n",
       "2  unskilled - resident          2        none            yes    bad  \n",
       "3      skilled employee          2        none            yes    bad  \n",
       "4      skilled employee          2        none            yes   good  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Loading Data\n",
    "data = pd.read_csv('./data/GermanCredit.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f41e135",
   "metadata": {},
   "source": [
    "### Data Unseen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ac1cf1cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data for model: (800, 21),\n",
      "Data for unseen predictions: (100, 21)\n"
     ]
    }
   ],
   "source": [
    "# Holding out Data as unseen from the model\n",
    "unseen = data.sample(n= 100)\n",
    "data = data[~data.index.isin(unseen.index)]\n",
    "print(f'Data for model: {data.shape},\\nData for unseen predictions: {unseen.shape}')\n",
    "unseen.to_csv('./data/Germancredit_unseen.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5c6843a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Creating X and y\n",
    "# data.columns!='credit'\n",
    "X = data.loc[: , data.columns!='credit']\n",
    "y = data.loc[: , data.columns=='credit'].values[:,0] # or df['species'].values"
   ]
  },
  {
   "cell_type": "raw",
   "id": "23d0a0c4",
   "metadata": {},
   "source": [
    "#### Encoding in Using pd.get_dummies\n",
    "X = pd.get_dummies(data = X,drop_first=True)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b92de248",
   "metadata": {},
   "source": [
    "###  Compare Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "694027f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "CatBoostClassifier:\n",
      " Precision: 98.9%\n",
      " Recall: 75.2%\n",
      " Accuracy: 92.2%\n",
      " F1 Score: 85.4%\n",
      "\n",
      "LogisticRegression:\n",
      " Precision: 100.0%\n",
      " Recall: 100.0%\n",
      " Accuracy: 100.0%\n",
      " F1 Score: 100.0%\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# preprocessing\n",
    "from sklearn.pipeline import make_pipeline, Pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler, LabelEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "# models\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegressionCV, LogisticRegression\n",
    "from catboost import CatBoostClassifier\n",
    "\n",
    "# Metrics\n",
    "from sklearn.metrics import f1_score, accuracy_score, precision_score, recall_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Create the train and test data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, shuffle=True, random_state=42)\n",
    "\n",
    "models = [\n",
    "    CatBoostClassifier(),\n",
    "    LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
    "                   intercept_scaling=1, l1_ratio=None, max_iter=1000,\n",
    "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
    "                   random_state=2930, solver='lbfgs', tol=0.0001, verbose=0,\n",
    "                   warm_start=False) #over fit but increasing the leaves\n",
    "]\n",
    "\n",
    "def score_model(X, y, estimator, **kwargs):\n",
    "    \"\"\"\n",
    "    Test various estimators.\n",
    "    \"\"\"\n",
    "    y = LabelEncoder().fit_transform(y)\n",
    "    model = Pipeline([\n",
    "        ('one_hot_encoder', OneHotEncoder()),\n",
    "        ('scaler', StandardScaler(with_mean=False)),\n",
    "        ('estimator', estimator)\n",
    "    ])\n",
    "\n",
    "    # Instantiate the classification model and visualizer\n",
    "    model.fit(X, y, **kwargs)\n",
    "\n",
    "    expected  = y\n",
    "    predicted = model.predict(X)\n",
    "\n",
    "    # Compute and return F1 (harmonic mean of precision and recall)\n",
    "    print(f\"{estimator.__class__.__name__}:\\n Precision: {precision_score(expected, predicted)* 100:,.1f}%\\n\"\n",
    "          f\" Recall: {recall_score(expected, predicted)* 100:,.1f}%\\n\"\n",
    "          f\" Accuracy: {accuracy_score(expected, predicted)* 100:,.1f}%\\n\"\n",
    "          f\" F1 Score: {f1_score(expected, predicted)* 100:,.1f}%\\n\"\n",
    "         )\n",
    "for model in models:\n",
    "    score_model(X, y, model)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0f1b2ec",
   "metadata": {},
   "source": [
    "### Build Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7abd2dae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Label encoding y\n",
    "y = LabelEncoder().fit_transform(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4e8dee5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['duration_mnth', 'credit_amt', 'installment_rate', 'Residence_LOS', 'age', 'existing_credit', 'dependents'] \n",
      " ['checking_acc', 'credit_hist', 'purpose', 'savings_acc', 'emp_status', 'pstatus_sex', 'guarantor', 'property', 'installment_plan', 'housing', 'job', 'telephone', 'foreign_worker']\n"
     ]
    }
   ],
   "source": [
    "# encoding \n",
    "# get the categorical and numeric column names\n",
    "num_cols = X_train.select_dtypes(exclude=['object']).columns.tolist()\n",
    "cat_cols = X_train.select_dtypes(include=['object']).columns.tolist()\n",
    "print(num_cols, '\\n', cat_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "94d82ccc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 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-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 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-6 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-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 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-6 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-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 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-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 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-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 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-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;simpleimputer&#x27;, SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                (&#x27;standardscaler&#x27;, StandardScaler())])</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-32\" type=\"checkbox\" ><label for=\"sk-estimator-id-32\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;simpleimputer&#x27;, SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                (&#x27;standardscaler&#x27;, StandardScaler())])</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-33\" type=\"checkbox\" ><label for=\"sk-estimator-id-33\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-34\" type=\"checkbox\" ><label for=\"sk-estimator-id-34\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='median')),\n",
       "                ('standardscaler', StandardScaler())])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pipeline for numerical columns\n",
    "num_pipe = make_pipeline(\n",
    "    SimpleImputer(strategy='median'),\n",
    "    StandardScaler()\n",
    ")\n",
    "num_pipe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7276c9c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 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-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 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-7 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-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 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-7 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-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 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-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 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-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 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-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                 SimpleImputer(fill_value=&#x27;N/A&#x27;, strategy=&#x27;constant&#x27;)),\n",
       "                (&#x27;onehotencoder&#x27;,\n",
       "                 OneHotEncoder(handle_unknown=&#x27;ignore&#x27;, sparse=False))])</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-35\" type=\"checkbox\" ><label for=\"sk-estimator-id-35\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                 SimpleImputer(fill_value=&#x27;N/A&#x27;, strategy=&#x27;constant&#x27;)),\n",
       "                (&#x27;onehotencoder&#x27;,\n",
       "                 OneHotEncoder(handle_unknown=&#x27;ignore&#x27;, sparse=False))])</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-36\" type=\"checkbox\" ><label for=\"sk-estimator-id-36\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(fill_value=&#x27;N/A&#x27;, strategy=&#x27;constant&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-37\" type=\"checkbox\" ><label for=\"sk-estimator-id-37\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;, sparse=False)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('simpleimputer',\n",
       "                 SimpleImputer(fill_value='N/A', strategy='constant')),\n",
       "                ('onehotencoder',\n",
       "                 OneHotEncoder(handle_unknown='ignore', sparse=False))])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pipeline for categorical columns\n",
    "cat_pipe = make_pipeline(\n",
    "    SimpleImputer(strategy='constant', fill_value='N/A'),\n",
    "    OneHotEncoder(handle_unknown='ignore', sparse=False)\n",
    ")\n",
    "cat_pipe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ebc1a8f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 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-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 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-8 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-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 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-8 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-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 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-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 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-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 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-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                 (&#x27;standardscaler&#x27;,\n",
       "                                                  StandardScaler())]),\n",
       "                                 [&#x27;duration_mnth&#x27;, &#x27;credit_amt&#x27;,\n",
       "                                  &#x27;installment_rate&#x27;, &#x27;Residence_LOS&#x27;, &#x27;age&#x27;,\n",
       "                                  &#x27;existing_credit&#x27;, &#x27;dependents&#x27;]),\n",
       "                                (&#x27;cat&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                  SimpleImputer(fill_value=&#x27;N/A&#x27;,\n",
       "                                                                strategy=&#x27;constant&#x27;)),\n",
       "                                                 (&#x27;onehotencoder&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
       "                                                                sparse=False))]),\n",
       "                                 [&#x27;checking_acc&#x27;, &#x27;credit_hist&#x27;, &#x27;purpose&#x27;,\n",
       "                                  &#x27;savings_acc&#x27;, &#x27;emp_status&#x27;, &#x27;pstatus_sex&#x27;,\n",
       "                                  &#x27;guarantor&#x27;, &#x27;property&#x27;, &#x27;installment_plan&#x27;,\n",
       "                                  &#x27;housing&#x27;, &#x27;job&#x27;, &#x27;telephone&#x27;,\n",
       "                                  &#x27;foreign_worker&#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-38\" type=\"checkbox\" ><label for=\"sk-estimator-id-38\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                 (&#x27;standardscaler&#x27;,\n",
       "                                                  StandardScaler())]),\n",
       "                                 [&#x27;duration_mnth&#x27;, &#x27;credit_amt&#x27;,\n",
       "                                  &#x27;installment_rate&#x27;, &#x27;Residence_LOS&#x27;, &#x27;age&#x27;,\n",
       "                                  &#x27;existing_credit&#x27;, &#x27;dependents&#x27;]),\n",
       "                                (&#x27;cat&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                  SimpleImputer(fill_value=&#x27;N/A&#x27;,\n",
       "                                                                strategy=&#x27;constant&#x27;)),\n",
       "                                                 (&#x27;onehotencoder&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
       "                                                                sparse=False))]),\n",
       "                                 [&#x27;checking_acc&#x27;, &#x27;credit_hist&#x27;, &#x27;purpose&#x27;,\n",
       "                                  &#x27;savings_acc&#x27;, &#x27;emp_status&#x27;, &#x27;pstatus_sex&#x27;,\n",
       "                                  &#x27;guarantor&#x27;, &#x27;property&#x27;, &#x27;installment_plan&#x27;,\n",
       "                                  &#x27;housing&#x27;, &#x27;job&#x27;, &#x27;telephone&#x27;,\n",
       "                                  &#x27;foreign_worker&#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-39\" type=\"checkbox\" ><label for=\"sk-estimator-id-39\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num</label><div class=\"sk-toggleable__content\"><pre>[&#x27;duration_mnth&#x27;, &#x27;credit_amt&#x27;, &#x27;installment_rate&#x27;, &#x27;Residence_LOS&#x27;, &#x27;age&#x27;, &#x27;existing_credit&#x27;, &#x27;dependents&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><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-40\" type=\"checkbox\" ><label for=\"sk-estimator-id-40\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-41\" type=\"checkbox\" ><label for=\"sk-estimator-id-41\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><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-42\" type=\"checkbox\" ><label for=\"sk-estimator-id-42\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat</label><div class=\"sk-toggleable__content\"><pre>[&#x27;checking_acc&#x27;, &#x27;credit_hist&#x27;, &#x27;purpose&#x27;, &#x27;savings_acc&#x27;, &#x27;emp_status&#x27;, &#x27;pstatus_sex&#x27;, &#x27;guarantor&#x27;, &#x27;property&#x27;, &#x27;installment_plan&#x27;, &#x27;housing&#x27;, &#x27;job&#x27;, &#x27;telephone&#x27;, &#x27;foreign_worker&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><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-43\" type=\"checkbox\" ><label for=\"sk-estimator-id-43\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(fill_value=&#x27;N/A&#x27;, strategy=&#x27;constant&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-44\" type=\"checkbox\" ><label for=\"sk-estimator-id-44\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;, sparse=False)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "ColumnTransformer(transformers=[('num',\n",
       "                                 Pipeline(steps=[('simpleimputer',\n",
       "                                                  SimpleImputer(strategy='median')),\n",
       "                                                 ('standardscaler',\n",
       "                                                  StandardScaler())]),\n",
       "                                 ['duration_mnth', 'credit_amt',\n",
       "                                  'installment_rate', 'Residence_LOS', 'age',\n",
       "                                  'existing_credit', 'dependents']),\n",
       "                                ('cat',\n",
       "                                 Pipeline(steps=[('simpleimputer',\n",
       "                                                  SimpleImputer(fill_value='N/A',\n",
       "                                                                strategy='constant')),\n",
       "                                                 ('onehotencoder',\n",
       "                                                  OneHotEncoder(handle_unknown='ignore',\n",
       "                                                                sparse=False))]),\n",
       "                                 ['checking_acc', 'credit_hist', 'purpose',\n",
       "                                  'savings_acc', 'emp_status', 'pstatus_sex',\n",
       "                                  'guarantor', 'property', 'installment_plan',\n",
       "                                  'housing', 'job', 'telephone',\n",
       "                                  'foreign_worker'])])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# combine both the pipelines\n",
    "pipeline = ColumnTransformer([\n",
    "    ('num', num_pipe, num_cols),\n",
    "    ('cat', cat_pipe, cat_cols)\n",
    "])\n",
    "pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "1760e36a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-13 {color: black;background-color: white;}#sk-container-id-13 pre{padding: 0;}#sk-container-id-13 div.sk-toggleable {background-color: white;}#sk-container-id-13 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-13 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-13 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-13 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-13 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 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-13 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-13 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-13 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 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-13 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-13 div.sk-item {position: relative;z-index: 1;}#sk-container-id-13 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-13 div.sk-item::before, #sk-container-id-13 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-13 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-13 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-13 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-13 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-13 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-13 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-13 div.sk-label-container {text-align: center;}#sk-container-id-13 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-13 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-13\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                  (&#x27;standardscaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;duration_mnth&#x27;,\n",
       "                                                   &#x27;credit_amt&#x27;,\n",
       "                                                   &#x27;installment_rate&#x27;,\n",
       "                                                   &#x27;Residence_LOS&#x27;, &#x27;age&#x27;,\n",
       "                                                   &#x27;existing_credit&#x27;,\n",
       "                                                   &#x27;dependents&#x27;]),\n",
       "                                                 (&#x27;cat&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                   SimpleImputer(fill_value=&#x27;N/A&#x27;,\n",
       "                                                                                 strategy=&#x27;constant&#x27;)),\n",
       "                                                                  (&#x27;onehotencoder&#x27;,\n",
       "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
       "                                                                                 sparse=False))]),\n",
       "                                                  [&#x27;checking_acc&#x27;,\n",
       "                                                   &#x27;credit_hist&#x27;, &#x27;purpose&#x27;,\n",
       "                                                   &#x27;savings_acc&#x27;, &#x27;emp_status&#x27;,\n",
       "                                                   &#x27;pstatus_sex&#x27;, &#x27;guarantor&#x27;,\n",
       "                                                   &#x27;property&#x27;,\n",
       "                                                   &#x27;installment_plan&#x27;,\n",
       "                                                   &#x27;housing&#x27;, &#x27;job&#x27;,\n",
       "                                                   &#x27;telephone&#x27;,\n",
       "                                                   &#x27;foreign_worker&#x27;])])),\n",
       "                (&#x27;logisticregression&#x27;,\n",
       "                 LogisticRegression(max_iter=1000, random_state=2930))])</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-81\" type=\"checkbox\" ><label for=\"sk-estimator-id-81\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                  (&#x27;standardscaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;duration_mnth&#x27;,\n",
       "                                                   &#x27;credit_amt&#x27;,\n",
       "                                                   &#x27;installment_rate&#x27;,\n",
       "                                                   &#x27;Residence_LOS&#x27;, &#x27;age&#x27;,\n",
       "                                                   &#x27;existing_credit&#x27;,\n",
       "                                                   &#x27;dependents&#x27;]),\n",
       "                                                 (&#x27;cat&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                   SimpleImputer(fill_value=&#x27;N/A&#x27;,\n",
       "                                                                                 strategy=&#x27;constant&#x27;)),\n",
       "                                                                  (&#x27;onehotencoder&#x27;,\n",
       "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
       "                                                                                 sparse=False))]),\n",
       "                                                  [&#x27;checking_acc&#x27;,\n",
       "                                                   &#x27;credit_hist&#x27;, &#x27;purpose&#x27;,\n",
       "                                                   &#x27;savings_acc&#x27;, &#x27;emp_status&#x27;,\n",
       "                                                   &#x27;pstatus_sex&#x27;, &#x27;guarantor&#x27;,\n",
       "                                                   &#x27;property&#x27;,\n",
       "                                                   &#x27;installment_plan&#x27;,\n",
       "                                                   &#x27;housing&#x27;, &#x27;job&#x27;,\n",
       "                                                   &#x27;telephone&#x27;,\n",
       "                                                   &#x27;foreign_worker&#x27;])])),\n",
       "                (&#x27;logisticregression&#x27;,\n",
       "                 LogisticRegression(max_iter=1000, random_state=2930))])</pre></div></div></div><div class=\"sk-serial\"><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-82\" type=\"checkbox\" ><label for=\"sk-estimator-id-82\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">columntransformer: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                 (&#x27;standardscaler&#x27;,\n",
       "                                                  StandardScaler())]),\n",
       "                                 [&#x27;duration_mnth&#x27;, &#x27;credit_amt&#x27;,\n",
       "                                  &#x27;installment_rate&#x27;, &#x27;Residence_LOS&#x27;, &#x27;age&#x27;,\n",
       "                                  &#x27;existing_credit&#x27;, &#x27;dependents&#x27;]),\n",
       "                                (&#x27;cat&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                  SimpleImputer(fill_value=&#x27;N/A&#x27;,\n",
       "                                                                strategy=&#x27;constant&#x27;)),\n",
       "                                                 (&#x27;onehotencoder&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
       "                                                                sparse=False))]),\n",
       "                                 [&#x27;checking_acc&#x27;, &#x27;credit_hist&#x27;, &#x27;purpose&#x27;,\n",
       "                                  &#x27;savings_acc&#x27;, &#x27;emp_status&#x27;, &#x27;pstatus_sex&#x27;,\n",
       "                                  &#x27;guarantor&#x27;, &#x27;property&#x27;, &#x27;installment_plan&#x27;,\n",
       "                                  &#x27;housing&#x27;, &#x27;job&#x27;, &#x27;telephone&#x27;,\n",
       "                                  &#x27;foreign_worker&#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-83\" type=\"checkbox\" ><label for=\"sk-estimator-id-83\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num</label><div class=\"sk-toggleable__content\"><pre>[&#x27;duration_mnth&#x27;, &#x27;credit_amt&#x27;, &#x27;installment_rate&#x27;, &#x27;Residence_LOS&#x27;, &#x27;age&#x27;, &#x27;existing_credit&#x27;, &#x27;dependents&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><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-84\" type=\"checkbox\" ><label for=\"sk-estimator-id-84\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-85\" type=\"checkbox\" ><label for=\"sk-estimator-id-85\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><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-86\" type=\"checkbox\" ><label for=\"sk-estimator-id-86\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat</label><div class=\"sk-toggleable__content\"><pre>[&#x27;checking_acc&#x27;, &#x27;credit_hist&#x27;, &#x27;purpose&#x27;, &#x27;savings_acc&#x27;, &#x27;emp_status&#x27;, &#x27;pstatus_sex&#x27;, &#x27;guarantor&#x27;, &#x27;property&#x27;, &#x27;installment_plan&#x27;, &#x27;housing&#x27;, &#x27;job&#x27;, &#x27;telephone&#x27;, &#x27;foreign_worker&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><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-87\" type=\"checkbox\" ><label for=\"sk-estimator-id-87\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(fill_value=&#x27;N/A&#x27;, strategy=&#x27;constant&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-88\" type=\"checkbox\" ><label for=\"sk-estimator-id-88\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;, sparse=False)</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-89\" type=\"checkbox\" ><label for=\"sk-estimator-id-89\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000, random_state=2930)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('columntransformer',\n",
       "                 ColumnTransformer(transformers=[('num',\n",
       "                                                  Pipeline(steps=[('simpleimputer',\n",
       "                                                                   SimpleImputer(strategy='median')),\n",
       "                                                                  ('standardscaler',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  ['duration_mnth',\n",
       "                                                   'credit_amt',\n",
       "                                                   'installment_rate',\n",
       "                                                   'Residence_LOS', 'age',\n",
       "                                                   'existing_credit',\n",
       "                                                   'dependents']),\n",
       "                                                 ('cat',\n",
       "                                                  Pipeline(steps=[('simpleimputer',\n",
       "                                                                   SimpleImputer(fill_value='N/A',\n",
       "                                                                                 strategy='constant')),\n",
       "                                                                  ('onehotencoder',\n",
       "                                                                   OneHotEncoder(handle_unknown='ignore',\n",
       "                                                                                 sparse=False))]),\n",
       "                                                  ['checking_acc',\n",
       "                                                   'credit_hist', 'purpose',\n",
       "                                                   'savings_acc', 'emp_status',\n",
       "                                                   'pstatus_sex', 'guarantor',\n",
       "                                                   'property',\n",
       "                                                   'installment_plan',\n",
       "                                                   'housing', 'job',\n",
       "                                                   'telephone',\n",
       "                                                   'foreign_worker'])])),\n",
       "                ('logisticregression',\n",
       "                 LogisticRegression(max_iter=1000, random_state=2930))])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# build the model\n",
    "lr_model = make_pipeline(pipeline, LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
    "                   intercept_scaling=1, l1_ratio=None, max_iter=1000,\n",
    "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
    "                   random_state=2930, solver='lbfgs', tol=0.0001, verbose=0,\n",
    "                   warm_start=False))\n",
    "lr_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "dcc0f775",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\miniconda3\\envs\\pyc310\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-14 {color: black;background-color: white;}#sk-container-id-14 pre{padding: 0;}#sk-container-id-14 div.sk-toggleable {background-color: white;}#sk-container-id-14 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-14 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-14 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-14 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-14 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-14 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-14 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-14 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-14 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 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-14 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-14 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-14 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-14 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 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-14 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-14 div.sk-item {position: relative;z-index: 1;}#sk-container-id-14 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-14 div.sk-item::before, #sk-container-id-14 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-14 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-14 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-14 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-14 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-14 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-14 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-14 div.sk-label-container {text-align: center;}#sk-container-id-14 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-14 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-14\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                  (&#x27;standardscaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;duration_mnth&#x27;,\n",
       "                                                   &#x27;credit_amt&#x27;,\n",
       "                                                   &#x27;installment_rate&#x27;,\n",
       "                                                   &#x27;Residence_LOS&#x27;, &#x27;age&#x27;,\n",
       "                                                   &#x27;existing_credit&#x27;,\n",
       "                                                   &#x27;dependents&#x27;]),\n",
       "                                                 (&#x27;cat&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                   SimpleImputer(fill_value=&#x27;N/A&#x27;,\n",
       "                                                                                 strategy=&#x27;constant&#x27;)),\n",
       "                                                                  (&#x27;onehotencoder&#x27;,\n",
       "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
       "                                                                                 sparse=False))]),\n",
       "                                                  [&#x27;checking_acc&#x27;,\n",
       "                                                   &#x27;credit_hist&#x27;, &#x27;purpose&#x27;,\n",
       "                                                   &#x27;savings_acc&#x27;, &#x27;emp_status&#x27;,\n",
       "                                                   &#x27;pstatus_sex&#x27;, &#x27;guarantor&#x27;,\n",
       "                                                   &#x27;property&#x27;,\n",
       "                                                   &#x27;installment_plan&#x27;,\n",
       "                                                   &#x27;housing&#x27;, &#x27;job&#x27;,\n",
       "                                                   &#x27;telephone&#x27;,\n",
       "                                                   &#x27;foreign_worker&#x27;])])),\n",
       "                (&#x27;logisticregression&#x27;,\n",
       "                 LogisticRegression(max_iter=1000, random_state=2930))])</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-90\" type=\"checkbox\" ><label for=\"sk-estimator-id-90\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                  (&#x27;standardscaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;duration_mnth&#x27;,\n",
       "                                                   &#x27;credit_amt&#x27;,\n",
       "                                                   &#x27;installment_rate&#x27;,\n",
       "                                                   &#x27;Residence_LOS&#x27;, &#x27;age&#x27;,\n",
       "                                                   &#x27;existing_credit&#x27;,\n",
       "                                                   &#x27;dependents&#x27;]),\n",
       "                                                 (&#x27;cat&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                   SimpleImputer(fill_value=&#x27;N/A&#x27;,\n",
       "                                                                                 strategy=&#x27;constant&#x27;)),\n",
       "                                                                  (&#x27;onehotencoder&#x27;,\n",
       "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
       "                                                                                 sparse=False))]),\n",
       "                                                  [&#x27;checking_acc&#x27;,\n",
       "                                                   &#x27;credit_hist&#x27;, &#x27;purpose&#x27;,\n",
       "                                                   &#x27;savings_acc&#x27;, &#x27;emp_status&#x27;,\n",
       "                                                   &#x27;pstatus_sex&#x27;, &#x27;guarantor&#x27;,\n",
       "                                                   &#x27;property&#x27;,\n",
       "                                                   &#x27;installment_plan&#x27;,\n",
       "                                                   &#x27;housing&#x27;, &#x27;job&#x27;,\n",
       "                                                   &#x27;telephone&#x27;,\n",
       "                                                   &#x27;foreign_worker&#x27;])])),\n",
       "                (&#x27;logisticregression&#x27;,\n",
       "                 LogisticRegression(max_iter=1000, random_state=2930))])</pre></div></div></div><div class=\"sk-serial\"><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-91\" type=\"checkbox\" ><label for=\"sk-estimator-id-91\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">columntransformer: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                 (&#x27;standardscaler&#x27;,\n",
       "                                                  StandardScaler())]),\n",
       "                                 [&#x27;duration_mnth&#x27;, &#x27;credit_amt&#x27;,\n",
       "                                  &#x27;installment_rate&#x27;, &#x27;Residence_LOS&#x27;, &#x27;age&#x27;,\n",
       "                                  &#x27;existing_credit&#x27;, &#x27;dependents&#x27;]),\n",
       "                                (&#x27;cat&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                  SimpleImputer(fill_value=&#x27;N/A&#x27;,\n",
       "                                                                strategy=&#x27;constant&#x27;)),\n",
       "                                                 (&#x27;onehotencoder&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
       "                                                                sparse=False))]),\n",
       "                                 [&#x27;checking_acc&#x27;, &#x27;credit_hist&#x27;, &#x27;purpose&#x27;,\n",
       "                                  &#x27;savings_acc&#x27;, &#x27;emp_status&#x27;, &#x27;pstatus_sex&#x27;,\n",
       "                                  &#x27;guarantor&#x27;, &#x27;property&#x27;, &#x27;installment_plan&#x27;,\n",
       "                                  &#x27;housing&#x27;, &#x27;job&#x27;, &#x27;telephone&#x27;,\n",
       "                                  &#x27;foreign_worker&#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-92\" type=\"checkbox\" ><label for=\"sk-estimator-id-92\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num</label><div class=\"sk-toggleable__content\"><pre>[&#x27;duration_mnth&#x27;, &#x27;credit_amt&#x27;, &#x27;installment_rate&#x27;, &#x27;Residence_LOS&#x27;, &#x27;age&#x27;, &#x27;existing_credit&#x27;, &#x27;dependents&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><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-93\" type=\"checkbox\" ><label for=\"sk-estimator-id-93\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-94\" type=\"checkbox\" ><label for=\"sk-estimator-id-94\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><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-95\" type=\"checkbox\" ><label for=\"sk-estimator-id-95\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat</label><div class=\"sk-toggleable__content\"><pre>[&#x27;checking_acc&#x27;, &#x27;credit_hist&#x27;, &#x27;purpose&#x27;, &#x27;savings_acc&#x27;, &#x27;emp_status&#x27;, &#x27;pstatus_sex&#x27;, &#x27;guarantor&#x27;, &#x27;property&#x27;, &#x27;installment_plan&#x27;, &#x27;housing&#x27;, &#x27;job&#x27;, &#x27;telephone&#x27;, &#x27;foreign_worker&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><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-96\" type=\"checkbox\" ><label for=\"sk-estimator-id-96\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(fill_value=&#x27;N/A&#x27;, strategy=&#x27;constant&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-97\" type=\"checkbox\" ><label for=\"sk-estimator-id-97\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;, sparse=False)</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-98\" type=\"checkbox\" ><label for=\"sk-estimator-id-98\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000, random_state=2930)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('columntransformer',\n",
       "                 ColumnTransformer(transformers=[('num',\n",
       "                                                  Pipeline(steps=[('simpleimputer',\n",
       "                                                                   SimpleImputer(strategy='median')),\n",
       "                                                                  ('standardscaler',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  ['duration_mnth',\n",
       "                                                   'credit_amt',\n",
       "                                                   'installment_rate',\n",
       "                                                   'Residence_LOS', 'age',\n",
       "                                                   'existing_credit',\n",
       "                                                   'dependents']),\n",
       "                                                 ('cat',\n",
       "                                                  Pipeline(steps=[('simpleimputer',\n",
       "                                                                   SimpleImputer(fill_value='N/A',\n",
       "                                                                                 strategy='constant')),\n",
       "                                                                  ('onehotencoder',\n",
       "                                                                   OneHotEncoder(handle_unknown='ignore',\n",
       "                                                                                 sparse=False))]),\n",
       "                                                  ['checking_acc',\n",
       "                                                   'credit_hist', 'purpose',\n",
       "                                                   'savings_acc', 'emp_status',\n",
       "                                                   'pstatus_sex', 'guarantor',\n",
       "                                                   'property',\n",
       "                                                   'installment_plan',\n",
       "                                                   'housing', 'job',\n",
       "                                                   'telephone',\n",
       "                                                   'foreign_worker'])])),\n",
       "                ('logisticregression',\n",
       "                 LogisticRegression(max_iter=1000, random_state=2930))])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train the model\n",
    "lr_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "703be315",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: title={'center': 'LogisticRegression Confusion Matrix'}, xlabel='Predicted Class', ylabel='True Class'>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Looking at the performace of the data over its test\n",
    "from yellowbrick.classifier import confusion_matrix\n",
    "# Instantiate the classification model and visualizer\n",
    "visualizer = confusion_matrix(lr_model, X_train, y_train, X_test, y_test,classes=['bad', 'good'], support=True)\n",
    "\n",
    "visualizer.fit(X_train, y_train)        # Fit the visualizer and the model\n",
    "visualizer.score(X_test, y_test)        # Evaluate the model on the test data\n",
    "visualizer.show()      "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "084c180e",
   "metadata": {},
   "source": [
    "### Test Unseen Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "23317090",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['bad' 'bad' 'bad' 'bad' 'good' 'bad' 'good' 'bad' 'bad' 'bad' 'bad' 'bad'\n",
      " 'bad' 'good' 'bad' 'bad' 'bad' 'bad' 'bad' 'bad' 'bad' 'bad' 'bad' 'good'\n",
      " 'good' 'bad' 'bad' 'bad' 'bad' 'bad' 'bad' 'bad' 'bad' 'bad' 'bad' 'good'\n",
      " 'bad' 'good' 'bad' 'bad' 'good' 'bad' 'bad' 'bad' 'bad' 'bad' 'good'\n",
      " 'bad' 'bad' 'bad' 'bad' 'good' 'good' 'bad' 'bad' 'bad' 'bad' 'good'\n",
      " 'bad' 'good' 'bad' 'bad' 'bad' 'bad' 'bad' 'bad' 'bad' 'bad' 'bad' 'bad'\n",
      " 'good' 'bad' 'bad' 'bad' 'bad' 'bad' 'bad' 'good' 'good' 'bad' 'good'\n",
      " 'good' 'bad' 'bad' 'bad' 'bad' 'bad' 'good' 'good' 'good' 'bad' 'bad'\n",
      " 'bad' 'bad' 'bad' 'bad' 'good' 'bad' 'bad' 'bad']\n"
     ]
    }
   ],
   "source": [
    "# Predict the target values for the test data using the trained model\n",
    "unseen_pred = lr_model.predict(unseen)\n",
    "\n",
    "# Print the predicted values\n",
    "print(unseen_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "a683a3c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 78.0\n",
      "Precision score: 72.8\n",
      "Recall score: 69.4\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(unseen['credit'], unseen_pred)\n",
    "precision = precision_score(unseen['credit'], unseen_pred, average='macro')\n",
    "recall = recall_score(unseen['credit'], unseen_pred, average='macro')\n",
    "\n",
    "# Print the scores\n",
    "print(f'Accuracy score: {accuracy *100:,.1f}' )\n",
    "print(f'Precision score: {precision *100:,.1f}')\n",
    "print(f'Recall score: {recall *100:,.1f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e4a2824",
   "metadata": {},
   "source": [
    "### Export Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c356c8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib\n",
    "joblib.dump(cb_model, './model/sk_german_credit.pkl')\n",
    "print(lr_model)"
   ]
  }
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