loan_prediction_using_non-feature_extraction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np                     # For mathematical calculations \n",
    "import seaborn as sns                  # For data visualization \n",
    "import matplotlib.pyplot as plt        # For plotting graphs \n",
    "%matplotlib inline \n",
    "import warnings                        # To ignore any warnings warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "train =pd.read_csv('train_1.csv')\n",
    "test = pd.read_csv('test_1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ApplicantIncome', 'CoapplicantIncome', 'Loan_Amount_Term',\n",
       "       'Credit_History', 'Loan_Status', 'LoanAmount_log', 'Gender_Female',\n",
       "       'Gender_Male', 'Married_No', 'Married_Yes', 'Dependents_3',\n",
       "       'Dependents_0', 'Dependents_1', 'Dependents_2', 'Education_Graduate',\n",
       "       'Education_Not Graduate', 'Self_Employed_No', 'Self_Employed_Yes',\n",
       "       'Property_Area_Rural', 'Property_Area_Semiurban',\n",
       "       'Property_Area_Urban'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Loan_Status</th>\n",
       "      <th>LoanAmount_log</th>\n",
       "      <th>Gender_Female</th>\n",
       "      <th>Gender_Male</th>\n",
       "      <th>Married_No</th>\n",
       "      <th>Married_Yes</th>\n",
       "      <th>...</th>\n",
       "      <th>Dependents_0</th>\n",
       "      <th>Dependents_1</th>\n",
       "      <th>Dependents_2</th>\n",
       "      <th>Education_Graduate</th>\n",
       "      <th>Education_Not Graduate</th>\n",
       "      <th>Self_Employed_No</th>\n",
       "      <th>Self_Employed_Yes</th>\n",
       "      <th>Property_Area_Rural</th>\n",
       "      <th>Property_Area_Semiurban</th>\n",
       "      <th>Property_Area_Urban</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>360.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.852030</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.189655</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.787492</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.948760</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ApplicantIncome  CoapplicantIncome  Loan_Amount_Term  Credit_History  \\\n",
       "0             5849                0.0             360.0             1.0   \n",
       "1             4583             1508.0             360.0             1.0   \n",
       "2             3000                0.0             360.0             1.0   \n",
       "3             2583             2358.0             360.0             1.0   \n",
       "4             6000                0.0             360.0             1.0   \n",
       "\n",
       "   Loan_Status  LoanAmount_log  Gender_Female  Gender_Male  Married_No  \\\n",
       "0            1        4.852030              0            1           1   \n",
       "1            0        4.852030              0            1           0   \n",
       "2            1        4.189655              0            1           0   \n",
       "3            1        4.787492              0            1           0   \n",
       "4            1        4.948760              0            1           1   \n",
       "\n",
       "   Married_Yes  ...  Dependents_0  Dependents_1  Dependents_2  \\\n",
       "0            0  ...             1             0             0   \n",
       "1            1  ...             0             1             0   \n",
       "2            1  ...             1             0             0   \n",
       "3            1  ...             1             0             0   \n",
       "4            0  ...             1             0             0   \n",
       "\n",
       "   Education_Graduate  Education_Not Graduate  Self_Employed_No  \\\n",
       "0                   1                       0                 1   \n",
       "1                   1                       0                 1   \n",
       "2                   1                       0                 0   \n",
       "3                   0                       1                 1   \n",
       "4                   1                       0                 1   \n",
       "\n",
       "   Self_Employed_Yes  Property_Area_Rural  Property_Area_Semiurban  \\\n",
       "0                  0                    0                        0   \n",
       "1                  0                    1                        0   \n",
       "2                  1                    0                        0   \n",
       "3                  0                    0                        0   \n",
       "4                  0                    0                        0   \n",
       "\n",
       "   Property_Area_Urban  \n",
       "0                    1  \n",
       "1                    0  \n",
       "2                    1  \n",
       "3                    1  \n",
       "4                    1  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ApplicantIncome            0\n",
       "CoapplicantIncome          0\n",
       "Loan_Amount_Term           0\n",
       "Credit_History             0\n",
       "Loan_Status                0\n",
       "LoanAmount_log             0\n",
       "Gender_Female              0\n",
       "Gender_Male                0\n",
       "Married_No                 0\n",
       "Married_Yes                0\n",
       "Dependents_3               0\n",
       "Dependents_0               0\n",
       "Dependents_1               0\n",
       "Dependents_2               0\n",
       "Education_Graduate         0\n",
       "Education_Not Graduate     0\n",
       "Self_Employed_No           0\n",
       "Self_Employed_Yes          0\n",
       "Property_Area_Rural        0\n",
       "Property_Area_Semiurban    0\n",
       "Property_Area_Urban        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train.drop('Loan_Status',1) \n",
    "y = train.Loan_Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>LoanAmount_log</th>\n",
       "      <th>Gender_Female</th>\n",
       "      <th>Gender_Male</th>\n",
       "      <th>Married_No</th>\n",
       "      <th>Married_Yes</th>\n",
       "      <th>Dependents_3</th>\n",
       "      <th>Dependents_0</th>\n",
       "      <th>Dependents_1</th>\n",
       "      <th>Dependents_2</th>\n",
       "      <th>Education_Graduate</th>\n",
       "      <th>Education_Not Graduate</th>\n",
       "      <th>Self_Employed_No</th>\n",
       "      <th>Self_Employed_Yes</th>\n",
       "      <th>Property_Area_Rural</th>\n",
       "      <th>Property_Area_Semiurban</th>\n",
       "      <th>Property_Area_Urban</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.85203</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ApplicantIncome  CoapplicantIncome  Loan_Amount_Term  Credit_History  \\\n",
       "0             5849                0.0             360.0             1.0   \n",
       "\n",
       "   LoanAmount_log  Gender_Female  Gender_Male  Married_No  Married_Yes  \\\n",
       "0         4.85203              0            1           1            0   \n",
       "\n",
       "   Dependents_3  Dependents_0  Dependents_1  Dependents_2  Education_Graduate  \\\n",
       "0             0             1             0             0                   1   \n",
       "\n",
       "   Education_Not Graduate  Self_Employed_No  Self_Employed_Yes  \\\n",
       "0                       0                 1                  0   \n",
       "\n",
       "   Property_Area_Rural  Property_Area_Semiurban  Property_Area_Urban  \n",
       "0                    0                        0                    1  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the train_test_split function from sklearn to divide our train dataset. So, first let us import train_test_split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train, x_cv, y_train, y_cv = train_test_split(X,y, test_size =0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will train the model on training dataset and make predictions for the test dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(max_iter=1000)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression \n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "model = LogisticRegression(max_iter=1000) \n",
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let’s predict the Loan_Status for validation set and calculate its accuracy.\n",
    "\n",
    "pred_cv = model.predict(x_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8216216216216217"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let us calculate how accurate our predictions are by calculating the accuracy.\n",
    "\n",
    "accuracy_score(y_cv,pred_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let’s make predictions for the test dataset.\n",
    "\n",
    "pred_test = model.predict(test)\n",
    "\n",
    "pred_test[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To check how robust our model is to unseen data, we can use Validation. It is a technique which involves reserving a particular sample of a dataset on which you do not train the model. Later, you test your model on this sample before finalizing it. Some of the common methods for validation are listed below:\n",
    "\n",
    "1. The validation set approach\n",
    "2. k-fold cross validation\n",
    "3. Leave one out cross validation (LOOCV)\n",
    "4. Stratified k-fold cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1 of kfold 5\n",
      "accuracy_score 0.8130081300813008\n",
      "\n",
      "2 of kfold 5\n",
      "accuracy_score 0.8211382113821138\n",
      "\n",
      "3 of kfold 5\n",
      "accuracy_score 0.7967479674796748\n",
      "\n",
      "4 of kfold 5\n",
      "accuracy_score 0.8211382113821138\n",
      "\n",
      "5 of kfold 5\n",
      "accuracy_score 0.7868852459016393\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "i=1 \n",
    "kf = StratifiedKFold(n_splits=5,random_state=1,shuffle=True) \n",
    "for train_index,test_index in kf.split(X,y):     \n",
    "    print('\\n{} of kfold {}'.format(i,kf.n_splits))  \n",
    "    xtr,xvl = X.loc[train_index],X.loc[test_index]  \n",
    "    ytr,yvl = y[train_index],y[test_index]  \n",
    "    model = LogisticRegression(max_iter = 10000,random_state=1)   \n",
    "    model.fit(xtr, ytr) \n",
    "    pred_test = model.predict(xvl) \n",
    "    score = accuracy_score(yvl,pred_test)  \n",
    "    print('accuracy_score',score)  \n",
    "    i+=1 \n",
    "    pred_test = model.predict(test)\n",
    "    pred=model.predict_proba(xvl)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import metrics \n",
    "fpr, tpr, _ = metrics.roc_curve(yvl,  pred) \n",
    "auc = metrics.roc_auc_score(yvl, pred) \n",
    "plt.figure(figsize=(12,8)) \n",
    "plt.plot(fpr,tpr,label=\"validation, auc=\"+str(auc))\n",
    "plt.xlabel('False Positive Rate') \n",
    "plt.ylabel('True Positive Rate') \n",
    "plt.legend(loc=4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mean validation accuracy for this model turns out to be 0.81. Let us visualize the roc curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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