loan_pred.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "RANDOM_STATE = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = pd.read_csv('test.csv')\n",
    "train = pd.read_csv(\"train.csv\")\n",
    "sample = pd.read_csv('sample.csv')\n",
    "\n",
    "#loading CSV files into the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Index(['Loan_ID', 'Gender', 'Married', 'Dependents', 'Education',\n",
       "       'Self_Employed', 'ApplicantIncome', 'CoapplicantIncome', 'LoanAmount',\n",
       "       'Loan_Amount_Term', 'Credit_History', 'Property_Area', 'Loan_Status'],\n",
       "      dtype='object')"
      ]
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_final = train.drop(columns=['Loan_ID'])\n",
    "test_final = test.drop(columns=['Loan_ID'])\n",
    "\n",
    "#removing not important columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Train File\n\nGender               13\nMarried               3\nDependents           15\nEducation             0\nSelf_Employed        32\nApplicantIncome       0\nCoapplicantIncome     0\nLoanAmount           22\nLoan_Amount_Term     14\nCredit_History       50\nProperty_Area         0\nLoan_Status           0\ndtype: int64\n\n\nTest File\n\nGender               11\nMarried               0\nDependents           10\nEducation             0\nSelf_Employed        23\nApplicantIncome       0\nCoapplicantIncome     0\nLoanAmount            5\nLoan_Amount_Term      6\nCredit_History       29\nProperty_Area         0\ndtype: int64\n"
     ]
    }
   ],
   "source": [
    "print('Train File')\n",
    "print()\n",
    "print(train_final.isna().sum())\n",
    "print()\n",
    "print()\n",
    "print('Test File')\n",
    "print()\n",
    "print(test_final.isna().sum())\n",
    "\n",
    "#checking if any values are missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_final['Gender'].fillna(train_final['Gender'].mode()[0],inplace=True)\n",
    "train_final['Married'].fillna(train_final['Married'].mode()[0],inplace=True)\n",
    "train_final['Dependents'].fillna('0',inplace=True)\n",
    "train_final['Self_Employed'].fillna(train_final['Self_Employed'].mode()[0],inplace=True)\n",
    "train_final['Loan_Amount_Term'].fillna(train_final['Loan_Amount_Term'].mode()[0],inplace=True)\n",
    "train_final['Credit_History'].fillna(train_final['Credit_History'].mode()[0],inplace=True)\n",
    "\n",
    "test_final['Gender'].fillna(test_final['Gender'].mode()[0],inplace=True)\n",
    "test_final['Married'].fillna(test_final['Married'].mode()[0],inplace=True)\n",
    "test_final['Dependents'].fillna('0',inplace=True)\n",
    "test_final['Self_Employed'].fillna(test_final['Self_Employed'].mode()[0],inplace=True)\n",
    "test_final['Loan_Amount_Term'].fillna(test_final['Loan_Amount_Term'].mode()[0],inplace=True)\n",
    "test_final['Credit_History'].fillna(test_final['Credit_History'].mode()[0],inplace=True)\n",
    "\n",
    "#Data processing for the missing values --1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_final = train_final.dropna()\n",
    "test_final['LoanAmount'].fillna(test_final['LoanAmount'].mean(),inplace=True) \n",
    "\n",
    "#Data processing for the missing values --2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder,MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LoanAmount'>"
      ]
     },
     "metadata": {},
     "execution_count": 10
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "sns.countplot(train_final['Gender'])\n",
    "\n",
    "plt.figure(figsize=(5,5))\n",
    "sns.countplot(train_final['Dependents'])\n",
    "\n",
    "plt.figure(figsize=(5,5))\n",
    "sns.distplot(train_final['LoanAmount'])\n",
    "\n",
    "#data visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "le_gender = LabelEncoder()\n",
    "le_married = LabelEncoder()\n",
    "le_dependent = LabelEncoder()\n",
    "le_education = LabelEncoder()\n",
    "le_employed = LabelEncoder()\n",
    "le_property = LabelEncoder()\n",
    "le_loan = LabelEncoder()\n",
    "\n",
    "#loadning and assigning Label Encoder Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
       "0  LP001002   Male      No          0      Graduate            No   \n",
       "1  LP001003   Male     Yes          1      Graduate            No   \n",
       "2  LP001005   Male     Yes          0      Graduate           Yes   \n",
       "3  LP001006   Male     Yes          0  Not Graduate            No   \n",
       "4  LP001008   Male      No          0      Graduate            No   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5849                0.0         NaN             360.0   \n",
       "1             4583             1508.0       128.0             360.0   \n",
       "2             3000                0.0        66.0             360.0   \n",
       "3             2583             2358.0       120.0             360.0   \n",
       "4             6000                0.0       141.0             360.0   \n",
       "\n",
       "   Credit_History Property_Area Loan_Status  \n",
       "0             1.0         Urban           Y  \n",
       "1             1.0         Rural           N  \n",
       "2             1.0         Urban           Y  \n",
       "3             1.0         Urban           Y  \n",
       "4             1.0         Urban           Y  "
      ],
      "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>Loan_ID</th>\n      <th>Gender</th>\n      <th>Married</th>\n      <th>Dependents</th>\n      <th>Education</th>\n      <th>Self_Employed</th>\n      <th>ApplicantIncome</th>\n      <th>CoapplicantIncome</th>\n      <th>LoanAmount</th>\n      <th>Loan_Amount_Term</th>\n      <th>Credit_History</th>\n      <th>Property_Area</th>\n      <th>Loan_Status</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>LP001002</td>\n      <td>Male</td>\n      <td>No</td>\n      <td>0</td>\n      <td>Graduate</td>\n      <td>No</td>\n      <td>5849</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>360.0</td>\n      <td>1.0</td>\n      <td>Urban</td>\n      <td>Y</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>LP001003</td>\n      <td>Male</td>\n      <td>Yes</td>\n      <td>1</td>\n      <td>Graduate</td>\n      <td>No</td>\n      <td>4583</td>\n      <td>1508.0</td>\n      <td>128.0</td>\n      <td>360.0</td>\n      <td>1.0</td>\n      <td>Rural</td>\n      <td>N</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>LP001005</td>\n      <td>Male</td>\n      <td>Yes</td>\n      <td>0</td>\n      <td>Graduate</td>\n      <td>Yes</td>\n      <td>3000</td>\n      <td>0.0</td>\n      <td>66.0</td>\n      <td>360.0</td>\n      <td>1.0</td>\n      <td>Urban</td>\n      <td>Y</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>LP001006</td>\n      <td>Male</td>\n      <td>Yes</td>\n      <td>0</td>\n      <td>Not Graduate</td>\n      <td>No</td>\n      <td>2583</td>\n      <td>2358.0</td>\n      <td>120.0</td>\n      <td>360.0</td>\n      <td>1.0</td>\n      <td>Urban</td>\n      <td>Y</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>LP001008</td>\n      <td>Male</td>\n      <td>No</td>\n      <td>0</td>\n      <td>Graduate</td>\n      <td>No</td>\n      <td>6000</td>\n      <td>0.0</td>\n      <td>141.0</td>\n      <td>360.0</td>\n      <td>1.0</td>\n      <td>Urban</td>\n      <td>Y</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_final['Gender'] = le_gender.fit_transform(train_final['Gender'])\n",
    "train_final['Married'] = le_married.fit_transform(train_final['Married'])\n",
    "train_final['Dependents'] = le_dependent.fit_transform(train_final['Dependents'])\n",
    "train_final['Education'] = le_education.fit_transform(train_final['Education'])\n",
    "train_final['Self_Employed'] = le_employed.fit_transform(train_final['Self_Employed'])\n",
    "train_final['Property_Area'] = le_property.fit_transform(train_final['Property_Area'])\n",
    "train_final['Loan_Status'] = le_loan.fit_transform(train_final['Loan_Status'])\n",
    "\n",
    "test_final['Gender'] = le_gender.fit_transform(test_final['Gender'])\n",
    "test_final['Married'] = le_married.fit_transform(test_final['Married'])\n",
    "test_final['Dependents'] = le_dependent.fit_transform(test_final['Dependents'])\n",
    "test_final['Education'] = le_education.fit_transform(test_final['Education'])\n",
    "test_final['Self_Employed'] = le_employed.fit_transform(test_final['Self_Employed'])\n",
    "test_final['Property_Area'] = le_property.fit_transform(test_final['Property_Area'])\n",
    "\n",
    "#Converting character variable to numerical variable "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = train_final.iloc[:,0:-1]\n",
    "y = train_final['Loan_Status'].values\n",
    "\n",
    "x_val = test_final\n",
    "\n",
    "#dividing the data set into train and test splits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    " from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2,random_state=RANDOM_STATE)"
   ]
  },
  {
   "source": [
    "### Machine Learning Model(s)"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score"
   ],
   "cell_type": "code",
   "metadata": {},
   "execution_count": 71,
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "metadata": {},
     "execution_count": 51
    }
   ],
   "source": [
    "dt = DecisionTreeClassifier()\n",
    "dt.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_dt = dt.predict(x_test)\n",
    "score_dt = accuracy_score(y_test,pred_dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_features=5)"
      ]
     },
     "metadata": {},
     "execution_count": 57
    }
   ],
   "source": [
    "rf = RandomForestClassifier(max_features=5,\n",
    "                        )\n",
    "rf.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_rf = rf.predict(x_test)\n",
    "score_rf = accuracy_score(y_test,pred_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(learning_rate=0.2, n_estimators=400)"
      ]
     },
     "metadata": {},
     "execution_count": 64
    }
   ],
   "source": [
    "gb = GradientBoostingClassifier(learning_rate=0.2,  n_estimators= 400)\n",
    "gb.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_gb = gb.predict(x_test)\n",
    "score_gb = accuracy_score(y_test,pred_gb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "metadata": {},
     "execution_count": 68
    }
   ],
   "source": [
    "logr = LogisticRegression()\n",
    "logr.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.7058823529411765"
      ]
     },
     "metadata": {},
     "execution_count": 70
    }
   ],
   "source": [
    "pred_logr = logr.predict(x_test)\n",
    "score_logr = accuracy_score(y_test,pred_logr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=15)"
      ]
     },
     "metadata": {},
     "execution_count": 81
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=15,\n",
    "                        weights='uniform')\n",
    "knn.fit(x_train,y_train)                        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.6470588235294118"
      ]
     },
     "metadata": {},
     "execution_count": 82
    }
   ],
   "source": [
    "pred_knn = knn.predict(x_test)\n",
    "score_knn = accuracy_score(y_test,pred_knn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}