Loan_Prediction-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The nltk version is 3.5.\n",
      "The scikit-learn version is 0.24.2.\n"
     ]
    }
   ],
   "source": [
    "import nltk\n",
    "import sklearn\n",
    "\n",
    "print('The nltk version is {}.'.format(nltk.__version__))\n",
    "print('The scikit-learn version is {}.'.format(sklearn.__version__))\n",
    "\n",
    "# The nltk version is 3.0.0.\n",
    "# The scikit-learn version is 0.15.2."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = pd.read_csv(\"train_ctrUa4K.csv\")\n",
    "test_df = pd.read_csv(\"test_lAUu6dG.csv\")\n",
    "test_copy = test_df.copy()\n",
    "sample_sub = pd.read_csv(\"sample_submission_49d68Cx.csv\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deducing information about data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 614 entries, 0 to 613\n",
      "Data columns (total 13 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Loan_ID            614 non-null    object \n",
      " 1   Gender             601 non-null    object \n",
      " 2   Married            611 non-null    object \n",
      " 3   Dependents         599 non-null    object \n",
      " 4   Education          614 non-null    object \n",
      " 5   Self_Employed      582 non-null    object \n",
      " 6   ApplicantIncome    614 non-null    int64  \n",
      " 7   CoapplicantIncome  614 non-null    float64\n",
      " 8   LoanAmount         592 non-null    float64\n",
      " 9   Loan_Amount_Term   600 non-null    float64\n",
      " 10  Credit_History     564 non-null    float64\n",
      " 11  Property_Area      614 non-null    object \n",
      " 12  Loan_Status        614 non-null    object \n",
      "dtypes: float64(4), int64(1), object(8)\n",
      "memory usage: 62.5+ KB\n"
     ]
    },
    {
     "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>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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LP001011</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5417</td>\n",
       "      <td>4196.0</td>\n",
       "      <td>267.0</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>6</th>\n",
       "      <td>LP001013</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2333</td>\n",
       "      <td>1516.0</td>\n",
       "      <td>95.0</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>7</th>\n",
       "      <td>LP001014</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3+</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3036</td>\n",
       "      <td>2504.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>LP001018</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4006</td>\n",
       "      <td>1526.0</td>\n",
       "      <td>168.0</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>9</th>\n",
       "      <td>LP001020</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>12841</td>\n",
       "      <td>10968.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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",
       "5  LP001011   Male     Yes          2      Graduate           Yes   \n",
       "6  LP001013   Male     Yes          0  Not Graduate            No   \n",
       "7  LP001014   Male     Yes         3+      Graduate            No   \n",
       "8  LP001018   Male     Yes          2      Graduate            No   \n",
       "9  LP001020   Male     Yes          1      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",
       "5             5417             4196.0       267.0             360.0   \n",
       "6             2333             1516.0        95.0             360.0   \n",
       "7             3036             2504.0       158.0             360.0   \n",
       "8             4006             1526.0       168.0             360.0   \n",
       "9            12841            10968.0       349.0             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  \n",
       "5             1.0         Urban           Y  \n",
       "6             1.0         Urban           Y  \n",
       "7             0.0     Semiurban           N  \n",
       "8             1.0         Urban           Y  \n",
       "9             1.0     Semiurban           N  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.info()\n",
    "train_df.describe()\n",
    "train_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Now that we know, not all features are numerical, there are categorical features as well in our train data. We can also figure out that we have some missing values in the train data that we need to take care of. We know that out target variable is Loan_Status"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Let's count the value we have for our target variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Semiurban    233\n",
       "Urban        202\n",
       "Rural        179\n",
       "Name: Property_Area, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.Loan_Status.value_counts()\n",
    "train_df.Property_Area.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have 422 yes values and 192 no values so we can deduce from here that we have more yes values than we have no values.\n",
    "Let's plot the graph to have a better look at it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_df[\"Loan_Status\"].value_counts().plot.bar() # important to put brackets in value counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's do some univariate analysis, that is anlayse the target variable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### In here we have three kind of variables:\n",
    "1. Numerical Variable\n",
    "2. Categorical Varible\n",
    "3. Ordinal Varible\n",
    "Let's analyze each of them individually"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "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')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10,10)) \n",
    "plt.subplot(221) \n",
    "train_df[\"Gender\"].value_counts().plot(kind = \"bar\", title = \"Gender\")\n",
    "plt.subplot(222)\n",
    "train_df.Married.value_counts().plot(kind = \"bar\", title = \"Marital Status\")\n",
    "plt.subplot(223)\n",
    "train_df.Credit_History.value_counts().plot(kind = \"bar\", title = \"Credit_History\")\n",
    "plt.subplot(224)\n",
    "train_df.Self_Employed.value_counts().plot(kind = \"bar\", title = \"Self_Employed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be inferred from above that more than 80% people are male, aboute 70% people are married who applied for the loan, more than 80% people who applied, have a history that they were able to clear the loan, about the same population are not self employed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "plt.subplot(221)\n",
    "train_df.Property_Area.value_counts().plot(kind = \"bar\", title = \"Property_Area\")\n",
    "plt.subplot(222)\n",
    "train_df.Education.value_counts().plot(kind = \"bar\", title = \"Education\")\n",
    "plt.subplot(223)\n",
    "train_df.Dependents.value_counts().plot(kind = \"bar\", title = \"Dependents\")\n",
    "plt.subplot(224)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From here we can infer that more people are from semiurban than urban than rural. More than 70% are graduate, most of the poeple have 0 dependents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.subplot(121)\n",
    "sns.distplot(train_df.ApplicantIncome)\n",
    "plt.subplot(122)\n",
    "train_df.ApplicantIncome.plot.box(figsize=(10,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df.boxplot(column=\"ApplicantIncome\", by = \"Education\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(1, figsize=(20, 15))\n",
    "plt.subplot(221)\n",
    "sns.distplot(train_df.CoapplicantIncome)\n",
    "plt.subplot(222)\n",
    "train_df.CoapplicantIncome.plot.box()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = train_df.dropna()\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.subplot(121)\n",
    "sns.distplot(train_df.LoanAmount)\n",
    "plt.subplot(122)\n",
    "train_df.LoanAmount.plot.box()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the distribution is normal while there are few outiers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's do some bivariate analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_vars = [\"Gender\", \"Married\", \"Self_Employed\", \"Credit_History\" ]\n",
    "for col in cat_vars:\n",
    "    sns.catplot(x = col, hue = \"Loan_Status\", kind = \"count\", data = df, height=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Married people have more loan_approval status rather than non married people\n",
    "There is nothing significant for self_employed people.\n",
    "Most of the people having bad credit_history got rejected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_vars = [\"Gender\", \"Married\", \"Self_Employed\", \"Credit_History\" ]\n",
    "ordinal_vars = [\"Dependents\", \"Education\", \"Property_Area\"]\n",
    "for val in ordinal_vars:\n",
    "    sns.catplot(x=val, hue = \"Loan_Status\", kind = \"count\", data = df, height=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Propotion of loans getting approved in semi urban area is higher than any other region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sns.catplot(x = \"ApplicantIncome\", kind= \"box\", hue = \"Loan_Status\", height=3, data = df)\n",
    "train_df.groupby(\"Loan_Status\")[\"ApplicantIncome\"].agg(\"mean\").plot(kind = \"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bins = [0, 2500, 4000, 6000, 81000] \n",
    "group = ['Low', \"Average\", \"High\", \"very high\"]\n",
    "train_df[\"Income_bin\"] = pd.cut(train_df.ApplicantIncome, bins, labels = group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.catplot(x = \"Income_bin\", hue = \"Loan_Status\", data = train_df, kind = \"count\", height = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "cols_to_drop = [\"Income_bin\"]\n",
    "train_df = train_df.drop(cols_to_drop, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df[\"Dependents\"].replace('3+', 3, inplace=True)\n",
    "train_df[\"Loan_Status\"].replace(\"N\", 0, inplace=True)\n",
    "test_df[\"Dependents\"].replace('3+', 3, inplace=True)\n",
    "train_df[\"Loan_Status\"].replace(\"Y\", 1, inplace=True)\n",
    "train_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_matrix = train_df.corr()\n",
    "f, ax = plt.subplots(figsize = (10,9))\n",
    "sns.heatmap(corr_matrix, vmax = 0.8, square = True, cmap=\"BuPu\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that ApplicantIncome is correlated with LoanAmount. Credit_History is correlated with Loan_Status, LoanAmount is correlated with CoApplicantIncome"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now let's fill the missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df.Gender.fillna(train_df.Gender.mode()[0], inplace = True)\n",
    "train_df.Married.fillna(train_df.Married.mode()[0], inplace=True)\n",
    "train_df.Dependents.fillna(train_df.Dependents.mode()[0], inplace=True)\n",
    "train_df.Self_Employed.fillna(train_df.Self_Employed.mode()[0], inplace=True)\n",
    "train_df.Credit_History.fillna(train_df.Credit_History.mode()[0], inplace=True)\n",
    "\n",
    "test_df.Gender.fillna(test_df.Gender.mode()[0], inplace = True)\n",
    "test_df.Married.fillna(test_df.Married.mode()[0], inplace=True)\n",
    "test_df.Dependents.fillna(test_df.Dependents.mode()[0], inplace=True)\n",
    "test_df.Self_Employed.fillna(test_df.Self_Employed.mode()[0], inplace=True)\n",
    "test_df.Credit_History.fillna(test_df.Credit_History.mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df.Loan_Amount_Term.value_counts()\n",
    "train_df.Loan_Amount_Term.fillna(train_df.Loan_Amount_Term.mode()[0], inplace=True)\n",
    "\n",
    "test_df.Loan_Amount_Term.value_counts()\n",
    "test_df.Loan_Amount_Term.fillna(test_df.Loan_Amount_Term.mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df.LoanAmount.fillna(train_df.LoanAmount.median(), inplace=True)\n",
    "\n",
    "test_df.LoanAmount.fillna(test_df.LoanAmount.median(), inplace=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we have seen that the plot of LoanAmount and we have seen that it is getting affected by the outliers so we can not impute the null values with mean because that will be affected by the outliers instead we will impute it by median values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now that no null values are there anymore so we are good to go\n",
    "\n",
    "Now since we know that have outliers really effect the algorithm in a way that mean and standard deviate deviates from what we are trying so lets transform the data and make it normally distributed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df['Loan_Amount_log'] = np.log(train_df.LoanAmount)\n",
    "test_df[\"Loan_Amount_log\"] = np.log(test_df.LoanAmount)\n",
    "sns.histplot(x= \"Loan_Amount_log\", data = train_df, bins= 20 , kde= True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's drop Loan ID from both train and test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = train_df.drop(\"Loan_ID\", axis = 1)\n",
    "test_df =  test_df.drop(\"Loan_ID\", axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.get_dummies(train_df)\n",
    "test = pd.get_dummies(test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = train_df.pop(\"Loan_Status\")\n",
    "X = train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.get_dummies(X)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's split the data in train and validation set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_cv, y_train, y_cv= train_test_split(X, y, test_size = 0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "model = LogisticRegression()\n",
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_cv = model.predict(x_cv)\n",
    "accuracy_score(y_cv, pred_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_test = model.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.read_csv(\"sample_submission_49d68Cx.csv\")\n",
    "submission.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.Loan_Status = pred_test\n",
    "submission.Loan_ID = test_copy.Loan_ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.Loan_Status.replace(0, 'N', inplace=True)\n",
    "submission.Loan_Status.replace(1, 'Y', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pd.DataFrame(submission, columns = [\"Loan_ID\", \"Loan_Status\"]).to_csv('logistic.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's do the stratified K fold cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedKFold\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 k fold {}'.format(i, kf.n_splits))\n",
    "    #print(train_index, test_index)\n",
    "    xtrain, xvalidation = X.iloc[train_index], X.iloc[test_index]\n",
    "    ytrain, yvalidation = y.iloc[train_index], y.iloc[test_index]\n",
    "    model = LogisticRegression(random_state = 0)\n",
    "    model.fit(xtrain, ytrain)\n",
    "    y_p = model.predict(xvalidation)\n",
    "    score = accuracy_score(y_p, yvalidation)\n",
    "    print(\"accuracy_score:{}\".format(score))\n",
    "    i+=1\n",
    "pred_test = model.predict(test)\n",
    "pred = model.predict_proba(xvalidation)[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "fpr, tpr, _ = metrics.roc_curve(yvalidation, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.Loan_Status = pred_test\n",
    "submission.Loan_ID = test_copy.Loan_ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.Loan_Status.replace(0, 'N', inplace=True)\n",
    "submission.Loan_Status.replace(1, 'Y', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(submission, columns = [\"Loan_ID\", \"Loan_Status\"]).to_csv('logistic.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Let's do some feature engineering!\n",
    "Let's create few new features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.columns\n",
    "train[\"Total_income\"] = train[\"ApplicantIncome\"] + train[\"CoapplicantIncome\"]\n",
    "test[\"Total_income\"] = test[\"ApplicantIncome\"] + test[\"CoapplicantIncome\"]\n",
    "train.columns\n",
    "train.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.distplot(train.Total_income)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train[\"EMI\"] = train.LoanAmount/train.Loan_Amount_Term\n",
    "test[\"EMI\"] = test.LoanAmount/test.Loan_Amount_Term\n",
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train[\"Balance_Income\"] = train.Total_income - (train[\"EMI\"]*1000)\n",
    "test[\"Balance_Income\"] = test.Total_income - (test[\"EMI\"]*1000)\n",
    "sns.distplot(train.Balance_Income)\n",
    "train.Balance_Income.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the graph is right skewed so let's do the transformation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train[\"Total_income\"] = np.log(1 + train.Total_income)\n",
    "train[\"Balance_Income\"] = np.log(1 + train[\"Balance_Income\"])\n",
    "sns.distplot(train.Total_income)\n",
    "test[\"Total_income\"] = np.log(1 + test.Total_income)\n",
    "test[\"Balance_Income\"] = np.log(1 + test[\"Balance_Income\"])\n",
    "\n",
    "train.Balance_Income.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can see that data is normally distributed so no worries now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.distplot(train.EMI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "cols = [\"ApplicantIncome\", \"CoapplicantIncome\", \"LoanAmount\", \"Loan_Amount_Term\"]\n",
    "train = train.drop(cols, axis = 1)\n",
    "test = test.drop(cols, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train.dropna()\n",
    "test.fillna(0, inplace = True)\n",
    "X = train.drop(\"Loan_Status\", axis = 1)\n",
    "y = train.Loan_Status\n",
    "test.Balance_Income.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's train on Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "i = 1\n",
    "kf = StratifiedKFold( n_splits=5, random_state = 0, shuffle = True)\n",
    "for train_index, test_index in kf.split(X, y):\n",
    "    print(\"\\n{} of kfold {}\".format(i, kf.n_splits))\n",
    "    X_train, X_val = X.iloc[train_index], X.iloc[test_index]\n",
    "    y_train, y_val = y.iloc[train_index], y.iloc[test_index]\n",
    "    model = LogisticRegression(random_state = 1)\n",
    "    model.fit(X_train, y_train)\n",
    "    y_pred = model.predict(X_val)\n",
    "    score = accuracy_score(y_pred, y_val)\n",
    "    print(score)\n",
    "    i+=1\n",
    "pred_test = model.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.Loan_Status = pred_test\n",
    "submission.Loan_ID = test_copy.Loan_ID\n",
    "submission.Loan_Status.replace(0, 'N', inplace=True)\n",
    "submission.Loan_Status.replace(1, 'Y', inplace=True)\n",
    "pd.DataFrame(submission, columns = [\"Loan_ID\", \"Loan_Status\"]).to_csv('Log2.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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