Loan Prediction Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1a797150-e66b-45be-a291-c9fd13b267d9",
   "metadata": {},
   "source": [
    "# Loan Prediction Project"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39982ced-1241-4d22-a51e-4f23b807d0b3",
   "metadata": {},
   "source": [
    "## Problem Statement : \n",
    "**Dream Housing Finance** company deals in all home loans. They have presence across all urban, semi urban and rural areas. Customer first apply for home loan after that company validates the customer eligibility for loan. Company wants to automate the loan eligibility process (real time) based on customer detail provided while filling online application form. These details are Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History and others. To automate this process, they have given a problem to identify the customers segments, those are eligible for loan amount so that they can specifically target these customers."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "185a9c67-9f59-4516-822e-7caf29f0833a",
   "metadata": {},
   "source": [
    "# Loading Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "703fc5b4-fe92-419c-a295-b396863fcc35",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# importing libraries\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt   # for plotting graphs\n",
    "%matplotlib inline\n",
    "import seaborn as sns   # for data visualisation\n",
    "import numpy as np   # for mathematical calculations\n",
    "import warnings           # to ignore any warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56e8ae0e-f9dd-42b4-b42a-d363a45fe33c",
   "metadata": {},
   "source": [
    "# Reading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2d14ad0a-f39b-44e7-a6f7-4950f207ca50",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"train_data.csv\")\n",
    "test = pd.read_csv(\"test_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e65c3eb8-5dc4-4da0-a7db-9dd407346c10",
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating copy of train and test data\n",
    "train_original = train.copy()\n",
    "test_original = test.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cd606c7-c371-4f9f-ab8d-e97bf9927731",
   "metadata": {},
   "source": [
    "## Understanding the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7044cc2c-b087-407e-a625-a883e4fa31ed",
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Structure of the training and test data\n",
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7c066747-82dd-401a-bf0a-05355c41f21a",
   "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'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0c9b7eb9-f54c-4c34-934a-fcbb09459c22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID               object\n",
       "Gender                object\n",
       "Married               object\n",
       "Dependents            object\n",
       "Education             object\n",
       "Self_Employed         object\n",
       "ApplicantIncome        int64\n",
       "CoapplicantIncome    float64\n",
       "LoanAmount           float64\n",
       "Loan_Amount_Term     float64\n",
       "Credit_History       float64\n",
       "Property_Area         object\n",
       "Loan_Status           object\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data types of variables in training data\n",
    "train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d36813dd-29f5-4346-bf2e-fa953dc097ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((614, 13), (367, 12))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shape of the dataset\n",
    "train.shape, test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e35f1d8-e887-41f8-8d1f-d43f54770596",
   "metadata": {},
   "source": [
    "# Univariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a7bdd84b-404a-481f-ae86-d6a3f30bf325",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Y    422\n",
       "N    192\n",
       "Name: Loan_Status, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First we will analyze the target variable : 'Loan_status'\n",
    "train['Loan_Status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9815fb49-16b1-47a9-bf6b-8cff610a0fa2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Y    0.687296\n",
       "N    0.312704\n",
       "Name: Loan_Status, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set normalize as True to print proportions instead of numeric values\n",
    "train['Loan_Status'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5b9a9130-702e-405e-8be6-50f73037bf71",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# Bar plot\n",
    "train['Loan_Status'].value_counts().plot.bar()\n",
    "plt.xlabel('Loan_Status')\n",
    "plt.ylabel('No. of customers')\n",
    "plt.title('Loan Status v/s no. of customers')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52fdc15e-4c29-4f3a-a08b-9498bfa7ab9a",
   "metadata": {},
   "source": [
    "Visualizing categorical and ordinal features first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6cc99f75-4f26-4ae5-8153-a6c65a45c293",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "plt.subplot(221)\n",
    "train['Gender'].value_counts(normalize=True).plot.bar(figsize=(15,10),title='Gender')\n",
    "plt.subplot(222)\n",
    "train['Married'].value_counts(normalize=True).plot.bar(title='Married')\n",
    "plt.subplot(223)\n",
    "train['Self_Employed'].value_counts(normalize=True).plot.bar(title='Self_Employed')\n",
    "plt.subplot(224)\n",
    "train['Credit_History'].value_counts(normalize=True).plot.bar(title='Credit_History')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8df29eb3-d770-4cc3-b3c7-014142dbfba8",
   "metadata": {},
   "source": [
    "From the bar plots above, following is inferred :\n",
    "\n",
    "1. 80% of applicants are male.\n",
    "2. 65% of the applicants are married.\n",
    "3. Around 15% are self employed.\n",
    "4. Around 85% have repaid their debts."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3afb2bb0-8d59-4a20-ae03-bd9820f4f880",
   "metadata": {},
   "source": [
    "Visualizing ordinal variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7810f350-d3a5-4f29-a027-bc54388a8a5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "plt.subplot(131)\n",
    "train['Dependents'].value_counts(normalize=True).plot.bar(figsize=(24,6),title='Dependents')\n",
    "plt.subplot(132)\n",
    "train['Education'].value_counts(normalize=True).plot.bar(title='Education')\n",
    "plt.subplot(133)\n",
    "train['Property_Area'].value_counts(normalize=True).plot.bar(title='Property_Area')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb9103f8-cb32-442d-8697-607893956d83",
   "metadata": {},
   "source": [
    "Inference :\n",
    "\n",
    "1. Most applicants have no dependents. This means that most applicants are fairly young.\n",
    "2. Around 80% of applicants are graduate.\n",
    "3. Most applicants are from semi-urban areas."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebc698d3-7a25-4143-b3a0-65ab288eb6fa",
   "metadata": {},
   "source": [
    "Visualizing numerical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "465de4b9-3569-4ba8-9055-521cdbf76214",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "plt.subplot(121)\n",
    "sns.distplot(train['ApplicantIncome'])\n",
    "plt.subplot(122)\n",
    "train['ApplicantIncome'].plot.box(figsize=(16,5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d44c7d3b-1f14-46af-9a6b-28b1dee7f293",
   "metadata": {},
   "source": [
    "Most of the data in the above distribution of applicant income is right-skewed or positively skewed, that is, it's not normally distributed."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbefb49c-2f82-4969-a5e9-fcacd43e73af",
   "metadata": {},
   "source": [
    "The boxplot shows the presence of lot of outliers/extreme values. This can be attributed to income disparity in the society. Part of this can be driven by the fact that we are looking at people with different education levels. Let us segregate them by Education :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3880a72c-f80e-4515-9a31-8d8c300a2d94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, '')"
      ]
     },
     "execution_count": 14,
     "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.boxplot(column=\"ApplicantIncome\", by=\"Education\")\n",
    "plt.suptitle(\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55e26f4b-5bb4-486a-ab39-2eda2b9fafa1",
   "metadata": {},
   "source": [
    "From above, we see that there are high number of graduates with very high income which are outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f257e220-3734-489d-844e-acfde1813983",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Coapplicant Income distribution\n",
    "plt.figure(1)\n",
    "plt.subplot(121)\n",
    "sns.distplot(train['CoapplicantIncome'])\n",
    "plt.subplot(122)\n",
    "train['CoapplicantIncome'].plot.box(figsize=(16,5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f89896ef-2c6b-4034-b2f0-fecc80975540",
   "metadata": {},
   "source": [
    "The distribution of coapplicant's income is similar to the distribution of applicant's income."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "292a1834-1c69-4153-848d-bfd02f4ffbfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Loan Amount distribution\n",
    "plt.figure(1)\n",
    "plt.subplot(121)\n",
    "df = train.dropna()\n",
    "sns.distplot(train['LoanAmount'])\n",
    "plt.subplot(122)\n",
    "train['LoanAmount'].plot.box(figsize=(16,5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7b92095-4b17-48cf-8bd6-9f1b43a77bb5",
   "metadata": {},
   "source": [
    "The distribution is fairly normal and has lot of outliers. Next, we see how each feature correlates with each Loan status."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac97edb3-e3ad-4363-94a8-244375e057b2",
   "metadata": {},
   "source": [
    "# Bivariate Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f1f75f3-65e1-449d-92e8-f0eba0a09414",
   "metadata": {},
   "source": [
    "We will test the following hypotheses using bivariate analysis\n",
    "\n",
    "* Applicants with high income should have more chances of loan approval\n",
    "* Applicants who have repaid their previous debts should have higher chance of loan approval\n",
    "* Loan approval should also depend on the loan amount\n",
    "* Lesser the amount to be paid monthly to repay the loan, higher the chances of loan approval"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de377dec-3804-491e-beab-5ec8f00a5f32",
   "metadata": {},
   "source": [
    "## Categorical Independent Variable vs Target variable"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35e2b9ec-1e03-47d3-95d9-dd17ca5826b5",
   "metadata": {},
   "source": [
    "### Gender and Loan Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "890fe540-5033-4364-b66e-1aad9ccf2853",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Gender = pd.crosstab(train['Gender'],train['Loan_Status'])\n",
    "Gender.div(Gender.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True, figsize=(6,6))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7682a909-4da5-4e5a-a8d3-5bce8853e5cd",
   "metadata": {},
   "source": [
    "It can be concluded that the proportion of both male and female applicants is more or less the same for both approved and unapproved loans."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1d53cc2b-0b0b-4724-9395-f2659a9c3c14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "plt.figure(1)\n",
    "# Married vs Loan Status\n",
    "Married = pd.crosstab(train['Married'],train['Loan_Status'])\n",
    "# Dependents vs Loan Status\n",
    "Dependents = pd.crosstab(train['Dependents'],train['Loan_Status'])\n",
    "# Education vs Loan Status\n",
    "Education = pd.crosstab(train['Education'],train['Loan_Status'])\n",
    "# Self_Employed Status vs Loan Status\n",
    "Self_Employed = pd.crosstab(train['Self_Employed'],train['Loan_Status'])\n",
    "\n",
    "Married.div(Married.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True)\n",
    "plt.show()\n",
    "\n",
    "Dependents.div(Dependents.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True)\n",
    "plt.show()\n",
    "\n",
    "Education.div(Education.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True)\n",
    "plt.show()\n",
    "\n",
    "Self_Employed.div(Self_Employed.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b128deca-02a3-4021-a55c-c3860e80ae3c",
   "metadata": {},
   "source": [
    "We see that\n",
    "\n",
    "* Proportion of unmarried applicants having unapproved loans is higher compared to married applicants. So, being married is more preferable than being unmarried especially when applying for loans.\n",
    "* Proportion of applicants with 2 dependents is higher for the approved loans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c843a4ec-451e-4cb2-819f-1ab06ece713b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "# Credit History vs Loan Status\n",
    "Credit_History = pd.crosstab(train['Credit_History'],train['Loan_Status'])\n",
    "# Property_Area vs Loan Status\n",
    "Property_Area = pd.crosstab(train['Property_Area'],train['Loan_Status'])\n",
    "\n",
    "Credit_History.div(Credit_History.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True)\n",
    "plt.show()\n",
    "\n",
    "Property_Area.div(Property_Area.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a28eff9-4562-415b-b4ba-830acef23dd0",
   "metadata": {},
   "source": [
    "* Applicants with credit history are more likely to get their loans approved\n",
    "* Semi-urban areas have higher chances of loan approval than rural and urban areas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef2e5a07-c50d-4b1a-9d37-2b97465bfce2",
   "metadata": {},
   "source": [
    "## Numerical Independent variable vs Target variable"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "400751e8-0207-4436-b7f9-6df9d139e009",
   "metadata": {},
   "source": [
    "Let's compare the mean income of applicants whose loan has been approved with the ones not having their loan approved"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "834b0da9-9cad-4a0b-b8e3-169379a7599f",
   "metadata": {},
   "outputs": [
    {
     "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.groupby('Loan_Status')['ApplicantIncome'].mean().plot.bar()\n",
    "plt.ylabel('Mean Applicant Income')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31d4f568-f1b4-440a-aad4-5bb6f5997ba3",
   "metadata": {},
   "source": [
    "There is no change in the mean income irrespective of the loan status. Let us make bins for the applicant income variable based on the values in it and analyze the corresponding loan status for each bin."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94402b4b-f06b-45ea-862c-2e345a3b2c9d",
   "metadata": {},
   "source": [
    "### Applicant Income vs Loan Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1035a566-3a30-4a6f-91e1-6eaa38ae4f93",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "bins = [0,2500,4000, 6000, 81000]\n",
    "group = ['Low', 'Average', 'High', 'Very high']\n",
    "train['Income_bin'] = pd.cut(train['ApplicantIncome'], bins, labels=group)\n",
    "\n",
    "Income_bin = pd.crosstab(train['Income_bin'], train['Loan_Status'])\n",
    "Income_bin.div(Income_bin.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True)\n",
    "plt.xlabel('Applicant Income')\n",
    "P = plt.ylabel('Percentage')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e84f8e1-1dbe-49a4-bd6c-b1fee89aeac1",
   "metadata": {},
   "source": [
    "We see that there is no effect of applicant income on the chances of loan approval. So, our hypothesis that higher income of applicant will get the loan approved is incorrect."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d56795d-069b-41b5-91de-5a4cee4f5486",
   "metadata": {},
   "source": [
    "### Co-applicant income and Loan Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "49ca4748-2f1c-4359-b5f0-145f0fe7e78f",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "bins = [0,1000,3200, 42000]\n",
    "group = ['Low', 'Average', 'High']\n",
    "train['Coapplicant_Income_bin'] = pd.cut(train['CoapplicantIncome'], bins, labels=group)\n",
    "\n",
    "Coapplicant_Income_bin = pd.crosstab(train['Coapplicant_Income_bin'], train['Loan_Status'])\n",
    "Coapplicant_Income_bin.div(Coapplicant_Income_bin.sum(1).astype(float), axis=0).plot(kind=\"bar\")\n",
    "plt.xlabel('Coapplicant Income')\n",
    "P = plt.ylabel('Percentage')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "785d84fa-e8a5-496f-aa68-bf4ffdc1f0cb",
   "metadata": {},
   "source": [
    "This shows that if the coapplicant's income is less, then the chances of loan approval are high. This looks absurd. The reason behind this could be that most of the applicants don't have a coapplicant, so the coapplicant income is zero for these applicants. So, the loan approval does not depend on coapplicant income. So, to get a better idea, we will combine the applicant's and coapplicant's income to visualize the combined effect on loan approval. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce4201ab-1762-4532-ad7a-4ef2a32a320f",
   "metadata": {},
   "source": [
    "### Total Income and Loan Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "260540ca-9d38-4f86-b929-3e6a32e90e6b",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# total income\n",
    "train['Total_income'] = train['ApplicantIncome']+train['CoapplicantIncome']\n",
    "\n",
    "bins = [0, 2500, 4000, 6000, 81000]\n",
    "group = ['Low', 'Average', 'High', 'Very High']\n",
    "train['Total_Income_bin'] = pd.cut(train['Total_income'], bins, labels=group)\n",
    "\n",
    "Total_Income_bin = pd.crosstab(train['Total_Income_bin'], train['Loan_Status'])\n",
    "Total_Income_bin.div(Total_Income_bin.sum(1).astype(float), axis=0).plot(kind=\"bar\")\n",
    "plt.xlabel('Total Income')\n",
    "P = plt.ylabel('Percentage')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e51a79f-5b8f-4da0-9372-42fc5f60164a",
   "metadata": {},
   "source": [
    "We see that the applicants having low total income have less chances of loan approval compared to applicants having average, high, and very high total income."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c2ec281-6fa8-411c-adfe-62baf1b6fd09",
   "metadata": {},
   "source": [
    "### Loan Amount and Loan Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6132059e-ff31-411f-80b3-2b3da1cc5dec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JcJGZmbVyaYugtvXSHl8wM7MWLG0RLJB0o6SDJB0o6ZvAwiyDmZlZZaQtgs+SXFB2DzAb2AhcklUoMzOrnAaHdyS1B34aESdXII+ZmVVYg3sEEbENeEtSzwrkMTOzCkt7wHcTsETSI8CbO2ZGxGWZpDIzs4pJWwQPlr7MzKyNSXtl8e2lew31i4hlGWcyM7MKSntl8WnAIpL7DSFpqKQ5GeYyM7MKSXv66CTgSGAdQEQsAvzUMjOzNiBtEWwtv8VESTR3GDMzq7y0RfC8pPOA9qXHVH4HeLKhN0k6RdIyScslXVXPeh+QtE3SWSnzmJlZM2nMlcWHkTyM5i5gPTChvjeULkS7GRgJDALOlTSojvWuBR5OndrMzJpNvWcNSeoCjAfeAywBjo6IrSm3fSSwPCJWlLZ1N3A68EKN9T4L/AT4QCNym5lZM2loj+B2YDhJCYwErm/EtvcHVpZNV5fm7SRpf+BMYFp9G5I0TtICSQtWr17diAhmZtaQhq4jGBQRRwBImgE83Yht1/Zw+5oHmL8FXBkR26TaVi+9KWI6MB1g+PDhPkhtZtaMGiqCLTteRMTW+n5Z16Ia6Fs2XQWsqrHOcODu0nb3AT4maWtE3N+Yb2RmZk3XUBEMkfR66bWArqVpARERPep57zPAQEkDgL8Ao4HzyleIiJ3XIki6DXjAJWBmAP033ZV3hEy9nHeAMvUWQUS0b+qGS3sQl5KcDdQemBkRSyWNLy2v97iAmZlVRqaPm4yIucDcGvNqLYCIuDDLLGZmVru01xGYmVkb5SIwMys4F4GZWcG5CMzMCs5FYGZWcC4CM7OCcxGYmRWci8DMrOBcBGZmBeciMDMrOBeBmVnBuQjMzArORWBmVnAuAjOzgsv0NtRtgR+OYWZtnfcIzMwKzkVgZlZwHhqyNs1De2YN8x6BmVnBuQjMzArORWBmVnAuAjOzgnMRmJkVnIvAzKzgXARmZgXnIjAzKzgXgZlZwbkIzMwKzkVgZlZwLgIzs4JzEZiZFZyLwMys4DItAkmnSFomabmkq2pZPkbS4tLXk5KGZJnHzMx2lVkRSGoP3AyMBAYB50oaVGO1PwIfjojBwDXA9KzymJlZ7bLcIzgSWB4RKyJiM3A3cHr5ChHxZES8VpqcD1RlmMfMzGqRZRHsD6wsm64uzavLRcDPa1sgaZykBZIWrF69uhkjmplZlkWgWuZFrStKJ5AUwZW1LY+I6RExPCKG9+7duxkjmplZls8srgb6lk1XAatqriRpMHALMDIi1maYx8zMapHlHsEzwEBJAyR1AkYDc8pXkNQPuBf4RET8PsMsZmZWh8z2CCJiq6RLgYeB9sDMiFgqaXxp+TTgK0Av4LuSALZGxPCsMpmZ2a6yHBoiIuYCc2vMm1b2+tPAp7PMYGZm9fOVxWZmBeciMDMrOBeBmVnBuQjMzArORWBmVnAuAjOzgnMRmJkVnIvAzKzgXARmZgXnIjAzKzgXgZlZwbkIzMwKzkVgZlZwLgIzs4JzEZiZFZyLwMys4FwEZmYF5yIwMys4F4GZWcG5CMzMCs5FYGZWcC4CM7OCcxGYmRWci8DMrOBcBGZmBeciMDMrOBeBmVnBuQjMzArORWBmVnAuAjOzgnMRmJkVnIvAzKzgXARmZgWXaRFIOkXSMknLJV1Vy3JJmlpavljSsCzzmJnZrjIrAkntgZuBkcAg4FxJg2qsNhIYWPoaB3wvqzxmZla7LPcIjgSWR8SKiNgM3A2cXmOd04E7IjEf2FPSvhlmMjOzGjpkuO39gZVl09XAUSnW2R94pXwlSeNI9hgANkha1rxRW5R9gDWV+ma6tlLfqTD8+bVebf2zO6CuBVkWgWqZF01Yh4iYDkxvjlAtnaQFETE87xzWNP78Wq8if3ZZDg1VA33LpquAVU1Yx8zMMpRlETwDDJQ0QFInYDQwp8Y6c4CxpbOHRgDrI+KVmhsyM7PsZDY0FBFbJV0KPAy0B2ZGxFJJ40vLpwFzgY8By4G3gE9mlacVKcQQWBvmz6/1Kuxnp4hdhuTNzKxAfGWxmVnBuQjMzArORWBmVnAughZAUpe8M5hZcflgcQsgaTnwN+AJYB7w64hYn28qS0vSscDAiLhVUm+gW0T8Me9clo6k/Umuut15FmVEzMsvUeW5CFoISf2A44BjSE6pXRcRQ3MNZQ2SNBEYDrw3Ig6WtB/wo4g4JudoloKka4FRwAvAttLsiIh/yS9V5WV5iwlLSVIVSQEcBwwBlgK/yjWUpXUm8D7gWYCIWCWpe76RrBHOICnxt/MOkicXQcvwZ5IrsSdHxPi8w1ijbI6IkBQAkt6VdyBrlBVAR8BFYLl7H3AscF7pAT4vAY9HxIx8Y1kKsyX9D8kt1C8GPgV8P+dM1gBJ3yG5weVbwCJJj1JWBhFxWV7Z8uBjBC2EpG4kZXAccD7JOGX/XENZKpI+Avwzyd10H46IR3KOZA2QdEF9yyPi9kplaQlcBC2ApAVAZ+BJkmMD8yLiT/mmMrOicBG0AJJ6R8TqvHNY40l6g12fobEeWABcERErKp/K0pK0hLo/v69FxNrKp6o8HyNoGTZLuhH4UGn6ceC/fC1Bq3AjyTM07iIZGhoN/BOwDJgJHJ9bMkvj5ySnjd5Vmh5N8jmuB24DTssnVmV5j6AFkPQT4Hlgx7jkJ4AhEfHx/FJZGpKeioijasybHxEjJP02Iobklc0aJunXNa/52DFP0pKIOCKvbJXkW0y0DAdFxMSIWFH6+ipwYN6hLJXtks6R1K70dU7ZMv+V1fJ1k7SzyCUdCXQrTW7NJ1LleWioZdgo6diI+BWApGOAjTlnsnTGAN8Gvkvyi38+cL6krsCleQazVD4NzCydtSfgdeDTpetBvpFrsgry0FALIGkIcAfQszTrNeCCiFicXyqz4pDUk+T34bq8s+TBRdCCSOoBEBGvS5oQEd/KOZI1oHTn2IuAw4Cdd5GNiE/lFsoaJOn8iLhT0uW1LY+IGyudKU8+RtCCRMTrEfF6abLW/4Fai/MDkrOEPkpytlcV8EauiSyNHbcC6V7HV6F4j6CFkrQyIvrmncPqJ+m5iHifpMURMVhSR5Kri0/MO5tZWj5Y3HK5oVuHLaX/rpN0OPBXoH9+cSwNSVPrW160ew25CHJUx1WpkJy90LXCcaxppkvaC/gSMIfk1MMv5xvJUlhY9vqrwMS8grQEHhoyayJJ7YCzImJ23lms6XYM7+WdI08+WGzWRBGxHV8r0BYU/q9hF4HZ7nlE0r9L6itp7x1feYcyawwPDZntBkm1PaQ+IsK3CGnBahyf24PkATWQHJ+LiOiRS7CcuAjMzArOQ0Nmu0HSHpK+JGl6aXqgpFPzzmXWGC4Cs91zK7AZ+GBpuhr4Wn5xzBrPRWC2ew6KiOsoXVgWERtJxpnNWg0Xgdnu2Vy65XQASDoIeDvfSGaN4yuLzXbPJOAhoK+kWcAxwIV5BjJrLJ81ZLabJPUCRpAMCc2PiDU5RzJrFO8RmO0GSXOAHwJzIuLNvPOYNYWPEZjtnhuA44AXJP1I0lmlh9WYtRoeGjJrBpLaAycCFwOnFO3KVGvdPDRktptKZw2dBowChgG35RrIrJE8NGS2GyTdA7xIsjdwE3AB0D7XUGaN5CIw2z23AmcDr5def5WkGMxaDQ8NmTWBpIOB0cC5wFrgHpJjbifkGsysCXyw2KwJJG0HngAuiojlpXkrfPtpa408NGTWNP9K8qD6/5X0fUkn4XsMWSvlPQKz3SDpXcAZJENEJwK3A/dFxC/yzGXWGC4Cs2ZSekTl2cCoiDgx7zxmabkIzMwKzscIzMwKzkVgZlZwLgJrUyRtqPD3O1NSSDqkkt+3lhwTJO2RZwZrvVwEZrvnXOBXJBeX5WkC4CKwJnERWJsnaaik+ZIWS7pP0l6l+RdLekbSbyX9ZMdf1JJukzRV0pOSVkg6q47tdiN5ItlFlBWBpOMlPS5ptqTfS5oiaYykpyUtKT3OEkkHSHq0lOtRSf3Kvv9ZZdvbULbdxyT9WNLvJM1S4jJgP5JrGv43k39Ea9NcBFYEdwBXRsRgYAkwsTT/3oj4QEQMIbk/0EVl79kXOBY4FZhSx3bPAB6KiN8Df5c0rGzZEOBzwBHAJ4CDI+JI4Bbgs6V1bgLuKOWaBUxN8bO8j+Sv/0HAgcAxETEVWAWc4FtcWFO4CKxNk9QT2DMiHi/Nuh34UOn14ZKekLQEGAMcVvbW+yNie0S8APSpY/PnAneXXt9dmt7hmYh4JSLeBv4A7LjAbAnQv/T6aOCu0usfkBRPQ56OiOqI2A4sKtuWWZP5pnNWZLcBZ0TEbyVdCBxftuztste73Dqi9JziE0nKJEhuPR2S/qOW928vm95O3f+/23FRz1ZKf6RJEtCpjlzb6tmWWWreI7A2LSLWA69JOq406xPAjr2D7sArkjqS7BE0xlkkwzoHRET/iOgL/JF0f9Xv8CT/OLYwhuSgM8DLwPtLr08HOqbY1hskP49Zo/mvCWtr9pBUXTZ9I8nDYqaVDgavAD5ZWvZl4CngTyRDNo35RXouux47+AlwHsktqdO4DJgp6QvA6rJc3wd+Kulp4FHgzRTbmg78XNIrPk5gjeVbTJiZFZyHhszMCs5FYGZWcC4CM7OCcxGYmRWci8DMrOBcBGZmBeciMDMruP8D6eer4sFy0ZUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = [0, 100, 200, 700]\n",
    "group = ['Low', 'Average', 'High']\n",
    "train['Loan_Amount_bin'] = pd.cut(train['LoanAmount'], bins, labels=group)\n",
    "\n",
    "Loan_Amount_bin = pd.crosstab(train['Loan_Amount_bin'], train['Loan_Status'])\n",
    "Loan_Amount_bin.div(Loan_Amount_bin.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True)\n",
    "plt.xlabel('Loan Amount')\n",
    "P = plt.ylabel('Percentage')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72f39699-9577-4a8e-a897-3b68c4003a28",
   "metadata": {},
   "source": [
    "Our hypothesis that the chances of loan approval will be high when the loan amount is less, is correct."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "97848aa1-a7c4-4781-b5a3-51ec11c5bb41",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# drop the bins created in the exploration part\n",
    "train = train.drop(['Income_bin','Coapplicant_Income_bin','Total_Income_bin','Loan_Amount_bin'],axis=1)\n",
    "\n",
    "# replace'3+' in dependents column as '3' to fit the data in the model\n",
    "train['Dependents'].replace('3+',3, inplace=True)\n",
    "test['Dependents'].replace('3+',3, inplace=True)\n",
    "\n",
    "# replace 'N' with 0 and 'Y' with 1\n",
    "train['Loan_Status'].replace('N',0,inplace=True)\n",
    "train['Loan_Status'].replace('Y',1, inplace=True)\n",
    "\n",
    "# replace Male with 1 and female with 0\n",
    "train['Gender'] = train['Gender'].map({'Male':1, 'Female':0})\n",
    "test['Gender'] = test['Gender'].map({'Male':1, 'Female':0})\n",
    "\n",
    "# replace in Married column : Yes with 1, No with 0\n",
    "train['Married'] = train['Married'].map({'Yes':1, 'No':0})\n",
    "test['Married'] = test['Married'].map({'Yes':1, 'No':0})\n",
    "\n",
    "# replace in Education : Graduate as 1, Not grad as 0\n",
    "train['Education'] = train['Education'].map({'Graduate':1, 'Not Graduate':0})\n",
    "test['Education'] = test['Education'].map({'Graduate':1, 'Not Graduate':0})\n",
    "\n",
    "# replace in Self_Employed : Yes with 1, No with 0\n",
    "train['Self_Employed'] = train['Self_Employed'].map({'Yes':1, 'No':0})\n",
    "test['Self_Employed'] = test['Self_Employed'].map({'Yes':1, 'No':0})\n",
    "\n",
    "# replace in Property_Area : Urban with 2, Rural with 0, Semi-urban with 1\n",
    "train['Property_Area'] = train['Property_Area'].map({'Urban':2, 'Rural':0, 'Semiurban':1})\n",
    "test['Property_Area'] = test['Property_Area'].map({'Urban':2, 'Rural':0, 'Semiurban':1})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42fb74b7-be97-414f-b943-9b97a3bf1154",
   "metadata": {},
   "source": [
    "### Correlation among numerical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2a684170-1a8c-46cd-a5aa-ba78c5f91bb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "matrix = train.corr()\n",
    "fig, ax = plt.subplots(figsize=(9,6))\n",
    "sns.heatmap(matrix, vmax=.8, square=True, cmap=\"BuPu\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93c46e71-cba6-494b-9c10-9f8330ad5007",
   "metadata": {},
   "source": [
    "* It can be inferred that the credit history is correlated to the loan status, as observed earlier.\n",
    "* Applicant Income is correlated to Loan Amount\n",
    "* Coapplicant Income is also correlated to the Loan Amount."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9aaa5cb-f04e-41ef-bd81-e7b908c845cd",
   "metadata": {},
   "source": [
    "# Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98055d5b-ce84-496c-8387-9a2ddee8a1c4",
   "metadata": {},
   "source": [
    "We will now check if there are anu missing values or outliers. We will impute any missing values and remove outliers so that they do not affect the model performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "92be4c6b-c7a0-4b54-b23c-f07db41878f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID               0\n",
       "Gender               13\n",
       "Married               3\n",
       "Dependents           15\n",
       "Education             0\n",
       "Self_Employed        32\n",
       "ApplicantIncome       0\n",
       "CoapplicantIncome     0\n",
       "LoanAmount           22\n",
       "Loan_Amount_Term     14\n",
       "Credit_History       50\n",
       "Property_Area         0\n",
       "Loan_Status           0\n",
       "Total_income          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6548449-0bd8-474a-abef-488fbb504ad3",
   "metadata": {},
   "source": [
    "We will use the following method to impute missing values :\n",
    "\n",
    "* **For numerical variables :** impute using mean/median\n",
    "* **For categorical variables :** impute using mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "bdb85e33-454b-44a2-8582-dcebebb1dcb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Gender'].fillna(train['Gender'].mode()[0],inplace=True)\n",
    "train['Married'].fillna(train['Married'].mode()[0],inplace=True)\n",
    "train['Dependents'].fillna(train['Dependents'].mode()[0],inplace=True)\n",
    "train['Self_Employed'].fillna(train['Self_Employed'].mode()[0],inplace=True)\n",
    "train['Credit_History'].fillna(train['Credit_History'].mode()[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "22afca14-d9da-4c74-ab3e-175a624ff4d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Next, we find a way to fill the missing values in the Loan Amount term feature\n",
    "\n",
    "# value counts of the Loan Amount term variable\n",
    "train['Loan_Amount_Term'].value_counts()\n",
    "\n",
    "# impute the missing values in this column with '360' since it has the highest value count\n",
    "train['Loan_Amount_Term'].fillna(train['Loan_Amount_Term'].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00761a5e-f534-415b-8bf6-6fabf2e7688c",
   "metadata": {},
   "source": [
    "Next, we impute the numerical variable 'LoanAmount' with median as it also has outliers and the mean is not a good approach as it is highly affected \n",
    "by outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c449539e-bb6a-40b9-9d8f-55dd73b6d0aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "train['LoanAmount'].fillna(train['LoanAmount'].median(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ab004162-c7ff-4788-8599-d9e2d7a44218",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID              0\n",
       "Gender               0\n",
       "Married              0\n",
       "Dependents           0\n",
       "Education            0\n",
       "Self_Employed        0\n",
       "ApplicantIncome      0\n",
       "CoapplicantIncome    0\n",
       "LoanAmount           0\n",
       "Loan_Amount_Term     0\n",
       "Credit_History       0\n",
       "Property_Area        0\n",
       "Loan_Status          0\n",
       "Total_income         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check if there are any missing value still left in the data\n",
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d3455f8-65e6-44ee-a947-a72931a7b466",
   "metadata": {},
   "source": [
    "We have imputed all missing values. So, we now impute the missing values from the test data with same approach as above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "07b78678-814a-4ef5-89d4-e257bc2e3bde",
   "metadata": {},
   "outputs": [],
   "source": [
    "test['Gender'].fillna(train['Gender'].mode()[0],inplace=True)\n",
    "test['Married'].fillna(train['Married'].mode()[0],inplace=True)\n",
    "test['Dependents'].fillna(train['Dependents'].mode()[0],inplace=True)\n",
    "test['Self_Employed'].fillna(train['Self_Employed'].mode()[0],inplace=True)\n",
    "test['Credit_History'].fillna(train['Credit_History'].mode()[0],inplace=True)\n",
    "test['Loan_Amount_Term'].fillna(train['Loan_Amount_Term'].mode()[0], inplace=True)\n",
    "test['LoanAmount'].fillna(train['LoanAmount'].median(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a1a53bef-45f4-4d08-bf1f-9a5d5885625c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID              0\n",
       "Gender               0\n",
       "Married              0\n",
       "Dependents           0\n",
       "Education            0\n",
       "Self_Employed        0\n",
       "ApplicantIncome      0\n",
       "CoapplicantIncome    0\n",
       "LoanAmount           0\n",
       "Loan_Amount_Term     0\n",
       "Credit_History       0\n",
       "Property_Area        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9e2df32-8bd2-4ae2-aa8b-663b65420df1",
   "metadata": {},
   "source": [
    "### Outlier Treatment"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bc98382-afb9-4b19-8c95-562b39b9e5c7",
   "metadata": {},
   "source": [
    "Having outliers in the data can have a significant effect on the mean and the standard deviation and thus, affecting the distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3ca56496-e4bf-48ee-b4aa-276f6233a0ca",
   "metadata": {},
   "outputs": [
    {
     "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['LoanAmount'].hist(bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2e86bf9-5c2e-4b54-87d9-19aa1074d912",
   "metadata": {},
   "source": [
    "We see that the bulk of the data is on the left and the right tail is longer. This is called **right-skewness**. So, we will apply log transfomation on this column. After log transformation, we will get a distribution similar to normal distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b04e375b-796a-4793-a093-78223173d9fa",
   "metadata": {},
   "outputs": [
    {
     "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['LoanAmount_log'] = np.log(train['LoanAmount'])\n",
    "train['LoanAmount_log'].hist(bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6a53f7fc-243d-42df-afbe-9999bd6142a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "test['LoanAmount_log'] = np.log(test['LoanAmount'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ac1975d-538d-4675-9fad-87d3312ccc70",
   "metadata": {},
   "source": [
    "Next, we will build logistic regresson model and make predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "749b0f88-5236-4863-a031-f320f2b12ca4",
   "metadata": {},
   "source": [
    "## Model Building 1 - Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8d556c29-5c3c-4244-ac2a-d97bfabb7ffc",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# drop the target variable from the 'LoanID' variable as it has no effect on 'LoanStatus'\n",
    "train = train.drop(['Loan_ID','Total_income'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "3a6bfe9e-6d30-4144-b199-090a2bb0866f",
   "metadata": {},
   "outputs": [],
   "source": [
    "test = test.drop('Loan_ID',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0112a662-4cb6-48d4-be2a-cac2c024d3ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# store the target variable in another dataset\n",
    "X = train.drop('Loan_Status',axis=1)\n",
    "y = train['Loan_Status']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f88762f-d435-4119-ad2b-77ede193bf75",
   "metadata": {},
   "source": [
    "### Splitting the dataset into train and validation data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c5041a77-966c-40b2-897a-1a7515cc7b55",
   "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": "code",
   "execution_count": 41,
   "id": "2355970c-61e5-4cfd-9f11-1f44670f3cc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "model = LogisticRegression()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a09d84c5-1c96-4cfe-9834-f127431dc0f9",
   "metadata": {},
   "source": [
    "Here C=1.0 represents the inverse of regularization strength. Regularization is the penalty applied to increasing the magnitude of parameter values. This is done to reduce overfitting of the model on the training data. Smaller values of C signify stronger regularization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f205dbaa-f294-43f3-aa35-e0bc44ee54f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# predict Loan Status on validation data\n",
    "pred_cv = model.predict(X_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0ccb601b-e584-480a-bfe9-6e6979b03a23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8054054054054054"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find accuracy score\n",
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_cv, pred_cv)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3514c25f-da73-4f75-b98d-423420c9aaf1",
   "metadata": {},
   "source": [
    "## Predict Loan Status on test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "10cf6449-1d5b-4e91-aa01-6fa6c66171d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_test = model.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b665aed7-6a9e-4788-b788-42040f931f8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import submission file 1\n",
    "submission = pd.read_csv('sample_submission_49d68Cx.csv')\n",
    "\n",
    "submission['Loan_Status'] = pred_test\n",
    "submission['Loan_ID'] = test_original['Loan_ID']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a03fb880-1270-469c-b19b-d124e76ef9d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace Y with 1 and N with 0\n",
    "submission['Loan_Status'].replace(0, 'N', inplace=True)\n",
    "submission['Loan_Status'].replace(1, 'Y', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "71ee5167-76f3-4304-85a5-bcd6276725aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# new file with final submission\n",
    "pd.DataFrame(submission, columns=['Loan_ID','Loan_Status']).to_csv('logistic.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9468ba3-21c0-4b79-86f8-929083001522",
   "metadata": {},
   "source": [
    "### Logistic Regression using Stratified k-folds cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "6e75b119-5095-4e9e-adf2-47c1915421c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1 of kfold 5 \n",
      "accuracy_score 0.8048780487804879\n",
      "\n",
      "2 of kfold 5 \n",
      "accuracy_score 0.8373983739837398\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",
    "\n",
    "    print ('\\n{} of kfold {} '.format(i,kf.n_splits))\n",
    "\n",
    "    xtr,xval = X.iloc[train_index],X.iloc[test_index]\n",
    "\n",
    "    ytr,yval = y.iloc[train_index],y.iloc[test_index]\n",
    "\n",
    "    model = LogisticRegression(random_state=1)\n",
    "\n",
    "    model.fit(xtr,ytr)\n",
    "\n",
    "    pred_test=model.predict(xval)\n",
    "\n",
    "    score=accuracy_score(yval,pred_test)\n",
    "\n",
    "    print ('accuracy_score',score)\n",
    "\n",
    "    i+=1\n",
    "\n",
    "pred_test2 = model.predict(test)\n",
    "\n",
    "pred = model.predict_proba(xval)[:,1]    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a994bad-c4d1-4a1e-921c-d6db571eb3ba",
   "metadata": {},
   "source": [
    "### Visualizing the ROC curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "15d813ab-932b-49b6-b687-bcfd38fdad85",
   "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",
    "\n",
    "fpr, tpr, _ = metrics.roc_curve(yval, pred)\n",
    "auc = metrics.roc_auc_score(yval, pred)\n",
    "\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",
   "id": "7cd04b81-ebbd-4abe-a94d-6a6900c7e536",
   "metadata": {},
   "source": [
    "We get an auc value of 0.74"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "332d7d4c-d0f4-407e-b5b8-54806f28c3ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import submission file 2\n",
    "submission['Loan_Status'] = pred_test2\n",
    "submission['Loan_ID'] = test_original['Loan_ID']\n",
    "\n",
    "submission['Loan_Status'].replace(0, 'N', inplace=True)\n",
    "submission['Loan_Status'].replace(1, 'Y', inplace=True)\n",
    "\n",
    "pd.DataFrame(submission, columns=['Loan_ID','Loan_Status']).to_csv('Logistic1.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "899a319a-75fd-44a8-a61b-68ecc10d90a6",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84315c66-a419-4078-8f69-d2aa7efcdb35",
   "metadata": {},
   "source": [
    "We can also make new features from existing features to make better predictions. Some new features are listed below:\n",
    "\n",
    "* Total Income - If total income (Applicant income+Coapplicant income) is high, then chanes of loan approval also become high\n",
    "* EMI - This is the monthly amount to be paid by the applicant to repay the loan. It can be calculated by taking the ratio of loan amount to the loan amount term\n",
    "* Balance Income - amount left after paying the EMI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b0682f6c-67d7-49fe-a326-39db133f647a",
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Total_Income'] = train['ApplicantIncome'] + train['CoapplicantIncome']\n",
    "test['Total_Income'] = test['ApplicantIncome'] + test['CoapplicantIncome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "8c097236-6749-429d-b8d7-1f95a019c920",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Total_Income', ylabel='Density'>"
      ]
     },
     "execution_count": 52,
     "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": [
    "sns.distplot(train['Total_Income'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98169d95-768d-48dc-a297-f3f9a0bb3999",
   "metadata": {},
   "source": [
    "We can see that the distribution is right-skewed. So, we take the log transformation to get normal distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "936ce0d3-c449-4f8d-8d31-4d3af1b7e76a",
   "metadata": {},
   "outputs": [
    {
     "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['Total_Income_log'] = np.log(train['Total_Income'])\n",
    "sns.distplot(train['Total_Income_log'])\n",
    "test['Total_Income_log'] = np.log(test['Total_Income'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8eceec23-8ab2-4d1b-939c-6a1ffea618ff",
   "metadata": {},
   "source": [
    "It can be seen that the effect of extreme values has been significantly subsided."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "472af1b2-8fb7-4bd4-81be-113ba903663f",
   "metadata": {},
   "outputs": [],
   "source": [
    "train['EMI'] = train['LoanAmount']/train['Loan_Amount_Term']\n",
    "test['EMI'] = test['LoanAmount']/test['Loan_Amount_Term']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "ec4ebd87-357e-4f4c-880b-63b99ed9268c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='EMI', ylabel='Density'>"
      ]
     },
     "execution_count": 55,
     "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": [
    "# distribution of EMI variable\n",
    "sns.distplot(train['EMI'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "aefe8d86-dcb6-4ead-9008-5da1a1a95459",
   "metadata": {},
   "outputs": [],
   "source": [
    "# multiply by 1000 to make the units equal\n",
    "train['Balance_Income'] = train['Total_Income']-(train['EMI']*1000)\n",
    "test['Balance_Income'] = test['Total_Income']-(test['EMI']*1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "5083c185-e20e-42bc-a9b0-9acd1e7a638f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Balance_Income', ylabel='Density'>"
      ]
     },
     "execution_count": 57,
     "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": [
    "# distribution of Balance income\n",
    "sns.distplot(train['Balance_Income'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9d04abf-16fd-45d2-9849-52e6813ecfac",
   "metadata": {},
   "source": [
    "We will now remove the features used to create these new features. This is because the correlation between the old and the new features will be very high and Logistic regression assumes that the variables are not correlated. Removing these features will also help remove the noise too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "a05215ae-3ee7-4030-a3f9-4832bb355487",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train.drop(['ApplicantIncome','CoapplicantIncome','LoanAmount','Loan_Amount_Term'],axis=1)\n",
    "test = test.drop(['ApplicantIncome','CoapplicantIncome','LoanAmount','Loan_Amount_Term'],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3218aaba-42e4-479e-b7f5-4062f8bf1a81",
   "metadata": {},
   "source": [
    "Let's prepare data to feed into the models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "400a9fa1-5b09-4a7a-9d4a-b33e27fc29c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train.drop('Loan_Status',1)\n",
    "y = train.Loan_Status"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64c7cb7b-0ee7-42f2-944a-880485dcdd3b",
   "metadata": {},
   "source": [
    "## 1- Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "38424f4d-9c11-4ecd-973b-92713242a22c",
   "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 0.6910569105691057\n",
      "\n",
      "3 of kfold 5 \n",
      "accuracy_score 0.6666666666666666\n",
      "\n",
      "4 of kfold 5 \n",
      "accuracy_score 0.8048780487804879\n",
      "\n",
      "5 of kfold 5 \n",
      "accuracy_score 0.680327868852459\n"
     ]
    }
   ],
   "source": [
    "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",
    "\n",
    "    print ('\\n{} of kfold {} '.format(i,kf.n_splits))\n",
    "\n",
    "    xtr,xval = X.iloc[train_index],X.iloc[test_index]\n",
    "\n",
    "    ytr,yval = y.iloc[train_index],y.iloc[test_index]\n",
    "\n",
    "    model = LogisticRegression(random_state=1)\n",
    "\n",
    "    model.fit(xtr,ytr)\n",
    "\n",
    "    pred_test=model.predict(xval)\n",
    "\n",
    "    score=accuracy_score(yval,pred_test)\n",
    "\n",
    "    print ('accuracy_score',score)\n",
    "\n",
    "    i+=1\n",
    "\n",
    "pred_test3 = model.predict(test)\n",
    "\n",
    "pred = model.predict_proba(xval)[:,1] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "10e5c04f-e254-4eff-ac3d-a454f1f9021e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import submission file 3\n",
    "submission['Loan_Status'] = pred_test3\n",
    "submission['Loan_ID'] = test_original['Loan_ID']\n",
    "\n",
    "submission['Loan_Status'].replace(0, 'N', inplace=True)\n",
    "submission['Loan_Status'].replace(1, 'Y', inplace=True)\n",
    "\n",
    "pd.DataFrame(submission, columns=['Loan_ID','Loan_Status']).to_csv('Logistic2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "ae6a19ef-2963-49b3-b20a-6745a0fcf151",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.731187524990004"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1 = np.array([0.8130081300813008,0.6910569105691057,0.6666666666666666,0.8048780487804879,0.680327868852459])\n",
    "mean_acc = np.mean(arr1)\n",
    "mean_acc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8480243-4f66-447e-9097-bd384b1d38fc",
   "metadata": {},
   "source": [
    "Mean accuracy of Logistic Regression is 73.11%"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8155e805-3269-4b41-b0cb-4d2c26caa8d7",
   "metadata": {},
   "source": [
    "## 2- Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "2803c625-1a19-4332-ad2f-74447565488b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1 of kfold 5 \n",
      "accuracy_score 0.7398373983739838\n",
      "\n",
      "2 of kfold 5 \n",
      "accuracy_score 0.6910569105691057\n",
      "\n",
      "3 of kfold 5 \n",
      "accuracy_score 0.7154471544715447\n",
      "\n",
      "4 of kfold 5 \n",
      "accuracy_score 0.7235772357723578\n",
      "\n",
      "5 of kfold 5 \n",
      "accuracy_score 0.6557377049180327\n"
     ]
    }
   ],
   "source": [
    "from sklearn import tree\n",
    "\n",
    "i=1\n",
    "kf_dt = StratifiedKFold(n_splits=5, random_state=1, shuffle=True)    \n",
    "for train_index,test_index in kf_dt.split(X,y):\n",
    "\n",
    "    print ('\\n{} of kfold {} '.format(i,kf_dt.n_splits))\n",
    "\n",
    "    xtr,xval = X.iloc[train_index],X.iloc[test_index]\n",
    "\n",
    "    ytr,yval = y.iloc[train_index],y.iloc[test_index]\n",
    "        \n",
    "    model = tree.DecisionTreeClassifier(random_state=1)\n",
    "\n",
    "    model.fit(xtr,ytr)\n",
    "\n",
    "    pred_test=model.predict(xval)\n",
    "\n",
    "    score=accuracy_score(yval,pred_test)\n",
    "\n",
    "    print ('accuracy_score',score)\n",
    "\n",
    "    i+=1\n",
    "\n",
    "pred_test4 = model.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "5d8ab489-8112-4b59-8b07-974cd00631cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import submission file 3\n",
    "submission['Loan_Status'] = pred_test4\n",
    "submission['Loan_ID'] = test_original['Loan_ID']\n",
    "\n",
    "submission['Loan_Status'].replace(0, 'N', inplace=True)\n",
    "submission['Loan_Status'].replace(1, 'Y', inplace=True)\n",
    "\n",
    "pd.DataFrame(submission, columns=['Loan_ID','Loan_Status']).to_csv('DecisionTree.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "7e7bd54b-330c-43c2-84c8-0b7f514bbe01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.705131280821005"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2 = np.array([0.7398373983739838,0.6910569105691057,0.7154471544715447,0.7235772357723578,0.6557377049180327])\n",
    "mean_acc2 = np.mean(arr2)\n",
    "mean_acc2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1481aa6b-357e-4816-8311-bd5782cbf78f",
   "metadata": {},
   "source": [
    "Mean accuracy of the Decision Tree Classifier is 70.51%"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75df30fe-b14e-4246-8de6-b54334c117fa",
   "metadata": {},
   "source": [
    "## 3- Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "dc2e9de5-4543-4824-91d9-aab93c4dbd68",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "09a8247a-758d-4202-b4c2-3d250b45ace1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1 of kfold 5 \n",
      "accuracy_score 0.8211382113821138\n",
      "\n",
      "2 of kfold 5 \n",
      "accuracy_score 0.8130081300813008\n",
      "\n",
      "3 of kfold 5 \n",
      "accuracy_score 0.8048780487804879\n",
      "\n",
      "4 of kfold 5 \n",
      "accuracy_score 0.7723577235772358\n",
      "\n",
      "5 of kfold 5 \n",
      "accuracy_score 0.7622950819672131\n"
     ]
    }
   ],
   "source": [
    "i=1\n",
    "kf_rf = StratifiedKFold(n_splits=5, random_state=1, shuffle=True)    \n",
    "for train_index,test_index in kf_rf.split(X,y):\n",
    "\n",
    "    print ('\\n{} of kfold {} '.format(i,kf_rf.n_splits))\n",
    "\n",
    "    xtr,xval = X.iloc[train_index],X.iloc[test_index]\n",
    "\n",
    "    ytr,yval = y.iloc[train_index],y.iloc[test_index]\n",
    "        \n",
    "    model = RandomForestClassifier(random_state=1, max_depth=10)\n",
    "\n",
    "    model.fit(xtr,ytr)\n",
    "\n",
    "    pred_test=model.predict(xval)\n",
    "\n",
    "    score=accuracy_score(yval,pred_test)\n",
    "\n",
    "    print ('accuracy_score',score)\n",
    "\n",
    "    i+=1\n",
    "\n",
    "pred_test4 = model.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "9b9cda14-9876-45c7-bfea-39c288bb79a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7947354391576703"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.array([0.8211382113821138,0.8130081300813008,0.8048780487804879,0.7723577235772358,0.7622950819672131])\n",
    "Mean_accuracy = np.mean(arr)\n",
    "Mean_accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40e2dab2-2e2a-4d9f-939a-4536b36dd882",
   "metadata": {},
   "source": [
    "The mean accuracy for Random Forest classifier is 79.47%"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11c1f4a0-d2ff-4ddf-affd-081e471bd0d4",
   "metadata": {},
   "source": [
    "## Hyperparameter tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e937b294-0566-4c68-bb39-ee6214fe4004",
   "metadata": {},
   "source": [
    "We will try to improve accuracy by tuning the hyperparameters for the Random Forest model. We will use Grid search to get optimized value of hyperparameters. We will tune the parameters - max-depth and n_estimators. Max depth is the maximum depth of the tree and n_estimators is the number of decision trees used in Random Forest model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "c1769969-8911-47a3-9448-8661f8080897",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(estimator=RandomForestClassifier(random_state=1),\n",
       "             param_grid={'max_depth': [1, 3, 5, 7, 9, 11, 13, 15, 17, 19],\n",
       "                         'n_estimators': [1, 21, 41, 61, 81, 101, 121, 141, 161,\n",
       "                                          181]})"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "paramgrid = {'max_depth':list(range(1,20,2)),'n_estimators':list(range(1,200,20))}\n",
    "\n",
    "grid_search = GridSearchCV(RandomForestClassifier(random_state=1), paramgrid)\n",
    "\n",
    "# split training and test sets\n",
    "x_train, x_cv, y_train, y_cv = train_test_split(X, y, test_size=0.3, random_state=1)\n",
    "\n",
    "# fit the grid seach model\n",
    "grid_search.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "14828544-73a0-466f-b2c9-640c9abb9cd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=3, n_estimators=161, random_state=1)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find the best estimator\n",
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "7a8a56cb-a1b5-4da1-93a0-b5f1556cbdea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1 of kfold 5 \n",
      "accuracy_score 0.8292682926829268\n",
      "\n",
      "2 of kfold 5 \n",
      "accuracy_score 0.8455284552845529\n",
      "\n",
      "3 of kfold 5 \n",
      "accuracy_score 0.7967479674796748\n",
      "\n",
      "4 of kfold 5 \n",
      "accuracy_score 0.7723577235772358\n",
      "\n",
      "5 of kfold 5 \n",
      "accuracy_score 0.7868852459016393\n"
     ]
    }
   ],
   "source": [
    "# Random forest model with best estimators\n",
    "i=1\n",
    "kf_rf1 = StratifiedKFold(n_splits=5, random_state=1, shuffle=True)    \n",
    "for train_index,test_index in kf_rf1.split(X,y):\n",
    "\n",
    "    print ('\\n{} of kfold {} '.format(i,kf_rf1.n_splits))\n",
    "\n",
    "    xtr,xval = X.iloc[train_index],X.iloc[test_index]\n",
    "\n",
    "    ytr,yval = y.iloc[train_index],y.iloc[test_index]\n",
    "        \n",
    "    model_rf = RandomForestClassifier(random_state=1, max_depth=3, n_estimators=161)\n",
    "\n",
    "    model_rf.fit(xtr,ytr)\n",
    "\n",
    "    pred_test=model_rf.predict(xval)\n",
    "\n",
    "    score=accuracy_score(yval,pred_test)\n",
    "\n",
    "    print ('accuracy_score',score)\n",
    "\n",
    "    i+=1\n",
    "\n",
    "pred_test5 = model_rf.predict(test)\n",
    "\n",
    "pred_rf = model_rf.predict_proba(test)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "12d17e70-c778-4dbb-af71-17da162d2989",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.806157536985206"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_2 = np.array([0.8292682926829268,0.8455284552845529,0.7967479674796748,0.7723577235772358,0.7868852459016393])\n",
    "Mean_accuracy = np.mean(arr_2)\n",
    "Mean_accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46069b36-33b7-4431-bde8-b577e9bcd17b",
   "metadata": {},
   "source": [
    "The mean accuracy of Random Forest classifier after hyperparameter tuning is 80.61%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "6bc0c7e4-14ef-4f3b-9e26-7b1cd1ec4d01",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import submission file 4\n",
    "submission['Loan_Status'] = pred_test5\n",
    "submission['Loan_ID'] = test_original['Loan_ID']\n",
    "\n",
    "submission['Loan_Status'].replace(0, 'N', inplace=True)\n",
    "submission['Loan_Status'].replace(1, 'Y', inplace=True)\n",
    "\n",
    "pd.DataFrame(submission, columns=['Loan_ID','Loan_Status']).to_csv('RandomForest.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbcc2f3d-358b-46ee-a2e1-eccd6ef809b9",
   "metadata": {},
   "source": [
    "Important Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "73244536-f0cb-46bf-96d1-0333e35ee91b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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q78TTel2GZrD1K5f3ugRJkjQLJFnTvuznYXo9cyBJkiRphuj1DcmTJsl3gHlDmk+oqoFe1CNJkiRtbWZNOKiqZ/S6BkmSJGlrNmvCwWyweOECVrumXJIkST3iPQeSJEmSAMOBJEmSpMZwIEmSJAkwHEiSJElqDAeSJEmSAMOBJEmSpMZwIEmSJAkwHEiSJElqDAeSJEmSAMOBJEmSpMZwIEmSJAkwHEiSJElqDAeSJEmSAMOBJEmSpMZwIEmSJAkwHEiSJElqDAeSJEmSAJjb6wK0ycAdG+hfcWGvy5hx1q9c3usSJEmStgnOHEiSJEkCDAeSJEmSGsOBJEmSJMBwIEmSJKkxHEiSJEkCxhEOktw9lYW0c/xNkvuSLJjqc22mjr8bQ58pfz0kSZKk6TTTZg6OA64FjupxHZsNB5IkSdJss0XhIMmSJFcnWZfk/CR/1NpfmeTaJDck+VySHVr7WUnel+RbSX6U5OiusfYCdgLeRickDLaflOQLSb6Y5LYkr0nyxiTXt3M/ajO1XJZkaXu8e5L1XeN+PslXk3w/yf9u7SuB+UnWJjl7DK9Bkpya5MYkA0mOae2PSPKhJDcl+VKSL3c/X0mSJGmm2dKZg48Db6mq/YAB4O2t/fNVtayq9ge+C7yi65g+4BDgT4GVXe3HAecA3wSenOTRXfv2Bf4bcCDwD8A9VfU04NvASzdTy2iWAMcAi4Fjkjy+qlYA91bVkqo6fgxjvLiNsz9wBHBqkr7W3t/G/ivgoOEOTnJyktVJVm+8Z8MYTidJkiRNjQmHg3ZfwK5VdXlr+hhwaHu8b5JvJhkAjgf26Tr0C1X1UFXdDDymq/1Y4Nyqegj4PPDnXfsuraq7quoXwAbgi619AOjfTC2juaSqNlTVfcDNwBPHcMxQhwDnVNXGqvoZcDmwrLWf157rT4FLhzu4qlZV1dKqWjpnh57eaiFJkqRt3NwpGvcs4MiquiHJScBhXfvu73ocgCT7AYuAryUBeCTwI+CDwxzzUNf2Q2z+OTzIphC0/ZB93eNuHMNYw8k42yVJkqQZacIzB1W1Afh1kme1phPofGoOsDPwkyTb0Zk52JzjgHdUVX/7eRywMMmYPsnfTC3rgQPa47Gu+X+g1T4WV9BZkjQnyR50ZiyuAa4EXtLuPXgMDw9IkiRJ0owznk/Kd0hye9f2PwMnAqe3G45/BLys7fufwHeAH9NZ+rPzZsY+FnjBkLbzW/vPxljfSLW8B/hMkhOAb4xxrFXAuiTXjeG+g/Pp3E9wA1DAm6vqp0k+BzwXuBG4lc7r4U0FkiRJmrFSVb2uYdZKslNV3Z1kNzqzCc9s9x8Ma17fouo78bRpq29rsX7l8l6XIEmSNKskWVNVS4e2T9U9B+r4UpJd6dxD8e7RgoEkSZLUa4aDUbRP/C8ZZtdzq+pXmzu+qg6b9KIkSZKkKWI4GEULAEt6XYckSZI0HQwHM8jihQtY7fp6SZIk9ciW/oVkSZIkSbOE4UCSJEkSYDiQJEmS1BgOJEmSJAGGA0mSJEmN4UCSJEkSYDiQJEmS1BgOJEmSJAGGA0mSJEmN4UCSJEkSYDiQJEmS1BgOJEmSJAGGA0mSJEmN4UCSJEkSYDiQJEmS1BgOJEmSJAGGA0mSJEnN3F4XoE0G7thA/4oLe13GtFm/cnmvS5AkSVIXZw4kSZIkAYYDSZIkSY3hQJIkSRJgOJAkSZLUGA4kSZIkAVMcDpJsTLI2yY1Jzkuyw1Seb5jzv2FLzpnkqCSV5CmTWZckSZI0E031zMG9VbWkqvYFfg+8untnkjlTdeI29huALQkkxwFXAseOcg5JkiRpVpjOZUXfBJ6U5LAklyb5FDCQZPskH00ykOT6JIcDJDkpyf9N8tUk30vy9sGBkvxlkmvarMSHB9+kJ7k7ybuSfAd4K/A44NJ2vlckeW/XGK9M8s8jFZtkJ+CZwCvoCgfD1D8nyalJrk2yLsmrBo9PckmS69pze9EkvpaSJEnSpJuWP4KWZC7wAuCrrelAYN+qui3JfweoqsVt+c7FSfbu7gfcA1yb5ELgd8AxwDOr6oEkHwKOBz4O7AjcWFV/3877cuDwqvplkh2BdUneXFUPAC8DXjVK2UcCX62qW5P8R5KnV9V1w9R/MrChqpYlmQdcleRi4N+Bo6rqt0l2B65OckFV1ZDX5mTgZIA5u+wxzldWkiRJmjxTHQ7mJ1nbHn8T+D/AwcA1VXVbaz8EeD9AVd2S5MfAYDj4WlX9CiDJ51vfB4ED6IQFgPnAz1v/jcDnhiukqn6X5BvAnyb5LrBdVQ2MUvtxwGnt8bltezAcdNf/fGC/JEe37QXAIuB24B+THAo8BCwEHgP8dEhdq4BVAPP6Fj0sOEiSJEnTaarDwb1VtaS7ob2h/1130yjHD32zXK3/x6rqfwzT/76q2jjKeGcAfwfcAnx0pE5JdgOeA+ybpIA5QCV5c+sytP7XVtVFQ8Y4CdgDOKDNcKwHth+lNkmSJKmnZsJXmV5BZ1kQbTnRE4DvtX3PS/KoJPPpLPO5CrgEODrJo9sxj0ryxBHGvgvYeXCjqr4DPB74b8A5o9R0NPDxqnpiVfVX1eOB2+jMXAx1EXBKku0Gn0NbwrQA+HkLBocDI9UoSZIkzQgzIRx8CJiTZAD4NHBSVd3f9l0JfAJYC3yuqlZX1c3A2+jcm7AO+BrQN8LYq4CvJLm0q+0zwFVV9etRajoOOH9I2+fohIqhzgBuBq5LciPwYTozMmcDS5OsphN+bhnlfJIkSVLPZcj9sTNGW5aztKpeM8njfgl4b1VdMpnjToZ5fYuq78TTel3GtFm/cnmvS5AkSdomJVlTVUuHts+EmYNpkWTXJLfSuQ9ixgUDSZIkqdem5atMJ6KqzgLOmsTxfsOmb0EC/nDj8XBB4bmD35IkSZIkbStmbDiYDi0ALOl1HZIkSdJMsE2Hg5lm8cIFrHYdviRJknpkm7nnQJIkSdLoDAeSJEmSAMOBJEmSpMZwIEmSJAkwHEiSJElqDAeSJEmSAMOBJEmSpMZwIEmSJAkwHEiSJElqDAeSJEmSAMOBJEmSpMZwIEmSJAkwHEiSJElqDAeSJEmSAMOBJEmSpMZwIEmSJAkwHEiSJElq5va6AG0ycMcG+ldc2OsyJEmSNMXWr1ze6xKG5cyBJEmSJMBwIEmSJKkxHEiSJEkCDAeSJEmSGsOBJEmSJMBwIEmSJKmZ0nCQ5LFJzk3ywyQ3J/lykr0nONZZSY5uj89I8tT2+O/GcOzdQ7ZPSvKB9vjVSV46yrGHJTl4IjVLkiRJW5MpCwdJApwPXFZVe1XVU4G/Ax7T1WfORMauqr+qqpvb5mbDwWbGOr2qPj5Kl8OAcYWDJP79CEmSJG11pnLm4HDggao6fbChqtYCc5JcmuRTwECSOUlOTXJtknVJXgWdcJHkA23G4ULg0YPjJLksydIkK4H5SdYmOXsiRSZ5R5I3tceva+db12Y8+oFXA3/TzvGsJE9Mcknrc0mSJ7Rjz0ryz0kuBU5N8v0ke7R9j0jygyS7D3P+k5OsTrJ64z0bJvIUJEmSpEkxlZ9w7wusGWHfgcC+VXVbkpOBDVW1LMk84KokFwNPA54MLKYz23AzcGb3IFW1IslrqmrJZmqZn2Rt1/ajgAuG6bcC2LOq7k+ya1X9JsnpwN1V9R6AJF8EPl5VH0vycuB9wJHt+L2BI6pqY5LfAMcDpwFHADdU1S+HnrCqVgGrAOb1LarNPA9JkiRpyvTqhuRrquq29vj5wEvbm/fvALsBi4BDgXOqamNV3Ql8YwvOd29VLRn8Af5+hH7rgLOT/CXw4Ah9DgI+1R5/Ajika995VbWxPT4TGLyX4eXARydavCRJkjQdpjIc3AQcMMK+33U9DvDarjfve1bVxW3fdH+Svhz4IJ2614zx3oHuGv/wvKrq34GfJXkO8AzgK5NZqCRJkjTZpjIcfAOYl+SVgw1JlgHPHtLvIuCUJNu1Pnsn2RG4Aji23ZPQR+cehuE8MHjslkjyCODxVXUp8GZgV2An4C5g566u3wKObY+PB64cZdgzgE8Cn+maUZAkSZJmpCkLB1VVwFHA89pXmd4EvAO4c0jXM+jcT3BdkhuBD9O5F+J84PvAAPCvwOUjnGoVsG6iNyR3mQN8MskAcD3w3qr6DfBF4KjBG5KB1wEvS7IOOAF4/ShjXkAnYLikSJIkSTNeOu/hNRWSLKUTMp41lv7z+hZV34mnTW1RkiRJ6rn1K5f39PxJ1lTV0qHtfh//FEmyAjiFztIjSZIkacabNeEgyW7AJcPsem5V/Wq666mqlcDK6T6vJEmSNFGzJhy0ALCk13VIkiRJW6tZEw5mg8ULF7C6x+vPJEmStO3q1R9BkyRJkjTDGA4kSZIkAYYDSZIkSY3hQJIkSRJgOJAkSZLUGA4kSZIkAYYDSZIkSY3hQJIkSRJgOJAkSZLUGA4kSZIkAYYDSZIkSY3hQJIkSRJgOJAkSZLUGA4kSZIkAYYDSZIkSY3hQJIkSRJgOJAkSZLUzO11Adpk4I4N9K+4cMT961cun8ZqJEmStK1x5kCSJEkSYDiQJEmS1BgOJEmSJAGGA0mSJEmN4UCSJEkSMAXhIMlbk9yUZF2StUmeMUrfs5Ic3R4/qx23Nsn8Yfr2J7m37R/8eekk1Xz3ZIwzyvh/eJ6SJEnSTDWpX2Wa5CDgT4GnV9X9SXYHHjnGw48H3lNVHx2lzw+raskWlilJkiRpGJM9c9AH/LKq7geoql9W1Z1JDkhyeZI1SS5K0td9UJK/Av4C+PskZ4/3pEnuTvK/2vhfT3JgksuS/CjJn7U+JyX5v0m+muR7Sd4+zDhJcmqSG5MMJDmmtX8iyYu6+p2d5M+SzGn9r20zJa/qGucDSW5OciHw6PE+J0mSJGm6TXY4uBh4fJJbk3woybOTbAe8Hzi6qg4AzgT+ofugqjoDuAD426o6fpTx9xqyrOhZrX1H4LI2/l3A/wc8DzgKeFfX8QfSmaFYAvx5kqVDxn9x27c/cARwagsyZwAvA0iyADgY+DLwCmBDVS0DlgGvTLJnO++TgcXAK1v/YSU5OcnqJKs33rNhlKcuSZIkTa1JXVZUVXcnOQB4FnA48Gk6b9T3Bb6WBGAO8JMJnmKkZUW/B77aHg8A91fVA0kGgP6ufl+rql8BJPk8cAiwumv/IcA5VbUR+FmSy4FlVXVBkg8meTSdAPG5qnowyfOB/bruJ1gALAIO7RrnziTfGOkJVdUqYBXAvL5FNeZXQpIkSZpkkxoOANob4suAy9qb878Gbqqqgyb7XF0eqKrBN9YPAYPLmh5K0v0ch775HrqdUc7xCTqzDscCL+/q/9qquuhhgyQvHGZsSZIkaUab1GVFSZ6cZFFX0xLgu8Ae7WZlkmyXZJ/JPO84PC/Jo9q3IR0JXDVk/xXAMe1egj3ozABc0/adBbwBoKpuam0XAae0pVMk2TvJjm2cY9s4fXRmUSRJkqQZbbJnDnYC3p9kV+BB4AfAyXSWzbyvrdefC5wG3DTCGKPZK8naru0zq+p94zj+SjozAE8CPlVVq4fsPx84CLiBzif/b66qnwJU1c+SfBf4Qlf/M+gsW7ounTVTv6ATOs4HnkNnidOtwOXjqFGSJEnqiWxajTO7JTkJWFpVr5ng8TvQebP/9KqakjuH5/Utqr4TTxtx//qVy6fitJIkSdrGJFlTVUO/nMe/kDwWSY4AbgHeP1XBQJIkSeq1Sb8heUslWUxn6U+3+6tqxL+0PBZVdRad+wYmcuzXgSdsyfklSZKkmW7GhYOqGqBzI7MkSZKkaTTjwsG2bPHCBaz2vgJJkiT1iPccSJIkSQIMB5IkSZIaw4EkSZIkwHAgSZIkqTEcSJIkSQIMB5IkSZIaw4EkSZIkwHAgSZIkqTEcSJIkSQIMB5IkSZIaw4EkSZIkwHAgSZIkqTEcSJIkSQIMB5IkSZIaw4EkSZIkwHAgSZIkqTEcSJIkSQJgbq8L0CYDd2ygf8WF03a+9SuXT9u5JEmSNPM5cyBJkiQJMBxIkiRJagwHkiRJkgDDgSRJkqRmqwoHSTYmWdv1s2KYPocl+dIkn/ewJAd3bb86yUsn8xySJElSr21t31Z0b1Ut6cF5DwPuBr4FUFWn96AGSZIkaUptVTMHI0nyJ0luSXIl8OKu9nckeVPX9o1J+tvjlyZZl+SGJJ9obf81yXeSXJ/k60ke0/q/GvibNlvxrO5xkyxJcnUb6/wkf9TaL0vyv5Jck+TWJM+athdEkiRJmoCtLRzMH7Ks6Jgk2wMfAf4r8CzgsZsbJMk+wFuB51TV/sDr264rgf9SVU8DzgXeXFXrgdOB91bVkqr65pDhPg68par2AwaAt3ftm1tVBwJvGNIuSZIkzThb/bKiJEuA26rq+237k8DJmxnnOcBnq+qXAFX1H639j4FPJ+kDHgncNtogSRYAu1bV5a3pY8B5XV0+336vAfpHGOPkwXrn7LLHZsqWJEmSps7WNnMwkhqh/UEe/hy3b78zwjHvBz5QVYuBV3X1n6j72++NjBDEqmpVVS2tqqVzdliwhaeTJEmSJm42hINbgD2T7NW2j+vatx54OkCSpwN7tvZLgL9Islvb96jWvgC4oz0+sWucu4Cdh564qjYAv+66n+AE4PKh/SRJkqStwdYWDobec7Cyqu6jsyznwnZD8o+7+n8OeFSStcApwK0AVXUT8A/A5UluAP659X8HcF6SbwK/7Brni8BRgzckD6npRODUJOuAJcC7Ju/pSpIkSdMnVSOtyNF0m9e3qPpOPG3azrd+5fJpO5ckSZJmjiRrqmrp0PatbeZAkiRJ0hQxHEiSJEkCDAeSJEmSGsOBJEmSJGDr+yNos9rihQtY7U3CkiRJ6hFnDiRJkiQBhgNJkiRJjeFAkiRJEmA4kCRJktQYDiRJkiQBhgNJkiRJjeFAkiRJEmA4kCRJktQYDiRJkiQBhgNJkiRJjeFAkiRJEmA4kCRJktQYDiRJkiQBhgNJkiRJjeFAkiRJEmA4kCRJktQYDiRJkiQBMLfXBWiTgTs20L/iwjH3X79y+RRWI0mSpG2NMweSJEmSAMOBJEmSpMZwIEmSJAkwHEiSJElqeh4OkmxMsjbJTUluSPLGJD2rK8n6JLtP8Ngjkzx1smuSJEmSpkPPwwFwb1Utqap9gOcBLwTe3uOaJupIwHAgSZKkrdJMCAd/UFU/B04GXpOOOUlOTXJtknVJXgWQ5LAkVyQ5P8nNSU4fnG1I8vwk305yXZLzkuzU2tcneWdrH0jylNa+W5KLk1yf5MNAButJ8pdJrmkzGx9OMqe1353kH9pMx9VJHpPkYODPgFNb/72SvK7Vty7JudP6YkqSJEnjNKPCAUBV/YhOXY8GXgFsqKplwDLglUn2bF0PBP47sBjYC3hxWw70NuCIqno6sBp4Y9fwv2zt/wq8qbW9Hbiyqp4GXAA8ASDJfwaOAZ5ZVUuAjcDx7Zgdgauran/gCuCVVfWtdvzftpmQHwIrgKdV1X7AqyfrNZIkSZKmwkz9I2iDn94/H9gvydFtewGwCPg9cE0LEiQ5BzgEuI/Osp6rkgA8Evh217ifb7/XAC9ujw8dfFxVFyb5dWt/LnAAcG0baz7w87bv98CXusZ63gjPYx1wdpIvAF8Y9okmJ9OZLWHOLnuMMIwkSZI09WZcOEjyn+h8Sv9zOiHhtVV10ZA+hwE15NBq/b9WVceNMPz97fdGHv7ch45FG+tjVfU/htn3QFUNHjN0rG7L6YSPPwP+Z5J9qurBhxVdtQpYBTCvb9FwdUiSJEnTYkYtK0qyB3A68IH25vsi4JQk27X9eyfZsXU/MMme7V6DY4ArgauBZyZ5Uuu/Q5K9N3PaK2jLhZK8APij1n4JcHSSR7d9j0ryxM2MdRewc+v/CODxVXUp8GZgV2CnMbwMkiRJUk/MhJmD+UnWAtsBDwKfAP657TsD6AeuS2dtzy/ofCMQdJYLraRzz8EVwPlV9VCSk4Bzksxr/d4G3DrK+d/Z+l8HXA78G0BV3ZzkbcDF7Y3+A8BfAz8eZaxzgY8keR1wLPB/kiygMwvx3qr6zeZeDEmSJKlXsml1zNajLSt6U1X9aY9LmVTz+hZV34mnjbn/+pXLp64YSZIkzVpJ1lTV0qHtM2pZkSRJkqTemQnLisatqi4DLutxGZIkSdKs4syBJEmSJMBwIEmSJKnZKpcVzVaLFy5gtTcZS5IkqUecOZAkSZIEGA4kSZIkNYYDSZIkSYDhQJIkSVJjOJAkSZIEGA4kSZIkNYYDSZIkSYDhQJIkSVJjOJAkSZIEGA4kSZIkNYYDSZIkSYDhQJIkSVJjOJAkSZIEGA4kSZIkNYYDSZIkSYDhQJIkSVJjOJAkSZIEwNxeF6BNBu7YQP+KC6f0HOtXLp/S8SVJkrT1cuZAkiRJEmA4kCRJktQYDiRJkiQBhgNJkiRJjeFAkiRJEjDLw0GSSvKJru25SX6R5EtbOO7jknx2nMecleToLTmvJEmSNJVmdTgAfgfsm2R+234ecMd4Bkgyd+h2Vd1ZVb7RlyRJ0qwy28MBwFeAwS/3Pw44Z3BHkgOTfCvJ9e33k1v7SUnOS/JF4OJhtvuT3Nj6zklyapJrk6xL8qrWniQfSHJzkguBR0/nk5YkSZLGa1sIB+cCxybZHtgP+E7XvluAQ6vqacDfA//Yte8g4MSqes4I24NeAWyoqmXAMuCVSfYEjgKeDCwGXgkcPFxxSU5OsjrJ6o33bNiS5ylJkiRtkVn/F5Kral2SfjqzBl8esnsB8LEki4ACtuva97Wq+o9Rtgc9H9iv636CBcAi4FDgnKraCNyZ5Bsj1LcKWAUwr29RjevJSZIkSZNo1oeD5gLgPcBhwG5d7e8GLq2qo1qAuKxr3++GjDF0e1CA11bVRQ9rTF5IJ3BIkiRJW4VtYVkRwJnAu6pqYEj7AjbdoHzSBMe+CDglyXYASfZOsiNwBZ3lTHOS9AGHT3B8SZIkaVpsEzMHVXU78C/D7PrfdJYVvREYdtnPGJwB9APXJQnwC+BI4HzgOcAAcCtw+QTHlyRJkqZFqlz5MlPM61tUfSeeNqXnWL9y+eY7SZIkaVZLsqaqlg5t31aWFUmSJEnaDMOBJEmSJMBwIEmSJKkxHEiSJEkCtpFvK9paLF64gNXeMCxJkqQeceZAkiRJEmA4kCRJktQYDiRJkiQBhgNJkiRJjeFAkiRJEmA4kCRJktQYDiRJkiQBhgNJkiRJjeFAkiRJEmA4kCRJktQYDiRJkiQBhgNJkiRJjeFAkiRJEmA4kCRJktQYDiRJkiQBhgNJkiRJjeFAkiRJEgBze12ANhm4YwP9Ky4cU9/1K5dPcTWSJEna1jhzIEmSJAkwHEiSJElqDAeSJEmSAMOBJEmSpGabCAdJHpPkU0l+lGRNkm8nOWoSxj0syZcmo0ZJkiSp12Z9OEgS4AvAFVX1n6rqAOBY4I97UIvfDiVJkqQZa9aHA+A5wO+r6vTBhqr6cVW9P8mcJKcmuTbJuiSvgj/MCFyW5LNJbklydgsZJPmT1nYl8OLBMZPsmOTMNtb1SV7U2k9Kcl6SLwIXT+szlyRJksZhW/gkex/guhH2vQLYUFXLkswDrkoy+Ab+ae3YO4GrgGcmWQ18hE7g+AHw6a6x3gp8o6penmRX4JokX2/7DgL2q6r/mMTnJUmSJE2qbSEcPEySDwKHAL8Hfgzsl+TotnsBsKjtu6aqbm/HrAX6gbuB26rq+639k8DJ7djnA3+W5E1te3vgCe3x10YKBklOHhxjzi57TM6TlCRJkiZgWwgHNwEvGdyoqr9OsjuwGvg34LVVdVH3AUkOA+7vatrIpteqRjhPgJdU1feGjPUM4HcjFVdVq4BVAPP6Fo00tiRJkjTltoV7Dr4BbJ/klK62Hdrvi4BTkmwHkGTvJDuOMtYtwJ5J9mrbx3Xtuwh4bde9CU+blOolSZKkaTLrw0FVFXAk8OwktyW5BvgY8BbgDOBm4LokNwIfZpTZlKq6j84SoAvbDck/7tr9bmA7YF0b691T8HQkSZKkKZPOe2fNBPP6FlXfiaeNqe/6lcunthhJkiTNWknWVNXSoe2zfuZAkiRJ0tgYDiRJkiQBhgNJkiRJjeFAkiRJErBt/J2DrcbihQtY7Y3GkiRJ6hFnDiRJkiQBhgNJkiRJjeFAkiRJEmA4kCRJktQYDiRJkiQBhgNJkiRJjeFAkiRJEmA4kCRJktSkqnpdg5okdwHf63UdmrF2B37Z6yI0Y3l9aDReHxqN18e26YlVtcfQRv9C8szyvapa2usiNDMlWe31oZF4fWg0Xh8ajdeHurmsSJIkSRJgOJAkSZLUGA5mllW9LkAzmteHRuP1odF4fWg0Xh/6A29IliRJkgQ4cyBJkiSpMRz0QJI/SfK9JD9IsmKY/UnyvrZ/XZKn96JO9cYYro+nJPl2kvuTvKkXNao3xnBtHN/+z1iX5FtJ9u9FneqNMVwfL2rXxtokq5Mc0os61Rubuz66+i1LsjHJ0dNZn2YOlxVNsyRzgFuB5wG3A9cCx1XVzV19Xgi8Fngh8AzgX6rqGT0oV9NsjNfHo4EnAkcCv66q9/SgVE2zMV4bBwPfrapfJ3kB8A7/79g2jPH62An4XVVVkv2Az1TVU3pSsKbVWK6Prn5fA+4Dzqyqz053reo9Zw6m34HAD6rqR1X1e+Bc4EVD+rwI+Hh1XA3smqRvugtVT2z2+qiqn1fVtcADvShQPTOWa+NbVfXrtnk18MfTXKN6ZyzXx9216RPBHQE/Hdx2jOW9B3Q+mPwc8PPpLE4zi+Fg+i0E/r1r+/bWNt4+mp38t9dIxnttvAL4ypRWpJlkTNdHkqOS3AJcCLx8mmpT7232+kiyEDgKOH0a69IMZDiYfhmmbeinN2Ppo9nJf3uNZMzXRpLD6YSDt0xpRZpJxnR9VNX5bSnRkcC7p7oozRhjuT5OA95SVRunvhzNZHN7XcA26Hbg8V3bfwzcOYE+mp38t9dIxnRttLXkZwAvqKpfTVNt6r1x/d9RVVck2SvJ7lX1yymvTr02lutjKXBuEoDdgRcmebCqvjAtFWrGcOZg+l0LLEqyZ5JHAscCFwzpcwHw0vatRf8F2FBVP5nuQtUTY7k+tG3a7LWR5AnA54ETqurWHtSo3hnL9fGktHd+7VvwHgkYILcNm70+qmrPquqvqn7gs8D/azDYNjlzMM2q6sEkrwEuAubQ+TaAm5K8uu0/HfgynW8q+gFwD/CyXtWr6TWW6yPJY4HVwC7AQ0neADy1qn7bq7o19cb4f8ffA7sBH2rvAR+sqqW9qlnTZ4zXx0vofPD0AHAvcEzXDcqaxcZ4fUiAX2UqSZIkqXFZkSRJkiTAcCBJkiSpMRxIkiRJAgwHkiRJkhrDgSRJkiTAcCBJkiSpMRxIkiRJAgwHkiRJkpr/H6/ocXn7QsUsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "importance = pd.Series(model_rf.feature_importances_, index=X.columns)\n",
    "importance.plot(kind='barh',figsize=(12,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37bf15e6-4c2d-45fa-a915-75cdf861eb0b",
   "metadata": {},
   "source": [
    "## 4- XGBoost Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "4821ede6-c052-469e-bd02-db366b19cccd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: xgboost in c:\\users\\hp\\anaconda3\\lib\\site-packages (1.7.5)Note: you may need to restart the kernel to use updated packages.\n",
      "\n",
      "Requirement already satisfied: numpy in c:\\users\\hp\\anaconda3\\lib\\site-packages (from xgboost) (1.20.1)\n",
      "Requirement already satisfied: scipy in c:\\users\\hp\\anaconda3\\lib\\site-packages (from xgboost) (1.6.2)\n"
     ]
    }
   ],
   "source": [
    "pip install xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "d9e760c9-e9a4-4019-981c-9054362c5064",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.391667</td>\n",
       "      <td>5608.333333</td>\n",
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       "      <td>1</td>\n",
       "      <td>4.890349</td>\n",
       "      <td>4583.0</td>\n",
       "      <td>8.430109</td>\n",
       "      <td>0.369444</td>\n",
       "      <td>4213.555556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>614 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender  Married Dependents  Education  Self_Employed  Credit_History  \\\n",
       "0       1.0      0.0          0          1            0.0             1.0   \n",
       "1       1.0      1.0          1          1            0.0             1.0   \n",
       "2       1.0      1.0          0          1            1.0             1.0   \n",
       "3       1.0      1.0          0          0            0.0             1.0   \n",
       "4       1.0      0.0          0          1            0.0             1.0   \n",
       "..      ...      ...        ...        ...            ...             ...   \n",
       "609     0.0      0.0          0          1            0.0             1.0   \n",
       "610     1.0      1.0          3          1            0.0             1.0   \n",
       "611     1.0      1.0          1          1            0.0             1.0   \n",
       "612     1.0      1.0          2          1            0.0             1.0   \n",
       "613     0.0      0.0          0          1            1.0             0.0   \n",
       "\n",
       "     Property_Area  LoanAmount_log  Total_Income  Total_Income_log       EMI  \\\n",
       "0                2        4.852030        5849.0          8.674026  0.355556   \n",
       "1                0        4.852030        6091.0          8.714568  0.355556   \n",
       "2                2        4.189655        3000.0          8.006368  0.183333   \n",
       "3                2        4.787492        4941.0          8.505323  0.333333   \n",
       "4                2        4.948760        6000.0          8.699515  0.391667   \n",
       "..             ...             ...           ...               ...       ...   \n",
       "609              0        4.262680        2900.0          7.972466  0.197222   \n",
       "610              0        3.688879        4106.0          8.320205  0.222222   \n",
       "611              2        5.533389        8312.0          9.025456  0.702778   \n",
       "612              2        5.231109        7583.0          8.933664  0.519444   \n",
       "613              1        4.890349        4583.0          8.430109  0.369444   \n",
       "\n",
       "     Balance_Income  \n",
       "0       5493.444444  \n",
       "1       5735.444444  \n",
       "2       2816.666667  \n",
       "3       4607.666667  \n",
       "4       5608.333333  \n",
       "..              ...  \n",
       "609     2702.777778  \n",
       "610     3883.777778  \n",
       "611     7609.222222  \n",
       "612     7063.555556  \n",
       "613     4213.555556  \n",
       "\n",
       "[614 rows x 12 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "0822e762-a5cf-476e-909a-db63485fa000",
   "metadata": {},
   "outputs": [],
   "source": [
    "cats = X.select_dtypes(exclude=np.number).columns.tolist()\n",
    "\n",
    "# encode categorical column 'Dependents'\n",
    "#from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "#le = LabelEncoder()\n",
    "for col in cats:\n",
    "    X[col] = X[col].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "df50cad2-0af6-41fd-a349-21321c30697f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender              float64\n",
       "Married             float64\n",
       "Dependents            int64\n",
       "Education             int64\n",
       "Self_Employed       float64\n",
       "Credit_History      float64\n",
       "Property_Area         int64\n",
       "LoanAmount_log      float64\n",
       "Total_Income        float64\n",
       "Total_Income_log    float64\n",
       "EMI                 float64\n",
       "Balance_Income      float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "53f0c1ab-c1db-44e1-9663-cfc9a98c7720",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1 of kfold 5 \n",
      "accuracy_score 0.8048780487804879\n",
      "\n",
      "2 of kfold 5 \n",
      "accuracy_score 0.7804878048780488\n",
      "\n",
      "3 of kfold 5 \n",
      "accuracy_score 0.7886178861788617\n",
      "\n",
      "4 of kfold 5 \n",
      "accuracy_score 0.7398373983739838\n",
      "\n",
      "5 of kfold 5 \n",
      "accuracy_score 0.7459016393442623\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "DataFrame.dtypes for data must be int, float, bool or category. When categorical type is supplied, The experimental DMatrix parameter`enable_categorical` must be set to `True`.  Invalid columns:Dependents: object",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-79-5840530e2c51>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     24\u001b[0m     \u001b[0mi\u001b[0m\u001b[1;33m+=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     25\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 26\u001b[1;33m \u001b[0mpred_test6\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel_xgb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     27\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     28\u001b[0m \u001b[0mpred_xgb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel_xgb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict_proba\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\xgboost\\sklearn.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, X, output_margin, ntree_limit, validate_features, base_margin, iteration_range)\u001b[0m\n\u001b[0;32m   1523\u001b[0m     ) -> np.ndarray:\n\u001b[0;32m   1524\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mconfig_context\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mverbosity\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mverbosity\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1525\u001b[1;33m             class_probs = super().predict(\n\u001b[0m\u001b[0;32m   1526\u001b[0m                 \u001b[0mX\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1527\u001b[0m                 \u001b[0moutput_margin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0moutput_margin\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\xgboost\\sklearn.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, X, output_margin, ntree_limit, validate_features, base_margin, iteration_range)\u001b[0m\n\u001b[0;32m   1112\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_can_use_inplace_predict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1113\u001b[0m                 \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1114\u001b[1;33m                     predts = self.get_booster().inplace_predict(\n\u001b[0m\u001b[0;32m   1115\u001b[0m                         \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1116\u001b[0m                         \u001b[0miteration_range\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0miteration_range\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\xgboost\\core.py\u001b[0m in \u001b[0;36minplace_predict\u001b[1;34m(self, data, iteration_range, predict_type, missing, validate_features, base_margin, strict_shape)\u001b[0m\n\u001b[0;32m   2282\u001b[0m         \u001b[0menable_categorical\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_has_categorical\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2283\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0m_is_pandas_df\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2284\u001b[1;33m             \u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_transform_pandas_df\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0menable_categorical\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2285\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mvalidate_features\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2286\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_features\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\xgboost\\data.py\u001b[0m in \u001b[0;36m_transform_pandas_df\u001b[1;34m(data, enable_categorical, feature_names, feature_types, meta, meta_type)\u001b[0m\n\u001b[0;32m    389\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mdtype\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtypes\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    390\u001b[0m     ):\n\u001b[1;32m--> 391\u001b[1;33m         \u001b[0m_invalid_dataframe_dtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    392\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    393\u001b[0m     feature_names, feature_types = _pandas_feature_info(\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\xgboost\\data.py\u001b[0m in \u001b[0;36m_invalid_dataframe_dtype\u001b[1;34m(data)\u001b[0m\n\u001b[0;32m    281\u001b[0m     \u001b[0mtype_err\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"DataFrame.dtypes for data must be int, float, bool or category.\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    282\u001b[0m     \u001b[0mmsg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34mf\"\"\"{type_err} {_ENABLE_CAT_ERR} {err}\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 283\u001b[1;33m     \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    284\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    285\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: DataFrame.dtypes for data must be int, float, bool or category. When categorical type is supplied, The experimental DMatrix parameter`enable_categorical` must be set to `True`.  Invalid columns:Dependents: object"
     ]
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "i=1\n",
    "kf_xgb = StratifiedKFold(n_splits=5, random_state=1, shuffle=True)    \n",
    "for train_index,test_index in kf_xgb.split(X, y):\n",
    "\n",
    "    print ('\\n{} of kfold {} '.format(i,kf_xgb.n_splits))\n",
    "\n",
    "    xtr,xval = X.iloc[train_index],X.iloc[test_index]\n",
    "\n",
    "    ytr,yval = y.iloc[train_index],y.iloc[test_index]\n",
    "        \n",
    "    model_xgb = XGBClassifier(max_depth=4, n_estimators=50)\n",
    "    \n",
    "    model_xgb.fit(xtr._get,ytr)\n",
    "\n",
    "    pred_test=model_xgb.predict(xval)\n",
    "\n",
    "    score=accuracy_score(yval,pred_test)\n",
    "\n",
    "    print('accuracy_score',score)\n",
    "\n",
    "    i+=1\n",
    "\n",
    "pred_test6 = model_xgb.predict(test)\n",
    "\n",
    "pred_xgb = model_xgb.predict_proba(test)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c391b5f-6f02-4eaa-b9e3-f694b0ea8b29",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr_1 = np.array([0.8048780487804879,0.7804878048780488,0.7886178861788617,0.7398373983739838,0.7459016393442623])\n",
    "mean_acc_1 = np.mean(arr1)\n",
    "mean_acc_1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce856fb4-e407-492e-9243-b1cc738d085f",
   "metadata": {},
   "source": [
    "The mean accuracy of XGBoost Classifier is 73.11%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b6d4ef5-bd7d-4938-bb93-44493ca06a4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import submission file 5\n",
    "submission['Loan_Status'] = pred_test6\n",
    "submission['Loan_ID'] = test_original['Loan_ID']\n",
    "\n",
    "submission['Loan_Status'].replace(0, 'N', inplace=True)\n",
    "submission['Loan_Status'].replace(1, 'Y', inplace=True)\n",
    "\n",
    "pd.DataFrame(submission, columns=['Loan_ID','Loan_Status']).to_csv('XGBoost.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9f83fa6-47fc-4e8a-a2a8-66c4815d1914",
   "metadata": {},
   "outputs": [],
   "source": [
    "print('Accuracy score of Logistic Regression : {}'.format(mean_acc))\n",
    "print('Accuracy score of Decision Tree Classifier : {}'.format(mean_acc2))\n",
    "print('Accuracy score of Random Forest Classifier : {}'.format(Mean_accuracy))\n",
    "print('Accuracy score of XGBoost Classifier : {}'.format(mean_acc_1))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d168b398-f09e-4788-84fb-25249a0d203d",
   "metadata": {
    "tags": []
   },
   "source": [
    "So, Random Forest Classifier is the best model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c29331b-1383-44cc-911c-71749ca7a870",
   "metadata": {},
   "source": [
    "# Model Deployment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ebbda7e4-ff4a-47cf-a8c9-bd091b149faa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# saving the model \n",
    "import pickle \n",
    "pickle_out = open(\"loan_approval_model.pkl\", mode = \"wb\") \n",
    "pickle.dump(model_rf, pickle_out) \n",
    "pickle_out.close()"
   ]
  }
 ],
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