Loan Eligibility Prediction Solution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ab931317",
   "metadata": {},
   "source": [
    "# Loan Eligibility Prediction\n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2fc61eb",
   "metadata": {},
   "source": [
    "## Process Flow\n",
    "1. Understanding Problem Statement\n",
    "2. Hypothesis Generation\n",
    "3. Getting the system ready and loading the data\n",
    "4. Understanding the data\n",
    "5. Exploratory Data Analysis (EDA)\n",
    "   - Univariate Analysis\n",
    "   - Bivariate Analysis\n",
    "6. Missing Value and Outlier Treatment\n",
    "7. Evaluation Metrics for classification problems\n",
    "8. Model Building Part-I\n",
    "9. Logistics regression using stratified k-folds cross validation\n",
    "10. Feature Engineering\n",
    "11. Model Building Part-II\n",
    "   - Logistic Regression\n",
    "   - Decision Tree\n",
    "   - Random Forest\n",
    "   - XGBoost\n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e51acf88",
   "metadata": {},
   "source": [
    "## 1. Understanding Problem Statement\n",
    "It is a **Classification Problem** where we have to predict whether a loan would be approved or not. In a classification problem, we have to predict discrete values based on a given set of independent variables.\n",
    "Classification can be of two types:\n",
    "- **Binary Classification:** In this classification, we have to predict either of the two given classes. For example- classifying the gender as male ore female, predictiing the result as win or loss etc.\n",
    "- **Multiclass Classification:** Here we have to classify the data into three or more classes. For example- classifying a movie's genre as comedy, action or romantic, classify fruits as oranges, apples or pears etc.\n",
    "\n",
    "Loan Prediction is a very common real life problem that each retail bank faces atleast once in its lifetime. If done correctly, it can save a lot of man hours at the end of a retail bank.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "453009e6",
   "metadata": {},
   "source": [
    "## 2. Hypothesis Generation\n",
    "After looking at the problem statement, we will now move into hypothesis generation. It is a process of listing out all the possible factors that can affect the outcome.\n",
    "\n",
    "**What is Hypothesis Generation?**\n",
    "This is a very important stage in any data science/machine learning pipeline. It involves understanding the problem in detail by brainstorming as many factors as possible which can impact the outcome. It is done by understanding the problem statement throoughly and before looking at the data.\n",
    "\n",
    "Below are some of the factors which I think can affect the Loan Approval (dependent variable for this loan prediction problem):\n",
    "- **Salary:** Applicants with high income should have more chances of loan approval.\n",
    "- **Previous History:** Applicants who have repayed their previous debts should have higher chances of loan approval.\n",
    "- **Loan Amount:** Loan approval should also depend on the loan amount. If the loan amount is less, chances of loan approval should be high.\n",
    "- **Loan term:** Loan for less time period and less amount should have higher chances of approval.\n",
    "- **EMI:** Lesser the amount to be paid monthly  to repay the loan, higher the chances of loan approval.\n",
    "\n",
    "These are some of the factors which I think can affect the target variable, you can come up with many more factors."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d4d8883",
   "metadata": {},
   "source": [
    "## 3. Getting the system ready and loading the data\n",
    "\n",
    "**System Specifications***\n",
    "- Python - 3.7\n",
    "- pandas - 0.20.3\n",
    "- seaborn - 1.0.0\n",
    "- sklearn - 0.19.1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84014a69",
   "metadata": {},
   "source": [
    "#### Loading packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "22517a75",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loading packages\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np                   # For mathematical calculations\n",
    "import seaborn as sns                # For Data Visualization\n",
    "import matplotlib.pyplot as plt      # For plotiing graphs\n",
    "%matplotlib inline\n",
    "import warnings                      # To ignore any warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91cbd139",
   "metadata": {},
   "source": [
    "#### About Given Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abd94482",
   "metadata": {},
   "source": [
    "**Data** - For this practice problem, we have been given three csv files: train, test and sample submission.\n",
    "- **Train file** will be used for training the model, i.e. our model will learn from this file. It contains all the independent variables and the target varibale.\n",
    "- **Test file** contains all the independent variables, but not the target variable. We will apply the model to predict the target variable for the test data.\n",
    "- **Sample Submission** file contains the format in  which we have to submit our predictions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "460d612d",
   "metadata": {},
   "source": [
    "#### Reading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "78a81726",
   "metadata": {},
   "outputs": [],
   "source": [
    "train=pd.read_csv(\"train.csv\")\n",
    "test=pd.read_csv(\"test.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55ed68bd",
   "metadata": {},
   "source": [
    "Let's make a copy of train and test data so that even if we have to make any changes in these datasets we would not lose the original datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6b4acf00",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_original=train.copy()\n",
    "test_original=train.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c79a761",
   "metadata": {},
   "source": [
    "## 4. Undestanding the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5601db2b",
   "metadata": {},
   "source": [
    "#### Features present in our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b42fec7f",
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "761cca5f",
   "metadata": {},
   "source": [
    "**Train dataset** has **12 independent variables** and **1 target variable i.e. Loan_Status**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ba71142d",
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "525877dc",
   "metadata": {},
   "source": [
    "Similar features in the **test datasets** as the train datasets **except the Loan_Status**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6e14417",
   "metadata": {},
   "source": [
    "#### Data Description\n",
    "\n",
    "- **Variable** \tDescription\n",
    "- **Loan_ID** \tUnique Loan ID\n",
    "- **Gender** \tMale/ Female\n",
    "- **Married** \tApplicant married (Y/N)\n",
    "- **Dependents** \tNumber of dependents\n",
    "- **Education** \tApplicant Education (Graduate/ Under Graduate)\n",
    "- **Self_Employed** \tSelf employed (Y/N)\n",
    "- **ApplicantIncome** \tApplicant income\n",
    "- **CoapplicantIncome** \tCoapplicant income\n",
    "- **LoanAmount** \tLoan amount in thousands\n",
    "- **Loan_Amount_Term** \tTerm of loan in months\n",
    "- **Credit_History** \tcredit history meets guidelines\n",
    "- **Property_Area** \tUrban/ Semi Urban/ Rural\n",
    "- **Loan_Status** \t(Target) Loan approved (Y/N)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ca88127",
   "metadata": {},
   "source": [
    "#### Data Types for each variable in datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4450970b",
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8ac97760",
   "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      int64\n",
       "LoanAmount           float64\n",
       "Loan_Amount_Term     float64\n",
       "Credit_History       float64\n",
       "Property_Area         object\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2d5b073",
   "metadata": {},
   "source": [
    "We can see there are three format of data types:\n",
    "- **object:** Object format means variables are **categorical**. Categorical variables in our datasets are: **Loan_ID, Gender, Married, Dependents, Education, Self_Employed, Property_Area, Loan_Status**.\n",
    "- **int64:** It repesents integer variables. **ApplicantIncome** is of this format.\n",
    "- **float64:** It represents the variable which have some decimal values involved. They are also **numerical variables**. Numerical variables in our datasets are - **CoapplicantIncome, LoanAmount, Loan_Amount_Term and Credit_Hisory**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c369cc9e",
   "metadata": {},
   "source": [
    "#### Shape of the datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7ef4ac1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((614, 13), (367, 12))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape, test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8d02f85",
   "metadata": {},
   "source": [
    "- **Train Datasets:** **614 rows** and **13 columns**\n",
    "- **Test Datasets:** **367 rows** and **12 columns**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f032d14",
   "metadata": {},
   "source": [
    "## 5. Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcf2b893",
   "metadata": {},
   "source": [
    "### 5.1 Univariate Analysis\n",
    "\n",
    "It is the simplest form of analyzing data where we examine each variable individually. For categorical features, we can use **frequency table or bar plots** which will calculate the number of each category in a particular variable. For numerical features, **probability density plots** can be used to look at the distribution of the variable."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13d397ad",
   "metadata": {},
   "source": [
    "#### Target Variable (Loan_Status)\n",
    "As it is a categorical variable, let us look at its frequency table, percentage distribution and bar plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "95c08400",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Y    422\n",
       "N    192\n",
       "Name: Loan_Status, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Loan_Status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9981d538",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Y    0.687296\n",
       "N    0.312704\n",
       "Name: Loan_Status, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Loan_Status'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "872170ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['Loan_Status'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd0568ca",
   "metadata": {},
   "source": [
    "**Inference:** The loan of **422(~69%)** people out of  614 was **approved**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba000249",
   "metadata": {},
   "source": [
    "Now lets visualize each variable separately. Different types of variables are Categorical, Ordinal and Numerical.\n",
    "- **Categorical Features:** These features have categories **(Gender, Married, Self_Employed, Credit History, Loan_Status)**\n",
    "- **Ordinal Features:** Variables in categorical features having some order involved **(Dependents, Education, Property_Area)**\n",
    "- **Numerical Features:** These features have numerical values **(ApplicantIncome, CoapplicantIncome, LoanAmount, Loan_Amount_Term)**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97c2a878",
   "metadata": {},
   "source": [
    "**Visualize Independent Variable (Categorical)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "06099ea9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "plt.subplot(221)\n",
    "train['Gender'].value_counts(normalize=True).plot.bar(figsize=(20,10), 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": "369883e8",
   "metadata": {},
   "source": [
    "**Inference:** It can be inferred from the above bar plots that:\n",
    "- **80%** applicants in the dataset are **Male**.\n",
    "- **~65%** of the applicants in the datasets are **Married**.\n",
    "- **~15%** applicants in the datasets are **Self_Employed**.\n",
    "- **~85%** applicants have **repaid their debts**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "106963fc",
   "metadata": {},
   "source": [
    "**Visualize Independent Variable (Ordinal)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "dbc94c5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "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= 'Propertry_Area')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "044652d1",
   "metadata": {},
   "source": [
    "**Inference:** Following inferences can be made from the above bar plots:\n",
    "- Most of the applicants **don't have any dependents**.\n",
    "- Around **80%** of the applicants are **Graduate**.\n",
    "- Most of the applicants are from **Semiurban** area"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be51024f",
   "metadata": {},
   "source": [
    "**Visualize Independent Variable (Numerical)**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d50bbe9",
   "metadata": {},
   "source": [
    "- **Distibution of Applicant Income**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "67e4ea67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "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": "e450576a",
   "metadata": {},
   "source": [
    "**Inference:** It can be inferred that most of the data in the distribution of applicant income is towards left which means it is **not normally distributed**. We will try to make it normal in later sections as algorithms works better if the data is normally distributed.\n",
    "\n",
    "The boxplot confirms the **presence of a lot of outliers/extreme values**. This can be attributed to the **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": 17,
   "id": "c9a3cd1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, '')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.boxplot(column='ApplicantIncome', by = 'Education')\n",
    "plt.suptitle(\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ffe4168",
   "metadata": {},
   "source": [
    "**Inference:** We can see there are a higher number of graduates with very high incomes, which are appearing to be the outliers."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1928f0a",
   "metadata": {},
   "source": [
    "- **Distribution of Coapplicant Income**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3d62276c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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": "8f9a7976",
   "metadata": {},
   "source": [
    "**Inference:** We see similar distribution as that of the applicant income. Majority of coapplicant's income ranges from **0 to 5000**. We also see a lot of outliers in the coapplicant income and it is **not normally distributed**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fd5fe8b",
   "metadata": {},
   "source": [
    "- **Distribuion of Loan Amount variable**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "87de9b1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "plt.subplot(121)        #df=train.dropna() has not been run in this block\n",
    "sns.distplot(train['LoanAmount'])\n",
    "plt.subplot(122)\n",
    "train['LoanAmount'].plot.box(figsize=(16,5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e473437",
   "metadata": {},
   "source": [
    "**Inference:** We see a **lot of outliers** in this variable and the distribution is **fairly normal**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d4e6306",
   "metadata": {},
   "source": [
    "### 5.2 Bivariate Analysis\n",
    "\n",
    "Lets recall some of the hypothesis that we generated earlier:\n",
    "- **Salary:** Applicants with high income should have more chances of loan approval.\n",
    "- **Previous History:** Applicants who have repayed their previous debts should have higher chances of loan approval.\n",
    "- **Loan Amount:** Loan approval should also depend on the loan amount. If the loan amount is less, chances of loan approval should be high.\n",
    "- **EMI:** Lesser the amount to be paid monthly  to repay the loan, higher the chances of loan approval.\n",
    "\n",
    "Lets try to test the above mentioned hypothesis using bivariate analysis. \n",
    "\n",
    "After looking at every variable individually in univariate analysis, we will now explore them again with respect to the target variable.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40e513c3",
   "metadata": {},
   "source": [
    "- **Categorical Independent Variable Vs Target Variable**\n",
    ">First of all we will find the relation between **target variable and categorical independent variables**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "50494bef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Loan_Status</th>\n",
       "      <th>N</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>37</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>150</td>\n",
       "      <td>339</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Loan_Status    N    Y\n",
       "Gender               \n",
       "Female        37   75\n",
       "Male         150  339"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Gender=pd.crosstab(train['Gender'], train['Loan_Status'])\n",
    "Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6926e90e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Loan_Status</th>\n",
       "      <th>N</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>0.330357</td>\n",
       "      <td>0.669643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>0.306748</td>\n",
       "      <td>0.693252</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Loan_Status         N         Y\n",
       "Gender                         \n",
       "Female       0.330357  0.669643\n",
       "Male         0.306748  0.693252"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Gender.div(Gender.sum(1).astype(float), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1507fff7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Gender'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Gender.div(Gender.sum(1).astype(float), axis=0).plot(kind= \"bar\", stacked= True, figsize=(4,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38b9664a",
   "metadata": {},
   "source": [
    "**Inference:** It can be inferred that the proportion of male and female applicants is **more or less same** for both approved and unapproved loans."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "47842520",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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wbNmycveJjY1FTEyM8XlhYSG8vLyqUyqR1bO3t4e3tzdKSkpQWlpaOydZ+FTtHFcpY+9+OtbGxga2trZ3/ZeLRSHdtGlT2NjYmI2ac3NzzUbXtyQlJaFr166YMmUKAKBDhw5wdHREWFgY5syZA3d3829G0Gg00Gg0lpRGRJVQqVSws7ODnZ1d7ZzgcmbtHFcpWq3SFRhZNAa3t7dHcHAwUlNTTdpTU1PRpUuXcve5evWq2VDf5r8rUQlRz+axiIhqmMUTJTExMVi6dCmWL1+O48ePY+LEidDr9YiKigJwc6pixIgRxv4DBgzAhg0bsGjRImRkZGDPnj2Ijo7Ggw8+CA8Pj5p7JURE9ZDFc9JDhw7F+fPnMWvWLBgMBgQGBmLLli3w8fEBABgMBpN7piMjI3Hp0iUsXLgQkyZNQqNGjdCzZ0+89dZbNfcqiIjqKZW4B+YcCgsL4eLigoKCAjg7O9fuyRJcavf4denWCmX1RX16b4D69f7wvbFYVXONa3cQEUmMIU1EJDGGNBGRxBjSREQSY0gTEUmMIU1EJDGGNBGRxBjSREQSY0gTEUmMIU1EJDGGNBGRxBjSREQSY0gTEUmMIU1EJDGGNBGRxBjSREQSY0gTEUmMIU1EJDGGNBGRxBjSREQSY0gTEUmMIU1EJDGGNBGRxBjSREQSY0gTEUmMIU1EJDGGNBGRxBjSREQSY0gTEUmMIU1EJDGGNBGRxBjSREQSY0gTEUmMIU1EJDGGNBGRxBjSREQSY0gTEUmMIU1EJDGGNBGRxBjSREQSY0gTEUmMIU1EJDGGNBGRxBjSREQSY0gTEUmMIU1EJDGGNBGRxKoV0ikpKfDz84NWq0VwcDB2795daf+ioiLExcXBx8cHGo0GLVu2xPLly6tVMBGRNbG1dId169ZhwoQJSElJQdeuXfHBBx8gIiICx44dg7e3d7n7DBkyBOfOncOyZcsQEBCA3NxclJSU3HXxRET1ncUhPX/+fIwaNQqjR48GACQnJ+Obb77BokWLkJSUZNb/66+/xs6dO5GRkYEmTZoAAHx9fe+uaiIiK2HRdEdxcTEOHjyI8PBwk/bw8HDs3bu33H2++OILhISEYO7cufD09ETr1q0xefJkXLt2rcLzFBUVobCw0ORBRGSNLBpJ5+XlobS0FDqdzqRdp9MhJyen3H0yMjLwww8/QKvVYuPGjcjLy8OYMWNw4cKFCuelk5KSkJiYaElpRET1UrUuHKpUKpPnQgiztlvKysqgUqmwZs0aPPjgg+jXrx/mz5+PlStXVjiajo2NRUFBgfGRmZlZnTKJiO55Fo2kmzZtChsbG7NRc25urtno+hZ3d3d4enrCxcXF2NauXTsIIfDnn3+iVatWZvtoNBpoNBpLSiMiqpcsGknb29sjODgYqampJu2pqano0qVLuft07doV2dnZuHz5srHt999/h1qtRosWLapRMhGR9bB4uiMmJgZLly7F8uXLcfz4cUycOBF6vR5RUVEAbk5VjBgxwth/2LBhcHV1xfPPP49jx45h165dmDJlCl544QU0aNCg5l4JEVE9ZPEteEOHDsX58+cxa9YsGAwGBAYGYsuWLfDx8QEAGAwG6PV6Y38nJyekpqZi3LhxCAkJgaurK4YMGYI5c+bU3KsgIqqnVEIIoXQRd1JYWAgXFxcUFBTA2dm5dk+W4HLnPveKhAKlK6hZ9em9AerX+8P3xmJVzTWu3UFEJDGGNBGRxBjSREQSY0gTEUmMIU1EJDGGNBGRxBjSREQSY0gTEUmMIU1EJDGGNBGRxBjSREQSY0gTEUmMIU1EJDGGNBGRxBjSREQSY0gTEUmMIU1EJDGGNBGRxBjSREQSY0gTEUmMIU1EJDGGNBGRxBjSREQSY0gTEUmMIU1EJDGGNBGRxBjSREQSY0gTEUmMIU1EJDGGNBGRxBjSREQSY0gTEUmMIU1EJDGGNBGRxBjSREQSY0gTEUmMIU1EJDGGNBGRxBjSREQSY0gTEUmMIU1EJDGGNBGRxBjSREQSY0gTEUmMIU1EJDGGNBGRxBjSREQSY0gTEUmsWiGdkpICPz8/aLVaBAcHY/fu3VXab8+ePbC1tUWnTp2qc1oiIqtjcUivW7cOEyZMQFxcHNLT0xEWFoaIiAjo9fpK9ysoKMCIESPQq1evahdLRGRtLA7p+fPnY9SoURg9ejTatWuH5ORkeHl5YdGiRZXu9/LLL2PYsGEIDQ2tdrFERNbGopAuLi7GwYMHER4ebtIeHh6OvXv3VrjfihUrcOrUKcTHx1fpPEVFRSgsLDR5EBFZI4tCOi8vD6WlpdDpdCbtOp0OOTk55e7zxx9/YNq0aVizZg1sbW2rdJ6kpCS4uLgYH15eXpaUSURUb1TrwqFKpTJ5LoQwawOA0tJSDBs2DImJiWjdunWVjx8bG4uCggLjIzMzszplEhHd86o2tP2vpk2bwsbGxmzUnJubaza6BoBLly4hLS0N6enpGDt2LACgrKwMQgjY2tpi27Zt6Nmzp9l+Go0GGo3GktKIiOoli0bS9vb2CA4ORmpqqkl7amoqunTpYtbf2dkZv/zyCw4fPmx8REVFoU2bNjh8+DAeeuihu6ueiKies2gkDQAxMTF47rnnEBISgtDQUCxZsgR6vR5RUVEAbk5VZGVlYfXq1VCr1QgMDDTZv1mzZtBqtWbtsvC9/rHSJdSYM0oXQER3zeKQHjp0KM6fP49Zs2bBYDAgMDAQW7ZsgY+PDwDAYDDc8Z5pIiKqGpUQQihdxJ0UFhbCxcUFBQUFcHZ2rtVz+U77qlaPX5fO/LO/0iXUrAQXpSuoWQkFSldQc/jeWKyquca1O4iIJMaQJiKSGEOaiEhiDGkiIokxpImIJMaQJiKSGEOaiEhiDGkiIokxpImIJMaQJiKSmMVrdxAR3a4+LUwGyLU4GUfSREQSY0gTEUmMIU1EJDGGNBGRxBjSREQSY0gTEUmMIU1EJDGGNBGRxBjSREQSY0gTEUmMIU1EJDGGNBGRxBjSREQSY0gTEUmMIU1EJDGGNBGRxBjSREQSY0gTEUmMIU1EJDGGNBGRxBjSREQSY0gTEUmMIU1EJDGGNBGRxBjSREQSY0gTEUmMIU1EJDGGNBGRxBjSREQSY0gTEUmMIU1EJDFbpQsgqirf6x8rXUKNOqN0AXRP4EiaiEhiDGkiIokxpImIJMaQJiKSGEOaiEhi1QrplJQU+Pn5QavVIjg4GLt3766w74YNG/DYY4/Bzc0Nzs7OCA0NxTfffFPtgomIrInFIb1u3TpMmDABcXFxSE9PR1hYGCIiIqDX68vtv2vXLjz22GPYsmULDh48iB49emDAgAFIT0+/6+KJiOo7i0N6/vz5GDVqFEaPHo127dohOTkZXl5eWLRoUbn9k5OTMXXqVDzwwANo1aoV3nzzTbRq1QqbN2++6+KJiOo7i0K6uLgYBw8eRHh4uEl7eHg49u7dW6VjlJWV4dKlS2jSpEmFfYqKilBYWGjyICKyRhaFdF5eHkpLS6HT6UzadTodcnJyqnSMd955B1euXMGQIUMq7JOUlAQXFxfjw8vLy5IyiYjqjWpdOFSpVCbPhRBmbeX55JNPkJCQgHXr1qFZs2YV9ouNjUVBQYHxkZmZWZ0yiYjueRat3dG0aVPY2NiYjZpzc3PNRte3W7duHUaNGoX169ejd+/elfbVaDTQaDSWlEZEVC9ZNJK2t7dHcHAwUlNTTdpTU1PRpUuXCvf75JNPEBkZiY8//hj9+/evXqVERFbI4lXwYmJi8NxzzyEkJAShoaFYsmQJ9Ho9oqKiANycqsjKysLq1asB3AzoESNG4L333sPDDz9sHIU3aNAALi4uNfhSiIjqH4tDeujQoTh//jxmzZoFg8GAwMBAbNmyBT4+PgAAg8Fgcs/0Bx98gJKSErz66qt49dVXje0jR47EypUr7/4VEBHVY9VaT3rMmDEYM2ZMudtuD94dO3ZU5xRERASu3UFEJDWGNBGRxBjSREQSY0gTEUmMIU1EJDGGNBGRxBjSREQSY0gTEUmMIU1EJDGGNBGRxBjSREQSY0gTEUmMIU1EJDGGNBGRxBjSREQSY0gTEUmMIU1EJDGGNBGRxBjSREQSY0gTEUmMIU1EJDGGNBGRxBjSREQSY0gTEUmMIU1EJDGGNBGRxBjSREQSY0gTEUmMIU1EJDGGNBGRxBjSREQSY0gTEUmMIU1EJDGGNBGRxBjSREQSY0gTEUmMIU1EJDGGNBGRxBjSREQSY0gTEUmMIU1EJDGGNBGRxBjSREQSY0gTEUmMIU1EJDGGNBGRxBjSREQSY0gTEUmsWiGdkpICPz8/aLVaBAcHY/fu3ZX237lzJ4KDg6HVauHv74/FixdXq1giImtjcUivW7cOEyZMQFxcHNLT0xEWFoaIiAjo9fpy+58+fRr9+vVDWFgY0tPT8frrryM6Ohqff/75XRdPRFTf2Vq6w/z58zFq1CiMHj0aAJCcnIxvvvkGixYtQlJSkln/xYsXw9vbG8nJyQCAdu3aIS0tDfPmzcPgwYPLPUdRURGKioqMzwsKCgAAhYWFlpZrsbKiq7V+jrpSFz+vulSf3hugfr0/fG+qfw4hROUdhQWKioqEjY2N2LBhg0l7dHS0ePTRR8vdJywsTERHR5u0bdiwQdja2ori4uJy94mPjxcA+OCDDz7q/SMzM7PS3LVoJJ2Xl4fS0lLodDqTdp1Oh5ycnHL3ycnJKbd/SUkJ8vLy4O7ubrZPbGwsYmJijM/Lyspw4cIFuLq6QqVSWVKylAoLC+Hl5YXMzEw4OzsrXQ79D7438qpv740QApcuXYKHh0el/Sye7gBgFpRCiErDs7z+5bXfotFooNFoTNoaNWpUjUrl5uzsXC/+Y6uP+N7Iqz69Ny4uLnfsY9GFw6ZNm8LGxsZs1Jybm2s2Wr6lefPm5fa3tbWFq6urJacnIrI6FoW0vb09goODkZqaatKempqKLl26lLtPaGioWf9t27YhJCQEdnZ2FpZLRGRdLL4FLyYmBkuXLsXy5ctx/PhxTJw4EXq9HlFRUQBuziePGDHC2D8qKgpnz55FTEwMjh8/juXLl2PZsmWYPHlyzb2Ke4xGo0F8fLzZlA4pj++NvKz1vVEJcaf7P8ylpKRg7ty5MBgMCAwMxLvvvotHH30UABAZGYkzZ85gx44dxv47d+7ExIkTcfToUXh4eOC1114zhjoREVWsWiFNRER1g2t3EBFJjCFNRCQxhjQRkcQY0kREEmNIExFJrFofCyeqb8rKynDy5Enk5uairKzMZNut20uJlMCQJqu3f/9+DBs2DGfPnjVbNlKlUqG0tFShyuiW4uLicn+Bent7K1RR3eF90rUoKCioyqv2HTp0qJaroYp06tQJrVu3RmJiItzd3c3es6osgkO1448//sALL7yAvXv3mrTfWtTNGn6BciRdi5544gmlS6Aq+OOPP/DZZ58hICBA6VLoNpGRkbC1tcWXX35Z7i9Qa8CRNFm9nj17YurUqejbt6/SpdBtHB0dcfDgQbRt21bpUhTDkTRZvXHjxmHSpEnIycnB/fffb7Y6Y4cOHRSqjNq3b4+8vDyly1AUR9J1pLS0FO+++y4+/fRT6PV6FBcXm2y/cOGCQpWRWm1+J6pKpbKqeU9Zbd++HdOnT8ebb75Z7i/Q+rL4f2U4kq4jiYmJWLp0KWJiYjBjxgzExcXhzJkz2LRpE2bOnKl0eVbt9OnTSpdAFejduzcAoFevXibt1vQLlCPpOtKyZUssWLAA/fv3R8OGDXH48GFj2/79+/Hxxx8rXSKRdHbu3Fnp9m7dutVRJcphSNcRR0dHHD9+HN7e3nB3d8dXX32Fzp07IyMjA0FBQSgoKFC6RKt37NixcqeiBg4cqFBFRJzuqDMtWrSAwWCAt7c3AgICsG3bNnTu3Bk//fST1X3ThGwyMjIwaNAg/PLLL8a5aODvL0q2hj+pZXf16tVyf4Faw0Vdrt1RRwYNGoTvvvsOADB+/HjMmDEDrVq1wogRI/DCCy8oXJ11Gz9+PPz8/HDu3Dk4ODjg6NGj2LVrF0JCQky+YYjq3l9//YV//OMfaNiwIe677z4EBQWZPKwBpzsU8uOPP2LPnj0ICAjgn9MKa9q0KbZv344OHTrAxcUFBw4cQJs2bbB9+3ZMmjQJ6enpSpdotYYPH44zZ84gOTkZPXr0wMaNG3Hu3DnMmTMH77zzDvr37690ibWO0x114MaNG3jppZcwY8YM+Pv7AwAeeughPPTQQwpXRsDN6QwnJycANwM7Ozsbbdq0gY+PD06cOKFwddZt+/bt+M9//oMHHngAarUaPj4+eOyxx+Ds7IykpCSrCGlOd9QBOzs7bNy4UekyqAKBgYE4cuQIgJu/POfOnYs9e/Zg1qxZxl+qpIwrV66gWbNmAIAmTZrgr7/+AgDcf//9VrPeDUO6jgwaNAibNm1Sugwqx/Tp042rq82ZMwdnz55FWFgYtmzZggULFihcnXVr06aN8a+ZTp064YMPPkBWVhYWL14Md3d3haurG5yTriNvvPEG5s2bh169eiE4OBiOjo4m26OjoxWqjMpz4cIFNG7c2CoX9JHJmjVrcOPGDURGRiI9PR19+vTB+fPnYW9vj5UrV2Lo0KFKl1jrGNJ1xM/Pr8JtKpUKGRkZdVgNVSQzMxMqlQotWrRQuhQqx9WrV/Hbb7/B29sbTZs2VbqcOsGQJqtXUlKCxMRELFiwAJcvXwYAODk5Ydy4cYiPjzdbL4KUcfv969aCc9Jk9caOHYslS5Zg7ty5SE9PR3p6OubOnYtly5Zh3LhxSpdn9ZYtW4bAwEBotVpotVoEBgZi6dKlSpdVZziSriN3+sDK8uXL66gSup2LiwvWrl2LiIgIk/atW7fi6aef5kf2FTRjxgy8++67GDduHEJDQwEA+/btw8KFCzF+/HjMmTNH4QprH++TriMXL140eX7jxg38+uuvyM/PR8+ePRWqigBAq9XC19fXrN3X1xf29vZ1XxAZLVq0CB9++CGeeeYZY9vAgQPRoUMHjBs3jiFNNae8+6TLysowZswY3oursFdffRWzZ8/GihUrjOuoFBUV4Y033sDYsWMVrs66lZaWIiQkxKw9ODgYJSUlClRU9zjdobATJ06ge/fuMBgMSpdiVZ588kmT599++y00Gg06duwIAPj5559RXFyMXr16YcOGDUqUSLj5rTl2dnaYP3++SfvkyZNx7do1vP/++wpVVnc4klbYqVOnrGZEIJPbvwF88ODBJs+9vLzqshz6HzExMcZ/q1QqLF26FNu2bcPDDz8MANi/fz8yMzMxYsQIpUqsUxxJ15H//Q8PuHk7kcFgwJdffonIyEgsXLhQocqsmxACer0ebm5ucHBwULocAtCjR48q9VOpVNi+fXstV6M8hnQd6d69u8n9nWq1Gm5ubujZsyf69+8PT09PBauzXmVlZdBqtTh69ChatWqldDlEZjjdUUfKW5c4JycHb7zxBsaPH49r167VfVEEtVqNVq1a4fz58wxpyZSUlECr1eLw4cMIDAxUuhzF8MMstSw/Px/Dhw+Hm5sbPD09sWDBApSVlSE+Ph4tW7bE/v37eY+0wubOnYspU6bg119/VboU+h+2trbw8fGx+m/G4XRHLRszZgw2b96MoUOHYuvWrfjtt9/Qp08fXL9+HfHx8VbxRZqya9y4Ma5evYqSkhLY29ujQYMGJtsvXLigUGW0YsUKrF+/Hh999BGaNGmidDmKYEjXMh8fHyxbtgy9e/dGRkYGAgICEB0djeTkZKVLo/9atWpVpdtHjhxZR5XQ7YKCgnDy5EncuHEDPj4+ZqtHWsOa0pyTrmXZ2dlo3749AMDf3x9arRajR49WuCr6XwxheT3++ONWt6DS7TiSrmU2NjbIycmBm5sbAKBhw4Y4cuRIpUuXUt3S6/WVbvf29q6jSojMMaRrmVqtRkREhPHjxps3b0bPnj3N/mzjp9qUo1arKx2tWfuFKyVU9J44OzujTZs2mDp1qtmnRusrTnfUstv/lH722WcVqoQqcvu3gd+4cQPp6emYP38+3njjDYWqsm4VfSdofn4+Dhw4gGeffRarVq3CU089VceV1T2OpIkq8NVXX+Htt98u9x53Utb777+P1atX48cff1S6lFrH+6SJKtC6dWv89NNPSpdB5QgPD8fvv/+udBl1gtMdZPUKCwtNnt9aVyUhIYGfQpTUtWvXoNVqlS6jTjCkyeo1atTI7CKVEAJeXl5Yu3atQlVRZT788EMEBQUpXUadYEiT1fv+++9Nnt9a/CogIAC2tvy/iBJuXzXyloKCAqSlpeHUqVPYvXt3HVelDF44JCLpVLRcqbOzM9q2bYsxY8bAx8enjqtSBkOarNIXX3yBiIgI2NnZ4Ysvvqi078CBA+uoKiJzDGmySmq1Gjk5OWjWrBnU6opvclKpVPwwCymKIU1EJDHeJ01EJDFeuiYCcODAAezYsQO5ubkoKysz2Xb7N1UT1SWGNFm9N998E9OnT0ebNm2g0+lM7pm29mUySXmckyarp9Pp8NZbbyEyMlLpUojMcE6arJ5arUbXrl2VLoOoXAxpsnoTJ07E+++/r3QZROXidAdZvbKyMvTv3x+///472rdvDzs7O5Pt/EIGUhIvHJLVGzduHL7//nv06NEDrq6uvFhIUuFImqxew4YNsXbtWvTv31/pUojMcE6arF6TJk3QsmVLpcsgKhdDmqxeQkIC4uPjcfXqVaVLITLD6Q6yekFBQTh16hSEEPD19TW7cHjo0CGFKiPihUMiPPHEE0qXQFQhjqSJiCTGOWkiAPn5+Vi6dCliY2Nx4cIFADenObKyshSujKwdR9Jk9Y4cOYLevXvDxcUFZ86cwYkTJ+Dv748ZM2bg7NmzWL16tdIlkhXjSJqsXkxMDCIjI/HHH39Aq9Ua2yMiIrBr1y4FKyNiSBPhp59+wssvv2zW7unpiZycHAUqIvobQ5qsnlarRWFhoVn7iRMn4ObmpkBFRH9jSJPVe/zxxzFr1izcuHEDwM2F/vV6PaZNm4bBgwcrXB1ZO144JKtXWFiIfv364ejRo7h06RI8PDxgMBgQGhqKrVu3wtHRUekSyYoxpIn+a/v27Th06BDKysoQHByMXr16KV0SEac7yHr9+OOP2Lp1q/F5z5494ebmhpSUFDzzzDN46aWXUFRUpGCFRAxpsmIJCQk4cuSI8fkvv/yCF198EY899himTZuGzZs3IykpScEKiTjdQVbM3d0dmzdvRkhICAAgLi4OO3fuxA8//AAAWL9+PeLj43Hs2DElyyQrx5E0Wa2LFy9Cp9MZn+/cuRN9+/Y1Pn/ggQeQmZmpRGlERgxpslo6nQ6nT58GABQXF+PQoUMIDQ01br906ZLZsqVEdY0hTVarb9++mDZtGnbv3o3Y2Fg4ODggLCzMuP3IkSP8xhZSHNeTJqs1Z84cPPnkk+jWrRucnJywatUq2NvbG7cvX74c4eHhClZIxAuHRCgoKICTkxNsbGxM2i9cuAAnJyeT4CaqawxpIiKJcU6aiEhiDGkiIokxpImIJMaQJiKSGEOaiEhiDGmqcZGRkVCpVFCpVLCzs4O/vz8mT56MK1euKF1auVauXIlGjRrVyrH//PNP2Nvbo23btrVyfKr/GNJUK/r27QuDwYCMjAzMmTMHKSkpmDx5slm/W9+GopTaPv/KlSsxZMgQXL16FXv27FG8Hrr3MKSpVmg0GjRv3hxeXl4YNmwYhg8fjk2bNiEhIQGdOnXC8uXL4e/vD41GAyEE9Ho9Hn/8cTg5OcHZ2RlDhgzBuXPnjMe7td8HH3wALy8vODg44KmnnkJ+fr7JeVesWIF27dpBq9Wibdu2SElJMW47c+YMVCoVPv30U3Tv3h1arRYfffQRnn/+eRQUFBhH/wkJCZg1axbuv/9+s9cVHByMmTNnVulnIITAihUr8Nxzz2HYsGFYtmyZyfaK6rnT6wCA1157Da1bt4aDgwP8/f0xY8YMBnx9JYhq2MiRI8Xjjz9u0jZu3Djh6uoq4uPjhaOjo+jTp484dOiQ+Pnnn0VZWZkICgoSjzzyiEhLSxP79+8XnTt3Ft26dTPuf2u/nj17ivT0dLFz504REBAghg0bZuyzZMkS4e7uLj7//HORkZEhPv/8c9GkSROxcuVKIYQQp0+fFgCEr6+vsc/Zs2dFcnKycHZ2FgaDQRgMBnHp0iWRmZkp1Gq1OHDggPH4P//8s1CpVOLUqVNV+jl89913onnz5qKkpET8+uuvwtHRURQWFhq3l1dPVlbWHV+HEELMnj1b7NmzR5w+fVp88cUXQqfTibfeesuSt4nuEQxpqnG3h/SPP/4oXF1dxZAhQ0R8fLyws7MTubm5xu3btm0TNjY2Qq/XG9uOHj0qABhDMj4+XtjY2IjMzExjn61btwq1Wi0MBoMQQggvLy/x8ccfm9Qye/ZsERoaKoT4OxSTk5NN+qxYsUK4uLiYvY6IiAjxyiuvGJ9PmDBBdO/evco/h2HDhokJEyYYn3fs2FF8+OGHxucV1XOn11GeuXPniuDg4CrXRvcOhjTVuJEjRwobGxvh6OgoNBqNUKvVYtCgQeLcuXMiPj5eBAQEmPR/7733hK+vr9lxGjVqJFatWiWEuBnSfn5+Jtvz8/MFALFjxw6Rm5srAIgGDRoIR0dH40Oj0YhmzZoJIf4OxR9++MHkOBWF9IYNG0SjRo3EtWvXRHFxsXBzcxOrV6+u0s/g4sWLQqvVirS0NGPb22+/bRK05dVTldchhBDr168XXbt2FTqdzrjdzc2tSrXRvYWr4FGt6NGjBxYtWgQ7Ozt4eHiYrMt8+7dvCyGgUqnMjlFR+y23tqlUKpSVlQEAPvzwQzz00EMm/W5fOKmq3/49YMAAaDQabNy4ERqNBkVFRRg8eHCV9v34449x/fp1k1qEECgrK8OxY8fQvn37cuupyuvYv38/nn76aSQmJqJPnz5wcXHB2rVr8c4771SpNrq3MKSpVjg6OiIgIKBKfdu3bw+9Xo/MzEx4eXkBAI4dO4aCggK0a9fO2E+v1yM7OxseHh4AgH379kGtVqN169bQ6XTw9PRERkYGhg8fblGt9vb2KC0tNWu3tbXFyJEjsWLFCmg0Gjz99NNwcHCo0jGXLVuGSZMmITIy0qQ9Ojoay5cvx7x588rdryqvY8+ePfDx8UFcXJyx7ezZs1Wqi+49DGlSXO/evdGhQwcMHz4cycnJKCkpwZgxY9CtWzfj9w8CgFarxciRIzFv3jwUFhYiOjoaQ4YMQfPmzQHcvAMkOjoazs7OiIiIQFFREdLS0nDx4kXExMRUeH5fX19cvnwZ3333HTp27AgHBwdjGI8ePdr4i6Iqt9ABwOHDh3Ho0CGsWbPG7P7oZ555BnFxcZV+we2dXkdAQAD0ej3Wrl2LBx54AF999RU2btxYpdroHqTwdAvVQ+Xd3XFLfHy86Nixo1n72bNnxcCBA4Wjo6No2LCheOqpp0ROTo7ZfikpKcLDw0NotVrx5JNPigsXLpgcZ82aNaJTp07C3t5eNG7cWDz66KNiw4YNQoi/54DT09PNzh8VFSVcXV0FABEfH2+yLSwsTLRv377Kr3/s2LEV9s/NzRU2Njbi888/r7Seyl6HEEJMmTJFuLq6CicnJzF06FDx7rvvljuvTvc+ridN94SEhARs2rQJhw8frtPzCiHQtm1bvPzyy5WOxolqC6c7iCqQm5uLf//738jKysLzzz+vdDlkpRjSRBXQ6XRo2rQplixZgsaNG5tsc3JyqnC/rVu3mnyhLdHd4HQHUTWcPHmywm2enp5o0KBBHVZD9RlDmohIYlxgiYhIYgxpIiKJMaSJiCTGkCYikhhDmohIYgxpIiKJMaSJiCT2/53ZlzRssbbjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Married=pd.crosstab(train['Married'], train['Loan_Status'])\n",
    "Dependents=pd.crosstab(train['Dependents'], train['Loan_Status'])\n",
    "Education=pd.crosstab(train['Education'], train['Loan_Status'])\n",
    "Self_Employed=pd.crosstab(train['Self_Employed'], train['Loan_Status'])\n",
    "Credit_History=pd.crosstab(train['Credit_History'], train['Loan_Status'])\n",
    "Property_Area=pd.crosstab(train['Property_Area'], train['Loan_Status'])\n",
    "\n",
    "Married.div(Married.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked= True, figsize=(4,4))\n",
    "Dependents.div(Dependents.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked= True, figsize=(4,4))\n",
    "Education.div(Education.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked= True, figsize=(4,4))\n",
    "Self_Employed.div(Self_Employed.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked= True, figsize=(4,4))\n",
    "Credit_History.div(Credit_History.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked= True, figsize=(4,4))\n",
    "Property_Area.div(Property_Area.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked= True, figsize=(4,4))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d0ff426",
   "metadata": {},
   "source": [
    "**Inference:** \n",
    "- Proportion of **Married** applicants is **higher** for the approved loans.\n",
    "- Distribution of applicants with **1 or 3+ dependents** is **similar** across both categories of Loan_Status\n",
    "- There is **nothing significant** we can infer from **Self_Employed Vs Loan_Status** plot.\n",
    "- It seems people with **credit history as 1** are more likely to get their loans **approved**.\n",
    "- Proportion of loans getting approved in **Semiurban** area  is **higher** compared to that in Rural or Urban areas."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17b2aa43",
   "metadata": {},
   "source": [
    "- **Numerical Independent Variable Vs Target Variable**\n",
    ">We will try to find the mean income of the people for which loan has been approved Vs mean income of people for which loan has not been approved."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29c3c0dc",
   "metadata": {},
   "source": [
    "- - **ApplicantIncome Vs Loan_Status Analysis**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b10b92a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Loan_Status'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.groupby('Loan_Status')['ApplicantIncome'].mean().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25af3a0b",
   "metadata": {},
   "source": [
    "Here the Y-axis represents the mean applicant income. We don't see any change in the mean income. So, let's make bins for the applicant income variable based on the values in it and analyse the corresponding loan status for each bin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "98e5add8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Percentage')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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('ApplicantIncome')\n",
    "plt.ylabel('Percentage')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff9326b2",
   "metadata": {},
   "source": [
    "**Inference:** It can be inferred that ApplicantIncome **does not affect** the chances of loan approval which **contradicts our hypothesis** in which we assumed that if the applicant income is high the chances of loan approval will also be high."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b87a2d20",
   "metadata": {},
   "source": [
    "- - **CoapplicantIncome vs Loan_Status Analysis**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "079fa41d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Percentage')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins=[0,1000,3000,42000]\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\", stacked= True)\n",
    "plt.xlabel('CoapplicantIncome')\n",
    "plt.ylabel('Percentage')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3a1359c",
   "metadata": {},
   "source": [
    "**Inference:** It shows that if *coapplicant's income* is **less** the *chances of loan approval* are **high**. But this does not look right. The possible reason behind this may be that most of the applicants  don't have any coapplicant so the coapplicant income for such applicants is 0 and hence the loan approval is not dependent on it. So, we can make a new variable in which we will combine the applicant's and coapplicant's income to visualize the combined effect of income on loan approval."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0ef0c67a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Percentage')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['Total_Income']=train['ApplicantIncome']+train['CoapplicantIncome']\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\", stacked=True)\n",
    "plt.xlabel('Total_Income')\n",
    "plt.ylabel('Percentage')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1aba2f02",
   "metadata": {},
   "source": [
    "**Inference:** We can see that Proportion of *loans getting approved* for *applicants having* **low Total_Income** is very **less** as compared to that of applicants with Average, High and Very High."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56fe44c4",
   "metadata": {},
   "source": [
    "- - **LoanAmount vs Loan_Status Analysis**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2744ed75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Percentage')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins=[0,100,200,700]\n",
    "group=['Low','Average','High']\n",
    "train['LoanAmount_bin']=pd.cut(train['LoanAmount'], bins, labels=group)\n",
    "\n",
    "LoanAmount_bin=pd.crosstab(train['LoanAmount_bin'], train['Loan_Status'])\n",
    "LoanAmount_bin.div(LoanAmount_bin.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True)\n",
    "plt.xlabel('LoanAmount')\n",
    "plt.ylabel('Percentage')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "498f8d44",
   "metadata": {},
   "source": [
    "**Inference:** It can be seen that the proportion of *approved loan* is **higher** for **Low and Average Loan Amoun**t as compared to that of High Loan Amount which **supports our hypothesis** in which we considered that the chances of loan approval will be high when the loan amount is less."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a012299",
   "metadata": {},
   "source": [
    "Let's drop the bins which we created for the exploration part. We will change the 3+ in dependents variable to 3 to make it a numerical variable. We will also convert the target variable's categories into 0 and 1 so that we can find its correlation with numerical variables. One more reason to do so is few models like logistic regression takes only numeric values as input. We will replace N with 0 and Y with 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e96a2b91",
   "metadata": {},
   "outputs": [],
   "source": [
    "train=train.drop(['Income_bin', 'Coapplicant_Income_bin', 'LoanAmount_bin', 'Total_Income_bin', 'Total_Income'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "004721f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Dependents'].replace('3+', 3, inplace=True)\n",
    "train['Loan_Status'].replace('N', 0, inplace=True)\n",
    "train['Loan_Status'].replace('Y', 1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2fba979",
   "metadata": {},
   "source": [
    "Now lets look at the correlation between all the numerical variables. We will use the **heat map** to visualize the correlation. Heatmaps visualize data through variations in coloring. The variables with darker color means their correlation is more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "14c2dacc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAGAPlWYAAADYRk8zlWYAAADAHirNAAAAsI2eZpJmAAAA2EF7Bu0ZAAAAgD1UmvHQfJGy0ugQXE77XC8YHYLL6bL5PaNDcDnuVJAclpZmNjoEl+PtRcrh0kzUWbkDAAAAgB187AMAAIBNJr6RotIMAAAA2EOlGQAAALZRaabSDAAAANhDpRkAAAC2sbgJSTMAAADsoD2D9gwAAADAHirNAAAAsMlEewaVZgAAAMAeKs0AAACwjZ5mKs0AAACAPVSaAQAAYBs9zVSaAQAAAHuoNAMAAMA2eppJmgEAAGAHSTPtGQAAAIA9VJoBAABgE4ubUGkGAAAA7CJpBgAAgG1upuzb7sOcOXMUGBgoLy8v1apVS5s2bbJ5fHJyskaPHq0yZcrI09NT5cqV04IFCxy6Ju0ZAAAAcBlLlizRoEGDNGfOHIWHh2vevHlq2rSpDhw4oNKlS2f4mvbt2+vMmTOKjo5WUFCQzp49q5SUFIeuS9IMAAAA27Kxpzk5OVnJyclWY56envL09Mzw+BkzZqh379565ZVXJEkzZ87Ud999p7lz52ry5Mnpjl+zZo02btyoX3/9VQUKFJAkBQQEOBwn7RkAAAAwzOTJk+Xn52e1ZZT8StLNmzcVGxur5557zmr8ueee09atWzN8zcqVK1W7dm1NnTpVJUuWVHBwsIYNG6YbN244FCeVZgAAANiWjfM0R0ZGasiQIVZjmVWZz507p9TUVBUtWtRqvGjRojp9+nSGr/n111+1efNmeXl5acWKFTp37pz69eunCxcuONTXTNIMAAAAw9hqxcjMvVPgmc3mTKfFS0tLk8lk0meffSY/Pz9Jt1s8XnzxRb3//vvKkydPlq5JewYAAABsMplM2bY5olChQnJ3d09XVT579my66vMdxYsXV8mSJS0JsyRVqlRJZrNZJ0+ezPK1SZoBAABgm5NMOefh4aFatWpp7dq1VuNr165V/fr1M3xNeHi4/vjjD129etUydvjwYbm5uemxxx7L+i1wKFIAAADAQEOGDNH8+fO1YMECJSQkaPDgwUpMTFRERISk2z3S3bp1sxzfuXNnFSxYUD179tSBAwf0008/afjw4erVq1eWWzMkepoBAABgTzY+COioDh066Pz585owYYKSkpIUEhKi1atXq0yZMpKkpKQkJSYmWo738fHR2rVrNWDAANWuXVsFCxZU+/btNXHiRIeuazKbzeYH+k6ATPydmmZ0CC6nfa4XjA7B5XTZ/J7RIbicZ6qXNDoEl3Pt71tGh+ByvL2o0zmqUF7HHo7LTlPC52XbuUdteTXbzv0g8TsYAAAAtmXj4iauIkf0NB8/flwmk0lxcXGSpA0bNshkMunSpUuGxgUAAADX8I+S5tOnT2vAgAEqW7asPD09VapUKbVs2VLr1q17UPFli/r16yspKclq6pHsFhMTI39//3TjTz31lAYNGvTQ4gAAAHCYk8yeYaT7bs84fvy4wsPD5e/vr6lTpyo0NFS3bt3Sd999p3//+986ePDgg4zzgfLw8FCxYsWMDgMAAAAu4r4rzf369ZPJZNLOnTv14osvKjg4WFWqVNGQIUO0fft2SVJiYqJatWolHx8f+fr6qn379jpz5ozlHMeOHVOrVq1UtGhR+fj4qE6dOvrhhx+srhMQEKA33nhDnTt3lo+Pj0qUKKH33rN+0MdkMmnu3Llq2rSp8uTJo8DAQC1dujTT2DNqz9iyZYuefPJJeXt7K3/+/GrSpIkuXrwoSVqzZo2eeOIJ+fv7q2DBgmrRooWOHTtmee2d9o/ly5erUaNG8vb2VrVq1bRt2zbL9Xr27KnLly9bJvIeP358hrEFBATozTffVK9evZQvXz6VLl1aH374odUxJ0+eVMeOHVWgQAHlzZtXtWvX1o4dOyz7586dq3LlysnDw0MVKlTQJ598ku5+zZs3Ty1atJC3t7cqVaqkbdu26ejRo3rqqaeUN29ehYWFWb1HSfrvf/+rWrVqycvLS2XLllVUVJRSUlIyvc8AAODR4CyLmxjpvpLmCxcuaM2aNfr3v/+tvHnzptvv7+8vs9ms1q1b68KFC9q4caPWrl2rY8eOqUOHDpbjrl69qmbNmumHH37Q3r171aRJE7Vs2dJqmhBJmjZtmkJDQ7Vnzx5FRkZq8ODB6Sa1HjNmjNq1a6f4+Hh16dJFnTp1UkJCQpbeT1xcnJ555hlVqVJF27Zt0+bNm9WyZUulpqZKkq5du6YhQ4Zo165dWrdundzc3NSmTRulpVnPBjF69GgNGzZMcXFxCg4OVqdOnZSSkqL69etr5syZ8vX1VVJSkpKSkjRs2LBM45k+fbpq166tvXv3ql+/furbt6+lcn/16lU9+eST+uOPP7Ry5UrFx8drxIgRllhWrFih1157TUOHDtXPP/+sV199VT179tSPP/5odY033nhD3bp1U1xcnCpWrKjOnTvr1VdfVWRkpHbv3i1J6t+/v+X47777Tl26dNHAgQN14MABzZs3TzExMZo0aVKW7jEAAHBhtGfc35RzO3fuVL169bR8+XK1adMmw2PWrl2rpk2b6rffflOpUqUkSQcOHFCVKlW0c+dO1alTJ8PXValSRX379rUkbAEBAapUqZK+/fZbyzEdO3bUlStXtHr16ttvwmRSRESE5s6daznm8ccfV82aNTVnzhwdP35cgYGB2rt3r6pXr64NGzaoUaNGunjxovz9/dW5c2clJiZq8+bNWXr/f/75p4oUKaL9+/crJCTEcv758+erd+/eVu81ISFBFStWVExMjAYNGpTu4cOnnnpK1atX18yZMy3vt0GDBpbqsNlsVrFixRQVFaWIiAh9+OGHGjZsmI4fP64CBQqkiy08PFxVqlSxqk63b99e165d0zfffGO5X6+//rreeOMNSdL27dsVFham6Oho9erVS5K0ePFi9ezZUzdu3JAkNWzYUE2bNlVkZKTlvJ9++qlGjBihP/74I10cycnJSk5Othoz58rt8NryOR1TzjmOKeccx5RzjmPKOccx5ZzjnGnKubcaRWfbuUf+2Dvbzv0g3Vel+U6ebauknpCQoFKlSlkSZkmqXLmy/P39LRXga9euacSIEZZxHx8fHTx4MF2lOSwsLN3P91aRs3JMZu5UmjNz7Ngxde7cWWXLlpWvr68CAwMlKV2coaGhll8XL15c0u210B1193lMJpOKFStmOU9cXJxq1KiRYcIs3b7v4eHhVmPh4eHp7sXd17izVnvVqlWtxv7++29duXJFkhQbG6sJEybIx8fHsvXp00dJSUm6fv16ujgmT54sPz8/q23alCmO3AYAAOAsTKbs21zEfX3sK1++vEwmkxISEtS6desMjzGbzRkm1XePDx8+XN99953efvttBQUFKU+ePHrxxRd18+ZNuzFkpQcmq30y9pZQbNmypUqVKqWPPvpIJUqUUFpamkJCQtLFmTt37nTXvreFIyvuPs+dc905T1aWe7z3fWf0/yKjWG3Fn5aWpqioKLVt2zbd9by8vNKNRUZGasiQIdZx5Mqd7jgAAABXcF+V5gIFCqhJkyZ6//33de3atXT7L126pMqVKysxMVG///67ZfzAgQO6fPmyKlWqJEnatGmTevTooTZt2qhq1aoqVqyYjh8/nu58dx4svPvnihUrOnxMZkJDQzOdJu/8+fNKSEjQ66+/rmeeeUaVKlWyPCDoCA8PD0uP9D8RGhqquLg4XbhwIcP9lSpVStdmsnXrVss9v181a9bUoUOHFBQUlG5zc0v/28jT01O+vr5WG60ZAAC4KHqa73/KuTlz5qh+/fqqW7euJkyYoNDQUKWkpGjt2rWaO3euDhw4oNDQUL388suaOXOmUlJS1K9fPz355JOqXbu2JCkoKEjLly9Xy5YtZTKZNGbMmAwrs1u2bNHUqVPVunVrrV27VkuXLrX0596xdOlS1a5dW0888YQ+++wz7dy5U9HRWeu/iYyMVNWqVdWvXz9FRETIw8NDP/74o1566SUVKFBABQsW1IcffqjixYsrMTFRo0aNcvh+BQQE6OrVq1q3bp2qVasmb29veXt7O3yeTp066c0331Tr1q01efJkFS9eXHv37lWJEiUUFham4cOHq3379qpZs6aeeeYZ/fe//9Xy5cvTzUriqLFjx6pFixYqVaqUXnrpJbm5uWnfvn3av3+/w2u3AwAAuJr7nnIuMDBQe/bsUaNGjTR06FCFhISocePGWrdunebOnSuTyaSvvvpK+fPnV8OGDfXss8+qbNmyWrJkieUc77zzjvLnz6/69eurZcuWatKkiWrWrJnuWkOHDlVsbKxq1KihN954Q9OnT1eTJk2sjomKitLixYsVGhqqRYsW6bPPPlPlypWz9F6Cg4P1/fffKz4+XnXr1lVYWJi+/vpr5cqVS25ublq8eLFiY2MVEhKiwYMHa9q0aQ7fr/r16ysiIkIdOnRQ4cKFNXXqVIfPId2uWH///fcqUqSImjVrpqpVq2rKlClyd3eXJLVu3VqzZs3StGnTVKVKFc2bN08LFy7UU089dV/Xu6NJkyZatWqV1q5dqzp16ujxxx/XjBkzVKZMmX90XgAA4AJM2bi5iPuaPeNhCggI0KBBg2yummcymbRixYpM+6vhHP5Odby/O6dj9gzHMXuG45g9w3HMnuE4Zs9wnFPNntF4Ybade+Tantl27geJ38EAAACwzYVmucguJM0AAACwyeRCD+xlF6dPmjOaTeNeTt5hAgAAABfn9EkzAAAADEah+f5nzwAAAAByCirNAAAAsI0HAak0AwAAAPZQaQYAAIBtzJ5BpRkAAACwh0ozAAAAbKPQTNIMAAAAO3gQkPYMAAAAwB4qzQAAALCNMiu3AAAAALCHSjMAAABso6eZSjMAAABgD5VmAAAA2GSi0kylGQAAALCHSjMAAABso9BM0gwAAAA73Miaac8AAAAA7KDSDAAAANt4EJBKMwAAAGAPlWYAAADYRqGZSjMAAABgD5VmAAAA2MbsGVSaAQAAAHuoNAMAAMA2Cs0kzQAAALCDKedozwAAAADsodIMAAAAm0w8CEilGQAAALCHSjMAAABso9BM0oyHZ9azC40OweV02fye0SG4nE+fGGB0CC6nyu+fGh2Cy8nn7WF0CC4nJdVsdAjAP0LSDAAAANuYPYOeZgAAAMAeKs0AAACwjdkzSJoBAABgBzkz7RkAAACAPVSaAQAAYBsPAlJpBgAAAOyh0gwAAADbKLNyCwAAAAB7qDQDAADANnqaqTQDAAAA9lBpBgAAgE0mKs0kzQAAALCD3gRuAQAAAGAPlWYAAADYRnsGlWYAAADAHirNAAAAsI1KM5VmAAAAwB4qzQAAALCNMiu3AAAAALCHSjMAAABso6eZSjMAAABgD5VmAAAA2EalmaQZAAAAdtCbwC0AAAAA7KHSDAAAANtoz6DSDAAAANhDpRkAAAC2UWmm0gwAAADYQ6UZAAAAtlFm5RYAAAAA9lBpBgAAgG30NOesSnOPHj3UunVro8OQJFWoUEEeHh46deqU0aE4JCYmRv7+/kaHAQAAHiaTKfs2F5GjkmZnsXnzZv3999966aWXFBMTY3Q4AAAAsIOk+f/buHGj6tatK09PTxUvXlyjRo1SSkqKZf+aNWv0xBNPyN/fXwULFlSLFi107Ngxy/7jx4/LZDJp+fLlatSokby9vVWtWjVt27Yt3bWio6PVuXNnde3aVQsWLJDZbLbaHxAQoIkTJ6pbt27y8fFRmTJl9PXXX+vPP/9Uq1at5OPjo6pVq2r37t1Wr1u2bJmqVKkiT09PBQQEaPr06Vb7TSaTvvrqK6sxf39/S+Ju7z1s2LBBPXv21OXLl2UymWQymTR+/HhHbzUAAHA1btm4uQgXCjX7nDp1Ss2aNVOdOnUUHx+vuXPnKjo6WhMnTrQcc+3aNQ0ZMkS7du3SunXr5ObmpjZt2igtLc3qXKNHj9awYcMUFxen4OBgderUySr5/uuvv7R06VJ16dJFjRs31rVr17Rhw4Z0Mb3zzjsKDw/X3r171bx5c3Xt2lXdunVTly5dtGfPHgUFBalbt26WhDs2Nlbt27dXx44dtX//fo0fP15jxoy5r0p2Zu+hfv36mjlzpnx9fZWUlKSkpCQNGzbM4fMDAAC4Gh4ElDRnzhyVKlVKs2fPlslkUsWKFfXHH39o5MiRGjt2rNzc3NSuXTur10RHR6tIkSI6cOCAQkJCLOPDhg1T8+bNJUlRUVGqUqWKjh49qooVK0qSFi9erPLly6tKlSqSpI4dOyo6OlqNGjWyOn+zZs306quvSpLGjh2ruXPnqk6dOnrppZckSSNHjlRYWJjOnDmjYsWKacaMGXrmmWc0ZswYSVJwcLAOHDigadOmqUePHg7dD1vvwc/PTyaTScWKFbN5juTkZCUnJ1uNpaTdUi633A7FAgAAnIAL9R5nFyrNkhISEhQWFibTXb8hwsPDdfXqVZ08eVKSdOzYMXXu3Flly5aVr6+vAgMDJUmJiYlW5woNDbX8unjx4pKks2fPWsaio6PVpUsXy89dunTR8uXLdenSpUzPU7RoUUlS1apV043dOXdCQoLCw8OtzhEeHq4jR44oNTU1K7chy+8hKyZPniw/Pz+r7ccTqx06BwAAgLMgaZZkNputEuY7Y5Is4y1bttT58+f10UcfaceOHdqxY4ck6ebNm1avy537f5XUO6+908Jx4MAB7dixQyNGjFCuXLmUK1cuPf7447px44Y+//xzu+exdW5b7+Hu19w7duvWrXT3w9Z1sioyMlKXL1+22hqVaebQOQAAgJMwZePmImjPkFS5cmUtW7bMKvHcunWr8uXLp5IlS+r8+fNKSEjQvHnz1KBBA0m3Z8BwVHR0tBo2bKj333/favyTTz5RdHS0+vbt+4/ew70xbd26VcHBwXJ3d5ckFS5cWElJSZb9R44c0fXr1x26joeHR5Yq156envL09LQaozUDAAC4qhyXNF++fFlxcXFWY//61780c+ZMDRgwQP3799ehQ4c0btw4DRkyRG5ubsqfP78KFiyoDz/8UMWLF1diYqJGjRrl0HVv3bqlTz75RBMmTLDqgZakV155RVOnTlV8fLyqVat2X+9r6NChqlOnjt544w116NBB27Zt0+zZszVnzhzLMU8//bRmz56txx9/XGlpaRo5cqRVVTkrAgICdPXqVa1bt07VqlWTt7e3vL297ytmAADgItxcqCScTXJce8aGDRtUo0YNq23cuHFavXq1du7cqWrVqikiIkK9e/fW66+/Lklyc3PT4sWLFRsbq5CQEA0ePFjTpk1z6LorV67U+fPn1aZNm3T7ypcvr6pVqyo6Ovq+31fNmjX1xRdfaPHixQoJCdHYsWM1YcIEq4cAp0+frlKlSqlhw4bq3Lmzhg0b5nDCW79+fUVERKhDhw4qXLiwpk6det8xAwAAF8HiJjKZ721yBbLJW43u/0NBThU48WmjQ3A5nz4xwOgQXM7k3z81OgSXk8/bw+gQXI5HrhxXp/vHivl6GR2CxfSxa7Pt3EMnNM62cz9I/A4GAACAbU72IOCcOXMUGBgoLy8v1apVS5s2bcrS67Zs2aJcuXKpevXqDl+TpBkAAAAuY8mSJRo0aJBGjx6tvXv3qkGDBmratGm6aYDvdfnyZXXr1k3PPPPMfV2XpBkAAAC2uZmybUtOTtaVK1estnsXSLvbjBkz1Lt3b73yyiuqVKmSZs6cqVKlSmnu3Lk238Krr76qzp07Kyws7P5uwX29CgAAAHgAMloQbfLkyRkee/PmTcXGxuq5556zGn/uuee0devWTK+xcOFCHTt2TOPGjbvvOHPclHMAAABwUDbOchE5KlJDhgyxGrt3rYc7zp07p9TUVMvKyHcULVpUp0+fzvA1R44c0ahRo7Rp0yblynX/qS9JMwAAAAyT0YJo9mS0CvK9Y5KUmpqqzp07KyoqSsHBwf8oTpJmAAAA2OYk0ykXKlRI7u7u6arKZ8+eTVd9lqS//vpLu3fv1t69e9W/f39JUlpamsxms3LlyqXvv/9eTz+dteldSZoBAABgm5OsCOjh4aFatWpp7dq1VgvGrV27Vq1atUp3vK+vr/bv3281NmfOHK1fv15ffvmlAgMDs3xtkmYAAAC4jCFDhqhr166qXbu2wsLC9OGHHyoxMVERERGSpMjISJ06dUoff/yx3NzcFBISYvX6IkWKyMvLK924PSTNAAAAsM2Jlrvu0KGDzp8/rwkTJigpKUkhISFavXq1ypQpI0lKSkqyO2fz/SBpBgAAgEvp16+f+vXrl+G+mJgYm68dP368xo8f7/A1SZoBAABgm/MUmg3D4iYAAACAHVSaAQAAYJuTzJ5hJCrNAAAAgB1UmgEAAGCbE82eYRSSZgAAANhGbwK3AAAAALCHSjMAAABsoz2DSjMAAABgD5VmAAAA2EalmUozAAAAYA+VZgAAANhGmZVbAAAAANhDpRkAAAC20dNM0gwAAAA7SJppzwAAAADsodIMAAAA2yizcgsAAAAAe6g0AwAAwDZ6mqk0AwAAAPZQaQYAAIBtVJqpNAMAAAD2UGnGQ9NzZRejQ3A57m58sndUld8/NToElxNZij+bjnrv3BKjQ3A5qWlmo0PAP0GZlaQZAAAAtploz+BzAwAAAGAPlWYAAADYRqWZSjMAAABgD5VmAAAA2EShmUozAAAAYBeVZgAAANjE7BlUmgEAAAC7qDQDAADANsqsJM0AAACwjfYMPjcAAAAAdlFpBgAAgG1Umqk0AwAAAPZQaQYAAIBNFJqpNAMAAAB2UWkGAACAbZSaqTQDAAAA9lBpBgAAgE0mNyrNVJoBAAAAO6g0AwAAwDYKzSTNAAAAsI1ltGnPAAAAAOyi0gwAAACbKDRTaQYAAADsotIMAAAA2yg1U2kGAAAA7KHSDAAAAJuYPYNKMwAAAGAXlWYAAADYRpmVpBkAAAC20Z7B5wYAAADALirNAAAAsI1Ks2OV5h49eqh169bZFMqDU6FCBXl4eOjUqVNGh+KQmJgY+fv7Z+nY48ePy2Qy2dzGjx+frfECAADkFI9cpXnz5s36+++/9dJLLykmJkajR482OqRsUapUKSUlJVl+fvvtt7VmzRr98MMPljEfHx+Hznnr1i3lzp37gcUIAAAeDRSaH2BP88aNG1W3bl15enqqePHiGjVqlFJSUiz716xZoyeeeEL+/v4qWLCgWrRooWPHjln236mcLl++XI0aNZK3t7eqVaumbdu2ORRHdHS0OnfurK5du2rBggUym81W+wMCAjRx4kR169ZNPj4+KlOmjL7++mv9+eefatWqlXx8fFS1alXt3r3b6nXLli1TlSpV5OnpqYCAAE2fPt1qv8lk0ldffWU15u/vr5iYmCy9vw0bNqhnz566fPlylirF7u7uKlasmGXz8fFRrly5rMaWLl2qSpUqycvLSxUrVtScOXPS3e8vvvhCTz31lLy8vPTpp59avk148803VbRoUfn7+ysqKkopKSkaPny4ChQooMcee0wLFixw6P8LAACAK3sgSfOpU6fUrFkz1alTR/Hx8Zo7d66io6M1ceJEyzHXrl3TkCFDtGvXLq1bt05ubm5q06aN0tLSrM41evRoDRs2THFxcQoODlanTp2skm9b/vrrLy1dulRdunRR48aNde3aNW3YsCHdce+8847Cw8O1d+9eNW/eXF27dlW3bt3UpUsX7dmzR0FBQerWrZsl4Y6NjVX79u3VsWNH7d+/X+PHj9eYMWMsCbEjMnt/9evX18yZM+Xr66ukpCQlJSVp2LBhDp//jo8++kijR4/WpEmTlJCQoDfffFNjxozRokWLrI4bOXKkBg4cqISEBDVp0kSStH79ev3xxx/66aefNGPGDI0fP14tWrRQ/vz5tWPHDkVERCgiIkK///77fccHAABch72W0H+yuYoH0p4xZ84clSpVSrNnz5bJZFLFihX1xx9/aOTIkRo7dqzc3NzUrl07q9dER0erSJEiOnDggEJCQizjw4YNU/PmzSVJUVFRqlKlio4ePaqKFSvajWPx4sUqX768qlSpIknq2LGjoqOj1ahRI6vjmjVrpldffVWSNHbsWM2dO1d16tTRSy+9JOl2IhkWFqYzZ86oWLFimjFjhp555hmNGTNGkhQcHKwDBw5o2rRp6tGjh0P3ytb78/Pzk8lkUrFixRw6Z0beeOMNTZ8+XW3btpUkBQYG6sCBA5o3b566d+9uOW7QoEGWY+4oUKCA3n33Xbm5ualChQqaOnWqrl+/rv/85z+SpMjISE2ZMkVbtmxRx44dM7x+cnKykpOTrcduSp6env/4vQEAADxsD6TSnJCQoLCwMKtPC+Hh4bp69apOnjwpSTp27Jg6d+6ssmXLytfXV4GBgZKkxMREq3OFhoZafl28eHFJ0tmzZ7MUR3R0tLp06WL5uUuXLlq+fLkuXbqU6TWKFi0qSapatWq6sTvXTUhIUHh4uNU5wsPDdeTIEaWmpmYptoyu7ej7y6o///xTv//+u3r37i0fHx/LNnHiRKuWGEmqXbt2utdXqVJFbm7/+61RtGhRq/vj7u6uggUL2ox78uTJ8vPzs9renT71Abw7AADw0Lll4+YiHkil2Ww2pyuv32ltuDPesmVLlSpVSh999JFKlCihtLQ0hYSE6ObNm1avu/tBtDuvvbeFIyMHDhzQjh07tGvXLo0cOdIynpqaqs8//1x9+/a1eQ1b17X1/u5+zb1jt27dShfn/b4/R9w530cffaR69epZ7XN3d7f6OW/evDZjlG7HmdGYrbgjIyM1ZMgQq7HLNzM5GAAAODVXaqPILg8kaa5cubKWLVtmlVxu3bpV+fLlU8mSJXX+/HklJCRo3rx5atCggaTbs1w8SNHR0WrYsKHef/99q/FPPvlE0dHRVkmzoypXrpwu3q1btyo4ONiShBYuXNhqNosjR47o+vXrDl3Hw8PD4cp1RooWLaqSJUvq119/1csvv/yPz3c/PD0907Vi/P1XciZHAwAAODeHk+bLly8rLi7Oauxf//qXZs6cqQEDBqh///46dOiQxo0bpyFDhsjNzU358+dXwYIF9eGHH6p48eJKTEzUqFGjHtR70K1bt/TJJ59owoQJVv3RkvTKK69o6tSpio+PV7Vq1e7r/EOHDlWdOnX0xhtvqEOHDtq2bZtmz55tNRvF008/rdmzZ+vxxx9XWlqaRo4c6fD0bQEBAbp69arWrVunatWqydvbW97e3vcV8/jx4zVw4ED5+vqqadOmSk5O1u7du3Xx4sV0FWAAAACbqDQ73kmyYcMG1ahRw2obN26cVq9erZ07d6patWqKiIhQ79699frrr9++iJubFi9erNjYWIWEhGjw4MGaNm3aA3sTK1eu1Pnz59WmTZt0+8qXL6+qVasqOjr6vs9fs2ZNffHFF1q8eLFCQkI0duxYTZgwweohwOnTp6tUqVJq2LChOnfurGHDhjmc8NavX18RERHq0KGDChcurKlT778H+JVXXtH8+fMVExOjqlWr6sknn1RMTIyllxwAAABZZzLf24gLZJOztGc4zN2NT/aOOn3RsbYoSJGlutg/CFbeO7fE6BBcTi53F3riy0mU9M9jdAgWH606kG3n7tOicrad+0HidzAAAABgh8skzU2bNrWaPu3u7c033zQ6vGyxadOmTN+zo0tkAwAA3DeTKfs2F/FAZs94GObPn68bN25kuK9AgQIPOZqHo3bt2ukeugQAAMDD5zJJc8mSJY0O4aHLkyePgoKCjA4DAADkcCaesXGdpBkAAADGcKEuimzjMj3NAAAAgFGoNAMAAMA2Ss1UmgEAAAB7qDQDAADAJhOVZirNAAAAgD1UmgEAAGAbhWYqzQAAAIA9VJoBAABgE4ubkDQDAADADlJm2jMAAAAAu6g0AwAAwCamnKPSDAAAANhFpRkAAAA2UWim0gwAAADYRaUZAAAANlFpptIMAAAA2EWlGQAAADaZmKmZpBkAAAC20Z5BewYAAABczJw5cxQYGCgvLy/VqlVLmzZtyvTY5cuXq3HjxipcuLB8fX0VFham7777zuFrkjQDAADAJpMp+zZHLVmyRIMGDdLo0aO1d+9eNWjQQE2bNlViYmKGx//0009q3LixVq9erdjYWDVq1EgtW7bU3r17HbsHZrPZ7Hi4gOPO/pVsdAgux92N78McdfridaNDcDmRpboYHYLLee/cEqNDcDm53KnTOaqkfx6jQ7D4v5+OZdu5Ozcs59Dx9erVU82aNTV37lzLWKVKldS6dWtNnjw5S+eoUqWKOnTooLFjx2b5uvQ0AwAAwKbsXEY7OTlZycnWhTVPT095enqmO/bmzZuKjY3VqFGjrMafe+45bd26NUvXS0tL019//aUCBQo4FCcf+wAAAGCYyZMny8/Pz2rLrGJ87tw5paamqmjRolbjRYsW1enTp7N0venTp+vatWtq3769Q3FSaQYAAIBN2dksGBkZqSFDhliNZVRltornnsq32WzOUjX8888/1/jx4/X111+rSJEiDsVJ0gwAAADDZNaKkZFChQrJ3d09XVX57Nmz6arP91qyZIl69+6tpUuX6tlnn3U4TtozAAAAYJPJZMq2zREeHh6qVauW1q5dazW+du1a1a9fP9PXff755+rRo4f+7//+T82bN7+ve0ClGQAAADY50+ImQ4YMUdeuXVW7dm2FhYXpww8/VGJioiIiIiTdbvc4deqUPv74Y0m3E+Zu3bpp1qxZevzxxy1V6jx58sjPzy/L1yVpBgAAgMvo0KGDzp8/rwkTJigpKUkhISFavXq1ypQpI0lKSkqymrN53rx5SklJ0b///W/9+9//tox3795dMTExWb4u8zTjoTnJ/LkOS0vjj6fDnKkc4iL4Z8BxAwp1MDoElxP+bDujQ3A5I9f2NDoEi6Vbfsu2c78UHpht536Q6GkGAAAA7KA9AwAAADZl5+ImroJKMwAAAGAHlWYAAADYRKGZSjMAAABgF5VmAAAA2ERPM0kzAAAA7CBlpj0DAAAAsItKMwAAAGyiO4NKMwAAAGAXlWYAAADYxIOAVJoBAAAAu6g0AwAAwCbqzFSaAQAAALuoNAMAAMAmWppJmgEAAGAHDwLSngEAAADYRaUZAAAANlFoptIMAAAA2EWlGQAAADaZmHSOSjMAAABgD5VmAAAA2ERPM5VmAAAAwC4qzQAAALCJSjOVZgAAAMAuKs0AAACwyY3ZM0iaAQAAYBvtGbRnAAAAAHZRaQYAAIBNVJqpNAMAAAB2UWkGAACATSZKzVSa74fJZNJXX30lSTp+/LhMJpPi4uKy/boxMTHy9/fP9usAAADA2iOTNJ8+fVoDBgxQ2bJl5enpqVKlSqlly5Zat25dtl63VKlSSkpKUkhIiCRpw4YNMplMunTpUpbP0aNHD7Vu3Trd+L3n6tChgw4fPpylc5JgAwCAB8WUjZureCTaM44fP67w8HD5+/tr6tSpCg0N1a1bt/Tdd9/p3//+tw4ePJjuNbdu3VLu3Ln/8bXd3d1VrFixf3yerMiTJ4/y5MnzUK51R2pqqkwmk9zcHpnPVwAAAA57JDKhfv36yWQyaefOnXrxxRcVHBysKlWqaMiQIdq+fbuk2y0VH3zwgVq1aqW8efNq4sSJkqT//ve/qlWrlry8vFS2bFlFRUUpJSXFcu4jR46oYcOG8vLyUuXKlbV27Vqra9/dnnH8+HE1atRIkpQ/f36ZTCb16NHjgb3Pe6vH8fHxatSokfLlyydfX1/VqlVLu3fv1oYNG9SzZ09dvnxZJpNJJpNJ48ePlyRdvHhR3bp1U/78+eXt7a2mTZvqyJEj6a6xatUqVa5cWZ6entq0aZNy586t06dPW8UzdOhQNWzY8IG9PwAA4Jzu5BPZsbkKl0+aL1y4oDVr1ujf//638ubNm27/3UnmuHHj1KpVK+3fv1+9evXSd999py5dumjgwIE6cOCA5s2bp5iYGE2aNEmSlJaWprZt28rd3V3bt2/XBx98oJEjR2YaS6lSpbRs2TJJ0qFDh5SUlKRZs2Y92Dd8l5dfflmPPfaYdu3apdjYWI0aNUq5c+dW/fr1NXPmTPn6+iopKUlJSUkaNmyYpNutILt379bKlSu1bds2mc1mNWvWTLdu3bKc9/r165o8ebLmz5+vX375RbVr11bZsmX1ySefWI5JSUnRp59+qp49e2bb+wMAAM7BZMq+zVW4fHvG0aNHZTabVbFiRbvHdu7cWb169bL83LVrV40aNUrdu3eXJJUtW1ZvvPGGRowYoXHjxumHH35QQkKCjh8/rscee0yS9Oabb6pp06YZnt/d3V0FChSQJBUpUsShnuJVq1bJx8fHaiw1NdXmaxITEzV8+HDLey9fvrxln5+fn0wmk1XryJEjR7Ry5Upt2bJF9evXlyR99tlnKlWqlL766iu99NJLkm63rsyZM0fVqlWzvLZ3795auHChhg8fLkn65ptvdP36dbVv3z7D2JKTk5WcnHzPWKo8PT1tvicAAABn5PKVZrPZLClrU6HUrl3b6ufY2FhNmDBBPj4+lq1Pnz5KSkrS9evXlZCQoNKlS1sSZkkKCwt7sG/g/2vUqJHi4uKstvnz59t8zZAhQ/TKK6/o2Wef1ZQpU3Ts2DGbxyckJChXrlyqV6+eZaxgwYKqUKGCEhISLGMeHh4KDQ21em2PHj109OhRS7vLggUL1L59+wyr+5I0efJk+fn5WW3vv/O2zfgAAIBzotL8CFSay5cvL5PJpISEhAxnoLjbvQleWlqaoqKi1LZt23THenl5WRLyu2VX703evHkVFBRkNXby5Embrxk/frw6d+6sb775Rt9++63GjRunxYsXq02bNhken9H7uTN+9/vKkydPuvdZpEgRtWzZUgsXLlTZsmW1evVqbdiwIdPYIiMjNWTIEKuxP6/brpwDAAA4K5dPmgsUKKAmTZro/fff18CBA9MlxpcuXcq0TaJmzZo6dOhQumT1jsqVKysxMVF//PGHSpQoIUnatm2bzXg8PDwk2W+teFCCg4MVHByswYMHq1OnTlq4cKHatGkjDw+PdDFUrlxZKSkp2rFjh6U94/z58zp8+LAqVapk91qvvPKKOnbsqMcee0zlypVTeHh4psd6enqma8W4knr9Pt4hAAAwmsmlJofLHi7fniFJc+bMUWpqqurWratly5bpyJEjSkhI0LvvvmuznWLs2LH6+OOPNX78eP3yyy9KSEjQkiVL9Prrr0uSnn32WVWoUEHdunVTfHy8Nm3apNGjR9uMpUyZMjKZTFq1apX+/PNPXb169YG+1ztu3Lih/v37a8OGDTpx4oS2bNmiXbt2WZLfgIAAXb16VevWrdO5c+d0/fp1lS9fXq1atVKfPn20efNmxcfHq0uXLipZsqRatWpl95pNmjSRn5+fJk6cyAOAAAAgR3kkkubAwEDt2bNHjRo10tChQxUSEqLGjRtr3bp1mjt3bqava9KkiVatWqW1a9eqTp06evzxxzVjxgyVKVNGkuTm5qYVK1YoOTlZdevW1SuvvGKZWSMzJUuWVFRUlEaNGqWiRYuqf//+D/S93uHu7q7z58+rW7duCg4OVvv27dW0aVNFRUVJkurXr6+IiAh16NBBhQsX1tSpUyVJCxcuVK1atdSiRQuFhYXJbDZr9erVWZqz2s3NTT169FBqaqq6deuWLe8LAAA4H3qaJZM5s0ZXIAN9+vTRmTNntHLlSodfe/Ii7RmOSkvjj6fDXOlvYCfBPwOOG1Cog9EhuJzwZ9sZHYLLGbnWeb7V3XTgtP2D7lODyg9nkbh/yuV7mvFwXL58Wbt27dJnn32mr7/+2uhwAADAQ+RKi5BkF5LmbJaYmKjKlStnuv/AgQMqXbr0Q4zo/rRq1Uo7d+7Uq6++qsaNGxsdDgAAeIjImUmas12JEiUUFxdnc78rsDW9HAAAwKOOpDmb5cqVK9Mp7QAAAFwB7RmPyOwZAAAAQHai0gwAAACbqDNTaQYAAADsotIMAAAAm2hpptIMAAAA2EWlGQAAADYxewZJMwAAAOwgZ6Y9AwAAALCLSjMAAABsMjHpHJVmAAAAwB4qzQAAALCJnmYqzQAAAIBdVJoBAABgE1POUWkGAAAA7KLSDAAAAJsoNJM0AwAAwA6SZtozAAAAALuoNAMAAMAmFjeh0gwAAADYRaUZAAAANtHTTKUZAAAAsItKMwAAAGxicRMqzQAAAIBdVJoBAABgE4VmkmYAAADYwZRztGcAAAAAdlFpBgAAgE20Z1BpBgAAAOyi0gwAAACb3Cg1kzTj4UlLMxsdgsvx9uKPqKNSUvl95qhU/mw6LPzZdkaH4HK2/LDM6BBcUE+jA8Bd+BcZAAAANlFopqcZAAAAsItKMwAAAGyi0kzSDAAAADtY3IT2DAAAAMAuKs0AAACwifYMKs0AAACAXVSaAQAAYJOJUjOVZgAAAMAeKs0AAACwiUIzlWYAAADALirNAAAAsImeZpJmAAAA2EHKTHsGAAAAXMycOXMUGBgoLy8v1apVS5s2bbJ5/MaNG1WrVi15eXmpbNmy+uCDDxy+JkkzAAAAbDKZTNm2OWrJkiUaNGiQRo8erb1796pBgwZq2rSpEhMTMzz+t99+U7NmzdSgQQPt3btX//nPfzRw4EAtW7bMsXtgNpvNDkcL3IfE89eMDsHleHvRQeWolFT+SnNUahr3zFGfvrTY6BBczpYfHEtQIK00rzI6BIvj57Lv3/CAQnkdOr5evXqqWbOm5s6daxmrVKmSWrdurcmTJ6c7fuTIkVq5cqUSEhIsYxEREYqPj9e2bduyfF0qzQAAALDJZMq+LTk5WVeuXLHakpOTM4zj5s2bio2N1XPPPWc1/txzz2nr1q0Zvmbbtm3pjm/SpIl2796tW7duZfkekDQDAADAMJMnT5afn5/VllHFWJLOnTun1NRUFS1a1Gq8aNGiOn36dIavOX36dIbHp6Sk6Ny5c1mOk+9+AQAAYFN2zp4RGRmpIUOGWI15enrajueeXmiz2WyzPzqj4zMat4WkGQAAAIbx9PS0myTfUahQIbm7u6erKp89ezZdNfmOYsWKZXh8rly5VLBgwSzHSXsGAAAAbMvOpmYHeHh4qFatWlq7dq3V+Nq1a1W/fv0MXxMWFpbu+O+//161a9dW7ty5s3xtkmYAAAC4jCFDhmj+/PlasGCBEhISNHjwYCUmJioiIkLS7XaPbt26WY6PiIjQiRMnNGTIECUkJGjBggWKjo7WsGHDHLou7RkAAACwyZlWBOzQoYPOnz+vCRMmKCkpSSEhIVq9erXKlCkjSUpKSrKaszkwMFCrV6/W4MGD9f7776tEiRJ699131a5dO4euyzzNeGiYp9lxzNPsOOZpdhzzNDuOeZodxzzNjnOmeZpPXryebed+LL93tp37QaI9AwAAALCDMhYAAABscqb2DKNQaQYAAADsoNIMAAAA2xycGu5RRKUZAAAAsINKMwAAAGyizkylGQAAALCLSjMAAABsoqU5B1eae/ToodatWxsdRqbmzZunatWqKW/evPL391eNGjX01ltvWfbfb/zjx49X9erVH1ygAAAgBzBl4+YaqDQ7oejoaA0ZMkTvvvuunnzySSUnJ2vfvn06cOCA0aEBAADkSDm20mzLxo0bVbduXXl6eqp48eIaNWqUUlJSLPvXrFmjJ554Qv7+/ipYsKBatGihY8eOWfYfP35cJpNJy5cvV6NGjeTt7a1q1app27ZtWbr+f//7X7Vv3169e/dWUFCQqlSpok6dOumNN96QdLtavGjRIn399dcymUwymUzasGGDJGnkyJEKDg6Wt7e3ypYtqzFjxujWrVuSpJiYGEVFRSk+Pt7yupiYGEu8cXFxlhguXbpkdd6LFy/q5ZdfVuHChZUnTx6VL19eCxcu/Ad3GQAAuAqTKfs2V0Gl+R6nTp1Ss2bN1KNHD3388cc6ePCg+vTpIy8vL40fP16SdO3aNQ0ZMkRVq1bVtWvXNHbsWLVp00ZxcXFyc/vf55DRo0fr7bffVvny5TV69Gh16tRJR48eVa5ctm97sWLFtHHjRp04cUJlypRJt3/YsGFKSEjQlStXLIlrgQIFJEn58uVTTEyMSpQoof3796tPnz7Kly+fRowYoQ4dOujnn3/WmjVr9MMPP0iS/Pz8dObMGbv3ZcyYMTpw4IC+/fZbFSpUSEePHtWNGzeydE8BAABcHUnzPebMmaNSpUpp9uzZMplMqlixov744w+NHDlSY8eOlZubm9q1a2f1mujoaBUpUkQHDhxQSEiIZXzYsGFq3ry5JCkqKkpVqlTR0aNHVbFiRZsxjBs3Tm3btlVAQICCg4MVFhamZs2a6cUXX5Sbm5t8fHyUJ08eJScnq1ixYlavff311y2/DggI0NChQ7VkyRKNGDFCefLkkY+Pj3LlypXudfYkJiaqRo0aql27tuXctiQnJys5OfmesRR5eno6dF0AAGA8FyoIZxvaM+6RkJCgsLAwme76viA8PFxXr17VyZMnJUnHjh1T586dVbZsWfn6+iowMFDS7cTybqGhoZZfFy9eXJJ09uxZuzEUL15c27Zt0/79+zVw4EDdunVL3bt31/PPP6+0tDSbr/3yyy/1xBNPqFixYvLx8dGYMWPSxXU/+vbtq8WLF6t69eoaMWKEtm7davP4yZMny8/Pz2qbM/PtfxwHAACAEUia72E2m60S5jtjkizjLVu21Pnz5/XRRx9px44d2rFjhyTp5s2bVq/LnTu35dd3Xmsv6b1bSEiI/v3vf+uzzz7T2rVrtXbtWm3cuDHT47dv366OHTuqadOmWrVqlfbu3avRo0eni+ted1pK7rxPSZY+6DuaNm2qEydOaNCgQfrjjz/0zDPPaNiwYZmeMzIyUpcvX7ba+g3K/HgAAOC86GmmPSOdypUra9myZVbJ89atW5UvXz6VLFlS58+fV0JCgubNm6cGDRpIkjZv3vxQ4pJu91NLkoeHh1JTU62O2bJli8qUKaPRo0dbxk6cOGF1TEavK1y4sCQpKSlJNWrUkCSrhwLvPq5Hjx7q0aOHGjRooOHDh+vttzOuHnt6eqZrxbh065q9twkAAOCUcnTSfPny5XTJ4b/+9S/NnDlTAwYMUP/+/XXo0CGNGzdOQ4YMkZubm/Lnz6+CBQvqww8/VPHixZWYmKhRo0Y90Lj69u2rEiVK6Omnn9Zjjz2mpKQkTZw4UYULF1ZYWJik2z3F3333nQ4dOqSCBQvKz89PQUFBSkxM1OLFi1WnTh198803WrFihdW5AwIC9NtvvykuLk6PPfaY8uXLpzx58ujxxx/XlClTFBAQoHPnzln1RkvS2LFjVatWLVWpUkXJyclatWqVKlWq9EDfNwAAcFYuVBLOJjm6PWPDhg2qUaOG1TZu3DitXr1aO3fuVLVq1RQREaHevXtbkkg3NzctXrxYsbGxCgkJ0eDBgzVt2rQHGtezzz6r7du366WXXlJwcLDatWsnLy8vrVu3TgULFpQk9enTRxUqVFDt2rVVuHBhbdmyRa1atdLgwYPVv39/Va9eXVu3btWYMWOszt2uXTs9//zzatSokQoXLqzPP/9ckrRgwQLdunVLtWvX1muvvaaJEydavc7Dw0ORkZEKDQ1Vw4YN5e7ursWLFz/Q9w0AAJwT7RmSyXx3IyuQjRLP057hKG+vHP1l0H1JSeWvNEelpnHPHPXpSxQNHLXlh2VGh+ByVppXGR2CxZ9Xk+0fdJ8K+7jGzFr8iwwAAACbXKggnG1ydHuGUZo2bSofH58MtzfffNPo8AAAAHAPKs0GmD9/fqar6d1Z2Q8AAMBpUGomaTZCyZIljQ4BAAAADiBpBgAAgE0mSs30NAMAAAD2UGkGAACATa40n3J2IWkGAACATeTMtGcAAAAAdlFpBgAAgG30Z1BpBgAAAOyh0gwAAACbqDNTaQYAAADsotIMAAAAm2hpptIMAAAA2EWlGQAAADZRaCZpBgAAgD30Z9CeAQAAANhDpRkAAAA2UWem0gwAAADYRaUZAAAANtHSTKUZAAAAsItKMwAAAOyg1EylGQAAALCDSjMAAABsoqeZpBkAAAB2kDPTngEAAADYRaUZAAAANtGeQaUZAAAAsItKMwAAAOyg1EylGQAAALCDSjMAAABsoqeZSjMAAABgl8lsNpuNDgIwUnJysiZPnqzIyEh5enoaHY5L4J45jnvmOO6Z47hnjuOeIatImpHjXblyRX5+frp8+bJ8fX2NDsclcM8cxz1zHPfMcdwzx3HPkFW0ZwAAAAB2kDQDAAAAdpA0AwAAAHaQNCPH8/T01Lhx43gAxAHcM8dxzxzHPXMc98xx3DNkFQ8CAgAAAHZQaQYAAADsIGkGAAAA7CBpBgAAAOwgaQYAAADsIGkGAAAA7CBpBuCwv//+2+gQAAB4qEiakSMdO3ZMr7/+ujp16qSzZ89KktasWaNffvnF4MicV1pamt544w2VLFlSPj4++vXXXyVJY8aMUXR0tMHROacJEybo+vXr6cZv3LihCRMmGBARHkVPPfWUPv74Y924ccPoUIBHGkkzcpyNGzeqatWq2rFjh5YvX66rV69Kkvbt26dx48YZHJ3zmjhxomJiYjR16lR5eHhYxqtWrar58+cbGJnzioqKsvz+utv169cVFRVlQESu4dSpU/riiy80e/Zsvfvuu1Yb0qtVq5ZGjBihYsWKqU+fPtq+fbvRITm9RYsW6ZtvvrH8PGLECPn7+6t+/fo6ceKEgZHBmbG4CXKcsLAwvfTSSxoyZIjy5cun+Ph4lS1bVrt27VLr1q116tQpo0N0SkFBQZo3b56eeeYZq/t28OBBhYWF6eLFi0aH6HTc3Nx05swZFS5c2Gp8/fr16tChg/7880+DInNeCxcuVEREhDw8PFSwYEGZTCbLPpPJZPmGA9ZSU1O1atUqLVy4UKtXr1ZQUJB69eqlrl27qmjRokaH53QqVKiguXPn6umnn9a2bdv0zDPPaObMmVq1apVy5cql5cuXGx0inBBJM3IcHx8f7d+/X4GBgVbJ3/Hjx1WxYkX6dTORJ08eHTx4UGXKlLG6bwcOHFDdunUzrKjmVPnz55fJZNLly5fl6+trlfilpqbq6tWrioiI0Pvvv29glM6pVKlSioiIUGRkpNzc+DL0fvz555+aN2+eJk2apNTUVDVr1kwDBw7U008/bXRoTsPb21sHDx5U6dKlNXLkSCUlJenjjz/WL7/8oqeeeooPtMhQLqMDAB42f39/JSUlKTAw0Gp87969KlmypEFROb8qVapo06ZNKlOmjNX40qVLVaNGDYOick4zZ86U2WxWr169FBUVJT8/P8s+Dw8PBQQEKCwszMAIndf169fVsWNHEub7tHPnTi1cuFCff/65ihQpoh49eigpKUktW7ZU37599fbbbxsdolPw8fHR+fPnVbp0aX3//fcaPHiwJMnLy4vecGSKpBk5TufOnTVy5EgtXbpUJpNJaWlp2rJli4YNG6Zu3boZHZ7TGjdunLp27apTp04pLS1Ny5cv16FDh/Txxx9r1apVRofnVLp37y5JCgwMVP369ZU7d26DI3IdvXv31tKlSzVq1CijQ3EZZ8+e1SeffKKFCxfqyJEjatmypRYvXqwmTZpYvuVo3769WrduTdL8/zVu3FivvPKKatSoocOHD6t58+aSpF9++UUBAQHGBgenRXsGcpxbt26pR48eWrx4scxms3LlyqXU1FR17txZMTExcnd3NzpEp/Xdd9/pzTffVGxsrNLS0lSzZk2NHTtWzz33nNGhOa20tDQdPXpUZ8+eVVpamtW+hg0bGhSV80pNTVWLFi1048YNVa1aNd0HjhkzZhgUmfPy8PBQuXLl1KtXL/Xo0SNdD70kXblyRa1atdKPP/5oQITO59KlS3r99df1+++/q2/fvnr++ecl3S4OeHh4aPTo0QZHCGdE0owc69ixY9q7d6/S0tJUo0YNlS9f3uiQ8IjZvn27OnfurBMnTujev2pNJpNSU1MNisx5vfHGGxo3bpwqVKigokWLpnsQcP369QZG53zMZrM2bdqk2rVry9vb2+hwgEcaSTMAh129ejVd1dTX19egaJxX9erVFRwcrKioKBUvXtwqAZRk1euM2/Lnz6933nlHPXr0MDoUl5CWliYvLy/98ssvfPB3wE8//WRzP98CISP0NCPHMZvN+vLLL/Xjjz9m+JU5Uw1l7LffflP//v21YcMGqxlGzGYzVdNMHDlyRF9++aWCgoKMDsVleHp6Kjw83OgwXIabm5vKly+v8+fPkzQ74Kmnnko3du8sN8C9SJqR47z22mv68MMP1ahRo3Rf/yJzL7/8siRpwYIF3Lcsqlevno4ePUrS7IDXXntN7733HguZOGDq1KkaPny45s6dq5CQEKPDcQn3zit/69Yt7d27V2PGjNGkSZMMigrOjvYM5DgFChTQp59+qmbNmhkdikvx8fFRbGysKlSoYHQoLmPFihV6/fXXNXz48AwfagsNDTUoMufVpk0brV+/XgULFlSVKlXS3TO+CUovf/78un79ulJSUuTh4aE8efJY7b9w4YJBkbmen376SYMHD1ZsbKzRocAJUWlGjuPn56eyZcsaHYbLqVOnjn7//XeSZge0a9dOktSrVy/LmMlkoqXFBn9/f7Vt29boMFzKzJkzjQ7hkVG4cGEdOnTI6DDgpKg0I8dZtGiR1qxZowULFqSryCBzx44dU0REhLp06aKQkBCqpllw4sQJm/vvXSgmp0tJSdFnn32mJk2aqFixYkaHg0fYvn37rH42m81KSkrSlClTdOvWLW3ZssWgyODMSJqR41y/fl1t27bVli1bFBAQkC7527Nnj0GRObc706cdP37cMkbVFA+at7e3EhIS+EDhoNTUVH311VdKSEiQyWRS5cqV9cILLzDvfCbc3Nwsf3/d7fHHH9eCBQtUsWJFgyKDM6M9AzlOjx49FBsbqy5duvBAmwN69eqlGjVq6PPPP+e+ZdHHH39scz8rUKZXr1497d27l6TZAUePHlWzZs106tQpVahQQWazWYcPH1apUqX0zTffqFy5ckaH6HR+++03q5/d3NxUuHBheXl5GRQRXAGVZuQ4efPm1XfffacnnnjC6FBcSt68eRUfH89MEA7Inz+/1c+3bt3S9evX5eHhIW9vbx7QysCdJbQHDx6sWrVqKW/evFb7aQNKr1mzZjKbzfrss89UoEABSdL58+fVpUsXubm56ZtvvjE4Qufz8ccfq0OHDvL09LQav3nzphYvXswHWmSIpBk5TsWKFfXFF1/wj6+DWrZsqR49elgebsP9OXLkiPr27avhw4erSZMmRofjdNzc3NKN0QZkW968ebV9+3ZVrVrVajw+Pl7h4eG6evWqQZE5L3d3dyUlJalIkSJW4+fPn1eRIkX4fYYM0Z6BHGf69OkaMWKEPvjgAwUEBBgdjsto2bKlBg8erP3792c4fdoLL7xgUGSupXz58poyZYq6dOmigwcPGh2O07n3a3PY5+npqb/++ivd+NWrV+Xh4WFARM7vzoewe508eZKVOpEpKs3Ice6e09Tb2ztd8sdX5hnLqAJ4BxVAx+zdu1dPPvmkrly5YnQoeAR069ZNe/bsUXR0tOrWrStJ2rFjh/r06aNatWopJibG2ACdSI0aNWQymRQfH68qVaooV67/1Q5TU1P122+/6fnnn9cXX3xhYJRwVlSakeMwp+n9uXe5cdi3cuVKq5/vTGs1e/Zsloq24ZNPPtEHH3yg3377Tdu2bVOZMmU0c+ZMBQYGqlWrVkaH53Teffddde/eXWFhYZYiQEpKil544QX+vrtH69atJUlxcXFq0qSJfHx8LPs8PDwUEBBACxoyRaUZALLJvdV5k8mkwoUL6+mnn9b06dNVvHhxgyJzXnPnztXYsWM1aNAgTZo0ST///LPKli2rmJgYLVq0SD/++KPRITqto0ePKiEhQWazWZUrV+ahXRsWLVqkDh06MFsGHELSjByJOU3vz8aNG/X2229b7lulSpU0fPhwNWjQwOjQ8IioXLmy3nzzTbVu3Vr58uVTfHy8ypYtq59//llPPfWUzp07Z3SITmfChAkaNmyYvL29rcZv3LihadOmaezYsQZFBjxaSJqR4zCn6f359NNP1bNnT7Vt21bh4eEym83aunWrVqxYoZiYGHXu3NnoEJ3anb9qmd/atjx58ujgwYMqU6aMVdJ85MgRhYaG6saNG0aH6HSYCcJxqampeuedd/TFF18oMTFRN2/etNrPsy3ISOZP9gCPqIEDB6pcuXL6/ffftWfPHu3du1eJiYkKDAzUwIEDjQ7PaU2aNElTp07VkiVLNHDgQL322mtasmSJpkyZojfeeMPo8JzWxx9/rKpVqypPnjzKkyePQkND9cknnxgdltMKDAxUXFxcuvFvv/1WlStXfvgBuYDMZoKIj4+3zNsMa1FRUZoxY4bat2+vy5cva8iQIWrbtq3c3Nw0fvx4o8ODszIDOYy3t7d537596cbj4uLMefPmNSAi1+Dh4WE+cuRIuvEjR46YPT09DYjI+U2fPt3s7e1tHjFihPnrr782f/XVV+bhw4ebvb29zTNmzDA6PKcSFRVlvnbtmnnBggXmkiVLmhcvXmzOmzev+fPPPzdPnDjR8mv8j7+/vzl//vxmNzc3y6/vbL6+vmY3Nzdzv379jA7TKZUtW9a8atUqs9lsNvv4+JiPHj1qNpvN5lmzZpk7depkZGhwYsyegRyHOU3vT6lSpbRu3bp0DxetW7dOpUqVMigq5/bee+9p7ty5VquLtWrVSlWqVNH48eM1ePBgA6NzLlFRUYqIiFDPnj2VkpKiESNG6Pr16+rcubNKliypWbNmqWPHjkaH6VRmzpwps9msXr16KSoqymp+4TszQYSFhRkYofM6ffq0ZTEYHx8fXb58WZLUokULjRkzxsjQ4MRImpHjtGjRQv/617/SzWkaERHBAh02DB06VAMHDlRcXJzq168vk8mkzZs3KyYmRrNmzTI6PKeUlJSk+vXrpxuvX7++kpKSDIjIeZnverymT58+6tOnj86dO6e0tLR0vbq4rXv37pJut7SEh4dbzTkM2x577DElJSWpdOnSCgoK0vfff6+aNWtq165d6ZbWBu6gpxk5zrvvvqty5copLCxMXl5e8vLyUnh4uIKCgkj+bOjbt68WL16s/fv3a9CgQXrttdf0888/a8mSJXr11VeNDs8pBQUFZbhIwpIlS1S+fHkDInJu9/blFipUiIQ5C/Lly6eEhATLz19//bVat26t//znP+kecMNtbdq00bp16yRJr732msaMGaPy5curW7du6tWrl8HRwVkxewZyLOY0RXZbtmyZOnTooGeffVbh4eGW6vy6dev0xRdfqE2bNkaH6DTc3NwUEhJit1q6Z8+ehxSR66hTp45GjRqldu3a6ddff1XlypXVtm1b7dq1S82bN2eBkyzYsWOHtmzZoqCgIL5xRKZImgFkya5du5SWlqZ69epZje/YsUPu7u6qXbu2QZE5t9jYWL3zzjtWH9CGDh2qGjVqGB2aU3Fzc9PQoUOtVmjLyLhx4x5SRK7Dz89Pe/bsUbly5fTWW29p/fr1+u6777RlyxZ17NhRv//+u9EhOp2ffvpJ9evXT/chLSUlRVu3blXDhg0NigzOjKQZOc6LL76o2rVra9SoUVbj06ZN086dO7V06VKDInNudevW1YgRI/Tiiy9ajS9fvlxvvfWWduzYYVBkeBS4ubnp9OnTtGPcB19fX8XGxqp8+fJq3LixWrRooddee02JiYmqUKECc1tngLmtcT94agA5zsaNGzOsVj3//PN6++23DYjINRw4cEA1a9ZMN16jRg0dOHDAgIhcx9mzZ3X27FmlpaVZjYeGhhoUkfNh0Zf7V7t2bU2cOFHPPvusNm7cqLlz50qSfvvtNxUtWtTg6JyTOZO5rc+fP6+8efMaEBFcAUkzcpzMppbLnTu3rly5YkBErsHT01NnzpxR2bJlrcaTkpJ4aj8TsbGx6t69u6U1424mk4lq1l340vP+zZw5Uy+//LK++uorjR492vJ8xpdffpnh7C05Wdu2bSXd/vPXo0cPq5kyUlNTtW/fPu4ZMsW/dMhxQkJCtGTJEo0dO9ZqfPHixaw4ZkPjxo0VGRmpr7/+2jIf7KVLl/Sf//xHjRs3Njg659SzZ08FBwcrOjpaRYsWpZpqw2+//abChQtn+XhfX1/FxcWl+xCXE4WGhmr//v3pxqdNmyZ3d3cDInJed/7uMpvNypcvn/LkyWPZ5+Hhoccff1x9+vQxKjw4OXqakeOsXLlS7dq1U+fOnfX0009Lur1Ax+eff66lS5eqdevWxgbopE6dOqWGDRvq/PnzlofY4uLiVLRoUa1du5YFTjKQL18+7d27l5lZskG+fPkUHx9P0oz7EhUVpWHDhtGKAYeQNCNH+uabb/Tmm28qLi5OefLkUWhoqMaNG6cnn3zS6NCc2rVr1/TZZ58pPj7ect86deqk3LlzGx2aU2rdurW6du2qdu3aGR3KIyenJ80FChTQ4cOHVahQIeXPn9/mtxgXLlx4iJG5po0bN+ratWsKCwtT/vz5jQ4HToqkGQCyyblz59S9e3fVrVtXISEh6T5cMB/s/cvpSfOiRYvUsWNHeXp6atGiRTaPvbNyIG63rFy9elVRUVGSbrdpNG3aVN9//70kqUiRIlq3bp2qVKliZJhwUiTNyLFu3ryZ4YwGpUuXNigi53f48GFt2LAhw/t2b484brcCde3aVX/99Ve6fTwI+M/k9KQZ96dmzZoaOXKkOnToIElaunSpunfvrrVr16pSpUrq1q2bvL29M1zJEyBpRo5z5MgR9erVS1u3brUavzMFEYlMxj766CP17dtXhQoVUrFixay+DjaZTKzUloGAgAC1aNFCY8aMYeqvB4wHAZXl2X58fX2zORLXkT9/fm3dulWVKlWSdPth3ZSUFH3yySeSpO3bt+ull15iQRhkiNkzkOP06NFDuXLl0qpVq1S8eHFmNMiiiRMnatKkSRo5cqTRobiM8+fPa/DgwSTM2YB6j+Tv72/z7y8KAendunXLapq5bdu26bXXXrP8XKJECZ07d86I0OACSJqR48TFxSk2NlYVK1Y0OhSXcvHiRb300ktGh+FS2rZtqx9//FHlypUzOhSXMWHCBA0bNkze3t5W4zdu3NC0adMsbUDffvutSpYsaUSITuPHH3+0/NpsNqtZs2aaP39+jr8vtgQFBemnn35S2bJllZiYqMOHD1s9AH7y5EkVLFjQwAjhzGjPQI5Tp04dvfPOO3riiSeMDsWl9O7dW3Xq1FFERITRobiMSZMmaebMmWrevLmqVq2a7kHAgQMHGhSZ82J54/tHn7d98+bN09ChQ9WhQwdt375d/v7+2rJli2X/xIkTtWPHDv33v/81MEo4KyrNyHHeeustjRgxQm+++WaGiQz9fxkLCgrSmDFjtH37dhLALJo/f758fHy0ceNGbdy40WqfyWTinmUgs+WN4+PjVaBAAQMiwqPk1VdftbTnNWzYUOPGjbPa/8cff6hXr14GRQdnR6UZOY6bm5skpfuHmf4/2wIDAzPdZzKZ9Ouvvz7EaPCouTPX8OXLl+Xr62v15zM1NVVXr15VRESE3n//fQOjdG5Umh+8KVOmKCIiQv7+/kaHAidA0owc596K371Y4ATZbf/+/YqOjtbMmTONDsVpLFq0SGazWb169dLMmTMtyx1Lt5c3DggIUFhYmIEROr98+fJp3759Nj/gwjHM0oK70Z6BHIekGEa4cuWKPv/8c0VHR2v37t0KDQ01OiSncmcBjsDAQNWvX59VJrOgbdu2Vj///fffioiISLc09PLlyx9mWI8U6oq4G0kzcox9+/Zl6TiSGWtDhgzJ0nEzZszI5khc08aNGxUdHa1ly5bp77//1vDhw/V///d/CgoKMjo0p/Tkk08qLS1Nhw8fznARnYYNGxoUmfO5uxovSV26dDEoEiBnIGlGjlG9enWZTCablQN6mtPbu3ev0SG4nKSkJC1cuFALFizQtWvX1KlTJ23cuFFhYWHq1q0bCbMN27dvV+fOnXXixIl0f1b582lt4cKFDh1/8uRJlShRwvJcBwDHkDQjx/jtt9+MDsEl3T0XLLImMDBQL730kt5//301btyYJMUBERERql27tr755hsWH3rAKleuTH8u8A+QNCPHKFOmjCQpMTFRpUqVyvAf48TExIcdlsvI6qITuP17bfPmzSpdurTKlCnDQjoOOHLkiL788kuq8dmA/lzgn6H8gRwnMDBQf/75Z7rx8+fP89S5DVFRUbp69Wq68evXrysqKsqAiJzXoUOH9OmnnyopKUl16tRRrVq19M4770hKP9UhrNWrV09Hjx41OgxAktSgQQPlyZPH6DDgJKg0I8fJbPGEq1evysvLy4CIXAOLTjgmPDxc4eHhevfdd/X5559rwYIFSk1NVb9+/dS5c2e1bt1ahQsXNjpMpzNgwAANHTpUp0+fznARHR7UxYOSlpamo0eP2nzgdPXq1UaEBifFPM3IMe7MAjFr1iz16dPHqs0gNTVVO3bskLu7u9WSqmDRiQcpISFB8+fP16effqoLFy7o1q1bRofkdDLq/77zAC8PAv4zLH7yPzxwivtBpRk5xp1ZIMxms/bv3y8PDw/LPg8PD1WrVk3Dhg0zKjynNXPmTMuiE1FRUSw68Q9UqlRJ06dP11tvvaWVK1caHY5T4oHd7ENr0P/wwCnuB5Vm5Dg9e/bUrFmz5Ovra3QoLmXjxo0sOnEfsvIVMPAwUGn+n7x58yo+Pp4HTuEQkmYAWUYC6Bi+Anbcxx9/bHN/t27dHlIkrqNXr16aNWuW8uXLZzV+7do1DRgwQAsWLJAk/f777ypRooTc3d2NCNOpPP300xoxYoSef/55o0OBCyFpRo5z7do1TZkyRevWrcsw+fv1118Nisy5kQA6rnr16goODlZUVFSGXwHfu6IbbvfQ3+3WrVu6fv26PDw85O3trQsXLhgUmfNyd3dXUlKSihQpYjV+7tw5FStWTCkpKQZF5rxWrFih119/XcOHD+eBU2QZPc3IcV555RVt3LhRXbt2pZfNAfQAOo45hx138eLFdGNHjhxR3759NXz4cAMicl5XrlyR2WyW2WzWX3/9ZTX7T2pqqlavXp0ukcZt7dq1k3S7Sn8HD5zCHpJm5DjffvutvvnmG4WHhxsdikshAXTcnTmHuWf/TPny5TVlyhR16dJFBw8eNDocp+Hv7y+TySSTyaTg4OB0+00mE3OoZ4IHTnE/SJqR4+TPn595he8DCaDjmHP4wXF3d9cff/xhdBhO5ccff5TZbNbTTz+tZcuWWf295uHhoTJlyqhEiRIGRui87qwQCziCnmbkOJ9++qm+/vprLVq0KN2S0MgcPYCOY85hx907FZ/ZbFZSUpJmz56tUqVK6dtvvzUoMud14sQJlS5dmpap+3DgwAElJibq5s2bVuMvvPCCQRHBmZE0I8epUaOGjh07JrPZrICAgHTJ3549ewyKzLmRADruxIkTNvdT7Urv3t9nJpNJhQsX1tNPP63p06erePHiBkXmXPbt26eQkBC5ublp3759No/lA216v/76q9q0aaP9+/db/h6T/jeXNX+fISO0ZyDHad26tdEhuCR6AB1HUuy4e2ezQcaqV6+u06dPq0iRIqpevbpV4nc3PtBm7LXXXlNgYKB++OEHlS1bVjt37tT58+c1dOhQvf3220aHBydFpRkAshlfAd+fe6t/+J+7WzL4RsNxhQoV0vr16xUaGio/Pz/t3LlTFSpU0Pr16zV06FDLCrLA3ag0A3AICWDW8RXw/fn44481bdo0HTlyRJIUHBys4cOHq2vXrgZH5jzuToRJih2XmpoqHx8fSbcT6D/++EMVKlRQmTJldOjQIYOjg7MiaUaOk5qaqnfeeUdffPFFhskfiydkjATQcXwF7LgZM2ZozJgx6t+/v8LDw2U2m7VlyxZFRETo3LlzGjx4sNEhOoV7H5i0hQ+06YWEhGjfvn0qW7as6tWrp6lTp8rDw0Mffvghy4wjc2YghxkzZoy5ePHi5mnTppm9vLzMb7zxhrl3797mggULmmfNmmV0eE6rRYsW5latWpnPnj1r9vHxMR84cMC8adMmc926dc0//fST0eE5pYIFC5rj4+PNZrPZ7Ovraz548KDZbDab161bZ65evbqRoTmtgIAA86JFi9KNx8TEmAMCAgyIyDmZTCarzc3NLd3Pdzakt2bNGvOyZcvMZrPZfOzYMXOlSpXMJpPJXKhQIfO6desMjg7OiqQZOU7ZsmXNq1atMpvNZrOPj4/56NGjZrPZbJ41a5a5U6dORobm1EgAHefv728+duyY2Wy+/ftu/fr1ZrPZbD569Kg5T548RobmtDw9Pc1HjhxJN3748GGzp6enARE5v7Vr15pr1qxpXrNmjfny5cvmK1eumNesWWOuXbu2+fvvvzc6PJdx/vx5c1pamtFhwImln0MKeMTdWWhCknx8fHT58mVJUosWLfTNN98YGZpTy6gHUBI9gDbc+QpYkuUr4C1btmjChAl8BZyJoKAgffHFF+nGlyxZovLlyxsQkfMbNGiQZs2apSZNmsjX11f58uVTkyZNNGPGDA0cONDo8JzeyZMnderUKRUoUICHTmETPc3IcR577DElJSWpdOnSCgoK0vfff6+aNWtq165d8vT0NDo8p0UPoONef/11Xbt2TZI0ceJEtWjRQg0aNFDBggW1ZMkSg6NzTlFRUerQoYN++uknhYeHy2QyafPmzVq3bl2GyTSkY8eOyc/PL924n5+fjh8//vADcgFpaWmaOHGipk+frqtXr0qS8uXLp6FDh2r06NEZzksPMOUccpxRo0bJ19dX//nPf/Tll1+qU6dOCggIUGJiogYPHqwpU6YYHaJT+u6773Tt2jW1bdtWv/76q1q0aKGDBw9aEsCnn37a6BBdwoULF5Q/f34qWjbExsbqnXfeUUJCgsxmsypXrqyhQ4eqRo0aRofmlBo2bKjcuXPr008/tSz+cvr0aXXt2lU3b97Uxo0bDY7Q+URGRio6OlpRUVFWD5yOHz9effr00aRJk4wOEU6IpBk53o4dO7RlyxYFBQXxlLmDSACz7uTJkzKZTCpZsqTRoeARc/ToUbVp00aHDh1S6dKlJUmJiYkKDg7WV199paCgIIMjdD4lSpTQBx98kO7v/K+//lr9+vXTqVOnDIoMzoykGUCWXL58WampqSpQoIDV+IULF5QrVy75+voaFJnz4ivg+3f27FmdPXs23QqBLAmdMbPZrLVr1+rgwYOW6vyzzz7LB9pMeHl5ad++fQoODrYaP3TokKpXr64bN24YFBmcGUkzcpzJkyeraNGi6tWrl9X4ggUL9Oeff2rkyJEGRebcmjZtqpYtW6pfv35W4x988IFWrlyp1atXGxSZ8+IrYMfFxsaqe/fultaMu7EktH1///23PD09SZbtqFevnurVq6d3333XanzAgAHauXOnduzYYVBkcGYkzchxAgIC9H//93+qX7++1fiOHTvUsWNH/fbbbwZF5twKFCigLVu2qFKlSlbjBw8eVHh4uM6fP29QZM6Lr4AdFxoaqqCgII0cOVJFixZNl/yx+l16aWlpmjRpkj744AOdOXNGhw8fVtmyZTVmzBgFBASod+/eRofodDZu3KjmzZurdOnSCgsLk8lk0tatW/X7779r9erVatCggdEhwgnx3SBynNOnT1selrlb4cKFlZSUZEBEriE5OVkpKSnpxm/dusVXmZm4cOGCKlasmG68YsWKrDyZid9++01Tp05VvXr1FBAQoDJlylhtSG/ixImKiYmxzGhzR9WqVTV//nwDI3NeTz75pA4fPqw2bdro0qVLunDhgtq2batffvlFCxcuNDo8OCmSZuQ4pUqV0pYtW9KNb9myRSVKlDAgItdQp04dffjhh+nGP/jgA9WqVcuAiJxftWrVNHv27HTjs2fPpjc3E88884zi4+ONDsOlfPzxx/rwww/18ssvy93d3TIeGhqqgwcPGhiZcytRooQmTZqkZcuWafny5Zo4caIuXryoRYsWGR0anBTzNCPHeeWVVzRo0CDdunXLMk3aunXrNGLECA0dOtTg6JzXpEmT9Oyzzyo+Pl7PPPOMpNv3bdeuXfr+++8Njs45TZ06Vc2bN9cPP/yQ4VfASG/+/Pnq3r27fv75Z4WEhCh37txW+5nhJr1Tp05lOENGWlqabt26ZUBEwKOJpBk5zogRI3ThwgX169dPN2/elHT7SeqRI0cqMjLS4OicV3h4uLZt26Zp06bpiy++UJ48eRQaGqro6GhWasvEna+A33//fcusBm3bttW//vUvjR8/nr7JDGzdulWbN2/Wt99+m24fDwJmrEqVKtq0aVO69pWlS5cytzXwAPEgIHKsq1evKiEhQXny5FH58uVZDRAPTXx8vGrWrEkCmIGAgAC1aNFCY8aMUdGiRY0OxyX897//VdeuXRUZGakJEyYoKipKhw4d0scff6xVq1apcePGRofoMvizCVtImgFk6sqVK5b5l69cuWLzWOZpzjr+Yc5cvnz5FBcXp3Llyhkdikv57rvv9Oabbyo2NlZpaWmqWbOmxo4dq+eee87o0JxK27Ztbe6/dOmSNm7cyJ9NZIj2DOQIbdu2VUxMjHx9fe3+pbl8+fKHFJXzy58/v5KSklSkSBH5+/tnOPer2Wzma3M8MG3bttWPP/5I0pxFKSkpmjRpknr16sVy2Vng5+dnd3+3bt0eUjRwNSTNyBH8/PwsCZ+9vzTxP+vXr7esAPjjjz8aHA1yguDgYEVGRmrz5s2qWrVqugcBBw4caFBkzilXrlyaNm2aunfvbnQoLoHp5PBP0J4BAA8YXwHfv8DAwEz3mUwm/frrrw8xGtfQunVrtW7dWj169DA6FOCRRqUZQKb27duX5WOZd/h/+Ar4/rEip+OaNm2qyMhI/fzzz6pVq5by5s1rtZ9p+oAHg0ozcoQaNWpk2I+bkT179mRzNK7Dzc1NJpNJ9v6aoKcZ2W3//v2Kjo7WzJkzjQ7F6bi5Zb5OGX82gQeHSjNyhNatWxsdgkui6gcjXblyRZ9//rmio6O1e/duvs3IRFpamtEhADkClWYAgFPZuHGjoqOjtWzZMv39998aPny4XnnllQxXvcvJ1q9fr/79+2v79u3ppny8fPmy6tevrw8++IBFdIAHhKQZOdbu3buVkJAgk8mkSpUqqVatWkaH5PQOHTqk9957z3LfKlasqAEDBqhChQpGhwYXl5SUpIULF2rBggW6du2aOnXqpM6dOyssLEzx8fGqXLmy0SE6nRdeeEGNGjXS4MGDM9z/7rvv6scff9SKFSsecmTAoynzRijgEXXy5Ek1aNBAdevW1WuvvaaBAweqTp06euKJJ/T7778bHZ7T+vLLLxUSEqLY2FhVq1ZNoaGh2rNnj0JCQrR06VKjw4OLCwwMVEJCgt5//32dOnVKM2bMUO3atY0Oy6nFx8fr+eefz3T/c889p9jY2IcYEfBoI2lGjtOrVy/dunVLCQkJunDhgi5cuKCEhASZzWb17t3b6PCc1ogRIxQZGalt27ZpxowZmjFjhrZu3ar//Oc/GjlypNHhwcWVKVNGmzdv1k8//aTDhw8bHY5LOHPmTLp5rO+WK1cu/fnnnw8xIuDRRtKMHGfTpk2aO3euVUtBhQoV9N5772nTpk0GRubcTp8+neE0aV26dNHp06cNiAiPkkOHDunTTz9VUlKS6tSpo1q1aumdd96RpCzPfJPTlCxZUvv37890/759+1S8ePGHGBHwaCNpRo5TunRp3bp1K914SkqKSpYsaUBEruGpp57K8EPF5s2bedAID0R4eLgWLFigpKQkRURE6IsvvlBqaqr69eunjz76iKrpPZo1a6axY8fq77//Trfvxo0bGjdunFq0aGFAZMCjiQcBkeN8/fXXevPNN/X++++rVq1aMplM2r17twYMGKCRI0cyPV0mPvjgA40dO1bt27fX448/Lknavn27li5dqqioKJUoUcJyLIsp4EFJSEhQdHS0PvnkE124cCHDD7w51ZkzZ1SzZk25u7urf//+qlChgkwmk6U3PDU1VXv27FHRokWNDhV4JJA0I8fJnz+/rl+/rpSUFOXKdXuq8ju/vnclrQsXLhgRolOytYDC3VhMAdkhJSVFK1eutCxRPmXKFEVERMjf39/YwAx24sQJ9e3bV999951lESKTyaQmTZpozpw5CggIMDZA4BFC0owcZ9GiRVk+tnv37tkYCYD75evrq7i4OJUtW9boUJzCxYsXdfToUZnNZpUvX1758+c3OiTgkUPSDABwOfny5VN8fDxJM4CHhmW0kSOlpqZqxYoVVoubtGrVytKugYytW7dO77zzjtXiJoMGDdKzzz5rdGgAAGQrZs9AjvPzzz8rODhY3bt314oVK7R8+XJ1795d5cuXtzl9U043e/ZsPf/888qXL59lURhfX181a9ZMs2fPNjo8AACyFe0ZyHEef/xxFSlSRIsWLbL0/V28eFE9evTQ2bNntW3bNoMjdE4lS5ZUZGSk+vfvbzX+/vvva9KkSfrjjz8Migw5Ee0ZAB42kmbkOHny5NHu3btVpUoVq/Gff/5ZderU0Y0bNwyKzLnly5dPe/fuVVBQkNX4kSNHVKNGDV29etWgyJATkTQDeNhoz0COU6FCBZ05cybd+NmzZ9MlhPifF154QStWrEg3/vXXX6tly5YGRIScrEGDBsqTJ4/RYQDIQag0I8dZvXq1RowYofHjx1st0jFhwgRNmTJFTzzxhOVYX19fo8J0OhMnTtTbb7+t8PBwhYWFSbp937Zs2aKhQ4da3auBAwcaFSYeAWlpaTp69KjOnj2rtLQ0q30NGzY0KCoAOR1JM3KcuxfpMJlMkmS1KMCdn1mkw1pgYGCWjjOZTPr111+zORo8qrZv367OnTvrxIkTuvefJ/5MAjAS82shx/nxxx8z3bdnzx7VrFnzIUbjOn777TejQ0AOEBERodq1a+ubb75R8eLFLR9kAcBoVJqR412+fFmfffaZ5s+fr/j4eCpZgIHy5s2r+Ph4ni8A4HSoNCPHWr9+vRYsWKDly5erTJkyateunaKjo40Oy6mdPHlSK1euVGJiom7evGm1b8aMGQZFhUdJvXr1dPToUZJmAE6HpBk5ysmTJxUTE6MFCxbo2rVrat++vW7duqVly5apcuXKRofn1NatW6cXXnhBgYGBOnTokEJCQnT8+HGZzWZaWvDADBgwQEOHDtXp06dVtWpV5c6d22p/aGioQZEByOloz0CO0axZM23evFktWrTQyy+/rOeff17u7u7KnTu34uPjSZrtqFu3rp5//nlNmDDBMkdukSJFLPeyb9++RoeIR8DdD+reYTKZeDgXgOFImpFj5MqVSwMHDlTfvn1Vvnx5yzhJc9bky5dPcXFxKleunPLnz6/NmzerSpUqio+PV6tWrXT8+HGjQ8Qj4MSJEzb3lylT5iFFAgDWaM9AjrFp0yYtWLBAtWvXVsWKFdW1a1d16NDB6LBcRt68eZWcnCxJKlGihI4dO2ZZVfHcuXNGhoZHCEkxAGdF0owcIywsTGFhYZo1a5YWL16sBQsWaMiQIUpLS9PatWtVqlQp5cuXz+gwndbjjz+uLVu2qHLlymrevLmGDh2q/fv3a/ny5ZZFYoAH5cCBAxk+cPrCCy8YFBGAnI72DORohw4dUnR0tD755BNdunRJjRs31sqVK40Oyyn9+uuvunr1qkJDQ3X9+nUNGzZMmzdvVlBQkN555x0qhHggfv31V7Vp00b79++39DJL/1t4iJ5mAEYhaQZ0+x/i//73v1qwYAFJM2Cgli1byt3dXR999JHKli2rnTt36vz58xo6dKjefvttNWjQwOgQAeRQJM0AHBIbG6uEhASZTCZVrlxZNWrUMDokPEIKFSqk9evXKzQ0VH5+ftq5c6cqVKig9evXa+jQodq7d6/RIQLIoehpBpAlZ8+eVceOHbVhwwb5+/vLbDbr8uXLatSokRYvXqzChQsbHSIeAampqfLx8ZF0O4H+448/VKFCBZUpU0aHDh0yODoAOVn6CTEBIAMDBgzQlStX9Msvv+jChQu6ePGifv75Z125ckUDBw40Ojw8IkJCQrRv3z5Jt1cHnDp1qrZs2aIJEyaobNmyBkcHICejPQNAlvj5+emHH35QnTp1rMZ37typ5557TpcuXTImMDxSvvvuO127dk1t27bVr7/+qhYtWujgwYMqWLCglixZoqefftroEAHkULRnAMiStLS0dEsaS7cXh0lLSzMgIjyKmjRpYvl12bJldeDAAV24cEH58+e3zKABAEag0gwgS1q1aqVLly7p888/V4kSJSRJp06d0ssvv6z8+fNrxYoVBkeIR83JkydlMplUsmRJo0MBAHqaAWTN7Nmz9ddffykgIEDlypVTUFCQAgMD9ddff+m9994zOjw8ItLS0jRhwgT5+fmpTJkyKl26tPz9/fXGG2/wjQYAQ9GeASBLSpUqpT179mjt2rU6ePCgzGazKleurGeffdbo0PAIGT16tKKjozVlyhSFh4fLbDZry5YtGj9+vP7++29NmjTJ6BAB5FC0ZwCwaf369erfv7+2b98uX19fq32XL19W/fr19cEHH7DoBB6IEiVK6IMPPki3XPbXX3+tfv366dSpUwZFBiCnoz0DgE0zZ85Unz590iXM0u0ZNV599VXNmDHDgMjwKLpw4YIqVqyYbrxixYq6cOGCAREBwG0kzQBsio+P1/PPP5/p/ueee06xsbEPMSI8yqpVq6bZs2enG589e7ZCQ0MNiAgAbqOnGYBNZ86cyXCquTty5cqlP//88yFGhEfZ1KlT1bx5c/3www8KCwuTyWTS1q1b9fvvv2v16tVGhwcgB6PSDMCmkiVLav/+/Znu37dvn4oXL/4QI8Kj7Mknn9Thw4fVpk0bXbp0SRcuXFDbtm31yy+/aOHChUaHByAH40FAADYNGDBAGzZs0K5du+Tl5WW178aNG6pbt64aNWqkd99916AIkRPEx8erZs2aSk1NNToUADkUSTMAm86cOaOaNWvK3d1d/fv3V4UKFWQymZSQkKD3339fqamp2rNnj4oWLWp0qHiEkTQDMBo9zQBsKlq0qLZu3aq+ffsqMjJSdz5nm0wmNWnSRHPmzCFhBgA88qg0A8iyixcv6ujRozKbzSpfvrzy589vdEjIIag0AzAaSTMAwHBt27a1uf/SpUvauHEjSTMAw9CeAQAwnJ+fn9393bp1e0jRAEB6VJoBAAAAO5inGQAAALCDpBkAAACwg6QZAAAAsIOkGQAAALCDpBkAAACwg6QZAAAAsIOkGQAAALDj/wFlW66eGIby8QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 900x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matrix=train.corr()\n",
    "ax=plt.subplots(figsize=(9,6))\n",
    "sns.heatmap(matrix, vmax=0.8, square=True, cmap=\"BuPu\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59573ca3",
   "metadata": {},
   "source": [
    "**Inference:** We see that the most correlated variables are **(ApplicantIncome-LoanAmount)** and **(Credit_History-Loan_Status)**. LoanAmount is also correlated with CoapplicantIncome."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae726033",
   "metadata": {},
   "source": [
    "## 6. Missing Value and Outlier Treatment"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc8ff542",
   "metadata": {},
   "source": [
    "After exploring all the variables in our data, we can now impute the missing values and treat the outliers because missing data and outliers can have adverse effect on the model performance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8c3a069",
   "metadata": {},
   "source": [
    "#### Missing Value Imputation\n",
    "Let's list out feature-wise count of missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "7ee924ad",
   "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",
       "dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17e0e67c",
   "metadata": {},
   "source": [
    "**Inference:** There are missing values in Gender, Married, Dependents, Self_Employed, LoanAmount, Loan_Amount_Term and Credit_History features."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e9ee275",
   "metadata": {},
   "source": [
    "We will treat the missing values in all the features one by one.\n",
    "We can consider these methods to fill the missing values:\n",
    "- **For numerical variable:** Imputation using mean or median\n",
    "- **For Categorical variable:** Imputation using mode"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72b5f409",
   "metadata": {},
   "source": [
    "There are very less missing values in Gender, Married, Dependents, Credit_History and Self_Employed features so we can fill them using the mode of the features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "669d09ac",
   "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": "markdown",
   "id": "e6b803df",
   "metadata": {},
   "source": [
    "Now let's try to find a way to fill the missing values in Loan_Amount_Term. We will look at the value countof the Loan amount term variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "623d715b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "360.0    512\n",
       "180.0     44\n",
       "480.0     15\n",
       "300.0     13\n",
       "240.0      4\n",
       "84.0       4\n",
       "120.0      3\n",
       "60.0       2\n",
       "36.0       2\n",
       "12.0       1\n",
       "Name: Loan_Amount_Term, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Loan_Amount_Term'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "866d5106",
   "metadata": {},
   "source": [
    "It can be seen that in loan amount term variable, the value of 360 is repeating the most. So, we will replace the missing values in this variable using the mode of this variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b4ea771d",
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Loan_Amount_Term'].fillna(train['Loan_Amount_Term'].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df7082f1",
   "metadata": {},
   "source": [
    "Now we will see the LoanAmount variable. As it is a numerical variable, we can use mean or median to impute the missing values. We will use median to fill the null values as earlier we saw that loan amount have outliers so the mean will not be the proper approach as it is highly affected by the presence of outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b7045a34",
   "metadata": {},
   "outputs": [],
   "source": [
    "train['LoanAmount'].fillna(train['LoanAmount'].median(), inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b7a9f8e",
   "metadata": {},
   "source": [
    "Now lets check whether all the missing values are filled in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e1abb755",
   "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",
       "dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()     #Checking the above operation completion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ccc35f3",
   "metadata": {},
   "source": [
    "As we can see that all the missing values have been filled in the test dataset. Let's fill all the missing values in the test dataset too with the same approach."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "981c361e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID               0\n",
       "Gender               11\n",
       "Married               0\n",
       "Dependents           10\n",
       "Education             0\n",
       "Self_Employed        23\n",
       "ApplicantIncome       0\n",
       "CoapplicantIncome     0\n",
       "LoanAmount            5\n",
       "Loan_Amount_Term      6\n",
       "Credit_History       29\n",
       "Property_Area         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "ad48b0d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "test['Gender'].fillna(train['Gender'].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": 49,
   "id": "69bfb380",
   "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": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum()     #Checking the above operation completion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "428c6275",
   "metadata": {},
   "source": [
    "#### Outlier Treatment"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ecf06bb",
   "metadata": {},
   "source": [
    "As we saw earlier in Univariate analysis, LoanAmount contains outliers so we have to treat them as the presence of outliers affects the distribution of the data. Let's examine what can happen to a data set with outliers.\n",
    "it can be seen that having outliers often has a significant effect on the mean and standard deviation and hence affecting the distribution. We must  take steps to remove outliers from our datasets."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b357e177",
   "metadata": {},
   "source": [
    "Due to these outliers bulk of the data in the **loan amount** is at the left and the right tail is longer. This is called **right skewness**. One way to remove the skewness is by doing the **log transformation**. As we take the log transformation, it does not affect the smaller values much, but reduces the larger values. So, we get a distribution similar to normal distribution.\n",
    "Let's visualize the effect of log transformation. We will do the similar changes to the test file simultaneously."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "2997fa68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['LoanAmount_log'] = np.log(train['LoanAmount'])\n",
    "train['LoanAmount_log'].hist(bins=20)\n",
    "\n",
    "test['LoanAmount_log'] = np.log(test['LoanAmount'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a83b295",
   "metadata": {},
   "source": [
    "Now the distribution looks much closer to normal and effect of extreme value has been significantly subsided. Let's build a logistic regression model and make predictions for the test datasets."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e496e2d2",
   "metadata": {},
   "source": [
    "## 7. Evaluation Metrics for Classification Problems"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "252521f6",
   "metadata": {},
   "source": [
    "The process of model building is not complete without evaluation of model's performance. Suppose we have the predictions from the model, how can we decide whether the predictions are accurate ? We can plot the results and compare them with the actual values i.e. calculate the distance between the predictions and actual values. Lesser this distance more accurate will be the predictions. Since this is a classification problem, we can evaluate our models using any one of the following evaluation metrics:\n",
    "- **Accuracy:** Let us understand it using the confusion matrix which is a tabular representation o Actual Vs Predicted values. This is how a confusion matrix looks like.\n",
    "\n",
    "\n",
    "\n",
    "- True Positive - Targets which are actually True(Y) and we have predicted them True(Y)\n",
    "- True Negative\n",
    "- False Positive\n",
    "- False Negative\n",
    "\n",
    "Using this value we can canculate the accuracy of the model. The accuarcy is given by:\n",
    "\n",
    "\n",
    "\n",
    "- **Precision:** It is a measure of correctness achieved in True Prediction i.e. of observation\n",
    "\n",
    "\n",
    "## Theory of this parts need to be completed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e473e99f",
   "metadata": {},
   "source": [
    "## 8. Model Building : Part I"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "971ccf99",
   "metadata": {},
   "source": [
    "Let us make our first model to predict the target variable. We will start with Logistic Regression which is used for predicting binary outcome.\n",
    "\n",
    "- Logistic Regression is a classification algorithm. It is used to predict a binary outcome (1/0, Yes/No, True/False) given a set of independent variables.\n",
    "- Logistic Regression is an estimation of **Logit function**. Logit function is simply a log of odds in favor of the event.\n",
    "- This function creates a **S-shaped curve** with the probability estimate, which is very similar to the required step wise function.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "954f377d",
   "metadata": {},
   "source": [
    "Lets drop the Loan_ID variable as it do not have any effect on the loan status. We will do the same changes to the test dataset which we did for the training dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "4afd94b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train.drop('Loan_ID', axis=1)\n",
    "test = test.drop('Loan_ID', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d894e899",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "      <th>LoanAmount_log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "      <td>4.852030</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>0</td>\n",
       "      <td>4.852030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "      <td>4.189655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "      <td>4.787492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "      <td>4.948760</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender Married Dependents     Education Self_Employed  ApplicantIncome  \\\n",
       "0   Male      No          0      Graduate            No             5849   \n",
       "1   Male     Yes          1      Graduate            No             4583   \n",
       "2   Male     Yes          0      Graduate           Yes             3000   \n",
       "3   Male     Yes          0  Not Graduate            No             2583   \n",
       "4   Male      No          0      Graduate            No             6000   \n",
       "\n",
       "   CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
       "0                0.0       128.0             360.0             1.0   \n",
       "1             1508.0       128.0             360.0             1.0   \n",
       "2                0.0        66.0             360.0             1.0   \n",
       "3             2358.0       120.0             360.0             1.0   \n",
       "4                0.0       141.0             360.0             1.0   \n",
       "\n",
       "  Property_Area  Loan_Status  LoanAmount_log  \n",
       "0         Urban            1        4.852030  \n",
       "1         Rural            0        4.852030  \n",
       "2         Urban            1        4.189655  \n",
       "3         Urban            1        4.787492  \n",
       "4         Urban            1        4.948760  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "53f93458",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
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       "      <td>1.0</td>\n",
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       "      <td>208.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>5.337538</td>\n",
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       "      <td>Male</td>\n",
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       "      <td>100.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>4.605170</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
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       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
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       "      <td>0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>4.356709</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender Married Dependents     Education Self_Employed  ApplicantIncome  \\\n",
       "0   Male     Yes          0      Graduate            No             5720   \n",
       "1   Male     Yes          1      Graduate            No             3076   \n",
       "2   Male     Yes          2      Graduate            No             5000   \n",
       "3   Male     Yes          2      Graduate            No             2340   \n",
       "4   Male      No          0  Not Graduate            No             3276   \n",
       "\n",
       "   CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
       "0                  0       110.0             360.0             1.0   \n",
       "1               1500       126.0             360.0             1.0   \n",
       "2               1800       208.0             360.0             1.0   \n",
       "3               2546       100.0             360.0             1.0   \n",
       "4                  0        78.0             360.0             1.0   \n",
       "\n",
       "  Property_Area  LoanAmount_log  \n",
       "0         Urban        4.700480  \n",
       "1         Urban        4.836282  \n",
       "2         Urban        5.337538  \n",
       "3         Urban        4.605170  \n",
       "4         Urban        4.356709  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cc9d33b",
   "metadata": {},
   "source": [
    "We will use **scikit-learn(sklearn)** for making different models which is an open source library for Python. It is one of the most efficient tool which contains many inbuilt functions that can be used for modelling in Python.\n",
    "\n",
    "Sklearn requires the target variable in a separate dataset. So, we will drop out target variable from the train dataset and save it in another dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "3a68b639",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train.drop('Loan_Status',1)\n",
    "y = train.Loan_Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "42279f59",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>4.948760</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender Married Dependents     Education Self_Employed  ApplicantIncome  \\\n",
       "0   Male      No          0      Graduate            No             5849   \n",
       "1   Male     Yes          1      Graduate            No             4583   \n",
       "2   Male     Yes          0      Graduate           Yes             3000   \n",
       "3   Male     Yes          0  Not Graduate            No             2583   \n",
       "4   Male      No          0      Graduate            No             6000   \n",
       "\n",
       "   CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
       "0                0.0       128.0             360.0             1.0   \n",
       "1             1508.0       128.0             360.0             1.0   \n",
       "2                0.0        66.0             360.0             1.0   \n",
       "3             2358.0       120.0             360.0             1.0   \n",
       "4                0.0       141.0             360.0             1.0   \n",
       "\n",
       "  Property_Area  LoanAmount_log  \n",
       "0         Urban        4.852030  \n",
       "1         Rural        4.852030  \n",
       "2         Urban        4.189655  \n",
       "3         Urban        4.787492  \n",
       "4         Urban        4.948760  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "36ee88c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    0\n",
       "2    1\n",
       "3    1\n",
       "4    1\n",
       "Name: Loan_Status, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "127e7ade",
   "metadata": {},
   "source": [
    "Now we will make **dummy variables(One Hot Encoding)** for the categorical variables. Dummy variables turns categorical variables into a series of 0 and 1, making them lot easier to quantify and compare. Let us understand the process of dummies first:\n",
    "- Consider the \"General\" variable. It has two classes, Male and Female.\n",
    "- As Logistic Regression takes only the numerical values as input, we have to change male and female into numerical value.\n",
    "- Once we apply dummies to this variable, it will convert the \"Gender\" variable into two variables(Gender_Male and Gender_Female), one for each class, i.e. Male and Female.\n",
    "- Gender_Male will have a value of 0 if the gender is Female and a value of 1 if the gender is Male."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "20fd5b10",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.get_dummies(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "069c8d8e",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>2900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.262680</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>4106</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.688879</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>8072</td>\n",
       "      <td>240.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.533389</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>7583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.231109</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>4583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.890349</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>614 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0               5849                0.0       128.0             360.0   \n",
       "1               4583             1508.0       128.0             360.0   \n",
       "2               3000                0.0        66.0             360.0   \n",
       "3               2583             2358.0       120.0             360.0   \n",
       "4               6000                0.0       141.0             360.0   \n",
       "..               ...                ...         ...               ...   \n",
       "609             2900                0.0        71.0             360.0   \n",
       "610             4106                0.0        40.0             180.0   \n",
       "611             8072              240.0       253.0             360.0   \n",
       "612             7583                0.0       187.0             360.0   \n",
       "613             4583                0.0       133.0             360.0   \n",
       "\n",
       "     Credit_History  LoanAmount_log  Gender_Female  Gender_Male  Married_No  \\\n",
       "0               1.0        4.852030              0            1           1   \n",
       "1               1.0        4.852030              0            1           0   \n",
       "2               1.0        4.189655              0            1           0   \n",
       "3               1.0        4.787492              0            1           0   \n",
       "4               1.0        4.948760              0            1           1   \n",
       "..              ...             ...            ...          ...         ...   \n",
       "609             1.0        4.262680              1            0           1   \n",
       "610             1.0        3.688879              0            1           0   \n",
       "611             1.0        5.533389              0            1           0   \n",
       "612             1.0        5.231109              0            1           0   \n",
       "613             0.0        4.890349              1            0           1   \n",
       "\n",
       "     Married_Yes  ...  Dependents_0  Dependents_1  Dependents_2  \\\n",
       "0              0  ...             1             0             0   \n",
       "1              1  ...             0             1             0   \n",
       "2              1  ...             1             0             0   \n",
       "3              1  ...             1             0             0   \n",
       "4              0  ...             1             0             0   \n",
       "..           ...  ...           ...           ...           ...   \n",
       "609            0  ...             1             0             0   \n",
       "610            1  ...             0             0             0   \n",
       "611            1  ...             0             1             0   \n",
       "612            1  ...             0             0             1   \n",
       "613            0  ...             1             0             0   \n",
       "\n",
       "     Education_Graduate  Education_Not Graduate  Self_Employed_No  \\\n",
       "0                     1                       0                 1   \n",
       "1                     1                       0                 1   \n",
       "2                     1                       0                 0   \n",
       "3                     0                       1                 1   \n",
       "4                     1                       0                 1   \n",
       "..                  ...                     ...               ...   \n",
       "609                   1                       0                 1   \n",
       "610                   1                       0                 1   \n",
       "611                   1                       0                 1   \n",
       "612                   1                       0                 1   \n",
       "613                   1                       0                 0   \n",
       "\n",
       "     Self_Employed_Yes  Property_Area_Rural  Property_Area_Semiurban  \\\n",
       "0                    0                    0                        0   \n",
       "1                    0                    1                        0   \n",
       "2                    1                    0                        0   \n",
       "3                    0                    0                        0   \n",
       "4                    0                    0                        0   \n",
       "..                 ...                  ...                      ...   \n",
       "609                  0                    1                        0   \n",
       "610                  0                    1                        0   \n",
       "611                  0                    0                        0   \n",
       "612                  0                    0                        0   \n",
       "613                  1                    0                        1   \n",
       "\n",
       "     Property_Area_Urban  \n",
       "0                      1  \n",
       "1                      0  \n",
       "2                      1  \n",
       "3                      1  \n",
       "4                      1  \n",
       "..                   ...  \n",
       "609                    0  \n",
       "610                    0  \n",
       "611                    1  \n",
       "612                    1  \n",
       "613                    0  \n",
       "\n",
       "[614 rows x 21 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "a1333f3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.get_dummies(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "3362883c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>614 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0               5849                0.0       128.0             360.0   \n",
       "1               4583             1508.0       128.0             360.0   \n",
       "2               3000                0.0        66.0             360.0   \n",
       "3               2583             2358.0       120.0             360.0   \n",
       "4               6000                0.0       141.0             360.0   \n",
       "..               ...                ...         ...               ...   \n",
       "609             2900                0.0        71.0             360.0   \n",
       "610             4106                0.0        40.0             180.0   \n",
       "611             8072              240.0       253.0             360.0   \n",
       "612             7583                0.0       187.0             360.0   \n",
       "613             4583                0.0       133.0             360.0   \n",
       "\n",
       "     Credit_History  Loan_Status  LoanAmount_log  Gender_Female  Gender_Male  \\\n",
       "0               1.0            1        4.852030              0            1   \n",
       "1               1.0            0        4.852030              0            1   \n",
       "2               1.0            1        4.189655              0            1   \n",
       "3               1.0            1        4.787492              0            1   \n",
       "4               1.0            1        4.948760              0            1   \n",
       "..              ...          ...             ...            ...          ...   \n",
       "609             1.0            1        4.262680              1            0   \n",
       "610             1.0            1        3.688879              0            1   \n",
       "611             1.0            1        5.533389              0            1   \n",
       "612             1.0            1        5.231109              0            1   \n",
       "613             0.0            0        4.890349              1            0   \n",
       "\n",
       "     Married_No  ...  Dependents_0  Dependents_1  Dependents_2  \\\n",
       "0             1  ...             1             0             0   \n",
       "1             0  ...             0             1             0   \n",
       "2             0  ...             1             0             0   \n",
       "3             0  ...             1             0             0   \n",
       "4             1  ...             1             0             0   \n",
       "..          ...  ...           ...           ...           ...   \n",
       "609           1  ...             1             0             0   \n",
       "610           0  ...             0             0             0   \n",
       "611           0  ...             0             1             0   \n",
       "612           0  ...             0             0             1   \n",
       "613           1  ...             1             0             0   \n",
       "\n",
       "     Education_Graduate  Education_Not Graduate  Self_Employed_No  \\\n",
       "0                     1                       0                 1   \n",
       "1                     1                       0                 1   \n",
       "2                     1                       0                 0   \n",
       "3                     0                       1                 1   \n",
       "4                     1                       0                 1   \n",
       "..                  ...                     ...               ...   \n",
       "609                   1                       0                 1   \n",
       "610                   1                       0                 1   \n",
       "611                   1                       0                 1   \n",
       "612                   1                       0                 1   \n",
       "613                   1                       0                 0   \n",
       "\n",
       "     Self_Employed_Yes  Property_Area_Rural  Property_Area_Semiurban  \\\n",
       "0                    0                    0                        0   \n",
       "1                    0                    1                        0   \n",
       "2                    1                    0                        0   \n",
       "3                    0                    0                        0   \n",
       "4                    0                    0                        0   \n",
       "..                 ...                  ...                      ...   \n",
       "609                  0                    1                        0   \n",
       "610                  0                    1                        0   \n",
       "611                  0                    0                        0   \n",
       "612                  0                    0                        0   \n",
       "613                  1                    0                        1   \n",
       "\n",
       "     Property_Area_Urban  \n",
       "0                      1  \n",
       "1                      0  \n",
       "2                      1  \n",
       "3                      1  \n",
       "4                      1  \n",
       "..                   ...  \n",
       "609                    0  \n",
       "610                    0  \n",
       "611                    1  \n",
       "612                    1  \n",
       "613                    0  \n",
       "\n",
       "[614 rows x 22 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "365a212b",
   "metadata": {},
   "outputs": [],
   "source": [
    "test = pd.get_dummies(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "910a995b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>LoanAmount_log</th>\n",
       "      <th>Gender_Female</th>\n",
       "      <th>Gender_Male</th>\n",
       "      <th>Married_No</th>\n",
       "      <th>Married_Yes</th>\n",
       "      <th>...</th>\n",
       "      <th>Dependents_1</th>\n",
       "      <th>Dependents_2</th>\n",
       "      <th>Dependents_3+</th>\n",
       "      <th>Education_Graduate</th>\n",
       "      <th>Education_Not Graduate</th>\n",
       "      <th>Self_Employed_No</th>\n",
       "      <th>Self_Employed_Yes</th>\n",
       "      <th>Property_Area_Rural</th>\n",
       "      <th>Property_Area_Semiurban</th>\n",
       "      <th>Property_Area_Urban</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2340</td>\n",
       "      <td>2546</td>\n",
       "      <td>100.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.605170</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3276</td>\n",
       "      <td>0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>360.0</td>\n",
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       "      <th>362</th>\n",
       "      <td>4009</td>\n",
       "      <td>1777</td>\n",
       "      <td>113.0</td>\n",
       "      <td>360.0</td>\n",
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       "      <td>4158</td>\n",
       "      <td>709</td>\n",
       "      <td>115.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.744932</td>\n",
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       "      <td>0</td>\n",
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       "      <th>364</th>\n",
       "      <td>3250</td>\n",
       "      <td>1993</td>\n",
       "      <td>126.0</td>\n",
       "      <td>360.0</td>\n",
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       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.062595</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>9200</td>\n",
       "      <td>0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.584967</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>367 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0               5720                  0       110.0             360.0   \n",
       "1               3076               1500       126.0             360.0   \n",
       "2               5000               1800       208.0             360.0   \n",
       "3               2340               2546       100.0             360.0   \n",
       "4               3276                  0        78.0             360.0   \n",
       "..               ...                ...         ...               ...   \n",
       "362             4009               1777       113.0             360.0   \n",
       "363             4158                709       115.0             360.0   \n",
       "364             3250               1993       126.0             360.0   \n",
       "365             5000               2393       158.0             360.0   \n",
       "366             9200                  0        98.0             180.0   \n",
       "\n",
       "     Credit_History  LoanAmount_log  Gender_Female  Gender_Male  Married_No  \\\n",
       "0               1.0        4.700480              0            1           0   \n",
       "1               1.0        4.836282              0            1           0   \n",
       "2               1.0        5.337538              0            1           0   \n",
       "3               1.0        4.605170              0            1           0   \n",
       "4               1.0        4.356709              0            1           1   \n",
       "..              ...             ...            ...          ...         ...   \n",
       "362             1.0        4.727388              0            1           0   \n",
       "363             1.0        4.744932              0            1           0   \n",
       "364             1.0        4.836282              0            1           1   \n",
       "365             1.0        5.062595              0            1           0   \n",
       "366             1.0        4.584967              0            1           1   \n",
       "\n",
       "     Married_Yes  ...  Dependents_1  Dependents_2  Dependents_3+  \\\n",
       "0              1  ...             0             0              0   \n",
       "1              1  ...             1             0              0   \n",
       "2              1  ...             0             1              0   \n",
       "3              1  ...             0             1              0   \n",
       "4              0  ...             0             0              0   \n",
       "..           ...  ...           ...           ...            ...   \n",
       "362            1  ...             0             0              1   \n",
       "363            1  ...             0             0              0   \n",
       "364            0  ...             0             0              0   \n",
       "365            1  ...             0             0              0   \n",
       "366            0  ...             0             0              0   \n",
       "\n",
       "     Education_Graduate  Education_Not Graduate  Self_Employed_No  \\\n",
       "0                     1                       0                 1   \n",
       "1                     1                       0                 1   \n",
       "2                     1                       0                 1   \n",
       "3                     1                       0                 1   \n",
       "4                     0                       1                 1   \n",
       "..                  ...                     ...               ...   \n",
       "362                   0                       1                 0   \n",
       "363                   1                       0                 1   \n",
       "364                   1                       0                 1   \n",
       "365                   1                       0                 1   \n",
       "366                   1                       0                 0   \n",
       "\n",
       "     Self_Employed_Yes  Property_Area_Rural  Property_Area_Semiurban  \\\n",
       "0                    0                    0                        0   \n",
       "1                    0                    0                        0   \n",
       "2                    0                    0                        0   \n",
       "3                    0                    0                        0   \n",
       "4                    0                    0                        0   \n",
       "..                 ...                  ...                      ...   \n",
       "362                  1                    0                        0   \n",
       "363                  0                    0                        0   \n",
       "364                  0                    0                        1   \n",
       "365                  0                    1                        0   \n",
       "366                  1                    1                        0   \n",
       "\n",
       "     Property_Area_Urban  \n",
       "0                      1  \n",
       "1                      1  \n",
       "2                      1  \n",
       "3                      1  \n",
       "4                      1  \n",
       "..                   ...  \n",
       "362                    1  \n",
       "363                    1  \n",
       "364                    0  \n",
       "365                    0  \n",
       "366                    0  \n",
       "\n",
       "[367 rows x 21 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb021932",
   "metadata": {},
   "source": [
    "Now we will train the model on training dataset and make predictions for the test dataset. But can we validate these predictions ? One way of doing this is we can divide our train dataset into two parts: **train and validation**. We can train the model on this train part and using that make predictions for the validation part. In this way, we can validate our predictions as we have the true predictions for the validation part (which we do not have for the test dataset).\n",
    "\n",
    "We will use the train_test_split function  from sklearn to divide our train dataset. So, first let us import train_test_split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "7fb96f8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "c9f54d44",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_cv, y_train, y_cv = train_test_split(X,y,test_size = 0.3, random_state=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "33dfa0db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((429, 21), (185, 21), (429,), (185,))"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape, x_cv.shape, y_train.shape, y_cv.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29059a04",
   "metadata": {},
   "source": [
    "The dataset has been divided into training and validation part. Let us import Logistic Regression and accuracy_score from sklearn and fit the logistic regression model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "250bc54c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "4f11f869",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(multi_class='ovr', n_jobs=1, penalty='12', random_state=5,\n",
       "                   solver='liblinear')"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LogisticRegression()\n",
    "model.fit(x_train, y_train)\n",
    "\n",
    "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True, intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1, penalty='12', random_state=5, solver='liblinear', tol=0.0001, verbose=0, warm_start=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aed05f54",
   "metadata": {},
   "source": [
    "Hence, the **C parameter represents inverse of regularization strength**. Regularization is applying a penalty to increasing the magnitude of parameter values in order to reduce overfitting. Smaller values of C specify stronger regularization."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7584590",
   "metadata": {},
   "source": [
    "Let us **predict the Loan_Status for validation set** and calculate its accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "342f3fb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_cv = model.predict(x_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "20e1750d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1,\n",
       "       1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,\n",
       "       1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n",
       "       1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,\n",
       "       1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1,\n",
       "       1, 1, 0, 0, 1, 1, 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_cv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5f200f6",
   "metadata": {},
   "source": [
    "Let us calculate how accurate our predictions are by **calculating the accuracy**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "9aa5c5bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8054054054054054"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_cv,pred_cv)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a74d59da",
   "metadata": {},
   "source": [
    "### Inference: So, our predictions are **almost 80% accurate**, i.e. we have identified 80% of the loan status correctly."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f67e773c",
   "metadata": {},
   "source": [
    "Let's make **prediction for the test dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "5280eccd",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_test = model.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "6356eedd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1,\n",
       "       0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0,\n",
       "       1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0,\n",
       "       1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1,\n",
       "       0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0,\n",
       "       0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1,\n",
       "       1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0,\n",
       "       1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1,\n",
       "       0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0,\n",
       "       1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0,\n",
       "       1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5281cada",
   "metadata": {},
   "source": [
    "Lets import the submission file which we have to submit on the solution checker."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "e7958cd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.read_csv(\"sample_submission.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "15ccdd0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loan_ID</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001015</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001022</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001031</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001035</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001051</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>LP002971</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>LP002975</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>LP002980</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>LP002986</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>LP002989</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>367 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Loan_ID Loan_Status\n",
       "0    LP001015           N\n",
       "1    LP001022           N\n",
       "2    LP001031           N\n",
       "3    LP001035           N\n",
       "4    LP001051           N\n",
       "..        ...         ...\n",
       "362  LP002971           N\n",
       "363  LP002975           N\n",
       "364  LP002980           N\n",
       "365  LP002986           N\n",
       "366  LP002989           N\n",
       "\n",
       "[367 rows x 2 columns]"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0b7a2f0",
   "metadata": {},
   "source": [
    "We only need the Loan_ID and the corresponding Loan_Status for the final submission. We will fill these columns with the **Loan_ID of test dataset** and the **predictions that we made, i.e. pred_test** respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "60962baa",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission['Loan_Status'] = pred_test\n",
    "submission['Loan_ID'] = test_original['Loan_ID']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "4fb93c0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loan_ID</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001003</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001005</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001006</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001008</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>LP002175</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>LP002178</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>LP002180</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>LP002181</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>LP002187</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>367 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Loan_ID  Loan_Status\n",
       "0    LP001002            1\n",
       "1    LP001003            1\n",
       "2    LP001005            1\n",
       "3    LP001006            1\n",
       "4    LP001008            1\n",
       "..        ...          ...\n",
       "362  LP002175            1\n",
       "363  LP002178            1\n",
       "364  LP002180            1\n",
       "365  LP002181            1\n",
       "366  LP002187            1\n",
       "\n",
       "[367 rows x 2 columns]"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "add147c9",
   "metadata": {},
   "source": [
    "Remember we need predictions in Y and N. So, let's convert 1 and 0 to Y and N."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "cc4a17be",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission['Loan_Status'].replace(0,'N', inplace=True)\n",
    "submission['Loan_Status'].replace(1, 'Y', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "1d21c976",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loan_ID</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001003</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001005</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001006</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001008</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>LP002175</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>LP002178</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>LP002180</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>LP002181</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>LP002187</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>367 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Loan_ID Loan_Status\n",
       "0    LP001002           Y\n",
       "1    LP001003           Y\n",
       "2    LP001005           Y\n",
       "3    LP001006           Y\n",
       "4    LP001008           Y\n",
       "..        ...         ...\n",
       "362  LP002175           Y\n",
       "363  LP002178           Y\n",
       "364  LP002180           Y\n",
       "365  LP002181           Y\n",
       "366  LP002187           Y\n",
       "\n",
       "[367 rows x 2 columns]"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "0c657b70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID   Loan_Status\n",
       "LP001002  Y              1\n",
       "LP001841  Y              1\n",
       "LP001835  N              1\n",
       "LP001825  Y              1\n",
       "LP001824  Y              1\n",
       "                        ..\n",
       "LP001426  Y              1\n",
       "LP001422  N              1\n",
       "LP001421  N              1\n",
       "LP001405  N              1\n",
       "LP002187  Y              1\n",
       "Length: 367, dtype: int64"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab635bf8",
   "metadata": {},
   "source": [
    "Finally we will convert the submission to .csv format and make submission to check the accuracy on the leaderboard."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "ce393b99",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(submission, columns=['Loan_ID', 'Loan_Status']).to_csv('Loan_Logistic.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1eb420fd",
   "metadata": {},
   "source": [
    "Now submission file is ready upload on the leaderboard."
   ]
  }
 ],
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