Loan_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nsrVSYftukSG"
   },
   "source": [
    ">> > <span style='background :yellow'> To Predict whether the Loan is approved or not </span><hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xJY2LdKN3Jak"
   },
   "source": [
    "### <span style='background :yellow' >1. Problem Definition :</span> \n",
    "- To build a Machine Learning Model whether the Loan is approved or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rvLPuCU_3Gs1",
    "outputId": "2fc4d210-b71b-4fdb-9db3-16413f2b6d2a"
   },
   "outputs": [],
   "source": [
    "# importing important libraries\n",
    "import pickle\n",
    "import warnings\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "# from google.colab import drive\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Modeling & Accuracy Metrics\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from mlxtend.feature_selection import SequentialFeatureSelector as SFS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7XInObjX3Vl9"
   },
   "source": [
    "### <span style='background :yellow' >2. Data Extraction/Collection :</span> \n",
    " - Here we read dataset from the .csv file and store it to \"dataset\" dataframe and start out exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 266
    },
    "id": "jGa_imc-3XwQ",
    "outputId": "fa0c45c0-493b-4333-fb9d-c94d30140de7"
   },
   "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>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001003</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001005</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001006</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001008</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
       "0  LP001002   Male      No          0      Graduate            No   \n",
       "1  LP001003   Male     Yes          1      Graduate            No   \n",
       "2  LP001005   Male     Yes          0      Graduate           Yes   \n",
       "3  LP001006   Male     Yes          0  Not Graduate            No   \n",
       "4  LP001008   Male      No          0      Graduate            No   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5849                0.0         NaN             360.0   \n",
       "1             4583             1508.0       128.0             360.0   \n",
       "2             3000                0.0        66.0             360.0   \n",
       "3             2583             2358.0       120.0             360.0   \n",
       "4             6000                0.0       141.0             360.0   \n",
       "\n",
       "   Credit_History Property_Area Loan_Status  \n",
       "0             1.0         Urban           Y  \n",
       "1             1.0         Rural           N  \n",
       "2             1.0         Urban           Y  \n",
       "3             1.0         Urban           Y  \n",
       "4             1.0         Urban           Y  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(614, 13)\n"
     ]
    }
   ],
   "source": [
    "# reading the CSV file from local disk\n",
    "# drive.mount('/content/drive/')\n",
    "# dataset = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/Loan Prediction/train.csv')\n",
    "dataset = pd.read_csv('train.csv')\n",
    "display(dataset.head())\n",
    "print(dataset.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9T7jKJuP6GQa"
   },
   "source": [
    "### <span style='background :yellow' >3. Data Pre-Processing :</span> \n",
    " - **Data preprocessing** is a process of preparing the raw data and making it suitable for a machine learning model. It is the first and crucial step while creating a machine learning model. As we are working with real life datasets, it is not always a case that we come across the clean and formatted data. And while doing any operation with data, it is mandatory to clean it and put in a formatted way. So for this, we use data preprocessing task."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9adhFZam6K0G"
   },
   "source": [
    "**A. Handling Numerical Missing Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jzR8IHlf6Opb",
    "outputId": "fafaadb4-206e-4fa2-9d12-fc769318676f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull().values.any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rm_GsqTV6YS6"
   },
   "source": [
    "Confirm that the output is \"True,\" which indicates that there is at least one missing value somewhere in the dataset. The next step is to find out where the missing values are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BaDClcao6dwG",
    "outputId": "445403b2-ac03-4630-f66a-85b7dba4b583"
   },
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here we see the number of pecentage of null values in each column = total number of null values in each column / number of rows * 100\n",
    "dataset.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7sorZV-RRnp9",
    "outputId": "6626f18e-81f4-4ce2-c706-6b44d3101ba9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 614 entries, 0 to 613\n",
      "Data columns (total 13 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Loan_ID            614 non-null    object \n",
      " 1   Gender             601 non-null    object \n",
      " 2   Married            611 non-null    object \n",
      " 3   Dependents         599 non-null    object \n",
      " 4   Education          614 non-null    object \n",
      " 5   Self_Employed      582 non-null    object \n",
      " 6   ApplicantIncome    614 non-null    int64  \n",
      " 7   CoapplicantIncome  614 non-null    float64\n",
      " 8   LoanAmount         592 non-null    float64\n",
      " 9   Loan_Amount_Term   600 non-null    float64\n",
      " 10  Credit_History     564 non-null    float64\n",
      " 11  Property_Area      614 non-null    object \n",
      " 12  Loan_Status        614 non-null    object \n",
      "dtypes: float64(4), int64(1), object(8)\n",
      "memory usage: 62.5+ KB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "21LsnjrBRxom"
   },
   "outputs": [],
   "source": [
    "# handle numerical missing data\n",
    "dataset['LoanAmount'] = dataset['LoanAmount'].fillna(dataset['LoanAmount'].mean())\n",
    "dataset['Loan_Amount_Term'] = dataset['Loan_Amount_Term'].fillna(dataset['Loan_Amount_Term'].mean())\n",
    "dataset['Credit_History'] = dataset['Credit_History'].fillna(dataset['Credit_History'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZUjtKIAvR-_h",
    "outputId": "51d4a53a-e786-411a-9b08-ecc384c036eb"
   },
   "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            0\n",
       "Loan_Amount_Term      0\n",
       "Credit_History        0\n",
       "Property_Area         0\n",
       "Loan_Status           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "e-v51ZO8SGej"
   },
   "source": [
    "**B. Handling Categorical Missing Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "NdQFbpfTSMDy"
   },
   "outputs": [],
   "source": [
    "dataset['Gender'] = dataset['Gender'].fillna(dataset['Gender'].mode()[0])\n",
    "dataset['Married'] = dataset['Married'].fillna(dataset['Married'].mode()[0])\n",
    "dataset['Dependents'] = dataset['Dependents'].fillna(dataset['Dependents'].mode()[0])\n",
    "dataset['Self_Employed'] = dataset['Self_Employed'].fillna(dataset['Self_Employed'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4chAU9b5Sado",
    "outputId": "30f2e614-09b5-458a-beba-250ba390e94d"
   },
   "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "P-ku-eAF6so-"
   },
   "source": [
    "**C. Removing Duplicate Column (if any)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9fYndhdp6n04",
    "outputId": "54b504cd-1a96-458c-89fd-0afce76fc3dd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplicated Rows : 0\n"
     ]
    }
   ],
   "source": [
    "# For Duplicated Rows\n",
    "print(f\"Duplicated Rows : {dataset.duplicated().sum()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6dg2Nn-v9OyK"
   },
   "source": [
    "### <span style='background :yellow' >4. Exploratory Data Analysis :</span> \n",
    " - **Exploratory Data Analysis** is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. A statistical model can be used or not, but primarily EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis testing task. It is also used to filter the data from redundancy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "bmV9mDakTvbz",
    "outputId": "0032618a-7eba-442f-914c-5f386d5d1f93"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# categorical data\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "fig = plt.figure(figsize =(20,12)) \n",
    "plt.subplot(2, 2, 1)\n",
    "ax = sns.countplot(dataset['Gender'],hue = dataset['Loan_Status'])\n",
    "for p in ax.patches:\n",
    "    height = p.get_height()\n",
    "    ax.text(x = p.get_x() + (p.get_width()/2), y = height + 3, s = '{:.0f}'.format(height), ha = 'center')\n",
    "\n",
    "plt.subplot(2, 2, 2)\n",
    "ax = sns.countplot(dataset['Married'],hue = dataset['Loan_Status'])\n",
    "for p in ax.patches:\n",
    "    height = p.get_height()\n",
    "    ax.text(x = p.get_x() + (p.get_width()/2), y = height + 3, s = '{:.0f}'.format(height), ha = 'center')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XkhQOVKEXDVP"
   },
   "source": [
    "Hypothesis : More Males have recieved approval for loan and number of married persons is more who got loan approval than unmarried people."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "4iAsFezuTdL5",
    "outputId": "05549c34-66ae-4c85-98b9-affdcc741cc2"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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L2eN2dY75cUTMjohZEfGl/NJLkqT1KZfxl4UkSZK0SX7wgx9w00038e677+YdRWtaBnwhpdQPqAKGRsR+wGhgckppd2By9pqI6AmMAHoBQ4FrI6JlHsElSdKGlcP4y0KSJEnaJNtvvz3HHnssN910U95RVEcqeCd72Tr7SMDhwNisfSxwRPb8cGB8SmlZSmkOMBsY2HCJJUlSscph/NUqt88sSZI+sWJuF1tKP/zhD7n66qtzzaB1ZTOKngJ2A65JKT0REZ1SSgsBUkoLI6Jj1r0r8Hidw2uztrXPOQoYBbDTTjuVMr4kSWWtuY+/LCRJkqSN8s4776x+3qlTJ5YuXZpjGtUnpbQSqIqIdsC9EdF7A92jvlPUc84bgBsABgwYsM77kiSpdMpp/OXSNkmSpCYqpfQG8AiFvY9ejYjOANnjoqxbLbBjncMqgQUNl1KSJDUmFpIkSZKakIjokM1EIiI+BXwReAGYCJyYdTsRmJA9nwiMiIiKiOgO7A5MbdDQkiSp0XBpmyRJOUopEVHfyqLmJyVXS20mnYGx2T5JLYC7Ukr3RcRjwF0RcQrwCnAMQEppZkTcBTwHrABOzZbGSZLUZDkGK9iU8ZeFJEmScrLlllvy2muvscMOOzT7gUxKiddee40tt9wy7yiNXkppOrB3Pe2vAQev55gxwJgSR5MkqSw4BivY1PGXhSRJknJSWVlJbW0tixcvzjtKWdhyyy2prKzMO4YkSWriHIN9ZFPGXxaSJEnKSevWrenevXveMSRJkpoVx2CfjJttS5IkSZIkqSgWkiRJkiRJklQUC0mSJEmSJEkqioUkSZIkSZIkFcVCkiRJkiRJkopiIUmSJEmSJElFsZAkSZIkSZKkolhIkiRJkiRJUlEsJEmSJEmSJKkoJSskRcSOEfFwRDwfETMj4rSsffuIeCAiXsoet6tzzI8jYnZEzIqIL5UqmyRJkiRJkjZeKWckrQB+mFLqAewHnBoRPYHRwOSU0u7A5Ow12XsjgF7AUODaiGhZwnySJEmSJEnaCCUrJKWUFqaUpmXP3waeB7oChwNjs25jgSOy54cD41NKy1JKc4DZwMBS5ZMkSZIkSdLGaZA9kiKiG7A38ATQKaW0EArFJqBj1q0rMK/OYbVZ29rnGhUR1RFRvXjx4pLmliRJkiRJ0kdKXkiKiLbA74EfpJTe2lDXetrSOg0p3ZBSGpBSGtChQ4fNFVOSJEmSJEkfo6SFpIhoTaGINC6ldE/W/GpEdM7e7wwsytprgR3rHF4JLChlPkmSJEmSJBWvlHdtC+Am4PmU0q/qvDURODF7fiIwoU77iIioiIjuwO7A1FLlkyRJkiRJ0sZpVcJzfw44Hng2Imqytv8HXAzcFRGnAK8AxwCklGZGxF3AcxTu+HZqSmllCfNJkiRJkiRpI5SskJRS+hv173sEcPB6jhkDjClVJkmSJEmSJG26BrlrmyRJkiRJkho/C0mSJEmSJEkqioUkSZIkSZIkFcVCkiRJkiRJkopiIUmSJEmSJElFsZAkSZIkSZKkolhIkiRJkiRJUlEsJEmSJEmSJKkoFpIkSZIkSZJUFAtJkiRJkiRJKoqFJEmSJEmSJBXFQpIkSZIkSZKKYiFJkiSpCYmIHSPi4Yh4PiJmRsRpWfv5ETE/Imqyjy/XOebHETE7ImZFxJfySy9Jkspdq7wDSJIkabNaAfwwpTQtIrYGnoqIB7L3Lk8pXVq3c0T0BEYAvYAuwIMRsUdKaWWDppYkSY2CM5IkSZKakJTSwpTStOz528DzQNcNHHI4MD6ltCylNAeYDQwsfVJJktQYWUiSJElqoiKiG7A38ETW9N2ImB4RN0fEdllbV2BencNqqafwFBGjIqI6IqoXL15cytiSJKmMWUiSJElqgiKiLfB74AcppbeA64BdgSpgIXDZqq71HJ7WaUjphpTSgJTSgA4dOpQmtCRJKnsWkiRJkpqYiGhNoYg0LqV0D0BK6dWU0sqU0ofAjXy0fK0W2LHO4ZXAgobMK0mSGg8LSZIkSU1IRARwE/B8SulXddo71+l2JDAjez4RGBERFRHRHdgdmNpQeSVJUuPiXdskSZKals8BxwPPRkRN1vb/gOMioorCsrW5wLcAUkozI+Iu4DkKd3w71Tu2SZKk9bGQJEmS1ISklP5G/fse/WkDx4wBxpQslCRJajJc2iZJkiRJkqSiWEiSJEmSJElSUSwkSZIkSZIkqSgWkiRJkiRJklQUC0mSJEmSJEkqioUkSZIkSZIkFcVCkiRJkiRJkopiIUmSJEmSJElFsZAkSZIkSZKkolhIkiRJkiRJUlEsJEmSJEmSJKkoFpIkSZIkSZJUFAtJkiRJkiRJKoqFJEmSJEmSJBXFQpIkSZIkSZKKYiFJkiRJkiRJRbGQJEmSJEmSpKJYSJIkSZIkSVJRLCRJkiRJkiSpKBaSJEmSJEmSVBQLSZIkSZIkSSqKhSRJkiRJkiQVxUKSJEmSJEmSimIhSZIkSZIkSUWxkCRJkiRJkqSiWEiSJEmSJElSUSwkSZIkSZIkqSglKyRFxM0RsSgiZtRpOz8i5kdETfbx5Trv/TgiZkfErIj4UqlySZIkSZIkadOUckbSb4Ch9bRfnlKqyj7+BBARPYERQK/smGsjomUJs0mSJEmSJGkjlayQlFKaAvy7yO6HA+NTSstSSnOA2cDAUmWTJEmSJEnSxstjj6TvRsT0bOnbdllbV2BenT61Wds6ImJURFRHRPXixYtLnVWSJEmSJEmZhi4kXQfsClQBC4HLsvaop2+q7wQppRtSSgNSSgM6dOhQkpCSJEmSJElaV4MWklJKr6aUVqaUPgRu5KPla7XAjnW6VgILGjKbJEmSJEmSNqxBC0kR0bnOyyOBVXd0mwiMiIiKiOgO7A5MbchskiRJTUFE7BgRD0fE8xExMyJOy9q3j4gHIuKl7HG7Osd491xJklSUVqU6cUTcAQwG2kdELXAeMDgiqigsW5sLfAsgpTQzIu4CngNWAKemlFaWKpskSVITtgL4YUppWkRsDTwVEQ8AJwGTU0oXR8RoYDTwo7XuntsFeDAi9nAsJkmS6lOyQlJK6bh6mm/aQP8xwJhS5ZEkSWoOUkoLKexFSUrp7Yh4nsJNTA6ncJEPYCzwCPAj6tw9F5gTEavunvtYwyaXJEmNQR53bZMkSVIDiIhuwN7AE0CnrMi0qtjUMetW1N1zvXOuJEkCC0mSJElNUkS0BX4P/CCl9NaGutbTts7dc71zriRJAgtJkiRJTU5EtKZQRBqXUrona3511Y1PssdFWbt3z5UkSUWzkCRJktSERERQ2Jfy+ZTSr+q8NRE4MXt+IjChTrt3z5UkSUUp2WbbkiRJysXngOOBZyOiJmv7f8DFwF0RcQrwCnAMePdcSZK0cSwkSZIkNSEppb9R/75HAAev5xjvnitJkori0jZJkiRJkiQVpahCUkRMLqZNkiRJm4fjL0mSVI42uLQtIrYE2gDtI2I7PpomvQ3QpcTZJEmSmh3HX5IkqZx93B5J3wJ+QGHQ8hQfDWTeAq4pXSxJkqRmy/GXJEkqWxssJKWUfg38OiK+l1K6qoEySZIkNVuOvyRJUjkr6q5tKaWrIuKzQLe6x6SUbi1RLkmSpGbN8ZckSSpHRRWSIuI2YFegBliZNSfAgYwkSVIJOP6SJEnlqKhCEjAA6JlSSqUMI0mSpNUcf0mSpLLTosh+M4BPlzKIJEmS1uD4S5IklZ1iZyS1B56LiKnAslWNKaXDSpJKkiRJjr8kSVLZKbaQdH4pQ0iSJGkd5+cdQJIkaW3F3rXtr6UOIkmSpI84/pIkSeWo2Lu2vU3hLiEAWwCtgXdTStuUKpgkSVJz5vhLkiSVo2JnJG1d93VEHAEMLEUgSZIkOf6SJEnlqdi7tq0hpfQH4AubN4okSZLWx/GXJEkqB8UubRte52ULYAAfTbWWJEnSZub4S5IklaNi79r21TrPVwBzgcM3expJkiSt4vhLkiSVnWL3SPqPUgeRJEnSRxx/SZKkclTUHkkRURkR90bEooh4NSJ+HxGVpQ4nSZLUXDn+kiRJ5ajYzbZvASYCXYCuwB+zNkmSJJWG4y9JklR2ii0kdUgp3ZJSWpF9/AboUMJcaiAnn3wyHTt2pHfv3qvbfve739GrVy9atGhBdXX16vapU6dSVVVFVVUV/fr14957780jsiRJzYXjL0mSVHaKLSQtiYhvRETL7OMbwGulDKaGcdJJJzFp0qQ12nr37s0999zDAQccsE57dXU1NTU1TJo0iW9961usWLGiIeNKktScOP6SJEllp9hC0snAscD/AQuBowE3gGwCDjjgALbffvs12nr06MGee+65Tt82bdrQqlVhf/b333+fiGiQjJIkNVOOvyRJUtkptpD0M+DElFKHlFJHCgOb80uWSmXriSeeoFevXvTp04frr79+dWFJkiRtdo6/JElS2Sm2kNQ3pfT6qhcppX8De5cmksrZoEGDmDlzJk8++SQXXXQR77//ft6RJElqqhx/SZKkslNsIalFRGy36kVEbA84FaUZ69GjB1tttRUzZszIO4okSU2V4y9JklR2ih2MXAb8IyLuBhKF9fpjSpZKZWnOnDnsuOOOtGrVin/961/MmjWLbt265R1LkqSmyvGXJEkqO0UVklJKt0ZENfAFIIDhKaXnSppMDeK4447jkUceYcmSJVRWVnLBBRew/fbb873vfY/Fixfzla98haqqKu6//37+9re/cfHFF9O6dWtatGjBtddeS/v27fP+EiRJapIcf0mSpHJU9PTobODi4KWJueOOO+ptP/LII9dpO/744zn++ONLHUmSJGUcf0mSpHJT7B5JkiRJkiRJauYsJEmSJEmSJKko3vmjEXvlwj55R9gsdjr32bwjSJIkSZKkIjgjSZIkqQmJiJsjYlFEzKjTdn5EzI+Imuzjy3Xe+3FEzI6IWRHxpXxSS5KkxsJCkiRJUtPyG2BoPe2Xp5Sqso8/AURET2AE0Cs75tqIaNlgSSVJUqNjIUmSJKkJSSlNAf5dZPfDgfEppWUppTnAbGBgycJJDeTkk0+mY8eO9O7de533Lr30UiKCJUuWrG676KKL2G233dhzzz25//77GzKqJDU6FpIkSZKah+9GxPRs6dt2WVtXYF6dPrVZ2zoiYlREVEdE9eLFi0udVfpETjrpJCZNmrRO+7x583jggQfYaaedVrc999xzjB8/npkzZzJp0iT+67/+i5UrVzZkXElqVCwkSZIkNX3XAbsCVcBC4LKsPerpm+o7QUrphpTSgJTSgA4dOpQkpLS5HHDAAWy//fbrtJ9++un88pe/JOKj//QnTJjAiBEjqKiooHv37uy2225MnTq1IeNKUqNiIUmSJKmJSym9mlJamVL6ELiRj5av1QI71ulaCSxo6HxSQ5g4cSJdu3alX79+a7TPnz+fHXf86NegsrKS+fPnN3Q8SWo0WuUdQJIkSaUVEZ1TSguzl0cCq+7oNhH4bUT8CugC7A44FUNNztKlSxkzZgx/+ctf1nkvpXUn4dWdsSRJWpOFJEmSpCYkIu4ABgPtI6IWOA8YHBFVFJatzQW+BZBSmhkRdwHPASuAU1NKbg6jJufll19mzpw5q2cj1dbW0r9/f6ZOnUplZSXz5n20VVhtbS1dunTJK6oklT0LSZIkSU1ISum4eppv2kD/McCY0iWS8tenTx8WLVq0+nW3bt2orq6mffv2HHbYYXz961/njDPOYMGCBbz00ksMHOjNCyVpfdwjSZIkSVKTctxxx/GZz3yGWbNmUVlZyU03rbeWSq9evTj22GPp2bMnQ4cO5ZprrqFly5YNmFaSGpeSzUiKiJuBQ4FFKaXeWdv2wJ1ANwrTqo9NKb2evfdj4BRgJfD9lNL9pcomSZIkqem64447Nvj+3Llz13h9zjnncM4555QwkSQ1HaWckfQbYOhabaOBySml3YHJ2WsioicwAuiVHXNtRHgZQJIkSZIkqYyUbEZSSmlKRHRbq/lwCps/AowFHgF+lLWPTyktA+ZExGwKt6V9rFT5JEmSJG2cVy7sk3cErWWnc5/NO4KkZqah90jqtOrWs9ljx6y9KzCvTr/arG0dETEqIqojonrx4sUlDStJkiRJkqSPlMtm21FPW6qvY0rphpTSgJTSgA4dOpQ4liRJkiRJklZp6ELSqxHRGSB7XHUPzlpgxzr9KoEFDZxNkiRJkiRJG9DQhaSJwInZ8xOBCXXaR0RERUR0B3YHpjZwNkmSJEmSJG1AyTbbjog7KGys3T4iaoHzgIuBuyLiFOAV4BiAlNLMiLgLeA5YAZyaUlpZqmySJEmSJEnaeKW8a9tx63nr4PX0HwOMKVUeSZIkSZIkfTLlstm2JEmSJEmSypyFJEmSJEmSJBXFQpIkSZIkSZKKYiFJkiRJkiRJRbGQJEmSJEmSpKJYSJIkSZIkSVJRLCRJkiRJkiSpKBaSJEmSJEmSVBQLSZIkSZIkSSqKhSRJkiRJkiQVxUKSJEmSJEmSimIhSZI2wqxZs6iqqlr9sc0223DFFVcAcNVVV7HnnnvSq1cvzj777HyDSpIkSVIJtMo7gCQ1JnvuuSc1NTUArFy5kq5du3LkkUfy8MMPM2HCBKZPn05FRQWLFi3KN6gkSZIklYAzkiRpE02ePJldd92VnXfemeuuu47Ro0dTUVEBQMeOHXNOJ0mSJEmbn4UkSdpE48eP57jjjgPgxRdf5NFHH2XQoEEceOCBPPnkkzmnkyRJkqTNz0KSJG2CDz74gIkTJ3LMMccAsGLFCl5//XUef/xxLrnkEo499lhSSjmnlCRJkqTNy0KSJG2CP//5z/Tv359OnToBUFlZyfDhw4kIBg4cSIsWLViyZEnOKSVJkiRp87KQJEmb4I477li9rA3giCOO4KGHHgIKy9w++OAD2rdvn1c8SZIkSSoJC0mStJGWLl3KAw88wPDhw1e3nXzyyfzzn/+kd+/ejBgxgrFjxxIROaaUJEmSpM2vVd4BJKmxadOmDa+99toabVtssQW33357TokkSZIkqWE4I0mSJEmSJElFsZAkSZIkSZKkori0TVKTt89Zt+YdYbN46pIT8o4gSZIkqZlzRpL0CbzxxhscffTR7LXXXvTo0YPHHnsMgKuuuoo999yTXr16cfbZZ+ecUpLUnETEzRGxKCJm1GnbPiIeiIiXssft6rz344iYHRGzIuJL+aSWJEmNhTOSpE/gtNNOY+jQodx999188MEHLF26lIcffpgJEyYwffp0KioqWLRoUd4xJUnNy2+Aq4G60zFHA5NTShdHxOjs9Y8ioicwAugFdAEejIg9UkorGzizJElqJJyRJG2it956iylTpnDKKacAhbt2tWvXjuuuu47Ro0dTUVEBQMeOHfOMKUlqZlJKU4B/r9V8ODA2ez4WOKJO+/iU0rKU0hxgNjCwIXJKkqTGyUKStIn++c9/0qFDB/7jP/6Dvffem//8z//k3Xff5cUXX+TRRx9l0KBBHHjggTz55JN5R5UkqVNKaSFA9rjqKkdXYF6dfrVZ2zoiYlREVEdE9eLFi0saVpIklS8LSdImWrFiBdOmTeM73/kOTz/9NFtttRUXX3wxK1as4PXXX+fxxx/nkksu4dhjjyWllHdcSZLqE/W01fs/rZTSDSmlASmlAR06dChxLEmSVK4sJEmbqLKyksrKSgYNGgTA0UcfzbRp06isrGT48OFEBAMHDqRFixYsWbIk57SSpGbu1YjoDJA9rtrArxbYsU6/SmBBA2eTJEmNiIUkaRN9+tOfZscdd2TWrFkATJ48mZ49e3LEEUfw0EMPAfDiiy/ywQcf0L59+zyjSpI0ETgxe34iMKFO+4iIqIiI7sDuwNQc8kmSpEbCu7ZJn8BVV13FyJEj+eCDD9hll1245ZZb2GqrrTj55JPp3bs3W2yxBWPHjiWivpUDkiRtfhFxBzAYaB8RtcB5wMXAXRFxCvAKcAxASmlmRNwFPAesAE71jm2SJGlDLCRJn0BVVRXV1dXrtN9+++05pJEkCVJKx63nrYPX038MMKZ0iSSpabv88sv5n//5HyKCPn36cMstt/DTn/6UP/7xj2yxxRbsuuuu3HLLLbRr1y7vqNJm4dI2SZIkSZI2wfz587nyyiuprq5mxowZrFy5kvHjxzNkyBBmzJjB9OnT2WOPPbjooovyjiptNs5IUrOxz1m35h1hs3nqkhPyjiBJkiSJwt2c33vvPVq3bs3SpUvp0qULhxxyyOr399tvP+6+++4cE0qblzOSJEmSJEnaBF27duXMM89kp512onPnzmy77bZrFJEAbr75ZoYNG5ZTQmnzs5AkSZIkSdImeP3115kwYQJz5sxhwYIFvPvuu2vslzpmzBhatWrFyJEjc0wpbV4WkiRJkiRJ2gQPPvgg3bt3p0OHDrRu3Zrhw4fzj3/8A4CxY8dy3333MW7cOO/irCbFQpIkSZIkSZtgp5124vHHH2fp0qWklJg8eTI9evRg0qRJ/OIXv2DixIm0adMm75jSZuVm25IkSZIkbYJBgwZx9NFH079/f1q1asXee+/NqFGj6NWrF8uWLWPIkCFAYcPt66+/Pue00uZhIUmSJEmSpE10wQUXcMEFF6zRNnv27JzSSKXn0jZJkiRJkiQVxRlJkiRJkqTc7XPWrXlHUD2euuSEvCOozDgjSZIkSZIkSUWxkCRJkiRJkqSiWEiSJEmSJElSUSwkSZIkSZIkqSgWkiRJkiRJklQUC0mSJEmSJEkqioUkSZIkSZIkFaVVHp80IuYCbwMrgRUppQERsT1wJ9ANmAscm1J6PY98kiRJkiRJWleeM5IOSilVpZQGZK9HA5NTSrsDk7PXkiRJkiRJKhPltLTtcGBs9nwscER+USRJkiRJkrS2vApJCfhLRDwVEaOytk4ppYUA2WPH+g6MiFERUR0R1YsXL26guJIkSZIkScpljyTgcymlBRHREXggIl4o9sCU0g3ADQADBgxIpQooSZIkSZKkNeUyIymltCB7XATcCwwEXo2IzgDZ46I8skmSJEmSJKl+DV5IioitImLrVc+BQ4AZwETgxKzbicCEhs4mSZIkSZKk9ctjaVsn4N6IWPX5f5tSmhQRTwJ3RcQpwCvAMTlkkyRJkiRJjVy3bt3YeuutadmyJa1ataK6upqzzjqLP/7xj2yxxRbsuuuu3HLLLbRr1y7vqI1Og89ISin9M6XUL/volVIak7W/llI6OKW0e/b474bOJjVH77//PgMHDqRfv3706tWL8847b/V7V111FXvuuSe9evXi7LPPzjGlJEmSJG2chx9+mJqaGqqrqwEYMmQIM2bMYPr06eyxxx5cdNFFOSdsnPLabFtSmaioqOChhx6ibdu2LF++nP33359hw4bx3nvvMWHCBKZPn05FRQWLFrltmSRJkqTG65BDDln9fL/99uPuu+/OMU3jlctm25LKR0TQtm1bAJYvX87y5cuJCK677jpGjx5NRUUFAB07dswzpiRJkiQVLSI45JBD2GeffbjhhhvWef/mm29m2LBhOSRr/CwkSWLlypVUVVXRsWNHhgwZwqBBg3jxxRd59NFHGTRoEAceeCBPPvlk3jElSZIkqSh///vfmTZtGn/+85+55pprmDJlyur3xowZQ6tWrRg5cmSOCRsvC0mSaNmyJTU1NdTW1jJ16lRmzJjBihUreP3113n88ce55JJLOPbYY0kp5R1Vys369hP76U9/St++famqquKQQw5hwYIFOSeVJElSly5dgMLKiiOPPJKpU6cCMHbsWO677z7GjRtHdhMwbSQLSZJWa9euHYMHD2bSpElUVlYyfPhwIoKBAwfSokULlixZkndEKTer9hN75plnqKmpYdKkSTz++OOcddZZTJ8+nZqaGg499FAuvPDCvKNKkiQ1a++++y5vv/326ud/+ctf6N27N5MmTeIXv/gFEydOpE2bNjmnbLzcbFtq5hYvXkzr1q1p164d7733Hg8++CA/+tGPaNu2LQ899BCDBw/mxRdf5IMPPqB9+/Z5x5Vys779xLbZZpvVfd59912vbEmSJOXs1Vdf5cgjjwRgxYoVfP3rX2fo0KHstttuLFu2jCFDhgCFDbevv/76PKM2ShaSpGZu4cKFnHjiiaxcuZIPP/yQY489lkMPPZQPPviAk08+md69e7PFFlswduxY/4GsZm/lypXss88+zJ49m1NPPZVBgwYBcM4553Drrbey7bbb8vDDD+ecUlq/iJgLvA2sBFaklAZExPbAnUA3YC5wbErp9bwySpL0Se2yyy4888wz67TPnj07hzRNj4UkqZnr27cvTz/99DrtW2yxBbfffnsOiaTytWo/sTfeeIMjjzySGTNm0Lt3b8aMGcOYMWO46KKLuPrqq7ngggvyjiptyEEppbprlUcDk1NKF0fE6Oz1j/KJJkmSyp2FJEmSNlLd/cR69+69uv3rX/86X/nKVywkqbE5HBicPR8LPIKFJElS5pUL++QdQWvZ6dxnc/38brYtSVIRFi9ezBtvvAGwej+xvfbai5deeml1n4kTJ7LXXnvllFAqSgL+EhFPRcSorK1TSmkhQPbYsb4DI2JURFRHRPXixYsbKK4kSSo3zkiSGqGmclUg70q6tDHWt5/YUUcdxaxZs2jRogU777yzGzaq3H0upbQgIjoCD0TEC8UemFK6AbgBYMCAAalUASVJUnmzkCRJUhHWt5/Y73//+xzSSJsmpbQge1wUEfcCA4FXI6JzSmlhRHQGFuUaUpIklTWXtklSMzVv3jwOOuggevToQa9evfj1r38NwPnnn0/Xrl2pqqqiqqqKP/3pTzknlbQ5RMRWEbH1qufAIcAMYCJwYtbtRGBCPgklSVJj4IwkSWqmWrVqxWWXXUb//v15++232WeffRgyZAgAp59+OmeeeWbOCT+Zfc66Ne8Im81Tl5yQdwQ1DZ2AeyMCCmPA36aUJkXEk8BdEXEK8ApwTI4ZJUlSmbOQJEnNVOfOnencuTMAW2+9NT169GD+/Pk5p5JUKimlfwL96ml/DTi44RNJkqTGyKVtkiTmzp3L008/zaBBgwC4+uqr6du3LyeffDKvv/56zukkSZIklQsLSZLUzL3zzjscddRRXHHFFWyzzTZ85zvf4eWXX6ampobOnTvzwx/+MO+IkiRJksqEhSRJasaWL1/OUUcdxciRIxk+fDgAnTp1omXLlrRo0YJvfvObTJ06NeeUkiRJksqFhSRJaqZSSpxyyin06NGDM844Y3X7woULVz+/99576d27dx7xJEmSJJUhN9uWpGbq73//O7fddht9+vShqqoKgJ///Ofccccd1NTUEBF069aN//7v/843qCRJkqSyYSFJkpqp/fffn5TSOu1f/vKXc0ijhnTyySdz33330bFjR2bMmAFATU0N3/72t3n//fdp1aoV1157LQMHDsw5qSRJksqNS9skSWpmTjrpJCZNmrRG29lnn815551HTU0NF154IWeffXZO6SRJklTOnJEkSY3EKxf2yTvCZrPTuc/mHaFZO+CAA5g7d+4abRHBW2+9BcCbb75Jly5dckgmSZKkcmchSZIkccUVV/ClL32JM888kw8//JB//OMfeUeSJElSGXJpmyRJ4rrrruPyyy9n3rx5XH755Zxyyil5R5IkSVIZspAkSZIYO3Ysw4cPB+CYY45h6tSpOSeSJElSObKQJEmS6NKlC3/9618BeOihh9h9991zTiRJkqRy5B5JkiQ1M8cddxyPPPIIS5YsobKykgsuuIAbb7yR0047jRUrVrDllltyww035B1TkiRJZchCkiRJzcwdd9xRb/tTTz3VwEkkSZLU2Li0TZIkSZIkSUVxRpIkSWXulQv75B1hs9np3GfzjiBJkqRPwBlJkiRJkiRJKoqFJEmSJEmSJBXFQpIkSZIkSZKKYiFJkiRJkiRJRbGQJEmSJEmSpKJYSJIkSZIkSVJRLCRJkiRJkiSpKBaSJEmSJEmSVBQLSZIkSZIkSSqKhSRJkiRJkiQVxUKSJEmSJEmSimIhSZIkSZIkSUWxkCRJkiRJkqSiWEiSJEmSJElSUSwkSZIkSZIkqSgWkiRJkiRJklQUC0mSJEmSJEkqStkVkiJiaETMiojZETE67zySJElNneMvSZJUrLIqJEVES+AaYBjQEzguInrmm0qSJKnpcvwlSZI2RlkVkoCBwOyU0j9TSh8A44HDc84kSZLUlDn+kiRJRYuUUt4ZVouIo4GhKaX/zF4fDwxKKX23Tp9RwKjs5Z7ArAYPWj7aA0vyDqFc+LNvnvy5N1/N/We/c0qpQ94hmqpixl9Zu2MwgX+PpObI3/vmab3jr1YNneRjRD1ta1S6Uko3ADc0TJzyFhHVKaUBeedQw/Nn3zz5c2++/NmrxD52/AWOwVTg3yOp+fH3Xmsrt6VttcCOdV5XAgtyyiJJktQcOP6SJElFK7dC0pPA7hHRPSK2AEYAE3POJEmS1JQ5/pIkSUUrq6VtKaUVEfFd4H6gJXBzSmlmzrHKmdPLmy9/9s2TP/fmy5+9SsbxlzaSf4+k5sffe62hrDbbliRJkiRJUvkqt6VtkiRJkiRJKlMWkiRJkiRJklQUC0mNUEQMjYhZETE7IkbnnUcNIyJujohFETEj7yxqWBGxY0Q8HBHPR8TMiDgt70wqjYjYMiKmRsQz2c/6grwzSWqcIqJTRPw2Iv4ZEU9FxGMRceQnON/5EXHmJh7bLSK+vqmfW2rOIiJFxGV1Xp8ZEed/zDFHRETPDbz/jYiYno01nomI/4mIdp8w5zuf4NiTIqLLJ/n8algWkhqZiGgJXAMMA3oCx23oj4SalN8AQ/MOoVysAH6YUuoB7Aec6u99k7UM+EJKqR9QBQyNiP3qdoiIuTnkktSIREQAfwCmpJR2SSntQ+FufJVr9WuoG+90AywkSZtmGTA8ItpvxDFHUPi34joiYihwOjAspdQL6A/8A+hUT9+WG51205wEWEhqRCwkNT4DgdkppX+mlD4AxgOH55xJDSClNAX4d9451PBSSgtTStOy528DzwNd802lUkgFq67otc4+vCuGpI31BeCDlNL1qxpSSv9KKV2VXfn/XUT8EfhLRLSNiMkRMS0ino2I1ePKiDgnmwX/ILBnnfZHImJA9rz9qgJ3NvPo0exc0yLis9khFwOfj4iaiDg9IlpGxCUR8WQ2K+Jbpf+WSI3WCgp3TTt97TciYufs93d69rhT9nt3GHBJ9ju361qHnQOcmVKaD5BSWplSujmlNCs759yIODci/gYcExHfzH5Xn4mI30dEm6xf92ym45MR8bM6mQZHxH11Xl8dESdlz8/N+s+IiBui4GhgADAuy/upiNgnIv6azaa8PyI6b75vpzYHC0mNT1dgXp3XtfgPSqnZiIhuwN7AEzlHUYlk/8CqARYBD6SU/FlL2li9gGkbeP8zwIkppS8A7wNHppT6AwcBl2X/uFs1i2lvYDiwbxGfdxEwJDvX14Ars/bRwKMppaqU0uXAKcCbKaV9s/N+MyK6b/RXKTUf1wAjI2LbtdqvBm5NKfUFxgFXppT+AUwEzsp+515e65iP+/sA8H5Kaf+U0njgnpTSvtls6ecp/P4C/Bq4Lvs9/r8iv46rs3P1Bj4FHJpSuhuoBkamlKooFM6uAo7OZlPeDIwp8vxqIBaSGp+op82r1VIzEBFtgd8DP0gpvZV3HpVGdmWwisISlIER0TubFVCTFZi6rHoeEdfkGlZSoxAR12SzCZ7Mmh5IKa2a5RzAzyNiOvAghQuUnYDPA/emlJZm/8+ZWMSnag3cGBHPAr9jPUtrgEOAE7K/aU8AOwC7b8KXJjUL2e/grcD313rrM8Bvs+e3AftvzHkjok82nng5Ir5W56076zzvnc00fBYYSaEQBfA54I46n7sYB0XEE9m5vlDnXHXtCfQGHsj+RvyEtZblKn8NtS5am08tsGOd15XAgpyySGogEdGaQhFpXErpnrzzqPRSSm9ExCPA0JTSGLKrcRExNys0SdL6zASOWvUipXRqtr9Kddb0bp2+I4EOwD4ppeXZMrUtVx26nvOv4KML0lvWaT8deBXol73//nqOD+B7KaX7i/pqJAFcQWEm0S0b6FPMBIOZFPZFejil9CxQFRFXU5ghtErdvxG/AY5IKT2TLVEb/DGfr+7fB8j+RkTElsC1wICU0rwobBi+5bqHE8DMlNJnivhalBNnJDU+TwK7Z2tSt6Aw5biYK0SSGqls09SbgOdTSr/KO49KJyI6RHbXlIj4FPBF4IVcQ0lqjB4CtoyI79Rpa7OevtsCi7Ii0kHAzln7FODIbL+SrYGv1jlmLrBP9vzotc61MKX0IXA8sGqj3reBrev0ux/4TnaRhIjYIyK22pgvUGpuslmEd/HR0jIobJI9Ins+Evhb9nzt37m6LgIujYi6s3w+tZ6+ZOdZmP2+jqzT/ve1Pvcq/wJ6RkRFthTv4Kx9VdFoSTbLvu7fjrp5ZwEdIuIzULiYGhH1zVxSjiwkNTIppRXAdyn8D/h54K6U0sx8U6khRMQdwGPAnhFRGxGnfNwxajI+R2FA/oU6S5q+nHcolURn4OFsicmTFJaf3Pcxx0jSGlJKicJdmw6MiDkRMRUYC/yonu7jgAERUU3hH4MvZOeYRmF5Sw2FGbGP1jnmUgqFoH8Ade8kdS1wYkQ8DuzBR7MapgMrsuV1pwP/AzwHTIuIGcB/40oJqRiXsebv3PeB/8jGDccDp2Xt44GzIuLptTfbTin9icL+ZX+OiOey3+OVFP59WZ+fUliC+gBrXtw6jcKdhJ+kUERedf55FApe0yn8fXk6a38DuBF4lsJdJZ+sc67fANdnS9laUigy/SIinqHwN+izqKxE4f8zkiRJkiRJ0oY5I0mSJEmSJElFsZAkSZIkSZKkolhIkiRJkiRJUlEsJEmSJEmSJKkoFpIkSZIkSZJUFAtJkjZZRKzMbkU/M7ul7xkRkdvflYiYGxHtP75nvcceERE9N3cmSZKkhlBnXLbqY3Q9fQZHxH2b+fMOjojP1nn97Yg4YXN+DknlpVXeASQ1au+llKoAIqIj8FtgW+C8PENtoiOA+4Dncs4hSZK0KVaPyxrYYOAd4B8AKaXrc8ggqQE5I0nSZpFSWgSMAr4bBS0j4pKIeDIipkfEt2D1VaspEXFvRDwXEdevmsUUEYdExGMRMS0ifhcRbbP2uRFxQdb+bETslbXvEBF/iYinI+K/gViVJyK+ERFTsyty/x0RLbP2dyJiTDaD6vGI6JRdRTsMuCTrv2tEfD/LNz0ixjfoN1OSJGkziYihEfFCRPwNGF6n/fyIOLPO6xkR0S17fkI2BnomIm7L2r4aEU9k464HszFUN+DbwOnZGOrzdc8bEVXZeGt6NvbbLmt/JCJ+kY3VXoyIzzfYN0TSJ2YhSdJmk1L6J4W/Kx2BU4A3U0r7AvsC34yI7lnXgcAPgT7ArsDwbEnaT4AvppT6A9XAGXVOvyRrvw5YNeg5D/hbSmlvYCKwE0BE9AC+BnwuuzK3EhiZHbMV8HhKqR8wBfhmSukf2fFnpZSqUkovA6OBvVNKfSkMkCRJksrZp9Za2va1iNgSuBH4KvB54NMfd5KI6AWcA3whGy+dlr31N2C/bNw1Hjg7pTQXuB64PBtDPbrW6W4FfpSNp55lzVnrrVJKA4Ef0Dhns0vNlkvbJG1uq2YFHQL0jYijs9fbArsDHwBTs6ITEXEHsD/wPtAT+HtEAGwBPFbnvPdkj0/x0dW0A1Y9Tyn9b0S8nrUfDOwDPJmd61PAouy9DygsYVt1riHr+TqmA+Mi4g/AH4r6yiVJkvKzztK2iKgC5qSUXspe305hBvmGfAG4O6W0BCCl9O+svRK4MyI6UxinzdnQSSJiW6BdSumvWdNY4Hd1utQd23X7mEySyoiFJEmbTUTsQmH2zyIKBaXvpZTuX6vPYCCtdWjK+j+QUjpuPadflj2uZM2/XWufi+xcY1NKP67nveUppVXHrH2uur5CoVB1GPDTiOiVUlqxnr6SJEnlqr6xEsAK1lyhsmX2GOs55irgVymlidl47vxPmGt9YztJZc6lbZI2i4joQGFq89VZoeZ+4DsR0Tp7f4+I2CrrPjAiumd7I32NwlTpx4HPRcRuWf82EbHHx3zaKWRL1iJiGLBd1j4ZODrbAJyI2D4idv6Yc70NbJ31bwHsmFJ6GDgbaAe0LeLbIEmSVE5eALpHxK7Z67oX7OYC/QEioj+waguCycCxEbFD9t72Wfu2wPzs+Yl1zrN6DFVXSulN4PU6+x8dD/x17X6SGh8rv5I+iU9FRA3QmsJVrduAX2Xv/Q+FacrTorC+bDGFO6NBYcnaxRT2SJoC3JtS+jAiTgLuiIiKrN9PgBc38PkvyPpPozAweQUgpfRcRPwE+EtWFFoOnAr8awPnGg/cGBHfB0YAN2VTsoPCuv83Pu6bIUmSlKNV47JVJqWURkfEKOB/I2IJhYt3vbP3fw+ckB3zJNmYK6U0MyLGAH+NiJXA08BJFGYg/S4i5lO4ALiq8PRH4O6IOBz43lqZTgSuj4g2wD+B/9h8X66kvMRHKzwkqfSyqdBnppQOzTmKJEmSJGkjubRNkiRJkiRJRXFGkiRJkiRJkorijCRJkiRJkiQVxUKSJEmSJEmSimIhSZIkSZIkSUWxkCRJkiRJkqSiWEiSJEmSJElSUf4/NmEPrXMAHKYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# categorical data\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "fig = plt.figure(figsize =(20,12)) \n",
    "plt.subplot(2, 2, 1)\n",
    "ax = sns.countplot(dataset['Dependents'],hue = dataset['Loan_Status'])\n",
    "for p in ax.patches:\n",
    "    height = p.get_height()\n",
    "    ax.text(x = p.get_x() + (p.get_width()/2), y = height + 3, s = '{:.0f}'.format(height), ha = 'center')\n",
    "plt.subplot(2, 2, 2)\n",
    "ax = sns.countplot(dataset['Education'],hue = dataset['Loan_Status'])\n",
    "for p in ax.patches:\n",
    "    height = p.get_height()\n",
    "    ax.text(x = p.get_x() + (p.get_width()/2), y = height + 3, s = '{:.0f}'.format(height), ha = 'center')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "60SNjO99XcVG"
   },
   "source": [
    "Hypothesis : when no dependent is there, loan approval is more and graduate people have got more loan approval than non-graduate people"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "F2lFflT_ThJ3",
    "outputId": "8f06b93e-1363-4a2d-af6b-0c456102c6c2"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# categorical data\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "fig = plt.figure(figsize =(20,12)) \n",
    "plt.subplot(2, 2, 1)\n",
    "ax = sns.countplot(dataset['Self_Employed'],hue = dataset['Loan_Status'])\n",
    "for p in ax.patches:\n",
    "    height = p.get_height()\n",
    "    ax.text(x = p.get_x() + (p.get_width()/2), y = height + 3, s = '{:.0f}'.format(height), ha = 'center')\n",
    "\n",
    "plt.subplot(2, 2, 2)\n",
    "ax = sns.countplot(dataset['Property_Area'],hue = dataset['Loan_Status'])\n",
    "for p in ax.patches:\n",
    "    height = p.get_height()\n",
    "    ax.text(x = p.get_x() + (p.get_width()/2), y = height + 3, s = '{:.0f}'.format(height), ha = 'center')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cwiASnj6XxoY"
   },
   "source": [
    "Hypothesis : number of Self-employed people taking loan is less. Semiurban area has more number of loan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "wTs0h4DDTmk6",
    "outputId": "92cfd62b-8a24-48e5-c9be-ca6aec3ddbde"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bt0qSJEmSJOlEmaCa41JKHO7uZ1HD1BJUC7PtDnYPFDMsSZIkSZI0h5igmuN6+ocYHE5Tmn8KoKmuiqqKsAeVJEmSJEmaNiao5ri23kJPqKkmqCoiWNBQw6FuE1SSJEmSJGl6mKCa49p7BgGYV1c15TqLGqvtQSVJkiRJkqbNlBJUEfGPEfHmiDChVWbae46tBxUU5qE6aA8qSZJyY9tMkiSVm6k2aj4HvA94MiKujYgzixiTSqitd4AAmo+hB9XChhp6B4Zp63GidEmScmLbTJIklZUpJahSSj9IKb0fuADYDtwWEXdGxIciYupdbzTjtPcM0FhbRVXF1G/ALmwsPMlv58HuYoUlSZImYNtMkiSVmylnJSJiMfBB4P8C7gf+ikKj6LaiRKaSaO8dYF791HtPASxqKCSodh0yQSVJUl5sm0mSpHIypcxERHwTOBP4MvCrKaWWbNXXIuLeYgWn4mvvGWRBw7HdaF3YWNh+58GeYoQkSZImYdtMkiSVm6l2nfliSumW0QURUZtS6kspbSpCXCqR9t4BTlrccEx16qsrqa2qYKc9qCRJyottM0mSVFamOsTvT8cp+9l0BqLSGxgaprt/iHl1x9aDKiJY1FjjHFSSJOXHtpkkSSorE/agiogVwGqgPiJeCkS2ah5wbN1uNON09A4CMP8Y56CCwpP8dh5yiJ8kSaVk20ySJJWryTITv0Jh8s01wF+OKu8A/qBIMalE2noGAI65BxXAwoZq7ttxiJQSETF5BUmSNB1sm0mSpLI0YYIqpXQ9cH1EvD2l9I8likkl0t6bJajqjyNB1VhD78AwrZ39LG2une7QJEnSOGybSZKkcjXZEL9fTyl9BVgXEb83dn1K6S/HqaZZov0EelAtaqgBYOehbhNUkiSViG0zSZJUriabJL0xe28Cmsd5aRZr7xmgujKoq57qXPnPW9iYJaicKF2SpFI6obZZRFwWEVsjYltEXDPO+oiIz2TrH4qICyarGxGLIuK2iHgye1+YlV8UEQ9krwcj4tdO7NQlSVI5m2yI3//O3v+4NOGolNp7B5lXV31cc0gtzHpQ7XKidEmSSuZE2mYRUQl8FrgU2AXcExE3p5QeHbXZ5cCG7HUx8Dng4knqXgPcnlK6NktcXQP8PvAIsCmlNBgRK4EHI+KfUkqDx3XykiSprE2p60xE/FlEzIuI6oi4PSJaI+LXix2ciqu9Z+C45p8CqKmqYHFjDc8dNkElSVKpHWfb7CJgW0rp6ZRSP3AjsHnMNpuBG1LBXcCCLLk0Ud3NwPXZ8vXA2wBSSt2jklF1QDr+M5YkSeVuqmO7fjml1A68hcJds9OB/7toUakk2nsHmH+cCSqAVQvq2W2CSpKkPBxP22w1sHPU511Z2VS2maju8pRSC0D2vmxko4i4OCK2AA8DV4/XeyoiroqIeyPi3v37909yCpIkqVxNNUE1ksV4E/APKaWDRYpHJZJSyob4TTjKc0KrFtTxnEP8JEnKw/G0zcYb0z+2V9PRtplK3RdvkNLdKaWzgZcBn4iIunG2+XxKaVNKadPSpUsn26UkSSpTU01Q/VNEPA5sAm6PiKVAb/HCUrEd7OpnaDgd9xA/eL4HVUr22JckqcSOp222C1g76vMaYPcUt5mo7t5sGCDZ+76xB04pPQZ0AedMEqMkSZqjppSgSildA7ycwkSXAxQaGGPnLNAssqe90IadV3f8CarVC+rp6h+ivce5TiVJKqXjbJvdA2yIiPURUQO8B7h5zDY3A1dkT/O7BGjLhu1NVPdm4Mps+UrgOwDZtlXZ8snAGcD24z1nSZJU3o5lfNdZwLqRhkbmhmmORyWyr6MPgOYTGuJXD8Bzh3uY33D8iS5JknRcjqltlj1N72PArUAl8KWU0paIuDpbfx1wC4Vhg9uAbuBDE9XNdn0tcFNEfBjYAbwzK38VcE1EDADDwEdSSq3TcN6SJKkMTSk7ERFfBk4FHgCGsuKECapZq/VIgurEhvgB7D7cw8ZV86YlLkmSNLnjbZullG6hkIQaXXbdqOUEfHSqdbPyA8Abxyn/MvDlieKRJEkaMdXuM5uAjcnJhspGa2c/AE21JzZJOsDuNidKlySpxGybSZKksjLVSdIfAVYUMxCVVmtnHzWVFdRUTfWfwIstaaylpqqC5w6boJIkqcRsm0mSpLIy1e4zS4BHI+LnQN9IYUrprUWJSkXX2tlH0wnMPwVQURGsml/Hc4dMUEmSVGK2zSRJUlmZaobik8UMQqXX2tl3QsP7RqxaUM9ue1BJklRqn8w7AEmSpOk0pQxFSulH2eOBN6SUfhARDRSe4KJZqrWjf9oSVD950gfySJJUSrbNJElSuZnSBEQR8e+BbwD/OytaDXy7SDGpBKazB9Xejl4GhoanISpJkjQVts0kSVK5meoM2R8FXgm0A6SUngSWFSsoFdfg0DAHu/tPeA4qgNUL6kgJ9rT1TkNkkiRpimybSZKksjLVBFVfSql/5ENEVAE+1niWOtjdT0pMWw8qwHmoJEkqLdtmkiSprEw1QfWjiPgDoD4iLgW+DvxT8cJSMbV2FNqz05qgajNBJUlSCdk2kyRJZWWqCaprgP3Aw8B/AG4B/muxglJxtXYWnkY9HQmq1VmC6rlDJqgkSSoh22aSJKmsTPUpfsMR8W3g2yml/cUNScV2JEE1DXNQ1VVXsrixhucOOweVJEmlYttMkiSVmwl7UEXBJyOiFXgc2BoR+yPij0oTnophOntQQWGYn3NQSZJUfLbNJElSuZpsiN/vUHhCzMtSSotTSouAi4FXRsTvFjs4FUdrZz+1VRXUVk11hOfEVi2oM0ElSVJp/A62zSRJUhmaLENxBfDelNIzIwUppaeBX8/WaRba39HHkqZaImJa9jfSgyolHx4kSVKR2TaTJEllabIEVXVKqXVsYTbXQXVxQlKxtXb2saS5dtr2t3pBPV39Q7T3DE7bPiVJ0rhsm0mSpLI0WYKq/zjXaQbb39HH0qaaadvfqpEn+TnMT5KkYrNtJkmSytJkCarzIqJ9nFcH8JLJdh4Rl0XE1ojYFhHXjLM+IuIz2fqHIuKCyepGxKKIuC0inszeF2bliyPihxHRGRF/PeY4F0bEw9m+PhPTNbZtlmrt7GdJ0/T1oDJBJUlSyZxQ20zFdfODu/nyXc9ysMtcoSRJx2rCBFVKqTKlNG+cV3NKacJu5BFRCXwWuBzYCLw3IjaO2exyYEP2ugr43BTqXgPcnlLaANyefQboBf4Q+Pg44Xwu2//IsS6bKPZyNjScONjVN80JqjoAJ0qXJKnITqRtpuJ6prWLu58+wGMt7fzV7U/ws6cP5B2SJEmzyvQ8xm18FwHbUkpPp5T6gRuBzWO22QzckAruAhZExMpJ6m4Grs+WrwfeBpBS6kop/YRCouqIbH/zUko/S4VZvG8YqTMXHeruZzjBkmkc4reksZaaqgoTVJIkac66/s7tVETwkdedyrrFjfzTg7vZ2947eUVJkgQUN0G1Gtg56vOurGwq20xUd3lKqQUge182hTh2TRIHABFxVUTcGxH37t+/f5Ldzk6tnX0ALG2um7Z9VlQEq+bXOcRPkiTNSe29A3z93p2cu2Y+axY28I4L11ARcP+Ow3mHJknSrFHMBNV48zylKW4zlbrTGUehMKXPp5Q2pZQ2LV269DgPN7O1dhTmRJjOHlRQmIfKHlSSJGkuuumenXT1D/GK05YA0FxXzenLm3lg5yGG0/E2YSVJmluKmaDaBawd9XkNsHuK20xUd282bG9k+N6+KcSxZpI45oyRHlRLmqdvDioYSVDZjV2SJM0tQ8OJv7tzOxetW8Tq7MExAC89aSHtvYNs29eZY3SSJM0exUxQ3QNsiIj1EVEDvAe4ecw2NwNXZE/zuwRoy4btTVT3ZuDKbPlK4DsTBZHtryMiLsme3nfFZHXK2ZEE1TROkg6FBNXejl4Ghoandb+SJEkz2bZ9new61MO7Xrb2BeVnrWimvrqSX+w4lFNkkiTNLkVLUKWUBoGPAbcCjwE3pZS2RMTVEXF1ttktwNPANuALwEcmqpvVuRa4NCKeBC7NPgMQEduBvwQ+GBG7Rj357zeBL2bHeQr4XlFOehbY39lHTWUF8+qqpnW/qxfUkRLsabMXlSRJmjsebWkD4Nw1819QXlVZwblr5vPo7nZ6B4byCE2SpFllerMUY6SUbqGQhBpddt2o5QR8dKp1s/IDwBuPUmfdUcrvBc6ZatzlrLWjnyVNNRQ6k02fVVmX9ucO97B2UcO07luSJGmmeqylg5qqCk5Z0si921/YW+qCkxZy9zMHeXR3OxecvDCnCCVJmh2KmqDSzNPa2Tft80/B8wkqJ0qXJElzyaO72zlzRTNVlS8emLBmYT3NtVVs3dsxpQTVV+/eMeH691180nHHKUnSTFfMOag0A7V29k37/FPAkUlBTVBJkqS5IqXEoy3tnLVi3rjrI4INy5t5cl8HQ8M+zU+SpImYoJpjCgmqmmnfb111JYsba3jOJ/lJkqQ5Ym97Hwe7+tm4avwEFcDpy5voHRhm16HuEkYmSdLsY4JqDhkeThzo7C9KDyooDPOzB5UkSZorHmtpB5gwQbVhWTMBPLG3o0RRSZI0O5mgmkPaegYYHE5FTFDVmaCSJElzxqNZgurMFc1H3aa+ppKTFjXwxN7OUoUlSdKsZIJqDmnt7AMoyiTp8HwPqsLDGSVJksrbo7vbOWlRA8111RNut2F5M88d7mF/R1+JIpMkafYxQTWH7B9JUBVhDiooTJTe1T9EW89AUfYvSZI0kzza0s7GlUcf3jfijOWFHlb/9uT+YockSdKsZYJqDmnt7AdgaZGG+I08yW/XIYf5SZKk8tbVN8j2A10Tzj81YuWCOhprq/jhVhNUkiQdjQmqOaS1Y6QHVXESVGsXNQD4lBpJklT2Ht/TQUpMqQdVRQRnLm/mjq37GBgaLkF0kiTNPiao5pD9nX1UVQTz6yeeJ+F4rVloDypJkjQ3PL4nmyB95dEnSB/trJXNdPQOcs8zB4sZliRJs5YJqjmktaOPxU01VFREUfY/v76a5toqdh60B5UkSSpvOw50U1NVwar59VPa/rRlzdRUVXDbY3uLHJkkSbOTCao5pLWzr2jD+wAigtUL6+1BJUmSyt7OQ92sWVA/5Rt/NVUVvOq0Jdz26F6feCxJ0jhMUM0hrZ39RU1QQWEeqp3OQSVJksrczoM9rMnm35yqSzcuZ9ehHrbu7ShSVJIkzV4mqOaQYveggsI8VLsO9XhnUJIklbWdh7pZu3Bqw/tGvPHMZQD84FGH+UmSNJYJqjkipcSBzn6WNhe5B9XCBrr7hzjY1V/U40iSJOWlo3eAw90DR55gPFXL5tVx3toF3PbYviJFJknS7GWCao5o7xmkf2iYJU01RT2OT/KTJEnlbufBQjtn7cJjS1AB/MrZy3lw52Geae2a7rAkSZrVTFDNEfs7+wCK34Mqu5PoPFSSJM08EXFZRGyNiG0Rcc046yMiPpOtfygiLpisbkQsiojbIuLJ7H1hVn5pRNwXEQ9n728ozVkW30g7Z+2iYxviB/COC9ZQWRH8w893THdYkiTNalV5B6DSaM0SVNM5B9VX735xw6p3YAiAf36whbecu2rajiVJkk5MRFQCnwUuBXYB90TEzSmlR0dtdjmwIXtdDHwOuHiSutcAt6eUrs0SV9cAvw+0Ar+aUtodEecAtwKrS3GuxbbzYJagOo4eVMvm1fHLG5fz9Xt38nuXnk5ddeV0hydJ0qxkD6o5ohgJqvHUVVdSX13JwW7noJIkaYa5CNiWUno6pdQP3AhsHrPNZuCGVHAXsCAiVk5SdzNwfbZ8PfA2gJTS/Sml3Vn5FqAuIorbECmRXYd6aKqtYkFD9XHVf//FJ3Ooe4Bbt+yZ5sgkSZq9TFDNEa0dIwmq4s5BBbCosYbDJqgkSZppVgM7R33exYt7NB1tm4nqLk8ptQBk78vGOfbbgftTSn1jV0TEVRFxb0Tcu3///mM4nfzsPNjNmoX1RMRx1X/FqYs5eXEDf3+Xw/wkSRphgmqOaO3sp7IiWNhQ/ATVgoZqDnYNFP04kiTpmIyXTUlT3GYqdcc/aMTZwP8A/sN461NKn08pbUopbVq6dOlUdpm7nYe6j/kJfqNVVATvu+gkfr79II+1tE9jZJIkzV4mqOaI1s4+FjXWUFFxfHf6jsWihkIPqpSm1G6VJEmlsQtYO+rzGmD3FLeZqO7ebBgg2fu+kY0iYg3wLeCKlNJT03AOuUspsfNgz3HNPzXauzatpbmuiv9+y2O2mSRJwgTVnNHa2Vf0+adGLGisYXA4sb/jRb34JUlSfu4BNkTE+oioAd4D3Dxmm5uBK7Kn+V0CtGXD9iaqezNwZbZ8JfAdgIhYAHwX+ERK6adFPK+SOtDVT8/A0HE9wW+0hY01/O4vnc6/PdnK9x/dO03RSZI0e5mgmiP2d/aXZP4pgEXZhKE7D/WU5HiSJGlyKaVB4GMUnqb3GHBTSmlLRFwdEVdnm90CPA1sA74AfGSiulmda4FLI+JJCk/5uzYr/xhwGvCHEfFA9hpvfqpZ5USe4DfWFS8/mTOWN/P//vOjR56ELEnSXFWVdwAqjdaOPk5d0liSYy3I5rnadaibC09eWJJjSpKkyaWUbqGQhBpddt2o5QR8dKp1s/IDwBvHKf9T4E9PMOQZZ+QG3InMQTWiqrKCT771bN77hbv463/dxqoFJ9YrS5Kk2cweVHNASoXhdkvnlWaI38hE7DsOdJfkeJIkSaUy0oNqzcLpSSa9/NTFvP2CNfzNHdt4en/ntOxTkqTZyATVHNDeM0j/0DBLSzQHVU1VBfPrq3nmQFdJjidJklQquw51s7ixhsba6RuI8Mebz2bd4ka+du9OOvsGp22/kiTNJiao5oD9nb0ALG0uTYIKYHFjDdtbTVBJkqTysutQD2umYXjfaE21Vfyv972Unv4hvnHfTp/qJ0mak0xQzQH72gtP01vWXFeyYy5uqmW7Q/wkSVKZee5wD6sXTH+b6uxV87nsnBU8sbeTX+w4PO37lyRppjNBNQfs7ywkqErZg2pJUw0Hu/pp6xko2TElSZKKKaVEy+FeVs4vzmTml5yymJMXN3DLwy109NqGkiTNLSao5oAjPahKNEk6wOLGwrEc5idJksrF4e4BegaGWDm/OL3SKyL4dy9dw8DQMP/0UEtRjiFJ0kxlgmoO2N/ZR21VBc3TOJnnZBY3FZ7kt92J0iVJUpnY3dYDwOoFxelBBYUe7284cxmPPNfGtn0+1U+SNHeYoJoD9nf0sbS5logo2TEXNdYQAc/Yg0qSJJWJlsOFB8+sLGKCCuBVG5Ywr66KO7buK+pxJEmaSUxQzQH7OnpZVsL5pwCqKytYNb/eIX6SJKlstGQ9qFYVaYjfiKqKCl512hKebu1i50EfOiNJmhtMUM0BIz2oSm39kkae8Ul+kiSpTOxu66W6MljSVPx21cvWLaK+upIfPbG/6MeSJGkmMEE1B+zr6GNZc3Hv9I1n3ZIGe1BJkqSy0XK4h+Xz6qioKP60CbXVlVxyymIebWlnX3tv0Y8nSVLeTFCVub7BIQ53D+TSg2rd4kbaegY41NVf8mNLkiRNt92He1lV5PmnRnv5qYuprgzufOpAyY4pSVJeTFCVudbOQnKo1HNQQWGIH8AzPslPkiSVgd1tPUWff2q0ptoqNq6cx8PPtTE4PFyy40qSlAcTVGVuf0cfQD49qLIElcP8JEnSbDc8nNjb3lv0J/iNdf7aBfQMDPHk3s6SHleSpFIzQVXm8kxQrV3YQEWYoJIkSbNfa2cfA0OppD2oAE5b1kxDTSUP7Dxc0uNKklRqJqjK3L6OwqSaeUySXlNVwZqFDT7JT5IkzXq72wptqpXzS9uDqrIiOHfNfB5raaejd6Ckx5YkqZRMUJW5/R19RMDipppcjr9+SSNP7bNLuiRJmt12H+4BYOWC0t/0O3/NAgaHE7du2VvyY0uSVComqMrcvo4+FjXUUF2Zz6U+Y0Uz2/Z3MjSccjm+JEnSdBhJUK0u8RxUAGsXNbCosYbvPPBcyY8tSVKpmKAqc/s7+nKZf2rE6cub6R8c5lmf5CdJkmaxlrZe6qsrmV9fXfJjRwQvWT2fO586QFu3w/wkSeXJBFWZ25d7gqoJgCf2duQWgyRJ0olqaeth5YI6IiKX45+1opmh4cSPntyfy/ElSSq2qrwDUHG1dvRx6tLG3I5/2rImImDrnk4uOye3MCRJkk7I7sO9rDrBCdK/eveO4667Jhvmd/tje3nreatOKA5JkmYie1CVsZRS7kP8GmqqOGlRgz2oJEnSrLb7cA8r55d+gvQRFRG8/oxl3LF1P4NDw7nFIUlSsZigKmNtPQP0Dw2zrDm/xhQU5qEyQSVJkmar/sFh9nf2sSqHCdJHe+NZy2jrGeAXOw7nGockScVggqqM7evoA8i1BxXAGcubeaa1i77BoVzjkCRJOh5723tJCVYtyPem36s3LKG6Mrj9sb25xiFJUjGYoCpje9p6AVgxL+ceVCuaGRxOPNPqk/wkSdLs05K1qVae4BxUJ6q5rpqL1y/m9sf35RqHJEnFYIKqjLW09QDkOl8CFHpQAWzd4zA/SZI0+4y0qfLuQQXwhjOXsW1fJ88e8MafJKm8mKAqYy1tvUTA8px7UK1f0khVRTgPlSRJmpV2H54ZPaigkKAC+NET+3OORJKk6WWCqoy1HO5lSVMtNVX5XuaaqgrWL2lk657OXOOQJEk6HrsP9zCvrorG2qq8Q2HdkkZOXtzAj7aaoJIklZeiZi4i4rKI2BoR2yLimnHWR0R8Jlv/UERcMFndiFgUEbdFxJPZ+8JR6z6Rbb81In5lVPkdWdkD2WtZMc97pmhp7819eN+I01f4JD9JkjQ7tbT15P4Ev9Fee/pS7nzqgA+gkSSVlaIlqCKiEvgscDmwEXhvRGwcs9nlwIbsdRXwuSnUvQa4PaW0Abg9+0y2/j3A2cBlwN9k+xnx/pTS+dlrTsws2XK4Z8YkqM5Y3syOg9109Q3mHYokSdIx2X24d8YlqHoGhrh3+6G8Q5EkadoUswfVRcC2lNLTKaV+4EZg85htNgM3pIK7gAURsXKSupuB67Pl64G3jSq/MaXUl1J6BtiW7WfOamnrnRFzJQCcs3oeAFt2t+cciSRJ0rFpaZs5N/0ALjllMTWVFc5DJUkqK8VMUK0Gdo76vCsrm8o2E9VdnlJqAcjeR4brTXa8v82G9/1hRMR4AUfEVRFxb0Tcu3//7P6F39E7QGff4IxpTJ27ZgEAD+48nGsckiRJx6Knf4hD3QMzqgdVY20VL1u/0HmoJEllpZgJqvGSQGmK20yl7rEc7/0ppZcAr85eHxhvBymlz6eUNqWUNi1dunSSw81sLW3Z02ZmSGNqSVMtqxfU88Cuw3mHIkmSNGW723oAZsxNvxGvPX0pW/d20JLFJ0nSbFfMBNUuYO2oz2uA3VPcZqK6e7NhgGTvI/NJHbVOSum57L0D+CpzYOjfkQTVDGpMnb92gT2oJEnSrNJyuNCmmkk9qABee3phEMGPHeYnSSoTxUxQ3QNsiIj1EVFDYQLzm8dsczNwRfY0v0uAtmzY3kR1bwauzJavBL4zqvw9EVEbEespTLz+84ioioglABFRDbwFeKQYJzyTtByeeXf7zls7n12Hemjt7Ms7FEmSpCkZ6UG1aobM6zni9OVNrJhX5zxUkqSyUVWsHaeUBiPiY8CtQCXwpZTSloi4Olt/HXAL8CYKE5p3Ax+aqG6262uBmyLiw8AO4J1ZnS0RcRPwKDAIfDSlNBQRjcCtWXKqEvgB8IVinfdM0dLWSwQsnzeDElTZPFQP7TrMG85cnm8wkiRJUzDSg2r5/NqcI3mhiOC1py/le4+0MDg0TFVlMe87S5JUfEVLUAGklG6hkIQaXXbdqOUEfHSqdbPyA8Abj1LnvwH/bUxZF3DhscY+27W09bC0qZbqGdRYOWf1fCoCHtjZZoJKkiTNCi1tPSxpqqW2qjLvUF7ktWcs5Wv37uTBXYe58ORFeYcjSdIJmTnZC02rlrbeGTNB+ojG2ipOX97MQ06ULkmSZonnDvewasHM6ZE+2itPW0JlRfg0P0lSWTBBVaZa2npZOYOG9404b01hovRC5zlJkqSZraWtd0bN6Tna/Ppqzl+7wHmoJEllwQRVGUop0XK4h5Uz8G7fuWvnc6h7gJ0HfSSyJEma2UbaVDPtCX6jvfb0pTz0XBsHfAiNJGmWM0FVhjr6BunqH5qRd/tGJkq/f+ehfAORJEmaRHtvoU01057gN9prT19KSvCTba15hyJJ0gkxQVWGRp42s3IGNqbOXNFMc20Vdz19IO9QJEmacyLisojYGhHbIuKacdZHRHwmW/9QRFwwWd2IWBQRt0XEk9n7wqx8cUT8MCI6I+KvS3OG06ulrdDjeyb2Sh/xktXzWdRY4zxUkqRZzwRVGdqdNaZm4oSeVZUVXHLqYu/ySZJUYhFRCXwWuBzYCLw3IjaO2exyYEP2ugr43BTqXgPcnlLaANyefQboBf4Q+HixzqnYZvJNvxEVFcFrNizhjif2MzTsHJ+SpNnLBFUZ2tNWaEytmKGNqVeeupidB3vYcaA771AkSZpLLgK2pZSeTin1AzcCm8dssxm4IRXcBSyIiJWT1N0MXJ8tXw+8DSCl1JVS+gmFRNWstOtQoa2yegbPQQXwSxuXc7Crn1/scAoFSdLsZYKqDO0+3ENFwLLm2rxDGderNiwB4KdP2YtKkqQSWg3sHPV5V1Y2lW0mqrs8pdQCkL0vO5agIuKqiLg3Iu7dv39mDVPbcbCb2qqKGdumGvHa05dSXRl8f8uevEORJOm4maAqQ88e6Gb1wnqqK2fm5T11aRPL59XyU4f5SZJUSjFO2dgxYUfbZip1j0tK6fMppU0ppU1Lly6djl1Om2cPdHPSogYqKsY7/Zmjua6al5+6hNse3UtKDvOTJM1OMzODoRPy7IEu1i1uzDuMo4oIXnnqEu586gDDzpUgSVKp7ALWjvq8Btg9xW0mqrs3GwZI9r5vGmPO1Y6DhQTVbPDLG5ez/UA32/Z15h2KJEnHpSrvADT9th/o5lfPW5l3GBN65WlL+Ob9z/H4ng42rpqXdziSJM0F9wAbImI98BzwHuB9Y7a5GfhYRNwIXAy0pZRaImL/BHVvBq4Ers3ev1P0MymBlBI7Dnbz8lMX5x3KEV+9e8dR13X2DgLw/Uf3smF5c6lCkiRp2tiDqswc7u6nrWdgRveggkKCCnCYnyRJJZJSGgQ+BtwKPAbclFLaEhFXR8TV2Wa3AE8D24AvAB+ZqG5W51rg0oh4Erg0+wxARGwH/hL4YETsGuepgTPWga5+uvuHZk0Pqnn11axZWM/3H92bdyiSJB0Xe1CVme3Zk/FmeoJqxfw6Tl3ayI+f3M+/f80peYcjSdKckFK6hUISanTZdaOWE/DRqdbNyg8AbzxKnXUnEG6uns3aVCcvnh0JKoCzVs7jtkf3sqetlxXz6/IOR5KkY2IPqjKzvbULgHVLZn5j6pfPXsGdTx3gUFd/3qFIkiS9wM6DhQTVbOlBBXDOqvkA/PNDY6cWkyRp5jNBVWa2H+giAtYsnPmNqTe/ZCVDw4lbfSSyJEmaYZ490D1r2lQjljbXcu6a+Xz7gefyDkWSpGNmgqrMPHugm1Xz66mrrsw7lEmdvWoe6xY38N2HW/IORZIk6QV2HOxmxby6WdGmGm3z+at55Ll2tu3ryDsUSZKOiQmqMrP9QNesmSshInjTS1Zy51MHOOgwP0mSNIPsONjF2lk0vG/Er563koqAb9/vMD9J0uxigqrMPHugm5Nn+ATpo735XIf5SZKkmWfHwW5OnoUJqmXNdbzytCV858HnKMx5L0nS7GCCqoy09QxwsKufdbOkBxXAxpXzWL+kke8+5DA/SZI0M/QODLG3vW9WTZA+2ubzV7PzYA+/2HEo71AkSZoyE1RlZMeRxyHPnh5UEcGbX7KSnz19gJa2nrzDkSRJev4JfrPopt9ol52zgvrqSm66Z1feoUiSNGVVeQeg6bP9QBcA65bMjMbUV+/eMeH69118EgDv2rSWz96xjX+4ewe/98tnlCI0SZKko3o2u+k3W3tQNdVW8baXruZb9+/iE286kwUNNXmHJEnSpOxBVUaezRJUs60xddLiBt5wxjK++vOd9A8O5x2OJEma43YcnH290se64uUn0zswzNfvtReVJGl2MEFVRrYf6Gb5vFoaamZfx7grXrGO1s4+vveIc1FJkqR87TjYTVNtFQsbqvMO5bidtXIeF61fxJfvepahYSdLlyTNfCaoysj21i7WzdI7fa8+bQnrlzRyw8+ezTsUSZI0xz3d2sXJixuIiLxDOSFXvnwdOw5286Mn9uUdiiRJkzJBVSZSSmzd28Fpy5ryDuW4VFQEv37Jydz37CEe3tWWdziSJGkO27qnnTOWN+cdxgn75bOXs3xeLX/70+15hyJJ0qRm31gwjWvnwR46egc5e9X8vEOZsrGTqAdQV13Bf/7Gg3zg5euA5ydSlyRJKoXD3f3sbe/jzJWzP0FVXVnBh165nmu/9zi/2HGIC05amHdIkiQdlT2oysSW3YVeR2evmpdzJMevrrqSV522lMf2dBx5vLMkSVIpPb6nA4AzVszeNtVoH7jkZBY31vCp257IOxRJkiZkgqpMbNndTmVFcMaK2X2375WnLqahppIfPLY371AkSdIc9HhLOwBnzfI21YjG2ir+w2tP4d+ebOXe7QfzDkeSpKMyQVUmtuxu47SlTdRVV+Ydygmpra7ktacv5cl9nTzT2pV3OJIkaY7ZureDhQ3VLG2uzTuUafPrl5zMkqYaPvUDe1FJkmYuE1RlYsvu9lk9vG+0S05ZzLy6Kr73SIuPRZYkSSX1WEsHZ6xonvVP8ButoaaKq197Kj/ddoAfbvWJfpKkmckEVRnY39HHvo4+NpZJgqq6soLLzlnJrkM9fPXnOyavIEmSNA2GhxNP7O3gzDKZf2q0K16+jlOWNvLJm7fQOzCUdziSJL2IT/ErA89PkD57nuA3mfPWzOe+Zw/yZ//yOJedvaKsutlLkqSZaeehbrr7hzhzFs8/NfYpyaO97vRlfOmnz/C/f/Q0v/1LG0oYlSRJk7MHVRnYsrswmWe59KACiAjeet5q+gaG+dPvPpp3OJIkaQ54/gl+szdBNZHTljXxlnNX8jd3bOPZA871KUmaWUxQlYFHd7ezdlE98+ur8w5lWi1truXq153Kdx7Yzfcebsk7HEmSVOYeb+kgAk5fXp4JKoD/+uaNVFdW8PGvP+hcn5KkGcUhfmVgy+42zl5ZPsP7RvvY60/jjq37uOabD3P+SQtYOb8+75AkSVKZ2rq3nZMWNdBYW75N5H99fB+Xn7OCr9+3i6u/ch+vP2PZC9a/7+KTcopMkjTXle9v3zmio3eA7Qe6eceFa15QPtH8A7NJTVUFn373+bz5Mz/hP930IF/58MVUVJTPU3UkSdLM8fieDs4o495TI85fu4Ctezu4/bG9nLa0ibWLGvIOSZIkh/jNdvdsPwjA+WsX5hxJ8ZyytIlPvnUjdz51gE/f/mTe4UiSpDLU0TvA9tYuzlpZPnN6Hk1EsPm81cyrq+bGe3bQ1TeYd0iSJJmgmu1+/EQrddUVbFpXvgkqgHdtWss7LlzDZ25/0vmoJEnStLv76YMMJ7j4lEV5h1IS9TWVvPeik+joHeSrP9/hfFSSpNw5xG+W+9ET+7nklMXUVVfmHUpRjB6qeO7q+dz99AF++8YHeLSlnZXz650nQZIkTYs7nzpAbVUFF5xU3jf9Rlu7qIFfe+lqvn7fLr778G7eet7qvEOSJM1h9qCaxXYe7OaZ1i5ee/rSvEMpiarKCt5/ycnUVVdw/Z3bOdjVn3dIkiSpTNz5VCub1i0s25t+R/PSkxby6tOWcNfTB/nxE/vzDkeSNIeZoJrFfpQ1Il4zRxJUAPPqqvngK9czMJT40k+fYW97b94hSZKkWa61s4/H93TwilOX5B1KLn7lnBWcu2Y+/7JlD1+7pzwetCNJmn1MUM1iP35iP6sX1HPKksa8QympFfPq+OAr1tHZN8j7vnAXuw/35B2SJEmaxe56+gAArzh1cc6R5KMigndcuIYNy5r4xDcf5p8e3J13SJKkOcgE1Sw1MDTMnU8d4LVnLCUi8g6n5NYuauDKl69jX3sf/+5v7mTrno68Q5IkSbPUnU8doKm2ipesnp93KLmpqqjg/RefzKaTF/HbN97PP963K++QJElzjAmqWeoXzx6is2+Q12yYO8P7xlq/pJGbrn45wynxzuvu5Ptb9uQdkiRJmoV+9tQBLl6/iKrKud00rqmq4O9+42W8/NTFfPwbD/KVu57NOyRJ0hwyt38Lz2LffbiFmsoKXnHa3OyKPuKslfP45kdewdpFDVz15fv4L996mO7+wbzDkiRJs8Tuwz0809rFK06bm/NPjdVQU8X/ufJlvP6MZfzXbz/Ctd97nOHhlHdYkqQ5oCrvAHTs2noG+MZ9u/jV81Yxr64673Byt2ZhA9/8yCv4i+8/wed//DQ/eGwvv/NLp/POC9fM+TuhkiRpYv/ySKEH9qs3mKAC+OrdhUnSX3/GMjp7B7nuR0/xkyf38/YL1lBbXcn7Lj4p5wglSeXKv95noa/fu5Pu/iE+9Mp1eYcyY9RWVfIHbzqLr1/9clYtqOcT33yY1//FHXz6B0/w7IGuvMOTJEkz0PBw4it3PctLT1rA6cub8w5nRqmsCDafv4rLz1nBlt3tfPaObbS0+WAaSVLx2INqlhkaTlz/s+28bN1CzpnDE3kezcvWLeKbv/kKvv/oXm742Xb+6vYn+fQPnmT1gnouOHkhG5Y1sXpBPSvm1zG/vrrwaqimubZqTk42L0nSXPbTp1p5urWLT737vLxDmZEigldvWMrqhfV87Z6dfO6Op2iqreI3XrWeanupS5KmmQmqWeZfH9/HzoM9fOLys/IOZUYY6YY+nje/ZBV//o7z+JdH9nDfs4e4b/vBoz42OYD6mkrqqyuPvDfWVvFLZy3j9OXNnL68mbWLGqisMIklSVK5uP7OZ1ncWMObXrIy71BmtFOWNPEf37CBb93/HP/f9x7n2w/s5k82n83L1i3KOzRJUhkxQTWLDA4N89kfbmPV/Dp+eePyvMOZFVYtqOc3XrWe33jVegB6B4bYfbiHfR19tPUM8P0te+jpH6J7YIie/iF6Rr3v6+jjf37/iSP7qqoIVsyvY/WC+sJrYT3Lmuv4wMtPzuv0JEnScdp5sJt/fXwvv/m6U6mtqsw7nBmvqbaKD1xyMoubavh/vrOFd173M169YQkfe/1pXLR+kT3RJUknzATVLPI3dzzFAzsP81fvOd/Jv6dooh5WABeePPGdv74sUbW3vZd9HX3sbuvhgZ2HufuZgwBUVwbffuA5XrJ6PueuKbxOWdJExaieVpPF4GSjkiSV3v/5yTMAvP9ibzQdi185ewWv3rCEv79rB//7x0/x7s/fxSlLGnn7hWt441nLOGN5s8kqSdJxMUE1S9z37CH+6vYnedv5q9h8/uq8w5kzaqsrWbuogbWLGo6UDafEwc5+dh3u4blD3fQPDfO1e3byd3duB6ChppLTlzdz5orCa9ehHlbMq6Oh1v/dJEmaCe7Yuo+/u3M777/4JFYtqM87nFmnoaaKf/+aU3j/JSfxzw+18I17d/Hnt27lz2/dypKmWi48eQFnrZzHmSuaOXlxIycvbqChxnaQJGli/qaYBXYe7OZ3vnY/K+fX8SdvOyfvcOa8igiWNNeypLmW89cu4H0Xn8TQcOKp/Z08tKuNR55rY+ueDm7dsocb79l5pN68uiqWz6tjxbw6VsyvY+WCepY21eZ4JpIkzT172nr5vZse5MwVzfzhWzbmHc6s1lBTxbs2reVdm9ay+3APP9nWyk+z1/e37CWN2ra5rorFjTUsbqzlDWct46RFDaxb3MjJSxqYV1ed2zlIkmYOE1Qz3E+3tfLRr/6CoeHEDb9xkb/AZ6CxQ/hGJlV/y7kr6egbZE9bL3vbe9nT1sue9l7ufPoAQ8OFJlt1ZfDN+3dxzqr5nLN6Huesns+GZc3UVDmEU5Kk6ba3vZerv3IfvQNDfPb9F1BX7dxT02XVgvojyaqv3r2D/sFh9nf0caCrj4Nd/Rzo7OdAVx9P7Ovgvh2HXlC3oaaykLxqqmVxYw1vPX8Vpy1r4tSlTV4jSZpDipqgiojLgL8CKoEvppSuHbM+svVvArqBD6aUfjFR3YhYBHwNWAdsB96VUjqUrfsE8GFgCPitlNKtWfmFwN8B9cAtwG+nlEbf1JlxHmtp529/+gzfuG8Xpy1r4vMf2MS6JY15h6VjEBHMq6tmXl01py9vPlI+NJxo7exj9+Eedh/uYWA48a37n+PLdz0LQE1lBWesaOac1fM4dWkTaxY2sGZhPWsXNTC/3gTlZIaHE139g3T3D9HZN3gkGfjdh1qObFNTWUFtVQU1VRVH5nNzLjBJc8FMaZuVWkqJ7zywmz/6ziP0Dw3z6Xefz6lLm/IIZc6oqapg9cLCQ2XG6h8cHpO4KiSvtrd28eDOw9z++D4AIuCkRQ1sWNbEhuXNbFjWxMmLG1m1oI5lzXU+XVmSykzRElQRUQl8FrgU2AXcExE3p5QeHbXZ5cCG7HUx8Dng4knqXgPcnlK6NiKuyT7/fkRsBN4DnA2sAn4QEaenlIay/V4F3EUhQXUZ8L1inftkBoeG6R0cpndgiL7BYbr6Btnb3svuwz08/Fwb9z17mMda2qmvruTXLzmZ/3zZmTQ5f1HZqKwIls+rY/m8Ol560kLed/FJDA8nth/o4pHd7Wx5ro1Hdrdxy8N7aOsZeEHdptoqFjZWs7Chhvn11SxoqGFBfTULGqqZX19NfU0l9dWVNNRUUlddWB4pq8uWqysqqKgoxFERQWVFUBnxgondx5NSIqXCHFzDCRLPfx4YTPQPDTMwNEz/YPY+NMzAUDpS1j80zMBgoax/aOgodQrbD4xsPzRM/2AaZ7/ZfrKynoEhuvqG6OobpGdg6NiuRwS11RV8/sdPMb++mvnZdzu/vooF9SPL1cwb/T1XV1JdVUF1ZVBTWUh0VVdWUFUR0zYxbMq+56HhxHBKDA4nhrLX4HD23Wbf03jfb//I9zvqexseTlRVFuKuqqigqjKOxF1dVUFtZQXVVRXUVBbOpyZbrhk51yypV/g3VJzzTAmGUmF5aCgxMOpcB4ef//cw0fLIv63hlArnMXIuVc8nJmurKqiprMySlEF1RQXVVYXvpboyqBr5XiorZvwfQCP3W1LiyHCalNKo5cL/ryPLI4aGE4PZdzw4NPL9Zf8PDhXKRv6tjawfvc3o9RUR2b+nUd/hmH9jo//tjf2OR65BVWUcWZ6uf2N63gxrm5XUvzyyh9/52gO89KQF/MU7z+MUk1PHbbKHv0xFTVUFK+fXs3L+i5NXA0PDXHLKYp7c18GTezvZtr+TbXs7+dET+xkYev6HWGVFsKy5lhXz65hXV01DTSUNNVU01FRSW1VR+F0ynBhKiaHhQvt7cLjw+2FwaJjtrd1Hfr9GFJ7WPPJzq6qignNWz6Ohpoqm2sJ+G2sraaytojE7RmNtVfa5ktrqylHtqULbonIa2wSllNJIW+P5dsTgcKHN1Tc4THd2E7Cnf+jIDcEfP7GfgcFh+ka14foGh4+ULW+uG/M7uPLIdzjy/TbVVtFQO1JW+I4L71XU11RSGUEER77nCGbl91suRv9dkOBIG25wODGYtdMHh0faCi8sK7QrhhkaTgyMWQeF/xcrKyqy93j+vXL88sojnyueL68cVa+igoqc/r2MbqPBC9tpwJE28OBw9n0MpRd8HszaaqM/D2XtsCOfs++4IjjSbn1BG7/y+Z9rI+2v6qxd9oL21wz5mVXMrMdFwLaU0tMAEXEjsBkY3QjaDNyQ9Wa6KyIWRMRKCnfgjlZ3M/C6rP71wB3A72flN6aU+oBnImIbcFFEbAfmpZR+lu3rBuBt5JCgGh5OnPGH33vBL9exmmqrOH/tAv7Lm87iXZvWMr/BHjNzQUVFcMrSJk5Z2sRbz1sFFH5wtfUM8IV/e4ZDXf0c6u7ncM8APf1DdPcPcqirn+7+doaz7YanoU/gyC99KPxBO5ye/4VTKjWVzydDqsckSqqr4kjCoaGmkvn11VRXBvXVlew+3Puixk9NVeEX2WgpFX6o9w0OH2ls9Q4MsWxeHW09A7T1DLDzYDeHu/uP63sd/Tf1yA/5OPI5e2f0Ri94O5KgGZqOC1pkMSr2iMjeR8qC7L8j2wbxoiTUzO7LWhDBkf8vRj4fWR57AXnx9X7BdmPKxyaPjnwdRykfSTzNhu/tRFUEVFVW8OaXrORT7z4/73DKxYxomwE/K+I5juvSjcv5s3ecy9svWDPjk85zXXVlBfc9WxgCOHJD75WnLmFoOHGgq49DXf2cvqKZPW297D7cy572Hg5397P78BDdWRupb3D4yM23kRty1ZXP/6FWU1lBR9/AkUTS8DD0DQwzNDx45I/qZ1o76eobon9o+LjPZeT3R0WWuDqaiX6mT/jjfsJ6R1850fGGTuB3c8CoNluhTVZdWcHA0DBd/YOFG2pZ26urf5DuE/x+K+L5G64VWdJK0yuN+ltgbDJqthmd0ILRSaPxk0hMsv5I8unI5+LEXUoj39FIu/1PNp/NOzetLW0MRdz3amDnqM+7KNyJm2yb1ZPUXZ5SagFIKbVExLJR+7prnH0NZMtjy18kIq6i0NMKoDMith7t5IppC/D3owI5TkuA1mkIR9Nn3Gvy/hwC0RH+fzIzeV1mntyuyaeBT7+naLs/uWh7nplmStvsBWZK+2ua+PNr5vBazBxei5nDazFzzPhr8a4/Lerux22DFTNBNV4Oe2xe8WjbTKXuVI835X2llD4PfH6S48wKEXFvSmlT3nHoeV6TmcdrMjN5XWYer0nZmCltsxcW2P5SEXgtZg6vxczhtZg5vBbjK+ajwnYBo/uDrQF2T3Gbieruzbqak73vm8K+1kwShyRJUrmbKW0zSZKkFylmguoeYENErI+IGgqTZN48ZpubgSui4BKgLesiPlHdm4Ers+Urge+MKn9PRNRGxHoKk3v+PNtfR0Rckj2Z5opRdSRJkuaKGdE2K9bJSZKk2a1oQ/xSSoMR8THgVgqPI/5SSmlLRFydrb+OwhP13gRso/Ao4w9NVDfb9bXATRHxYWAH8M6szpaIuInCZJ2DwEdHPSXmN4G/A+opTI6e2xP8SqgsusqXGa/JzOM1mZm8LjOP16QMzLC2Wbny/5WZw2sxc3gtZg6vxczhtRhHpHKYbl6SJEmSJEmzVjGH+EmSJEmSJEmTMkElSZIkSZKkXJmgKjMRcVlEbI2IbRFxTd7xlJuIWBsRP4yIxyJiS0T8dla+KCJui4gns/eFo+p8IrseWyPiV0aVXxgRD2frPpNN4k82mezXsvK7I2JdyU90FoqIyoi4PyL+OfvsNclZRCyIiG9ExOPZ/zMv97rkKyJ+N/vZ9UhE/ENE1HlNpBNn+6s4IuJLEbEvIh4ZVebPrBzYBp45st/dP4+IB7Nr8cdZudciJ+HfIdPGBFUZiYhK4LPA5cBG4L0RsTHfqMrOIPCfUkpnAZcAH82+42uA21NKG4Dbs89k694DnA1cBvxNdp0APgdcReGpRhuy9QAfBg6llE4DPgX8j1KcWBn4beCxUZ+9Jvn7K+BfUkpnAudRuD5el5xExGrgt4BNKaVzKEx0/R68JtIJsf1VVH/H8z9fRvgzKx+2gWeOPuANKaXzgPOBy6Lw1FWvRX78O2SamKAqLxcB21JKT6eU+oEbgc05x1RWUkotKaVfZMsdFH4QrabwPV+fbXY98LZseTNwY0qpL6X0DIWnIl0UESuBeSmln6XCkwpuGFNnZF/fAN44kkHX+CJiDfBm4Iujir0mOYqIecBrgP8DkFLqTykdxuuStyqgPiKqgAZgN14T6UTZ/iqSlNKPgYNjiv2ZlQPbwDNHKujMPlZnr4TXIhf+HTK9TFCVl9XAzlGfd2VlKoKse+VLgbuB5SmlFij8AgeWZZsd7ZqszpbHlr+gTkppEGgDFhflJMrHp4H/DAyPKvOa5OsUYD/wt1mX5y9GRCNel9yklJ4D/iewA2gB2lJK38drIp0o21+l5c+snNkGzl82pOwBYB9wW0rJa5GfT+PfIdPGBFV5GS+TmkoexRwQEU3APwK/k1Jqn2jTccrSBOUT1dE4IuItwL6U0n1TrTJOmddk+lUBFwCfSym9FOgi6958FF6XIsvmP9gMrAdWAY0R8esTVRmnzGsivZj/7mcGf2aVgG3gmSGlNJRSOh9YQ6EHzjkTbO61KBL/Dpl+JqjKyy5g7ajPaygM39A0iohqCr+Y/z6l9M2seG/WNZPsfV9WfrRrsitbHlv+gjrZMJz5vLh7u573SuCtEbGdwrCKN0TEV/Ca5G0XsCu7oweFLskX4HXJ0y8Bz6SU9qeUBoBvAq/AayKdKNtfpeXPrJzYBp55sukT7qAwX5HXovT8O2SamaAqL/cAGyJifUTUUJiA7eacYyor2Xjf/wM8llL6y1GrbgauzJavBL4zqvw92dMX1lOY8O7nWVfPjoi4JNvnFWPqjOzrHcC/ZmORNY6U0idSSmtSSuso/Jv/15TSr+M1yVVKaQ+wMyLOyIreCDyK1yVPO4BLIqIh+y7fSGEOEa+JdGJsf5WWP7NyYBt45oiIpRGxIFuup3AD6nG8FiXn3yFFkFLyVUYv4E3AE8BTwH/JO55yewGvotCl8iHggez1JgrjgG8HnszeF42q81+y67EVuHxU+SbgkWzdXwORldcBX6cwad7PgVPyPu/Z8gJeB/xztuw1yf96nA/cm/3/8m1godcl92vyxxQasY8AXwZqvSa+fJ34y/ZX0b7Xf6AwZ94AhV4EH/ZnVm7XwjbwDHkB5wL3Z9fiEeCPsnKvRb7X5XX4d8gJv0ZOWpIkSZIkScqFQ/wkSZIkSZKUKxNUkiRJkiRJypUJKkmSJEmSJOXKBJUkSZIkSZJyZYJKkiRJkiRJuTJBJalkIuLXIiJFxJknsI+/i4h3ZMtfjIiN0xchRMQfjPncOZ37lyRJKoaIWBERN0bEUxHxaETcEhGnl+C4n4yIj2fLfxIRvzTN+/+diGgY9Xl7RCyZzmNImhlMUEkqpfcCPwHeMx07Syn9XymlR6djX6P8weSbSJIkzRwREcC3gDtSSqemlDZSaNMsL2UcKaU/Sin9YJp3+ztAw2QbSZr9TFBJKomIaAJeCXyYLEEVEa+LiB9HxLeyO33XRURFtq4zIv4iIn4REbdHxNJx9nlHRGzKli/Ltn0wIm7Pyi6KiDsj4v7s/Yys/IMR8c2I+JeIeDIi/iwrvxaoj4gHIuLvxxzrddnxvhERj0fE32eNQSLiZdn+H4yIn0dEc0TURcTfRsTD2fFfP+rY346If4qIZyLiYxHxe9k2d0XEomy7U7P47ouIfzuRXmeSJKnsvR4YSCldN1KQUnoA+ElE/HlEPJK1Sd4NhXZZ1r76RVa+OStfl7Vzro+Ih7J2T0O2bntE/I+srfPziDhtbBBjerqP1z5al7VrfpG9XpFtO247KyJ+C1gF/DAifjjmWOsi4rGI+EJEbImI70dEfbbutIj4QXbsX2TtqjjKd/G6iPhRRNwUEU9ExLUR8f4s5ocj4tRsu6UR8Y8RcU/2euU0X0NpzjNBJalU3gb8S0rpCeBgRFyQlV8E/CfgJcCpwL/LyhuBX6SULgB+BPw/R9txFJJXXwDenlI6D3hntupx4DUppZcCfwT891HVzgfenR333RGxNqV0DdCTUjo/pfT+cQ71Ugp38TYCpwCvjIga4GvAb2fH/iWgB/goQErpJRR6jl0fEXXZfs4B3ped+38DurMYfwZckW3zeeA/ppQuBD4O/M3Rzl+SJM155wD3jVP+7yi0eUbaKH8eESuBXuDXsnbW64G/GLnxBpwBfD6ldC7QDnxk1P7aU0oXAX8NfPpowUzQPtoHXJod993AZ0ZVe1E7K6X0GWA38PqU0uvHOdQG4LMppbOBw8Dbs/K/z8rPA14BtEzwXZCV/TaFduEHgNOz8/wi8B+zbf4K+FRK6WXZcb54tPOXdHyq8g5A0pzxXp5vyNyYff4u8POU0tMAEfEPwKuAbwDDFBo2AF8BvjnBvi8BfpxSegYgpXQwK59PITG0AUhA9ag6t6eU2rLjPgqcDOyc5Bx+nlLaldV5AFgHtAEtKaV7smO3Z+tfBfyvrOzxiHgWGJkH4ocppQ6gIyLagH/Kyh8Gzo1Cb7NXAF9/vq1I7SSxSZIkjfUq4B9SSkPA3oj4EfAy4HvAf4+I11Boc63m+eGAO1NKP82WvwL8FvA/s8//MOr9UxMc9wzGbx81An8dEecDQzzfNoLx21k/meT8nsl6ikEhQbcuIpqB1Smlb2XH7s32ebTvoh24J6XUkm33FPD9bJ8PU0jgQSGptXFU22xeRDRnbTpJ08AElaSii4jFwBuAcyIiAZUUEka3ZO+jjf08WTlAHGX9/0shGfRrEbEOuGPUur5Ry0NM7efheHWOduwYp2y8/QyP+jyc7bMCOJxSOn8KMUmSJG0B3jFO+dHaI+8HlgIXppQGImI7MNLTe6K22dGWxzvueOt/F9hLocdSBYWeXCOmo21Wz9HP+UTaZlCI9+UppZ4pxCXpODjET1IpvAO4IaV0ckppXUppLfAMhbt6F0XE+ijMPfVunr9TVsHzDa33MfEdtJ8Br42I9QAj8zhR6EH1XLb8wSnGOhAR1ZNvdsTjwKqIeFl27OaIqAJ+TKHxRxSeoHMSsHUqO8zuMj4TEe/M6kdEnHcMMUmSpLnlX4HaiPj3IwVZ2+QQhakMKrMpEV4D/JxCG2lflpx6PYWe5CNOioiXZ8sjD7gZ8e5R7z+bIJ6jtY/mU+hZNUxhKF3lFM6tA2iewnbAkXbUroh4W3bs2ijMo/Vjxv8upur7wMdGPmS9wCRNIxNUkkrhvRSeLDPaP1JIPP0MuBZ4hELSamS7LuDsiLiPQu+rPznazlNK+4GrgG9GxIM8PzTwz4D/LyJ+ytQaQFCY++mhGDNJ+gTH7qfQSPtf2bFvo3AH8m+Ayoh4OIvngymlvqPv6UXeD3w42+cWYPMx1JUkSXNISikBvwZcGhFPRcQW4JPAV4GHgAcpJLH+c0ppD4U5mjZFxL0U2hyPj9rdY8CVEfEQsAj43Kh1tRFxN4X5mn53gngmah9dGRF3URje1zWF0/s88L2xk6RP4gPAb2XncCewgkIbc7zvYqp+i8J39lA2PcTVx1BX0hRE4WeZJJVeRLwO+HhK6S3jrOtMKTWVPChJkqQ5KpsS4Z9TSueMs247sCml1FrquCTNDfagkiRJkiRJUq7sQSVJkiRJkqRc2YNKkiRJkiRJuTJBJUmSJEmSpFyZoJIkSZIkSVKuTFBJkiRJkiQpVyaoJEmSJEmSlKv/HxNSF9uivBhwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# numerical data\n",
    "fig = plt.figure(figsize =(20,12)) \n",
    "plt.subplot(2, 2, 1)\n",
    "sns.distplot(dataset['ApplicantIncome'])\n",
    "plt.subplot(2, 2, 2)\n",
    "sns.distplot(dataset['CoapplicantIncome'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "xrLZANTEVCI6",
    "outputId": "7bde5fcf-dcf1-4a37-f8b2-97c06d47b7d4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# numerical data\n",
    "fig = plt.figure(figsize =(20,12)) \n",
    "plt.subplot(2, 2, 1)\n",
    "sns.distplot(dataset['LoanAmount'])\n",
    "plt.subplot(2, 2, 2)\n",
    "sns.distplot(dataset['Loan_Amount_Term'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pB8F0MnoHXi4"
   },
   "source": [
    "### <span style='background :yellow' >5. Feature engineering :</span> \n",
    " - **Feature engineering** is the process of using domain knowledge of the data to create features that make machine learning algorithms work. Feature engineering is fundamental to the application of machine learning and is both difficult and expensive. It is simply using your existing knowledge of the dataset to create new features that can help a machine learning model perform better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "cIGM62OsjmeQ"
   },
   "outputs": [],
   "source": [
    "# Creating 'Total_income' as a new column\n",
    "dataset['Total_income'] = dataset['ApplicantIncome'] + dataset['CoapplicantIncome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "ZoBItHBV2JXG"
   },
   "outputs": [],
   "source": [
    "# creating instance of labelencoder\n",
    "labelencoder = LabelEncoder()\n",
    "dataset['Gender_encoded'] = labelencoder.fit_transform(dataset['Gender'])\n",
    "dataset['Married_encoded'] = labelencoder.fit_transform(dataset['Married'])\n",
    "dataset['Dependents_encoded'] = labelencoder.fit_transform(dataset['Dependents'])\n",
    "dataset['Education_encoded'] = labelencoder.fit_transform(dataset['Education'])\n",
    "dataset['Self_Employed_encoded'] = labelencoder.fit_transform(dataset['Self_Employed'])\n",
    "dataset['Property_Area_encoded'] = labelencoder.fit_transform(dataset['Property_Area'])\n",
    "dataset['Loan_Status_encoded'] = labelencoder.fit_transform(dataset['Loan_Status'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qfSvXydMagL9"
   },
   "source": [
    " - Creating dictionary for mapping the encoding for the test dataset\n",
    " - as the dictionary is created with the groupby method, but for every key we have a list of same element throughout the list as we have the groupby, so creating a main dictionary with one element from the list as the KEY and key of that dictionary as value of this dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "d-8ee2uwkMYU",
    "outputId": "5981896d-cf82-4a1a-c145-1493fda81336"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Female': 0, 'Male': 1}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For 'Gender_encoded' Column\n",
    "label_dict_temp, label_dict_gender = {}, {}\n",
    "label_dict_temp = dataset.groupby(['Gender_encoded'])['Gender'].unique().apply(list).to_dict()\n",
    "for i in label_dict_temp.keys():\n",
    "    label_dict_gender[label_dict_temp[i][0]] = i\n",
    "label_dict_gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pHnOZcpC5JBM",
    "outputId": "43fd1643-5e39-4383-d1e9-92c57c2bc158"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'No': 0, 'Yes': 1}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For 'Married_encoded' Column\n",
    "label_dict_temp2, label_dict_married = {}, {}\n",
    "label_dict_temp2 = dataset.groupby(['Married_encoded'])['Married'].unique().apply(list).to_dict()\n",
    "for i in label_dict_temp2.keys():\n",
    "    label_dict_married[label_dict_temp2[i][0]] = i\n",
    "label_dict_married"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "aLASGdJm5WVj",
    "outputId": "791e8ec6-d973-4c75-eaec-44c8e84a3f27"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'0': 0, '1': 1, '2': 2, '3+': 3}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For 'Dependents_encoded' Column\n",
    "label_dict_temp3, label_dict_dependents = {}, {}\n",
    "label_dict_temp3 = dataset.groupby(['Dependents_encoded'])['Dependents'].unique().apply(list).to_dict()\n",
    "for i in label_dict_temp3.keys():\n",
    "    label_dict_dependents[label_dict_temp3[i][0]] = i\n",
    "label_dict_dependents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Emf9aS3m5hSy",
    "outputId": "cce8f630-ba2a-432a-9e93-d2ccd33265f9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Graduate': 0, 'Not Graduate': 1}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For 'Education_encoded' Column\n",
    "label_dict_temp4, label_dict_education = {}, {}\n",
    "label_dict_temp4 = dataset.groupby(['Education_encoded'])['Education'].unique().apply(list).to_dict()\n",
    "for i in label_dict_temp4.keys():\n",
    "    label_dict_education[label_dict_temp4[i][0]] = i\n",
    "label_dict_education"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "A8D6HrhR6Jt3",
    "outputId": "f96a5bb9-9999-40e9-98f7-1bdd6d8469ed"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'No': 0, 'Yes': 1}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For 'Self_Employed_encoded' Column\n",
    "label_dict_temp5, label_dict_self_employed = {}, {}\n",
    "label_dict_temp5 = dataset.groupby(['Self_Employed_encoded'])['Self_Employed'].unique().apply(list).to_dict()\n",
    "for i in label_dict_temp5.keys():\n",
    "    label_dict_self_employed[label_dict_temp5[i][0]] = i\n",
    "label_dict_self_employed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "q9D53Um76dPQ",
    "outputId": "0ee8c644-8000-4646-9ee3-6893c5c340cd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Rural': 0, 'Semiurban': 1, 'Urban': 2}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For 'Property_Area_encoded' Column\n",
    "label_dict_temp6, label_dict_property_area = {}, {}\n",
    "label_dict_temp6 = dataset.groupby(['Property_Area_encoded'])['Property_Area'].unique().apply(list).to_dict()\n",
    "for i in label_dict_temp6.keys():\n",
    "    label_dict_property_area[label_dict_temp6[i][0]] = i\n",
    "label_dict_property_area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "laxQBrAv6pik",
    "outputId": "508c52a8-f00a-4baa-f9d5-3a0e6bc76e1b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'N': 0, 'Y': 1}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For 'Loan_Status_encoded' Column\n",
    "label_dict_temp7, label_dict_loan_status = {}, {}\n",
    "label_dict_temp7 = dataset.groupby(['Loan_Status_encoded'])['Loan_Status'].unique().apply(list).to_dict()\n",
    "for i in label_dict_temp7.keys():\n",
    "    label_dict_loan_status[label_dict_temp7[i][0]] = i\n",
    "label_dict_loan_status"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DkipgFVA7WIJ"
   },
   "source": [
    "**Applying Changes on Test Dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 249
    },
    "id": "mGv-0-S77bjU",
    "outputId": "9f1b311c-6761-4bed-a74b-f832acdb9a4c"
   },
   "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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001015</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5720</td>\n",
       "      <td>0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001022</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3076</td>\n",
       "      <td>1500</td>\n",
       "      <td>126.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001031</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5000</td>\n",
       "      <td>1800</td>\n",
       "      <td>208.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001035</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2340</td>\n",
       "      <td>2546</td>\n",
       "      <td>100.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001051</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3276</td>\n",
       "      <td>0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
       "0  LP001015   Male     Yes          0      Graduate            No   \n",
       "1  LP001022   Male     Yes          1      Graduate            No   \n",
       "2  LP001031   Male     Yes          2      Graduate            No   \n",
       "3  LP001035   Male     Yes          2      Graduate            No   \n",
       "4  LP001051   Male      No          0  Not Graduate            No   \n",
       "\n",
       "   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",
       "   Credit_History Property_Area  \n",
       "0             1.0         Urban  \n",
       "1             1.0         Urban  \n",
       "2             1.0         Urban  \n",
       "3             NaN         Urban  \n",
       "4             1.0         Urban  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(367, 12)\n"
     ]
    }
   ],
   "source": [
    "# test_dataset = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/Loan Prediction/test.csv')\n",
    "test_dataset = pd.read_csv('test.csv')\n",
    "display(test_dataset.head())\n",
    "print(test_dataset.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "qPOV8VzkkOZ3"
   },
   "outputs": [],
   "source": [
    "# filling numerical missing data\n",
    "test_dataset['LoanAmount'] = test_dataset['LoanAmount'].fillna(dataset['LoanAmount'].mean())\n",
    "test_dataset['Loan_Amount_Term'] = test_dataset['Loan_Amount_Term'].fillna(dataset['Loan_Amount_Term'].mean())\n",
    "test_dataset['Credit_History'] = test_dataset['Credit_History'].fillna(dataset['Credit_History'].mean())\n",
    "\n",
    "# filling categorical missing data\n",
    "test_dataset['Gender'] = test_dataset['Gender'].fillna(dataset['Gender'].mode()[0])\n",
    "test_dataset['Married'] = test_dataset['Married'].fillna(dataset['Married'].mode()[0])\n",
    "test_dataset['Dependents'] = test_dataset['Dependents'].fillna(dataset['Dependents'].mode()[0])\n",
    "test_dataset['Self_Employed'] = test_dataset['Self_Employed'].fillna(dataset['Self_Employed'].mode()[0])\n",
    "\n",
    "test_dataset['Total_income'] = test_dataset['ApplicantIncome'] + test_dataset['CoapplicantIncome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "XuvvOqqp7mQg"
   },
   "outputs": [],
   "source": [
    "# # Adding the dictionary to new columns\n",
    "\n",
    "test_dataset['Gender_encoded'] = test_dataset['Gender'].map(label_dict_gender)\n",
    "test_dataset['Married_encoded'] = test_dataset['Married'].map(label_dict_married)\n",
    "test_dataset['Dependents_encoded'] = test_dataset['Dependents'].map(label_dict_dependents)\n",
    "test_dataset['Education_encoded'] = test_dataset['Education'].map(label_dict_education)\n",
    "test_dataset['Self_Employed_encoded'] = test_dataset['Self_Employed'].map(label_dict_self_employed)\n",
    "test_dataset['Property_Area_encoded'] = test_dataset['Property_Area'].map(label_dict_property_area)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "QEaZ3nUitwHf"
   },
   "outputs": [],
   "source": [
    "# 'Loan_ID' is un-useful column, so deleting it.\n",
    "# As 'Gender', 'Married', \"Dependents\", \"Education\", \"Self_Employed\", 'Property_Area',\"Loan_Status\" columns have been encoded\n",
    "cols = ['Loan_ID','Gender', 'Married', \"Dependents\", \"Education\", \"Self_Employed\", 'Property_Area',\"Loan_Status\"]\n",
    "dataset.drop(columns=cols,inplace=True)\n",
    "cols.pop() # Removing \"Loan_Status\" from cols list\n",
    "test_dataset.drop(columns=cols,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "fjcpjoaQIXHt"
   },
   "outputs": [],
   "source": [
    "# specify input and output attributes\n",
    "x = dataset.drop(columns=['Loan_Status_encoded'], axis=1)\n",
    "y = dataset['Loan_Status_encoded']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "jB9coC3vIY71"
   },
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3KXQz4Cv6RZp"
   },
   "source": [
    "### <span style='background :yellow' >6. Feature Selection :</span> \n",
    " - **Feature selection** is the process of reducing the number of input variables when developing a predictive model to reduce the computational cost of modeling and, in some cases, to improve the performance of the model.\n",
    " - We are using Sequential Feature Selector to select the features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ojWDNyyiZYnY",
    "outputId": "67b72eb8-7107-4f58-e33e-6820a893ef85"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3220789523067532\n",
      "['CoapplicantIncome', 'LoanAmount', 'Loan_Amount_Term', 'Credit_History', 'Total_income', 'Gender_encoded', 'Married_encoded', 'Dependents_encoded', 'Education_encoded', 'Self_Employed_encoded', 'Property_Area_encoded']\n"
     ]
    }
   ],
   "source": [
    "# Sequential Forward Selection(sfs)\n",
    "feature_score_list = []\n",
    "feature_list = []\n",
    "for i in range(1,13):\n",
    "    sfs = SFS(LinearRegression(), k_features=i, forward=True, floating=False, scoring = 'r2', cv = 0)\n",
    "    sfs.fit(x_train, y_train)\n",
    "    selected_features = list(sfs.k_feature_names_)\n",
    "    feature_list.append(selected_features)\n",
    "    feature_score_list.append(sfs.k_score_)\n",
    "\n",
    "max_score = feature_score_list.index(max(feature_score_list))\n",
    "print(feature_score_list[max_score])\n",
    "print(feature_list[max_score])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "o49JV3Wcy_zR"
   },
   "source": [
    "### <span style='background :yellow' >7. Models :</span> \n",
    " - **Supervised learning** is where you have input variables (x) and an output variable (Y) and you use an\n",
    "algorithm to learn the mapping function from the input to the output.\n",
    "Y = f(X)\n",
    "The goal is to approximate the mapping function so well that when you have new input data (x) that you\n",
    "can predict the output variables (Y) for that data.\n",
    "It is called supervised learning because the process of an algorithm learning from the training dataset can\n",
    "be thought of as a teacher supervising the learning process. We know the correct answers, the algorithm\n",
    "iteratively makes predictions on the validation data and is corrected by the teacher."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ksCC-RIAbIG2"
   },
   "source": [
    "#### **1. Random Forest Regressor**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max:  45\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = []\n",
    "n_estimator_options = [10,15,20,25,30,35,40,45,50,55,60,65,70,75,80,85,90,95,100]\n",
    "# None means 1 unless in a joblib.parallel_backend context. -1 means using all processors\n",
    "for trees in n_estimator_options:\n",
    "    model = RandomForestClassifier(trees, oob_score=True, n_jobs=-1, random_state=101)\n",
    "    model.fit(x_train[feature_list[max_score]], y_train)\n",
    "    Y_pred_val = model.predict(x_test[feature_list[max_score]])\n",
    "    accuracy = np.mean(y_test == Y_pred_val)\n",
    "    results.append(accuracy)\n",
    "\n",
    "plt.figure(figsize=(8,4))\n",
    "pd.Series(results, n_estimator_options).plot(color=\"red\",linestyle='dashed', marker='o', markerfacecolor='blue')\n",
    "max_n_estimator = n_estimator_options[results.index(max(results))]\n",
    "print(\"Max: \",max_n_estimator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max:  auto\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = []\n",
    "max_features_options = ['auto',None,'sqrt',0.95,0.75,0.5,0.25,0.10]\n",
    "for trees in max_features_options:\n",
    "    model = RandomForestClassifier(n_estimators = max_n_estimator, oob_score=True, n_jobs=-1, random_state=101, max_features = trees)\n",
    "    model.fit(x_train[feature_list[max_score]], y_train)\n",
    "    Y_pred_val = model.predict(x_test[feature_list[max_score]])\n",
    "    accuracy = np.mean(y_test == Y_pred_val)\n",
    "    results.append(accuracy)\n",
    "\n",
    "plt.figure(figsize=(8,4))\n",
    "pd.Series(results, max_features_options).plot(kind=\"bar\",color=\"green\",ylim=(0.7,1))\n",
    "max_max_features = max_features_options[results.index(max(results))]\n",
    "print(\"Max: \",max_max_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max:  10\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = []\n",
    "min_samples_leaf_options = [5,10,15,20,25,30,35,40,45,50]\n",
    "for trees in min_samples_leaf_options:\n",
    "    model = RandomForestClassifier(n_estimators=max_n_estimator, oob_score=True, n_jobs=-1, random_state=101, max_features = max_max_features, min_samples_leaf = trees)\n",
    "    model.fit(x_train[feature_list[max_score]], y_train)\n",
    "    Y_pred_val = model.predict(x_test[feature_list[max_score]])\n",
    "    accuracy = np.mean(y_test == Y_pred_val)\n",
    "    results.append(accuracy)\n",
    "\n",
    "plt.figure(figsize=(8,4))\n",
    "pd.Series(results, min_samples_leaf_options).plot(color=\"blue\",linestyle='dashed', marker='o', markerfacecolor='yellow')\n",
    "max_min_samples_leaf = min_samples_leaf_options[results.index(max(results))]\n",
    "print(\"Max: \",max_min_samples_leaf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(min_samples_leaf=10, n_estimators=45, n_jobs=-1,\n",
       "                       oob_score=True, random_state=101)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Performing Random Forest Classifier Classifier\n",
    "model_rf = RandomForestClassifier(n_estimators=max_n_estimator, oob_score=True, n_jobs=-1, random_state=101, max_features = max_max_features, min_samples_leaf = max_min_samples_leaf)\n",
    "# n_estimators=70, oob_score=True, n_jobs=-1, random_state=101, max_features = None, min_samples_leaf = 30\n",
    "model_rf.fit(x_train[feature_list[max_score]], y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ggijpyV5IckF",
    "outputId": "06055da1-cd48-442e-b8ec-215b093f4db4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.39      0.55        54\n",
      "           1       0.75      0.98      0.85       100\n",
      "\n",
      "    accuracy                           0.77       154\n",
      "   macro avg       0.83      0.68      0.70       154\n",
      "weighted avg       0.81      0.77      0.74       154\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Testing on Val Set\n",
    "Y_pred_val = model_rf.predict(x_test[feature_list[max_score]])\n",
    "print(classification_report(y_test,Y_pred_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Score is 77.27272727272727\n",
      "\n",
      "Confusion Matrix: \n",
      " [[21 33]\n",
      " [ 2 98]]\n"
     ]
    }
   ],
   "source": [
    "print(\"Model Score is\", model_rf.score(x_test[feature_list[max_score]], y_test)*100)\n",
    "print(\"\\nConfusion Matrix: \\n\",confusion_matrix(y_test,Y_pred_val))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wOpgEH69x4Pc"
   },
   "source": [
    "#### **2. Decision Tree Classifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max:  4\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = []\n",
    "max_depth_options = [2,4,6,8,10,12,14,16,18,20]\n",
    "for trees in max_depth_options:\n",
    "    model = DecisionTreeClassifier(max_depth=trees, random_state=101)\n",
    "    model.fit(x_train[feature_list[max_score]], y_train)\n",
    "    Y_pred_val = model.predict(x_test[feature_list[max_score]])\n",
    "    accuracy = np.mean(y_test == Y_pred_val)\n",
    "    results.append(accuracy)\n",
    "\n",
    "plt.figure(figsize=(8,4))\n",
    "pd.Series(results, max_depth_options).plot(color=\"red\",linestyle='dashed', marker='o', markerfacecolor='blue')\n",
    "max_max_depth = max_depth_options[results.index(max(results))]\n",
    "print(\"Max: \",max_max_depth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max:  0.5\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = []\n",
    "max_features_options = ['auto',None,'sqrt',0.95,0.75,0.5,0.25,0.10]\n",
    "for trees in max_features_options:\n",
    "    model = DecisionTreeClassifier(max_depth=max_max_depth, random_state=101, max_features = trees)\n",
    "    model.fit(x_train[feature_list[max_score]], y_train)\n",
    "    Y_pred_val = model.predict(x_test[feature_list[max_score]])\n",
    "    accuracy = np.mean(y_test == Y_pred_val)\n",
    "    results.append(accuracy)\n",
    "\n",
    "plt.figure(figsize=(8,4))\n",
    "pd.Series(results, max_features_options).plot(kind=\"bar\",color=\"green\",ylim=(0.7,1))\n",
    "max_max_features = max_features_options[results.index(max(results))]\n",
    "print(\"Max: \",max_max_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max:  10\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = []\n",
    "min_samples_leaf_options = [5,10,15,20,25,30,35,40,45,50]\n",
    "for trees in min_samples_leaf_options:\n",
    "    model = DecisionTreeClassifier(max_depth=max_max_depth, random_state=101, max_features = max_max_features, min_samples_leaf = trees)\n",
    "    model.fit(x_train[feature_list[max_score]], y_train)\n",
    "    Y_pred_val = model.predict(x_test[feature_list[max_score]])\n",
    "    accuracy = np.mean(y_test == Y_pred_val)\n",
    "    results.append(accuracy)\n",
    "\n",
    "plt.figure(figsize=(8,4))\n",
    "pd.Series(results, min_samples_leaf_options).plot(color=\"blue\",linestyle='dashed', marker='o', markerfacecolor='yellow')\n",
    "max_min_samples_leaf = min_samples_leaf_options[results.index(max(results))]\n",
    "print(\"Max: \",max_min_samples_leaf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=4, max_features=0.5, min_samples_leaf=10,\n",
       "                       random_state=101)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_dtree = DecisionTreeClassifier(max_depth=max_max_depth, random_state=101, max_features = max_max_features, min_samples_leaf = max_min_samples_leaf)\n",
    "model_dtree.fit(x_train[feature_list[max_score]], y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VkvAD0nRIfPK",
    "outputId": "6d21ead1-6153-41a4-fa23-e365b56e7145"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.44      0.58        54\n",
      "           1       0.76      0.95      0.84       100\n",
      "\n",
      "    accuracy                           0.77       154\n",
      "   macro avg       0.79      0.70      0.71       154\n",
      "weighted avg       0.78      0.77      0.75       154\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Testing on Val Set\n",
    "Y_pred_val = model_dtree.predict(x_test[feature_list[max_score]])\n",
    "print(classification_report(y_test,Y_pred_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Score is 77.27272727272727\n",
      "\n",
      "Confusion Matrix: \n",
      " [[24 30]\n",
      " [ 5 95]]\n"
     ]
    }
   ],
   "source": [
    "print(\"Model Score is\", model_dtree.score(x_test[feature_list[max_score]], y_test)*100)\n",
    "print(\"\\nConfusion Matrix: \\n\",confusion_matrix(y_test,Y_pred_val))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kFhH2_2uyF7p"
   },
   "source": [
    "#### **3. Logistic Regression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qDLDtSYoIgaL",
    "outputId": "b039bcc3-b61d-45c2-8ab1-f7e029a89e2e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is 77.92207792207793\n"
     ]
    }
   ],
   "source": [
    "model3 = LogisticRegression()\n",
    "\n",
    "model3.fit(x_train[feature_list[max_score]], y_train)\n",
    "print(\"Accuracy is\", model3.score(x_test[feature_list[max_score]], y_test)*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aulvHNwvy0Cf"
   },
   "source": [
    "#### **Confusion Matrics**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tq2zRkugIjBi",
    "outputId": "06c500f3-6a1e-4e7b-a344-2ccb9f25e05a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[22, 32],\n",
       "       [ 2, 98]], dtype=int64)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = model3.predict(x_test[feature_list[max_score]])\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "id": "3scgB4maVzWF",
    "outputId": "39a38f6d-c40d-42e7-c9e1-aa8453a32b48"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cm,annot = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nBYCYcD0zGGy"
   },
   "source": [
    "### <span style='background :yellow' >8. Saving the Model</span> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "id": "_R-oa5uoIlry"
   },
   "outputs": [],
   "source": [
    "# Saving the model\n",
    "with open(\"model.bin\", 'wb') as f_out:\n",
    "    # model in this below parameter means, model variable from random forest classifier\n",
    "    pickle.dump(model3, f_out)\n",
    "    f_out.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the model from saved file\n",
    "with open(\"model.bin\", 'rb') as f_in:\n",
    "    # model in this below parameter means, model variable from random forest classifier\n",
    "    model = pickle.load(f_in)\n",
    "    f_out.close()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [
    "7XInObjX3Vl9",
    "9T7jKJuP6GQa",
    "6dg2Nn-v9OyK",
    "3KXQz4Cv6RZp"
   ],
   "name": "Loan Prediction.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
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