Project Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "6aa984e7-2543-4c43-8883-9f6fb4006bb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "import scipy.stats as stats\n",
    "import statistics \n",
    "from scipy.stats import norm\n",
    "from math import sqrt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "ccbd64c2-d59b-4905-920e-6a97bb26acdd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the dataset\n",
    "loan_data = pd.read_csv('loan_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "7d495186-c2a8-4c55-8176-5444d5886f38",
   "metadata": {},
   "outputs": [],
   "source": [
    "loan_data=loan_data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "df0a179e-3daa-4508-9b5d-568dd004f4ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 499 entries, 0 to 613\n",
      "Data columns (total 12 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   Gender              499 non-null    object \n",
      " 1   Married             499 non-null    object \n",
      " 2   Dependents          499 non-null    object \n",
      " 3   Education           499 non-null    object \n",
      " 4   Self_Employed       499 non-null    object \n",
      " 5   Applicant_Income    499 non-null    int64  \n",
      " 6   Coapplicant_Income  499 non-null    float64\n",
      " 7   Loan_Amount         499 non-null    int64  \n",
      " 8   Term                499 non-null    float64\n",
      " 9   Credit_History      499 non-null    float64\n",
      " 10  Area                499 non-null    object \n",
      " 11  Status              499 non-null    object \n",
      "dtypes: float64(3), int64(2), object(7)\n",
      "memory usage: 50.7+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Display basic information about the dataset\n",
    "print(loan_data.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "ba008881-52f1-4582-af1e-d5f342fb9c6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Applicant_Income</th>\n",
       "      <th>Coapplicant_Income</th>\n",
       "      <th>Loan_Amount</th>\n",
       "      <th>Term</th>\n",
       "      <th>Credit_History</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.990000e+02</td>\n",
       "      <td>4.990000e+02</td>\n",
       "      <td>4.990000e+02</td>\n",
       "      <td>499.000000</td>\n",
       "      <td>499.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.336128e+05</td>\n",
       "      <td>1.566996e+05</td>\n",
       "      <td>1.395251e+07</td>\n",
       "      <td>342.012024</td>\n",
       "      <td>0.851703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.618157e+05</td>\n",
       "      <td>2.580955e+05</td>\n",
       "      <td>8.345237e+06</td>\n",
       "      <td>64.860368</td>\n",
       "      <td>0.355750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.500000e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.885500e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>9.800000e+06</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.859000e+05</td>\n",
       "      <td>1.086000e+05</td>\n",
       "      <td>1.260000e+07</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.825000e+05</td>\n",
       "      <td>2.251500e+05</td>\n",
       "      <td>1.665000e+07</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.100000e+06</td>\n",
       "      <td>3.383700e+06</td>\n",
       "      <td>6.000000e+07</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Applicant_Income  Coapplicant_Income   Loan_Amount        Term  \\\n",
       "count      4.990000e+02        4.990000e+02  4.990000e+02  499.000000   \n",
       "mean       5.336128e+05        1.566996e+05  1.395251e+07  342.012024   \n",
       "std        5.618157e+05        2.580955e+05  8.345237e+06   64.860368   \n",
       "min        1.500000e+04        0.000000e+00  0.000000e+00   36.000000   \n",
       "25%        2.885500e+05        0.000000e+00  9.800000e+06  360.000000   \n",
       "50%        3.859000e+05        1.086000e+05  1.260000e+07  360.000000   \n",
       "75%        5.825000e+05        2.251500e+05  1.665000e+07  360.000000   \n",
       "max        8.100000e+06        3.383700e+06  6.000000e+07  480.000000   \n",
       "\n",
       "       Credit_History  \n",
       "count      499.000000  \n",
       "mean         0.851703  \n",
       "std          0.355750  \n",
       "min          0.000000  \n",
       "25%          1.000000  \n",
       "50%          1.000000  \n",
       "75%          1.000000  \n",
       "max          1.000000  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "eb962820-0aa1-4f9a-8b9e-587d6ff031d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature: Gender\n",
      "Gender\n",
      "Male      411\n",
      "Female     88\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Feature: Married\n",
      "Married\n",
      "Yes    324\n",
      "No     175\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Feature: Dependents\n",
      "Dependents\n",
      "0     284\n",
      "2      88\n",
      "1      84\n",
      "3+     43\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Feature: Education\n",
      "Education\n",
      "Graduate        395\n",
      "Not Graduate    104\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Feature: Self_Employed\n",
      "Self_Employed\n",
      "No     430\n",
      "Yes     69\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Feature: Area\n",
      "Area\n",
      "Semiurban    195\n",
      "Urban        159\n",
      "Rural        145\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Feature: Status\n",
      "Status\n",
      "Y    341\n",
      "N    158\n",
      "Name: count, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Get list of categorical features\n",
    "categorical_features = loan_data.select_dtypes(include=['object']).columns.tolist()\n",
    "\n",
    "# Calculate count of unique values for each categorical feature\n",
    "for feature in categorical_features:\n",
    "    unique_values_count = loan_data[feature].value_counts()\n",
    "    print(\"Feature:\", feature)\n",
    "    print(unique_values_count)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "047c7f8b-2b5f-4259-a730-2d8ce3d2486d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>Applicant_Income</th>\n",
       "      <th>Coapplicant_Income</th>\n",
       "      <th>Loan_Amount</th>\n",
       "      <th>Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Area</th>\n",
       "      <th>Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>584900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15000000</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>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>458300</td>\n",
       "      <td>150800.0</td>\n",
       "      <td>12800000</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>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>300000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6600000</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>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>258300</td>\n",
       "      <td>235800.0</td>\n",
       "      <td>12000000</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>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>600000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14100000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>541700</td>\n",
       "      <td>419600.0</td>\n",
       "      <td>26700000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>233300</td>\n",
       "      <td>151600.0</td>\n",
       "      <td>9500000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3+</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>303600</td>\n",
       "      <td>250400.0</td>\n",
       "      <td>15800000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>400600</td>\n",
       "      <td>152600.0</td>\n",
       "      <td>16800000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>1284100</td>\n",
       "      <td>1096800.0</td>\n",
       "      <td>34900000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>320000</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>7000000</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>12</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>307300</td>\n",
       "      <td>810600.0</td>\n",
       "      <td>20000000</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>13</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>185300</td>\n",
       "      <td>284000.0</td>\n",
       "      <td>11400000</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>14</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>129900</td>\n",
       "      <td>108600.0</td>\n",
       "      <td>1700000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>495000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12500000</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": [
       "   Gender Married Dependents     Education Self_Employed  Applicant_Income  \\\n",
       "0    Male      No          0      Graduate            No            584900   \n",
       "1    Male     Yes          1      Graduate            No            458300   \n",
       "2    Male     Yes          0      Graduate           Yes            300000   \n",
       "3    Male     Yes          0  Not Graduate            No            258300   \n",
       "4    Male      No          0      Graduate            No            600000   \n",
       "5    Male     Yes          2      Graduate           Yes            541700   \n",
       "6    Male     Yes          0  Not Graduate            No            233300   \n",
       "7    Male     Yes         3+      Graduate            No            303600   \n",
       "8    Male     Yes          2      Graduate            No            400600   \n",
       "9    Male     Yes          1      Graduate            No           1284100   \n",
       "10   Male     Yes          2      Graduate            No            320000   \n",
       "12   Male     Yes          2      Graduate            No            307300   \n",
       "13   Male      No          0      Graduate            No            185300   \n",
       "14   Male     Yes          2      Graduate            No            129900   \n",
       "15   Male      No          0      Graduate            No            495000   \n",
       "\n",
       "    Coapplicant_Income  Loan_Amount   Term  Credit_History       Area Status  \n",
       "0                  0.0     15000000  360.0             1.0      Urban      Y  \n",
       "1             150800.0     12800000  360.0             1.0      Rural      N  \n",
       "2                  0.0      6600000  360.0             1.0      Urban      Y  \n",
       "3             235800.0     12000000  360.0             1.0      Urban      Y  \n",
       "4                  0.0     14100000  360.0             1.0      Urban      Y  \n",
       "5             419600.0     26700000  360.0             1.0      Urban      Y  \n",
       "6             151600.0      9500000  360.0             1.0      Urban      Y  \n",
       "7             250400.0     15800000  360.0             0.0  Semiurban      N  \n",
       "8             152600.0     16800000  360.0             1.0      Urban      Y  \n",
       "9            1096800.0     34900000  360.0             1.0  Semiurban      N  \n",
       "10             70000.0      7000000  360.0             1.0      Urban      Y  \n",
       "12            810600.0     20000000  360.0             1.0      Urban      Y  \n",
       "13            284000.0     11400000  360.0             1.0      Rural      N  \n",
       "14            108600.0      1700000  120.0             1.0      Urban      Y  \n",
       "15                 0.0     12500000  360.0             1.0      Urban      Y  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_data.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5f98dda-5bb9-463c-82a0-c346f0afcac4",
   "metadata": {},
   "source": [
    "## Probability Mass Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "18be5a64-8aa5-489b-9a9a-ea957aeb6878",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PMF for 'Gender' field (Loan Approval: Yes):\n",
      "Gender\n",
      "Female    0.16129\n",
      "Male      0.83871\n",
      "Name: proportion, dtype: float64\n",
      "----------------------------------------------\n",
      "PMF for 'Married' field (Loan Approval: Yes):\n",
      "Married\n",
      "No     0.316716\n",
      "Yes    0.683284\n",
      "Name: proportion, dtype: float64\n",
      "----------------------------------------------\n",
      "PMF for 'Dependents' field (Loan Approval: Yes):\n",
      "Dependents\n",
      "0     0.563050\n",
      "1     0.161290\n",
      "2     0.193548\n",
      "3+    0.082111\n",
      "Name: proportion, dtype: float64\n",
      "----------------------------------------------\n",
      "PMF for 'Education' field (Loan Approval: Yes):\n",
      "Education\n",
      "Graduate        0.815249\n",
      "Not Graduate    0.184751\n",
      "Name: proportion, dtype: float64\n",
      "----------------------------------------------\n",
      "PMF for 'Self_Employed' field (Loan Approval: Yes):\n",
      "Self_Employed\n",
      "No     0.868035\n",
      "Yes    0.131965\n",
      "Name: proportion, dtype: float64\n",
      "----------------------------------------------\n",
      "PMF for 'Credit_History' field (Loan Approval: Yes):\n",
      "Credit_History\n",
      "0.0    0.020528\n",
      "1.0    0.979472\n",
      "Name: proportion, dtype: float64\n",
      "----------------------------------------------\n",
      "PMF for 'Area' field (Loan Approval: Yes):\n",
      "Area\n",
      "Rural        0.258065\n",
      "Semiurban    0.445748\n",
      "Urban        0.296188\n",
      "Name: proportion, dtype: float64\n",
      "----------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Function to calculate PMF for a categorical field given loan approval status \"Yes\"\n",
    "def calculate_pmf_loan_approval_yes(data, column):\n",
    "    filtered_data = data[data['Status'] == 'Y']\n",
    "    pmf = filtered_data[column].value_counts(normalize=True).sort_index()\n",
    "    return pmf\n",
    "\n",
    "# List of categorical fields\n",
    "categorical_fields = ['Gender', 'Married', 'Dependents', 'Education', 'Self_Employed', 'Credit_History', 'Area']\n",
    "\n",
    "# Calculate and print PMF for loan approval status \"Yes\" for each categorical field\n",
    "for field in categorical_fields:\n",
    "    pmf_loan_approval_yes = calculate_pmf_loan_approval_yes(loan_data, field)\n",
    "    print(\"PMF for '{}' field (Loan Approval: Yes):\".format(field))\n",
    "    print(pmf_loan_approval_yes)\n",
    "    print(\"----------------------------------------------\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da9f35c1-d80c-4891-8053-091d614feaac",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64a5b28a-b6d0-49e5-9323-cd2e22d817a6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e402fbf-9885-4f68-a99b-81ab0f864dc2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "aa058c5d-7acb-4df7-90ea-b45d892afb5f",
   "metadata": {},
   "source": [
    "## Frequency Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a4905f9a-7c2e-4633-8e15-11a09fa31963",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\ProgramData\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(loan_data['Loan_Amount'], bins=20, kde=True)\n",
    "plt.title('Distribution of Loan Amount')\n",
    "plt.xlabel('Loan Amount')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "57d4609f-dc33-4731-8a20-e16dd4b1bc14",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\ProgramData\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(loan_data['Loan_Amount'])\n",
    "plt.title('Distribution of Loan Amount')\n",
    "plt.xlabel('Loan Amount')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "384200b7-65b5-4ccf-9cb7-eb810ef6a857",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define loan amount ranges\n",
    "loan_ranges = [0, 5000000, 10000000, 15000000, 20000000, 25000000, 30000000, 35000000, 40000000, 45000000, 50000000, float('inf')]\n",
    "labels = ['0-5000k', '5000k-10M', '10M-15M', '15M-20M', '20M-25M', '25M-30M', '30M-35M', '35M-40M', '40M-45M', '45M-50M', '50M+']\n",
    "\n",
    "# Calculate count of loans and count of approved loans within each range\n",
    "loan_data['Loan_Amount_Range'] = pd.cut(loan_data['Loan_Amount'], bins=loan_ranges, labels=labels)\n",
    "class_distribution = loan_data['Loan_Amount_Range'].value_counts().sort_index()\n",
    "approved_loans_distribution = loan_data[loan_data['Status'] == 'Y']['Loan_Amount_Range'].value_counts().sort_index()\n",
    "\n",
    "# Plot the class distribution of loan amounts\n",
    "plt.figure(figsize=(12, 6))\n",
    "width = 0.4\n",
    "plt.bar(class_distribution.index, class_distribution.values, width, label='Total Loans', color='red')\n",
    "plt.bar(approved_loans_distribution.index, approved_loans_distribution.values, width, label='Approved Loans', color='green')\n",
    "plt.xlabel('Loan Amount Range')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Class Distribution of Loan Amounts and Approved Loans')\n",
    "plt.legend()\n",
    "plt.xticks(rotation=45)\n",
    "plt.grid(axis='y')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cba7d705-3a65-4522-ad85-3ef19e6d3b36",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\ProgramData\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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pscceU1RUlB588MEa1fqLX/xCmZmZ+s1vfqPNmzerX79+8ng8+uqrr9ShQweNGDFCAwcOlMPh0C9+8Qvdf//9Ki0t1YwZM5Sbm1ujcx2tdevWio6O1uuvv64OHTooLi5OGRkZJ53aWlu9e/fWr371Kz3xxBPat2+frr76ajmdTn3zzTeKiYnR2LFj1b59e/3f//2fpk+fLpvNpsGDB2vHjh165JFH1KxZs+O6AJ6J559/Xpdeeql69+6t3/72t2rZsqUKCgr0ww8/6P3339fixYvr7FwALCSwvS4AIHC8ne28N4fDYaamppp9+vQxJ0+ebObk5Bz3nGO7rq1YscK87rrrzBYtWphOp9NMTk42+/TpYy5YsMDveYsWLTIvuOAC0+l0mpLMkSNH+h1v//79pzyXaVZ2krvqqqvMt99+2+zYsaPpcDjMli1bmlOnTj3u+Vu2bDEHDRpkJiQkmI0aNTLHjh1rfvjhh8d17Tt06JB5ww03mA0aNDANw/A7p07QbXDdunXm0KFDzcTERNPhcJhdu3Y9rtPbiTq6meaRLnun0xnuv//9r3n55ZebsbGxZnR0tHnxxReb77///gmPd6qufdOmTTMlmfPnz692n7///e+mJPOdd94xTfP032vva3311VfN3/3ud2ajRo1Mp9Np9u7d26+LnGmeXtc+0zTNkpIS89FHHzXbtm1rOhwOMzk52bz88svN5cuX+/Z5//33za5du5pRUVFmkyZNzPvuu8/86KOPjvt8+/TpY3bs2PG41zty5MjjujW+8cYb5rnnnmtGRkZW22ny2Nd9oq59x/4+n6iLpNvtNp977jmzU6dOpsPhMBMTE82ePXv6fcZut9t86qmnzHbt2pmRkZFmSkqKecstt5i7d+8+7j080Wv0fobHkmSOGTPGb9v27dvNUaNGmU2aNDEjIyPNRo0amb169TKfeOKJat8DAOHNMM1TtKQCACAMtWzZUp06ddIHH3xw0v0+//xz9evXT2+99ZZuuOGGs1QdACDQWCMFAAAAADVEkAIAAACAGmJqHwAAAADUECNSAAAAAFBDBCkAAAAAqCGCFAAAAADUEBfkleTxeLR3717Fx8fX+CKPAAAAAKzDNE0VFBQoIyNDNlv1404EKUl79+5Vs2bNAl0GAAAAgCCxe/duNW3atNrHCVKS4uPjJVW+WQkJCQGuBgAAAECg5Ofnq1mzZr6MUB2ClOSbzpeQkECQAgAAAHDKJT80mwAAAACAGiJIAQAAAEANEaQAAAAAoIYIUgAAAABQQwQpAAAAAKghghQAAAAA1BBBCgAAAABqiCAFAAAAADVEkAIAAACAGiJIAQAAAEANEaQAAAAAoIYIUgAAAABQQwQpAAAAAKghghQAAAAA1BBBCgAAAABqiCAFAAAAADVEkAIAAACAGiJIhTlXhVurdxySx2MGuhQAAAAgZBCkwtxzC7fqhr+v0EPz1gW6FAAAACBkEKTCmGmaev/bvZKkuat2a85XuwJcEQAAABAaCFJhbGNWvn46XOK7/9iC9fp6V24AKwIAAABCA0EqjC3amCNJGtAhTVd2TFe529RvX1ujnILSAFcGAAAABDeCVBhbuClbkjTovDT95cauapMap335Lo17Y61Mk+YTAAAAQHUIUmEqK69E63/Kl2FIl3dIVZwzQi/9qrvsNkMrfjyo7HxGpQAAAIDqEKTC1KKN+yRJ3Zs3VEqcU5LUulGc2qbGSZI27s0PWG0AAABAsCNIhalPq4LUgPPS/Laf1zhBkrSBIAUAAABUiyAVhgpKy7Xyx4OSpIHHBqmMyiDFiBQAAABQPYJUGFq6Zb/K3abOaRSr1o3i/B7zBqkNWXmBKA0AAAAICQSpMORdHzWwQ9pxj3mn9u0+VKK8kvKzWhcAAAAQKghSYabc7dHi7yuvH3XstD5JahDjUJMG0ZKkTVlM7wMAAABOhCAVZn7cX6T80grFOyN0QfOGJ9yHdVIAAADAyRGkwsyuQ8WSpBYpMbLbjBPu453et5ERKQAAAOCECFJhZndVkGrWMKbafTpm0AIdAAAAOBmCVJjZnVsZpJonVR+kvFP7fsgpUFmF56zUBQAAAIQSglSY2X2oRJLU9CRBqkmDaCVGR6rcbWrLvoKzVRoAAAAQMghSYebI1L7oavcxDIN1UgAAAMBJEKTCiGmavql9zU4yIiXRuQ8AAAA4mYAGqRkzZqhLly5KSEhQQkKCevbsqY8++sj3uGmamjRpkjIyMhQdHa2+fftqw4YNfsdwuVwaO3asUlJSFBsbq2HDhmnPnj1n+6WEhENFZSouc0uS71pR1elIkAIAAACqFdAg1bRpUz355JNavXq1Vq9ercsvv1zXXHONLyw9/fTTmjp1ql544QWtWrVK6enpGjhwoAoKjqzbGT9+vObNm6e5c+dq2bJlKiws1NVXXy232x2olxW0dudWro9KS3AqKtJ+0n19I1JZ+fJ4zHqvDQAAAAglAQ1SQ4cO1ZAhQ9SuXTu1a9dOf/7znxUXF6eVK1fKNE1NmzZNDz/8sIYPH65OnTrplVdeUXFxsebMmSNJysvL08yZM/Xss89qwIABuuCCC/Taa69p3bp1WrRoUSBfWlDyro86Wcc+r9aN4uSIsKnQVeGbDggAAACgUtCskXK73Zo7d66KiorUs2dPbd++XdnZ2Ro0aJBvH6fTqT59+mj58uWSpDVr1qi8vNxvn4yMDHXq1Mm3z4m4XC7l5+f73cLBrtO4hpRXpN2m9mnxkpjeBwAAABwr4EFq3bp1iouLk9Pp1G9+8xvNmzdP5513nrKzsyVJaWlpfvunpaX5HsvOzpbD4VDDhg2r3edEpkyZosTERN+tWbNmdfyqgtOeqpGlk7U+P5q3cx8X5gUAAAD8BTxItW/fXmvXrtXKlSv129/+ViNHjtTGjRt9jxuG4be/aZrHbTvWqfZ58MEHlZeX57vt3r37zF5EiPBeQ+pkrc+P1iY1TpK08xBT+wAAAICjBTxIORwOtWnTRj169NCUKVPUtWtXPf/880pPT5ek40aWcnJyfKNU6enpKisrU25ubrX7nIjT6fR1CvTewsHptj73yqjq7Lf3cEm91QQAAACEooAHqWOZpimXy6VWrVopPT1dCxcu9D1WVlampUuXqlevXpKk7t27KzIy0m+frKwsrV+/3rcPKrk9pi8QnW6QalI1cvVTLkEKAAAAOFpEIE/+0EMPafDgwWrWrJkKCgo0d+5cff755/r4449lGIbGjx+vyZMnq23btmrbtq0mT56smJgY3XzzzZKkxMREjR49Wvfee6+Sk5OVlJSkiRMnqnPnzhowYEAgX1rQyc4vVbnbVKTdUHpC1Gk9J6NB5X77CkpV7vYo0h50uRsAAAAIiIAGqX379ulXv/qVsrKylJiYqC5duujjjz/WwIEDJUn333+/SkpKdNdddyk3N1cXXXSRPv30U8XHx/uO8dxzzykiIkI33nijSkpK1L9/f82aNUt2+8mvkxRudh2snNbXpEG07LaTrzHzSol1yhFhU1mFR9l5pac9kgUAAABYnWGaZthfbTU/P1+JiYnKy8uz7Hqpf6/erfvf/k6926bo1dEXnfbz+j6zRDsOFmvu/12si89JrscKAQAAgMA73WzAXK0wsaeq817T07iG1NFoOAEAAAAcjyAVJnbnehtNnF7rc68mBCkAAADgOASpMLG7akSqWS1HpH4iSAEAAAA+BKkwsetQza4h5dXEF6RK67wmAAAAIFQRpMJAablbOQUuSVLzmgaphkztAwAAAI5FkAoDe6rWR8U67GoYE1mj5/qm9uWWiAaPAAAAQCWCVBjYnXtkWp9hnN41pLwaJ1ZelLek3K3DxeV1XhsAAAAQighSYaC2rc8lKSrSrpQ4pyQaTgAAAABeBKkwUNvW515NGlSOShGkAAAAgEoEqTCwJ7f2I1ISDScAAACAYxGkwsCBgjJJUlqCs1bPz0g80nACAAAAAEEqLBwoqmx9nhxbyyBV1blvbx5BCgAAAJAIUmHhYGHliFRKnKNWz/dO7eOivAAAAECliEAXgOPt2rVLBw4cqJNjlbtN5ZVUti3/6cfNKvipZtk5JSVFTRokVj6fqX0AAACAJIJU0Nm1a5fO7dBBJcXFdXI8e1yymo55RabHrX69fiapZhfVjY6J0VffrJMkHSh0qbTcrahIe53UBgAAAIQqglSQOXDggEqKi/XLB55RWvPWZ3y8w2WGPsuWoiNsmvC3d2r03H27tun1p+5TWUGuoiPtKil3KzuvVC1TYs+4LgAAACCUEaSCVFrz1mratuMZH8d9sEjK3qu4GKeatm1Xq2MYhqGMBlHatr9IPx0uIUgBAAAg7NFswuKKy9ySpGjHmU3Ha1J1DSouygsAAAAQpCyvpCpIxUSe2eBjkwZRkrgoLwAAACARpCzPOyIVc4YjUlyUFwAAADiCIGVxxeUVkupiah8X5QUAAAC8CFIWV2cjUg2qghQX5QUAAAAIUlZXUlfNJqqC1E+HS+Tx1OxaVAAAAIDVEKQsrriOmk2kJ0bJZkhlFR4dLCqri9IAAACAkEWQsjDTNI907TvDEalIu03JcU5JUk4B0/sAAAAQ3ghSFlbm9shtVk7DO9OpfZLUyBekXGd8LAAAACCUEaQszDutL9JuKNJ+5h91o/jKILWfIAUAAIAwR5CysCPT+s5sfZRXKkEKAAAAkESQsjTviFR05JlP65MYkQIAAAC8CFIWVlxWeTHeM2004eUdkaLZBAAAAMIdQcrC6qpjn1ej+ChJjEgBAAAABCkLKy6vm4vxeqUm0LUPAAAAkAhSllbXzSa87c8ZkQIAAEC4I0hZWH01myguc6vIVVEnxwQAAABCEUHKwup6jVSsM0KxVcdieh8AAADCGUHKwuq6a59EC3QAAABAIkhZlttjqrTCI6numk1IUmpV5z5aoAMAACCcEaQsqrSqY58hKaqO1khJjEgBAAAAEkHKsryNJqIi7bIZRp0dt1E8LdABAAAAgpRF+dZHOetuNEpiRAoAAACQCFKW5evYV4fT+iQplSAFAAAAEKSsqrhqjVRdNpqQmNoHAAAASAQpyyr2XUMqok6Py9Q+AAAAgCBlWd6pfXU9IuVtf36wyKUKt6dOjw0AAACECoKURdXHxXglKSnWIZshmaZ0qKisTo8NAAAAhAqClEUV11OzCbvNUEoc66QAAAAQ3ghSFlVSXj9rpCTWSQEAAAAEKQsyTdM3IlXXa6SkIy3QcwpK6/zYAAAAQCggSFlQuduU22NKqvs1UhIjUgAAAABByoK8jSYi7YYi7XX/EROkAAAAEO4IUhbkXR8VXceNJry8LdBpNgEAAIBwRZCyoNLyyus7RdVTkGJECgAAAOGOIGVBpVUjUvUVpI40myBIAQAAIDwRpCzIF6Qi6ufjPXpEyjTNejkHAAAAEMwIUhZUWlE5tc9Zz1P7SsrdKnRV1Ms5AAAAgGBGkLIgl29qX/18vDGOCMU5Ky/0yzopAAAAhCOClAXVd7MJiYYTAAAACG8EKQsqrfCukar/IEXDCQAAAIQjgpQFuXwjUvX38TIiBQAAgHAW0CA1ZcoUXXjhhYqPj1dqaqquvfZabd682W+f2267TYZh+N0uvvhiv31cLpfGjh2rlJQUxcbGatiwYdqzZ8/ZfClBxdu1r76aTUi0QAcAAEB4C2iQWrp0qcaMGaOVK1dq4cKFqqio0KBBg1RUVOS335VXXqmsrCzf7T//+Y/f4+PHj9e8efM0d+5cLVu2TIWFhbr66qvldrvP5ssJGkem9tX/iFROQWm9nQMAAAAIVhGBPPnHH3/sdz8zM1Opqalas2aNLrvsMt92p9Op9PT0Ex4jLy9PM2fO1KuvvqoBAwZIkl577TU1a9ZMixYt0hVXXFF/LyAImaZ51NS+elwjFVcZpA4WltXbOQAAAIBgFVRrpPLy8iRJSUlJfts///xzpaamql27dvr1r3+tnJwc32Nr1qxReXm5Bg0a5NuWkZGhTp06afny5Sc8j8vlUn5+vt/NKsoqPPJeItdZj2ukUrxBqoipfQAAAAg/QROkTNPUhAkTdOmll6pTp06+7YMHD9brr7+uxYsX69lnn9WqVat0+eWXy+Wq/AKfnZ0th8Ohhg0b+h0vLS1N2dnZJzzXlClTlJiY6Ls1a9as/l7YWea9GG+k3VCErf4+3uQ4hyTpQAEjUgAAAAg/AZ3ad7S7775b3333nZYtW+a3/aabbvL93KlTJ/Xo0UMtWrTQhx9+qOHDh1d7PNM0ZRjGCR978MEHNWHCBN/9/Px8y4QpX6OJemx9LvmPSJ3svQYAAACsKChGpMaOHasFCxZoyZIlatq06Un3bdy4sVq0aKGtW7dKktLT01VWVqbc3Fy//XJycpSWlnbCYzidTiUkJPjdrMIbpOqz9bkkJcVWjkiVu03ll1bU67kAAACAYBPQIGWapu6++269++67Wrx4sVq1anXK5xw8eFC7d+9W48aNJUndu3dXZGSkFi5c6NsnKytL69evV69eveqt9mBV6m00Uc8jUlGRdsU7Kwc0DxSyTgoAAADhJaBT+8aMGaM5c+bovffeU3x8vG9NU2JioqKjo1VYWKhJkybp+uuvV+PGjbVjxw499NBDSklJ0XXXXefbd/To0br33nuVnJyspKQkTZw4UZ07d/Z18Qsn3tbn9dlowis5zqECV4UOFpapdaN6Px0AAAAQNAIapGbMmCFJ6tu3r9/2zMxM3XbbbbLb7Vq3bp1mz56tw4cPq3HjxurXr5/efPNNxcfH+/Z/7rnnFBERoRtvvFElJSXq37+/Zs2aJbu9fkdlgtHZaH3ulRLn1I6DxTrIiBQAAADCTECDlGmaJ308Ojpan3zyySmPExUVpenTp2v69Ol1VVrIOrJGqv6DlK9zH0EKAAAAYSYomk2g7nin9kVFnI2pfZWd+w5wUV4AAACEGYKUxZSe5al9EhflBQAAQPghSFmM7zpSZ6HZRAoX5QUAAECYIkhZjOsstT+XpORYRqQAAAAQnghSFuNbI3VWpvZVjkgdZI0UAAAAwgxBykJM0zyqa9/Zazaxn659AAAACDMEKQspd5vyVHWUP5sjUgWlFXJVjYQBAAAA4YAgZSHeaX12w1CEzaj38yVGR/rOc6iI6X0AAAAIHwQpC/E2mnBG2mQY9R+kDMPwXZSXdVIAAAAIJwQpCzmyPqr+p/V5eTv3sU4KAAAA4YQgZSG+IBVx9j7WlPiqFuiMSAEAACCMEKQspLSi6hpSZ3FEKiXWO7WPESkAAACED4KUhXhHpJxnofW5l3eN1AGCFAAAAMIIQcpCvM0mzuqIVBxT+wAAABB+CFIW4m1/HhVxFptNVAWpA7Q/BwAAQBghSFnIka59AZjaV8DUPgAAAIQPgpSFlAZgal8j79S+IoIUAAAAwgdBykK8U/ucZ7H9+dEX5DVN86ydFwAAAAgkgpSFBKLZRFJV+/MKj6m8kvKzdl4AAAAgkAhSFnJkjdTZC1LOCLsSoiIkSQfo3AcAAIAwQZCyiAq3RxWeyql1Z7PZhHR0C3TWSQEAACA8EKQsorSiclqfYUgO+9n9WI9clJcRKQAAAIQHgpRF+Kb1RdhlGMZZPXdyLJ37AAAAEF4IUhbhbTThPMvT+iQpJZ4RKQAAAIQXgpRFeFufR0WcvUYTXt4RqQOskQIAAECYIEhZxJGOfQEYkfJdS4ogBQAAgPBAkLKIQFxDyutI1z6m9gEAACA8EKQsIqBT++KY2gcAAIDwQpCyiNIANptI9k3tY0QKAAAA4YEgZRFH1kgFbmpfgavCVwcAAABgZQQpizgyte/sf6QJURGKtFdeu+pQEaNSAAAAsD6ClEUcuY7U2R+RMgyDFugAAAAIKwQpi3BVVAWpAIxISUcuyss6KQAAAIQDgpRFBHKNlMRFeQEAABBeCFIWYJqmygI8IuXt3HeAESkAAACEAYKUBZS5PTKrfg5UkGrkuygvI1IAAACwPoKUBXgbTdhthiLsgR2ROkjXPgAAAIQBgpQFBLrRhMQaKQAAAIQXgpQFuKquIRXIIJUS7w1SjEgBAADA+ghSFlBaNbUvUB37JCk51tv+nBEpAAAAWB9BygK8I1KOQI5IeZtNFJXJ4zFPsTcAAAAQ2ghSFuBdIxUVEbgRqaSqESm3x1ReSXnA6gAAAADOBoKUBXi79gVyjZQjwqbE6EhJ0sEipvcBAADA2ghSFuBrNhEZ2I/T2wJ9fwENJwAAAGBtBCkLCIapfdLR66QYkQIAAIC1EaQsoLS8qtlEgEekUrwX5aUFOgAAACyOIGUBwTIixUV5AQAAEC4IUhbgDVKBbDYhHVkjxUV5AQAAYHUEKQtwlQdHswnfGilGpAAAAGBxBCkLODIiFehmE94RKYIUAAAArI0gFeIqPB5VeExJUlTAp/Z5u/YxtQ8AAADWRpAKcd6L8UqVF8UNpCNT+whSAAAAsDaCVIg7utGEYRgBrcXbbKLQVeFryQ4AAABYEUEqxLkqqhpNBHg0SpLinRFy2CvrYJ0UAAAArCzw375xRrxT+5yRgW00IUmGYXBRXgAAAIQFglSIC5ZrSHkdaTjBiBQAAACsKzi+faPWSoNoap901EV5CxiRAgAAgHUFx7dv1FqwXEPKy9u57wAjUgAAALCwgAapKVOm6MILL1R8fLxSU1N17bXXavPmzX77mKapSZMmKSMjQ9HR0erbt682bNjgt4/L5dLYsWOVkpKi2NhYDRs2THv27DmbLyVgXFXd8aIigyMTJ7NGCgAAAGEgoN++ly5dqjFjxmjlypVauHChKioqNGjQIBUVFfn2efrppzV16lS98MILWrVqldLT0zVw4EAVFBT49hk/frzmzZunuXPnatmyZSosLNTVV18tt9v6LbiDbkQqtmpEiq59AAAAsLCIQJ78448/9rufmZmp1NRUrVmzRpdddplM09S0adP08MMPa/jw4ZKkV155RWlpaZozZ47uvPNO5eXlaebMmXr11Vc1YMAASdJrr72mZs2aadGiRbriiivO+us6m3xd+4JkjVRKPCNSAAAAsL7g+PZdJS8vT5KUlJQkSdq+fbuys7M1aNAg3z5Op1N9+vTR8uXLJUlr1qxReXm53z4ZGRnq1KmTb59juVwu5efn+91Cle86UsEytY8RKQAAAISB4Pj2rcq1UBMmTNCll16qTp06SZKys7MlSWlpaX77pqWl+R7Lzs6Ww+FQw4YNq93nWFOmTFFiYqLv1qxZs7p+OWdNsE3t83XtY0QKAAAAFhY0Qeruu+/Wd999pzfeeOO4xwzD8LtvmuZx2451sn0efPBB5eXl+W67d++ufeEB5g1SwdJsolFV175DRS55PGaAqwEAAADqR1B8+x47dqwWLFigJUuWqGnTpr7t6enpknTcyFJOTo5vlCo9PV1lZWXKzc2tdp9jOZ1OJSQk+N1CVWm59zpSwTEi1TC2ckTKY0qHS8oDXA0AAABQPwIapEzT1N133613331XixcvVqtWrfweb9WqldLT07Vw4ULftrKyMi1dulS9evWSJHXv3l2RkZF++2RlZWn9+vW+fazKNM2jpvYFRSZWpN2mBjGRklgnBQAAAOsKaNe+MWPGaM6cOXrvvfcUHx/vG3lKTExUdHS0DMPQ+PHjNXnyZLVt21Zt27bV5MmTFRMTo5tvvtm37+jRo3XvvfcqOTlZSUlJmjhxojp37uzr4mdVZW6P7+dgCVJS5UV5DxeX60ChS+3S4gNdDgAAAFDnAhqkZsyYIUnq27ev3/bMzEzddtttkqT7779fJSUluuuuu5Sbm6uLLrpIn376qeLjj3xBf+655xQREaEbb7xRJSUl6t+/v2bNmiW7PTimu9UXb+tzu81QhD14glRyrEM/iBboAAAAsK6ABinTPHUzAsMwNGnSJE2aNKnafaKiojR9+nRNnz69DqsLfsE2rc8rJY4W6AAAALC24PoGjhrxNpqICpJGE14pcVyUFwAAANZGkAphvhGpIGl97pVcNSJ1sIgRKQAAAFhTcH0DR424Krytz4PrY/RelHd/ASNSAAAAsKbg+gaOGjmyRirYpvaxRgoAAADWRpAKYd6ufcE2tY8gBQAAAKsLrm/gqJFgndqXGl8ZpPYXuE6rMyMAAAAQaoLrGzhqpLRqal/wde2rDFKuCo8KXRUBrgYAAACoewSpEOaqan8ebFP7oh12xTkrL1G2v4DpfQAAALCeWn0D3759e13XgVoI1mYTktToqOl9AAAAgNXUKki1adNG/fr102uvvabS0tK6rgmn6UiQCq4RKenIRXkPcFFeAAAAWFCtvoF/++23uuCCC3TvvfcqPT1dd955p/73v//VdW04hWCd2icdPSJF0AYAAID11OobeKdOnTR16lT99NNPyszMVHZ2ti699FJ17NhRU6dO1f79++u6TpxAsDabkI5ugc6IFAAAAKznjIYyIiIidN111+nf//63nnrqKW3btk0TJ05U06ZNdeuttyorK6uu6sQxKtweuT2VrcWDcWpfozjWSAEAAMC6zugb+OrVq3XXXXepcePGmjp1qiZOnKht27Zp8eLF+umnn3TNNdfUVZ04hnd9lCQ5gjFIxXNRXgAAAFhXRG2eNHXqVGVmZmrz5s0aMmSIZs+erSFDhshmq/xC36pVK7300ks699xz67RYHHF0ownDMAJczfG8U/v2E6QAAABgQbUKUjNmzNCoUaN0++23Kz09/YT7NG/eXDNnzjyj4lA9V0VVo4kgHI2SaH8OAAAAa6tVkNq6desp93E4HBo5cmRtDo/T4CqvGpGKDL5GE5KUctTUPtM0g3LUDAAAAKitWg1nZGZm6q233jpu+1tvvaVXXnnljIvCqZUG+YiU9zpS5W5TeSXlAa4GAAAAqFu1+hb+5JNPKiUl5bjtqampmjx58hkXhVML5ovxSpIzwq7E6EhJNJwAAACA9dTqW/jOnTvVqlWr47a3aNFCu3btOuOicGreqX1RQTq1TzoyKpXDOikAAABYTK2CVGpqqr777rvjtn/77bdKTk4+46JwasHebEKi4QQAAACsq1bfwkeMGKHf/e53WrJkidxut9xutxYvXqxx48ZpxIgRdV0jTuDI1L5gHpHyNpwoC3AlAAAAQN2qVde+J554Qjt37lT//v0VEVF5CI/Ho1tvvZU1UmeJr2sfI1IAAADAWVerIOVwOPTmm2/qT3/6k7799ltFR0erc+fOatGiRV3Xh2r4uvZFBm+QOjIiRZACAACAtdQqSHm1a9dO7dq1q6taUANlVVP7ooJ4ah8jUgAAALCqWgUpt9utWbNm6bPPPlNOTo48Ho/f44sXL66T4lC90vLgH5EiSAEAAMCqahWkxo0bp1mzZumqq65Sp06dZBhGXdeFUwiFZhONmNoHAAAAi6pVkJo7d67+/e9/a8iQIXVdD06DaZpBf0Fe6ciI1MGiMnk8pmw2AjcAAACsoVbfwh0Oh9q0aVPXteA0eddHScEdpJJiKy/I6/aYyi2mBToAAACso1bfwu+99149//zzMk2zruvBafCORtlthiLswRukIu02X5jaz/Q+AAAAWEitpvYtW7ZMS5Ys0UcffaSOHTsqMjLS7/F33323TorDiXlbn0cF8WiUV0qcQ4eKyrS/wKVz0wNdDQAAAFA3ahWkGjRooOuuu66ua8FpOnIx3uBtNOHVKN6pLfsKaTgBAAAAS6lVkMrMzKzrOlADvkYTQdz63MvbuY8W6AAAALCSWn8Tr6io0KJFi/TSSy+poKBAkrR3714VFhbWWXE4MVfV1L5gbjThleJrgU6zCQAAAFhHrUakdu7cqSuvvFK7du2Sy+XSwIEDFR8fr6efflqlpaX6+9//Xtd14ii+qX2RoTG1T2JECgAAANZSqyGNcePGqUePHsrNzVV0dLRv+3XXXafPPvuszorDiYXCNaS8UrgoLwAAACyo1l37vvzySzkcDr/tLVq00E8//VQnhaF6R7r2MSIFAAAABEKthjQ8Ho/cbvdx2/fs2aP4+PgzLgonF4ojUgQpAAAAWEmtvokPHDhQ06ZN8903DEOFhYV67LHHNGTIkLqqDdVwlVc1mwiFrn1VI1KHistU4fYEuBoAAACgbtRqat9zzz2nfv366bzzzlNpaaluvvlmbd26VSkpKXrjjTfqukYc48iIVPBP7UuKdchmSB5TOlRUptSEqECXBAAAAJyxWgWpjIwMrV27Vm+88Ya+/vpreTwejR49Wr/85S/9mk+gfhy5IG/wj0jZbYaSYp06UOjS/kIXQQoAAACWUKsgJUnR0dEaNWqURo0aVZf14DR4ryMVFQLtz6XK6X0HCl3KKXCpY6CLAQAAAOpArYLU7NmzT/r4rbfeWqticHpKQ6jZhCSlJTi1KUvan0/DCQAAAFhDrYLUuHHj/O6Xl5eruLhYDodDMTExBKl6VOH2yO0xJYVGswlJSouvnM6XnV8a4EoAAACAulGrb+K5ubl+t8LCQm3evFmXXnopzSbqmbfRhCQ57CESpBIqO/ftI0gBAADAIursm3jbtm315JNPHjdahbp19DWkDMMIcDWnJy2xckRqH1P7AAAAYBF1OqRht9u1d+/eujwkjuFtNBEq66OkI1P7cgoYkQIAAIA11GqN1IIFC/zum6aprKwsvfDCC7rkkkvqpDCcWGlV6/NQ6dgnSWkJ3hEpghQAAACsoVZB6tprr/W7bxiGGjVqpMsvv1zPPvtsXdSFanhHpByhNCJVtUZqf4FLFW6PIkJkbRcAAABQnVoFKY/Hc+qdUC+8F+ONigidEankOKfsNkNuj6mDRWW+ESoAAAAgVDE0EGJ8zSZCpPW5JNlthhrF0bkPAAAA1lGrEakJEyac9r5Tp06tzSlQjVBsNiFVTu/Lzi+lcx8AAAAsoVZB6ptvvtHXX3+tiooKtW/fXpK0ZcsW2e12devWzbdfqLTnDiVH2p+HztQ+SUpNiJKUx0V5AQAAYAm1ClJDhw5VfHy8XnnlFTVs2FBS5UV6b7/9dvXu3Vv33ntvnRaJI0rLq0akQmhqn3Sk4UQOQQoAAAAWUKtv488++6ymTJniC1GS1LBhQz3xxBN07atnR1+QN5Sk0wIdAAAAFlKrb+P5+fnat2/fcdtzcnJUUFBwxkWhet4gFUpd+yTv1D6xRgoAAACWUKsgdd111+n222/X22+/rT179mjPnj16++23NXr0aA0fPryua8RRXCE7tY8RKQAAAFhHrdZI/f3vf9fEiRN1yy23qLy8vPJAEREaPXq0nnnmmTotEP5CtdmEd40UQQoAAABWUKsgFRMToxdffFHPPPOMtm3bJtM01aZNG8XGxtZ1fTiKaZohu0YqLb5yRCq3uFyuCnfIBUEAAADgaGf0bTwrK0tZWVlq166dYmNjZZpmjZ7/xRdfaOjQocrIyJBhGJo/f77f47fddpsMw/C7XXzxxX77uFwujR07VikpKYqNjdWwYcO0Z8+eM3lZQausKkRJoTe1r0FMpBxV4S+HdVIAAAAIcbX6Nn7w4EH1799f7dq105AhQ5SVlSVJuuOOO2rU+ryoqEhdu3bVCy+8UO0+V155pS+wZWVl6T//+Y/f4+PHj9e8efM0d+5cLVu2TIWFhbr66qvldrtr89KCWmlVkIqwGYqwhVaQMgzjSAv0Aqb3AQAAILTVamrfPffco8jISO3atUsdOnTwbb/pppt0zz33nHYL9MGDB2vw4MEn3cfpdCo9Pf2Ej+Xl5WnmzJl69dVXNWDAAEnSa6+9pmbNmmnRokW64oorTvMVhQZXRVWjiRCb1ueVFh+l3YdKlJ3HiBQAAABCW62+kX/66ad66qmn1LRpU7/tbdu21c6dO+ukMK/PP/9cqampateunX79618rJyfH99iaNWtUXl6uQYMG+bZlZGSoU6dOWr58ebXHdLlcys/P97uFAld51fqoyNBcX0TnPgAAAFhFrYJUUVGRYmJijtt+4MABOZ3OMy7Ka/DgwXr99de1ePFiPfvss1q1apUuv/xyuVyVIxrZ2dlyOBx+FwaWpLS0NGVnZ1d73ClTpigxMdF3a9asWZ3VXJ9CtdGEly9IMbUPAAAAIa5W38gvu+wyzZ4923ffMAx5PB4988wz6tevX50Vd9NNN+mqq65Sp06dNHToUH300UfasmWLPvzww5M+zzRNGYZR7eMPPvig8vLyfLfdu3fXWc31qTTUp/Z510jRbAIAAAAhrlZrpJ555hn17dtXq1evVllZme6//35t2LBBhw4d0pdfflnXNfo0btxYLVq00NatWyVJ6enpKisrU25urt+oVE5Ojnr16lXtcZxOZ52OnJ0tZUztAwAAAIJCrYY2zjvvPH333Xf62c9+poEDB6qoqEjDhw/XN998o9atW9d1jT4HDx7U7t271bhxY0lS9+7dFRkZqYULF/r2ycrK0vr1608apEKVd0QqKkRHpFKrRqSyCVIAAAAIcTUekfI2d3jppZf0+OOPn9HJCwsL9cMPP/jub9++XWvXrlVSUpKSkpI0adIkXX/99WrcuLF27Nihhx56SCkpKbruuuskSYmJiRo9erTuvfdeJScnKykpSRMnTlTnzp19Xfys5MgaqdAckUqvGpFiah8AAABCXY2DVGRkpNavX3/SNUina/Xq1X5rqiZMmCBJGjlypGbMmKF169Zp9uzZOnz4sBo3bqx+/frpzTffVHx8vO85zz33nCIiInTjjTeqpKRE/fv316xZs2S3h2bYOJkjXftCdUSqMkgVuipU6KpQnLNWM0sBAACAgKvVN9lbb71VM2fO1JNPPnlGJ+/bt69M06z28U8++eSUx4iKitL06dM1ffr0M6olFATqOlKbNm2qs2NFRxgqqTD1+cqvlRFf81+/lJQUNW/evM7qAQAAAGqjVkGqrKxM//znP7Vw4UL16NFDsbGxfo9PnTq1ToqDv7M9tS//0H5J0i233FJnx8y4Y4Yik5vphlvvkGvXuho/PzomRt9v2kSYAgAAQEDVKEj9+OOPatmypdavX69u3bpJkrZs2eK3T11M+cOJlZaf3RGpksLKCxVfdefDat+le50c84t9Edrvkobe/Sc1j/XU6Ln7dm3T60/dpwMHDhCkAAAAEFA1ClJt27ZVVlaWlixZIqnyOk9//etflZaWVi/FwZ93RCrqLLc/T85ooaZtO9bNscqytT+7QM6G6WraouGpnwAAAAAEoRoNbRy7numjjz5SUVFRnRaEEzNN0zciFRWizSYkKbaqwUShqyLAlQAAAAC1d0bfyE/WKAJ1q9xtylP1dp/tEam6FOuorL2IIAUAAIAQVqMgZRjGcWugWBN1dnhHo+w2QxG20H3P4xiRAgAAgAXUaI2UaZq67bbb5HQ6JUmlpaX6zW9+c1zXvnfffbfuKoQkqbSq9XlUhC2kw2tcFEEKAAAAoa9GQWrkyJF+9+uyLTZOrrQ8MI0m6lq8M1JSZZDymKZsIRwKAQAAEL5qFKQyMzPrqw6cgsvb+jyEG01IUozTLpsheczKdVLxUZGBLgkAAACosdD+Vh5GfCNSZ+livPXFZhi+dVIFpUzvAwAAQGgiSIUI3xqpEJ/aJ8k3CkWQAgAAQKgiSIUIK1xDyiu+quFEfml5gCsBAAAAaif0v5WHCe/UPqcFRqQSGJECAABAiCNIhQhX1dS+6BBfIyUdGZEqYEQKAAAAIYogFSKOtD8P/Y/MF6S4lhQAAABCVOh/Kw8Tpb7251YYkaqa2ldCkAIAAEBoIkiFiCNd+0L/I/OOSJW5Pb4piwAAAEAoCf1v5WHANE3LXEdKkiLtNl8gpOEEAAAAQhFBKgRUeEy5PaYka1xHSjoyvY8W6AAAAAhFBKkQ4F0fZTOkSLsR4GrqRrzT27mPESkAAACEHoJUCPBdQyrCLsOwRpDiWlIAAAAIZQSpEOCyUKMJL64lBQAAgFBmnW/mFnbkGlLWWB8lHR2kGJECAABA6CFIhQDvGilrBSmm9gEAACB0EaRCgO8aUhHW+bi8I1JFrgpfR0IAAAAgVFjnm7mF+ZpNWGhEKsZhl90wZKoyTAEAAAChhCAVAlxVU/uiLRSkDMNQHOukAAAAEKIIUiHgyIiUtT4uOvcBAAAgVFnrm7lFHVkjZZ0RKelIkMpnah8AAABCDEEqBBzp2metj8vXua+EESkAAACEFmt9M7coK15HSpISvFP7GJECAABAiCFIhQArXkdK4lpSAAAACF0EqSBX4faoouo6S1a6jpTk32zCNLmWFAAAAEKHtb6ZW5CronJanyHJYbUg5awMUuVu0/c6AQAAgFBgrW/mFuSd1ueMtMkwjABXU7ci7DbftbGY3gcAAIBQQpAKcr5GExZrfe7la4HOtaQAAAAQQghSQc53DSmLNZrwSoiubDiRTwt0AAAAhBCCVJA7emqfFTWoClKHCVIAAAAIIdb8dm4hLoteQ8qrQUxlkMorJkgBAAAgdBCkgpxvap/FOvZ5NYh2SGJECgAAAKHFmt/OLaTU4iNSiVUjUvml5XJ7uJYUAAAAQgNBKsh510hZNUjFOuyKsBkyzcoL8wIAAAChgCAV5I4EKWt+VIZh+EalmN4HAACAUGHNb+cWUlph7etISUd17qPhBAAAAEIEQSrIWX1qn3Sk4QSd+wAAABAqCFJBztv+3KrXkZJ01NS+sgBXAgAAAJwe6347twC3x1SZ29pd+yQuygsAAIDQQ5AKYq6qa0hJktOi15GSjmqBXlIuDy3QAQAAEAKs++3cArzXkHJG2GQzjABXU3/inRGy2wx5TKnAVRHocgAAAIBTIkgFsXBoNCFVtUCP8nbuY50UAAAAgh9BKoiVVk3ts/K0Pi/v9L481kkBAAAgBFj/G3oI807ts/qIlETDCQAAAIQWglQQKymrHJGKdlg/SPlGpLiWFAAAAEIAQSqIlVStkYpmRAoAAAAIKgSpIBZOI1INYhySKtdImSYt0AEAABDcCFJBzDsiFRMGI1LxzgjZjMqLEBfSAh0AAABBjiAVxMJpRMpmM5Tga4HO9D4AAAAEN4JUECsuqxyZCYc1UhIt0AEAABA6CFJBzNdsIgxGpCSpYXTlOikaTgAAACDYBTRIffHFFxo6dKgyMjJkGIbmz5/v97hpmpo0aZIyMjIUHR2tvn37asOGDX77uFwujR07VikpKYqNjdWwYcO0Z8+es/gq6ofblMrdlU0XwmGNlHRkROpwcVmAKwEAAABOLqBBqqioSF27dtULL7xwwseffvppTZ06VS+88IJWrVql9PR0DRw4UAUFBb59xo8fr3nz5mnu3LlatmyZCgsLdfXVV8vtdp+tl1EvXFXl2wzJEREeA4e0QAcAAECoiAjkyQcPHqzBgwef8DHTNDVt2jQ9/PDDGj58uCTplVdeUVpamubMmaM777xTeXl5mjlzpl599VUNGDBAkvTaa6+pWbNmWrRoka644ooTHtvlcsnlcvnu5+fn1/ErO3MujyGpcn2UYRgBrubsaBBzpNmExzRlC5PXDQAAgNATtEMd27dvV3Z2tgYNGuTb5nQ61adPHy1fvlyStGbNGpWXl/vtk5GRoU6dOvn2OZEpU6YoMTHRd2vWrFn9vZBa8o5Ihcv6KElKiI5UhM2Q22PScAIAAABBLWiDVHZ2tiQpLS3Nb3taWprvsezsbDkcDjVs2LDafU7kwQcfVF5enu+2e/fuOq7+zPlGpMIoSNkMQ0mxlQ0nDhayTgoAAADBK6BT+07HsdPaTNM85VS3U+3jdDrldDrrpL764huRCpNGE17JcQ7lFLh0sMilNooLdDkAAADACQXtiFR6erokHTeylJOT4xulSk9PV1lZmXJzc6vdJ1SVVY1IxUQGfdatU8mxlQGXESkAAAAEs6ANUq1atVJ6eroWLlzo21ZWVqalS5eqV69ekqTu3bsrMjLSb5+srCytX7/et0+oCsc1UlLliJQkHSwiSAEAACB4BXS4o7CwUD/88IPv/vbt27V27VolJSWpefPmGj9+vCZPnqy2bduqbdu2mjx5smJiYnTzzTdLkhITEzV69Gjde++9Sk5OVlJSkiZOnKjOnTv7uviFqnBcIyVJyVVrpA4Xl6nC41GELWizPgAAAMJYQIPU6tWr1a9fP9/9CRMmSJJGjhypWbNm6f7771dJSYnuuusu5ebm6qKLLtKnn36q+Ph433Oee+45RURE6MYbb1RJSYn69++vWbNmyW4P7QASrmuk4pwRckTYVFbh0eHicqXEBfdaNgAAAISngAapvn37yjTNah83DEOTJk3SpEmTqt0nKipK06dP1/Tp0+uhwsAJ1xEpwzCUHOtQVl6pDhaWEaQAAAAQlJg3FaS8I1IxYTYiJR29Tsp1ij0BAACAwCBIBSN7hCrM8ByRkqQUOvcBAAAgyBGkgpA9OlGSZDMkZ0T4fUR07gMAAECwC79v6SHAFlMZpKIi7ae8+LAVJVV17ssrKVe52xPgagAAAIDjEaSCkL0qSIXjtD5JinFEKKbqtTMqBQAAgGBEkApCviAVho0mvLzXkzpYSMMJAAAABB+CVBCyxSRICs+OfV7JVW3PGZECAABAMCJIBSFvs4lwndonHT0iRZACAABA8CFIBSFbmK+RkriWFAAAAIIbQSoIsUbqSOe+IpdbpeXuAFcDAAAA+CNIBSFGpCRnhF3xURGSmN4HAACA4EOQCkLeEamYyIgAVxJY3nVSB+jcBwAAgCBDkApCjEhVSo2PkiTtKygNcCUAAACAP4JUkKnwmLJHxUkiSKUlVLZA35fPiBQAAACCC0EqyOS7PJIkQ6aiIsL740lLqByROlRUJlcFDScAAAAQPML7m3oQyqsKUg6bZBhGgKsJrFhnhK/hRA6jUgAAAAgiBKkg4x2RctrNAFcSHNKrRqWy81knBQAAgOBBkAoyviDFJyPpSJDaR5ACAABAEOHrepDJL2VE6mhpjEgBAAAgCBGkgszRa6QgpSY4ZRhSkcutkopAVwMAAABU4ut6kMljjZSfSLvNd2HeQ2Xh3XwDAAAAwYMgFWTyXZVtvlkjdYR3ndShMt4UAAAABAe+mQYZuvYdz7tOKtfFiBQAAACCA0EqyNC173jpiVVBqsyQDN4YAAAABB7fSoMMa6SOlxTrUKTdUIVpKDK5aaDLAQAAAAhSweb28xOUu+RfirYHupLgYTMMpcZXjko5GrcLcDUAAAAAQSro9GsZo/z/vatIPhk/3oYTToIUAAAAggBf1xES0hKckhiRAgAAQHAgSCEkeBtOOFJbqaTcE+BqAAAAEO4IUggJ8VGRio0wZdjs2rC/LNDlAAAAIMwRpBAyGjkrR6LW5RCkAAAAEFgEKYSM1KjKlvDf7XMFuBIAAACEO4IUQkZqVOWI1M68Ch0oJEwBAAAgcAhSCBlOu1SWs12StGLbwQBXAwAAgHBGkEJIKd35rSTpyx8OBLgSAAAAhDOCFEJK6Y61kqQvtxGkAAAAEDgEKYSU0j0bZDek3YdKtPtQcaDLAQAAQJgiSCGkmGUlapMUKYnpfQAAAAgcghRCTpc0pyTpSxpOAAAAIEAIUgg5XVIdkqQV2w7INM0AVwMAAIBwRJBCyGmX7FBUpE0HCsu0eV9BoMsBAABAGCJIIeRE2g39rFWyJGnZVtZJAQAA4OwjSCEkXdY2RZK0cOO+AFcCAACAcESQQki6omO6JGnVjkM6WOgKcDUAAAAINwQphKRmSTHqmJEgjykt2sSoFAAAAM4ughRC1pVVo1Ifr88OcCUAAAAINwQphKwrO1UGqS9/OKiC0vIAVwMAAIBwQpBCyGqTGqdzGsWqzO3Rks37A10OAAAAwghBCiHLMAxf04lPmN4HAACAs4gghZDmXSe1ZHOOSsvdAa4GAAAA4YIghZDWpWmiGidGqbjMrf9ycV4AAACcJQQphDS/6X0bmN4HAACAs4MghZDn7d63cOM+lbs9Aa4GAAAA4YAghZB3YcskpcQ5lVdSriXf5wS6HAAAAIQBghRCnt1maHi3JpKkt9bsCXA1AAAACAcEKVjCDd2bSpKWfJ+jA4WuAFcDAAAAqyNIwRLapcWra9NEVXhMzf/mp0CXAwAAAIsjSMEybujRTJL09po9Mk0zwNUAAADAyoI6SE2aNEmGYfjd0tPTfY+bpqlJkyYpIyND0dHR6tu3rzZs2BDAihFIw7pkyBFh0/fZBVr/U36gywEAAICFBXWQkqSOHTsqKyvLd1u3bp3vsaefflpTp07VCy+8oFWrVik9PV0DBw5UQUFBACtGoCTGRGrQeWmSpLfX7A5wNQAAALCyoA9SERERSk9P990aNWokqXI0atq0aXr44Yc1fPhwderUSa+88oqKi4s1Z86cAFeNQPl51fS+977dK1eFO8DVAAAAwKqCPkht3bpVGRkZatWqlUaMGKEff/xRkrR9+3ZlZ2dr0KBBvn2dTqf69Omj5cuXn/SYLpdL+fn5fjdYw6VtUpSeEKXDxeVatJFrSgEAAKB+BHWQuuiiizR79mx98skn+sc//qHs7Gz16tVLBw8eVHZ2tiQpLS3N7zlpaWm+x6ozZcoUJSYm+m7NmjWrt9eAs+voa0rN+d/OAFcDAAAAqwrqIDV48GBdf/316ty5swYMGKAPP/xQkvTKK6/49jEMw+85pmket+1YDz74oPLy8ny33btZT2MlN1/UXDZD+vKHg9qyj/VyAAAAqHtBHaSOFRsbq86dO2vr1q2+7n3Hjj7l5OQcN0p1LKfTqYSEBL8brKNpwxgNOq/y9yPzyx2BLQYAAACWFFJByuVyadOmTWrcuLFatWql9PR0LVy40Pd4WVmZli5dql69egWwSgSD2y9pKUma980eHS4uC2wxAAAAsJygDlITJ07U0qVLtX37dn311Ve64YYblJ+fr5EjR8owDI0fP16TJ0/WvHnztH79et12222KiYnRzTffHOjSEWA/a5WkDo0TVFru0dxVTN0EAABA3QrqILVnzx794he/UPv27TV8+HA5HA6tXLlSLVq0kCTdf//9Gj9+vO666y716NFDP/30kz799FPFx8cHuHIEmmEYvlGpV1fsVIXbE9iCAAAAYCkRgS7gZObOnXvSxw3D0KRJkzRp0qSzUxBCyrCuGXryo+/10+ESLdy4T4M7Nw50SQAAALCIoB6RAs5EVKRdN/+suSQpc/mOwBYDAAAASyFIwdJuubiFImyG/rf9kL7dfTjQ5QAAAMAiCFKwtPTEKA07P0OS9OLnPwS4GgAAAFgFQQqWd1ff1jIM6ZMN+7hALwAAAOoEQQqW1yY1Xld2rLxA74tLGJUCAADAmSNIISyM6ddGkrTg273adbA4wNUAAAAg1AV1+3PgRDZt2lSr53VLd+rrbJf+9M5X+m2PxDOuIyUlRc2bNz/j4wAAACD0EKQQMvIP7Zck3XLLLbV6vrPJeUq/5Wl9uiVPmff+XO7Cg2dUT3RMjL7ftIkwBQAAEIYIUggZJYX5kqSr7nxY7bt0r9Uxlu7z6IAi1ff3/1LXhu5a17Jv1za9/tR9OnDgAEEKAAAgDBGkEHKSM1qoaduOtXpu7+RizfvmJ20vitBlXVorPiqyjqsDAABAOKDZBMJKs4bRatogWm6Pqa+2Hwp0OQAAAAhRBCmEFcMw1LN1siRpY1a+covLAlwRAAAAQhFBCmEno0G0WqXEyjSllT+eWcMJAAAAhCeCFMJSz3MqR6W27CvU/gJXgKsBAABAqCFIISw1ineqXVqcJGn5tgMBrgYAAAChhiCFsHXxOckyDGnHwWLtyS0OdDkAAAAIIQQphK2GMQ51ykiUJH2x5YA8phngigAAABAqCFIIaxefkyRnhE37C13auDc/0OUAAAAgRBCkENZiHBG6qFWSJGn5toNyVbgDXBEAAABCAUEKYa9L0wZqGBOpknK3/sdFegEAAHAaCFIIe3abod5tG0mS1u4+zEV6AQAAcEoEKUBSq5RYtUiOkceUlm7ZL5PGEwAAADgJghRQpU/bRrIbhnYeLNaWfYWBLgcAAABBjCAFVGkY69CFrRpKqhyVKimj8QQAAABOjCAFHKVHiyQlxzpUUu7WF1v3B7ocAAAABCmCFHAUu83QgA5pkqTvswu042BRgCsCAABAMCJIAcdIT4zS+c0aSJIWf5+jsgpPYAsCAABA0CFIASfQq3WyEqIiVFBaoSWbc+jiBwAAAD8EKeAEIu02DeqYLsOonOK3MSs/0CUBAAAgiBCkgGo0aRCtnuckS5I+37xfBwtdAa4IAAAAwYIgBZxEjxYN1SIpRhUeU/9Zl61yN+ulAAAAQJACTsowDA3qmKZYh12Hisv02feslwIAAABBCjilGEeEruxUuV5qc3aBvtx2MNAlAQAAIMAIUsBpaNowRv3PTZUkrdmZqy35/KcDAAAQzvg2CJymjhmJuqR1ZfOJdYcjFNuxX4ArAgAAQKAQpIAa6N6ioS6oulhv8uBxWrqzOLAFAQAAICAIUkANGIah3m1T1DzGLcMeoee/ytNfPtksj4cGFAAAAOGEIAXUkGEY6pHsVt6KtyRJLyz5QXe/8bVKytwBrgwAAABnC0EKqAXDkA5/8YrG/ixRkXZD/1mXrWEvLNPyHw4EujQAAACcBQQp4Az0axmjOb++WClxDm3NKdTN//xKv3l1jXYfYu0UAACAlRGkgDN0YcskLZrQR7f1aim7zdDHG7LVf+pSjXn9a328Pkul5Uz5AwAAsJqIQBcAWEGDGIcmDeuoX/ysuR5/f4OWbzuoD9dl6cN1WYpzRqhn62S1TY1T60ZxapkSq1inXQ67TY4Im0xTclW4VVru8f1ZWu6Wq8KjCo+pSJshu81QZIRNSTEOpSVEKSXOoQg7/w4CAAAQKAQpoA61T4/X63dcpA1787Xg2716/9u9ysor1cKN+7Rw4746O49hSOkJUTqvcYI6ZiTovIwEdW+RpEbxzjo7BwAAAKpHkALqmGEY6tQkUZ2aJOr3V56rb3bn6rs9edq2v1Dbcoq061CxSsvdKnN7VFbhkSRFRdoVFWmTM6Lyz6hIu6Ii7LLZJLfHVLnbVFmFR4eKyrS/0CW3x1RWXqmy8kr12fc5vnOf1zhBvdum6LJ2jXRRqyRGrQAAAOoJQQo4A5s2bTrlPoakrtFS1+aSmjskOc7gjLFye0wVlHmUVeDWj4fLteNwuXbkm9p2sFQbs/K1MStfL33xoxrGROrKTuka0rmxep6TTKgCAACoQwQpoBbyD+2XJN1yyy0BrqRSdEyMVqxZpx2lTi3dsl+fb96vQ0VleuN/u/XG/3YrJc6hoV0zNPyCpurUJEGGYQS6ZAAAgJBGkAJqoaQwX5J01Z0Pq32X7gGtZd+ubXr9qfvkLj6sa7p10zXnN1GF26OVPx7Sh+uy9MmGbB0oLFPmlzuU+eUOtUmN03UXNNG1FzRRkwbRAa0dAAAgVBGkgDOQnNFCTdt2DHQZx4mw23Rp2xRd2jZFf7ymo77Ysl/zvvlJCzfu0w85hXrmk8165pPNuqhVkoZ3a6LBnRsrISoy0GUDAACEDIIUYBEnW6/VUNKoc6WbzmmklXtKtXRnidbvL9NX2w/pq+2H9Mj8deqREaW+LaJ1frpTEbbaT/1LSUlR8+bNa/18AACAUECQAkJcbddr2eMbKbZjX8V27CelNNfy3aVavrtU7qLDKtr0hYo2LFFZ9tYa1xMdE6PvN20iTAEAAEsjSAEh7kzXa5mmdLi8XLuKbNpdZJMrtoESegxTQo9hiosw1STGoybRHjVwmDpVjwrveq0DBw4QpAAAgKURpACLOJP1Ws0kdZbk8ZjadahYm7Lz9eP+IhVWSJvz7dqcb1d8VIRaN4pTm9Q4NU6Mko3OfwAAIIwRpAD42GyGWqbEqmVKrMoqPNpxsEg/5BRqx8EiFZRWaO3uw1q7+7BiHHZfqGrSIFr2M1hTBQAAEIoIUgBOyBFhU7u0eLVLi1eF26Odh4r1Q06hfjxQpOIyt9b9lKd1P+UpKsKmVo1i1SY1TnYz0FUDAACcHQQpAKcUYbepdaM4tW4UJ7fH1J7cylC1bX+RSsrd2pRVoE1ZBYowIpUy9D4t312i9h0rFOvkfzEAAMCa+JYDoEbsNkMtkmPVIjlW/c41tfdwiS9UFboqFHteH/1lxWFNX7VQfdo10pWd0tX/3DQlxnCdKgAAYB0EKQC1ZjMMNW0Yo6YNY9SnnanvNmzSewsWqN3lN2pfkVufbtynTzfuU4TN0MXnJGtQxzQNPC9NjROjA106AADAGSFIAagThmEo2Wnq8OeZevEvYxTVuI0+Xp+tj9dna2tOoZb9cEDLfjigR9/boC5NEzXovDQN6piutqlxMugACAAAQgxBCkCdMwxDHTMS1TEjUfcOaq/tB4q0cGO2Pt2wT2t25eq7PXn6bk+e/vLpFjVpEK3ebVN0adsUXdI6RQ1jHYEuH2fZrl27dODAgWofd3tM5bk8Olzq0eFSd9Wflbc8l1tFZaZKK0yVuj1yVZgyVXl9NEmyGZLDbsgZYchpN5TgtKlBlF0NomxVt8qfG0bZlOC0KbVRI66BBgA4LZYJUi+++KKeeeYZZWVlqWPHjpo2bZp69+4d6LIASGqVEqv/u6y1/u+y1tpf4NJnmyqn/C374YB+Olyiuat2a+6q3ZKktqlx6tGyobo1b6jzmzVQq5RYRdhtAX4FqCumaaq4zK380nLllZRr646f9Ks77lSFESl7TAPZYhvIHttQdu+fMQ1ki0mQYdT/74BZUSZ3wde6uHM7tc1oWDVtNbrqFqNGcU7ZaPUPAKhiiSD15ptvavz48XrxxRd1ySWX6KWXXtLgwYO1ceNG/mURCIBNmzad9PF2EVK7LnbdcV4jbdxfpm/3lWlttku78yu0NadQW3MK9cb/KoNVpE1qmhChFomRSo+zKy0uQumxdqXE2JUYZVPESb7YpqSk8P8AVY7oFJVVqNjlVlFZhYpcFdq+e6/2HTyskgpTpeWmSis8lT9XmL4/j95W7q4c4vGY8o34eMwTbfP+XDky5DErt1f+bKqk3JT7mDb5Da5+4DRehakom+S0m4qyV/1Zdd9hkyJspuyGFGFIvt8Io/Lc7qpbhWnI5ZZcbkOlbqnUU3m/1G3I5ZGMCIciGmZo9Z5Crd5TeFwFDrtNTXzBKtoXtBrFO5UU61BSjEMNYhxyRBD8AYSGU80IqIlyd+XfHyXllX93eH8uraj8f3/l3y9Vj5VXPl7mNuUxK/9e8JhS5yaJevyGC+uknrPBEkFq6tSpGj16tO644w5J0rRp0/TJJ59oxowZmjJlSoCrA8JH/qH9kqRbbrmlVs+3RSfI2aSD7+ZIbaVyZ4y2H67Q9sMVJ3yOuzhP7qLD8pQVyywrkaespOrPUtnNcv3urt8oJamh7DbJbrPJblR2HrTbbLLbKhtmRNgN2YzK28mc6jJZpnlmF9LymKYq3KbcHlMVnqP/9Mjtkdwejyo8lfu4KtwqKXertNxT+WeZW6UVbpWUuVVc5vYLTqXlnjOqqz6Y7gp5SgvlcRXJU1qojObnqFFKkmIcdsU4Iqr+rPw51mlXVKT9lJ/PmXB7TP2weaNm/eUxPfHci7InpGpPbon25BZrT26JsvJKVOb2aPuBIm0/UHTSY8U7I9Qw1qGkWIfioyIUFVn5WqIjK19HdNXPjgib7IYhm83w/V5W/nzkT++203nlp3p7Tucopz7GmTmd/0JO9Z+ReRpHOZ3/FOv7v+fTr+PMX8/Zer2n9Y6chc+vLn6PTqeWunhfT+cgdfLfRS3Ok5ubqz//ebLKy8uqthgy7HYZdoeMCO8tsvJPu0M66mcj0iGbI1o2R7QM75/2M+/Ou2Llpxr9s7SQ+UfQkA9SZWVlWrNmjX7/+9/7bR80aJCWL19+wue4XC65XC7f/by8PElSfn5+/RV6mgoLK/8VdM/WDXKVFAe0ln27tkmSsnds0bbYmIDWIgVXPdRyYjs2fiNJunDwTWraqu0ZHq1AZtl3cpU5VGJEqcSIUpnhUKkRKZfhVLkiJcOQYY9UREKjao/yj/9ul7T9DGuxBpshRUUYipBbB7J/UlxcvJyRdtnkkd00ZZdbhjyyyyOb6Zbd97NHtqq/go2j/io2/P5a9j5e+bNx1Hbvz4ZpyiaPIuSWTR4ZknZtX6c1i95Tl9snqk3D86USVd4kVUjKr7qdDbl7tsu1e50SD21S+0YedU+XlC5JEarwxCm31K0DRR7tL3Zrf7FbB6r+zHd5VFBmqqjMI48p5bmkvHxpx1mqGwBqK/bim+rkOGZFucyKckmq+jvDXfV3i/fvErdspln5d4rcvr9nbDJlVN1KCg5r/fL3tGNHbzVo0KBO6qotbyY4VUA1zLr4J5cA2rt3r5o0aaIvv/xSvXr18m2fPHmyXnnlFW3evPm450yaNEmPP/742SwTAAAAQAjZvXu3mjZtWu3jIT8i5XVs+2TTNKttqfzggw9qwoQJvvsej0eHDh1ScnJyQNsw5+fnq1mzZtq9e7cSEhICVocV8d7WH97b+sN7W394b+sX72/94b2tP7y39SfU3lvTNFVQUKCMjIyT7hfyQSolJUV2u13Z2dl+23NycpSWlnbC5zidTjmdTr9tgR5CPFpCQkJI/JKFIt7b+sN7W394b+sP72394v2tP7y39Yf3tv6E0nubmJh4yn1CvrWQw+FQ9+7dtXDhQr/tCxcu9JvqBwAAAAB1JeRHpCRpwoQJ+tWvfqUePXqoZ8+eevnll7Vr1y795je/CXRpAAAAACzIEkHqpptu0sGDB/XHP/5RWVlZ6tSpk/7zn/+oRYsWgS6tRpxOpx577LHjph3izPHe1h/e2/rDe1t/eG/rF+9v/eG9rT+8t/XHqu9tyHftAwAAAICzLeTXSAEAAADA2UaQAgAAAIAaIkgBAAAAQA0RpAAAAACghghSQeLFF19Uq1atFBUVpe7du+u///1voEuyhC+++EJDhw5VRkaGDMPQ/PnzA12SZUyZMkUXXnih4uPjlZqaqmuvvVabN28OdFmWMGPGDHXp0sV34cKePXvqo48+CnRZljRlyhQZhqHx48cHupSQN2nSJBmG4XdLT08PdFmW8dNPP+mWW25RcnKyYmJidP7552vNmjWBLssSWrZsedzvrmEYGjNmTKBLC3kVFRX6wx/+oFatWik6OlrnnHOO/vjHP8rj8QS6tDpBkAoCb775psaPH6+HH35Y33zzjXr37q3Bgwdr165dgS4t5BUVFalr16564YUXAl2K5SxdulRjxozRypUrtXDhQlVUVGjQoEEqKioKdGkhr2nTpnryySe1evVqrV69WpdffrmuueYabdiwIdClWcqqVav08ssvq0uXLoEuxTI6duyorKws323dunWBLskScnNzdckllygyMlIfffSRNm7cqGeffVYNGjQIdGmWsGrVKr/f24ULF0qSfv7znwe4stD31FNP6e9//7teeOEFbdq0SU8//bSeeeYZTZ8+PdCl1QnanweBiy66SN26ddOMGTN82zp06KBrr71WU6ZMCWBl1mIYhubNm6drr7020KVY0v79+5WamqqlS5fqsssuC3Q5lpOUlKRnnnlGo0ePDnQpllBYWKhu3brpxRdf1BNPPKHzzz9f06ZNC3RZIW3SpEmaP3++1q5dG+hSLOf3v/+9vvzyS2arnCXjx4/XBx98oK1bt8owjECXE9KuvvpqpaWlaebMmb5t119/vWJiYvTqq68GsLK6wYhUgJWVlWnNmjUaNGiQ3/ZBgwZp+fLlAaoKqLm8vDxJlV/4UXfcbrfmzp2roqIi9ezZM9DlWMaYMWN01VVXacCAAYEuxVK2bt2qjIwMtWrVSiNGjNCPP/4Y6JIsYcGCBerRo4d+/vOfKzU1VRdccIH+8Y9/BLosSyorK9Nrr72mUaNGEaLqwKWXXqrPPvtMW7ZskSR9++23WrZsmYYMGRLgyupGRKALCHcHDhyQ2+1WWlqa3/a0tDRlZ2cHqCqgZkzT1IQJE3TppZeqU6dOgS7HEtatW6eePXuqtLRUcXFxmjdvns4777xAl2UJc+fO1ddff61Vq1YFuhRLueiiizR79my1a9dO+/bt0xNPPKFevXppw4YNSk5ODnR5Ie3HH3/UjBkzNGHCBD300EP63//+p9/97ndyOp269dZbA12epcyfP1+HDx/WbbfdFuhSLOGBBx5QXl6ezj33XNntdrndbv35z3/WL37xi0CXVicIUkHi2H/1ME2TfwlByLj77rv13XffadmyZYEuxTLat2+vtWvX6vDhw3rnnXc0cuRILV26lDB1hnbv3q1x48bp008/VVRUVKDLsZTBgwf7fu7cubN69uyp1q1b65VXXtGECRMCWFno83g86tGjhyZPnixJuuCCC7RhwwbNmDGDIFXHZs6cqcGDBysjIyPQpVjCm2++qddee01z5sxRx44dtXbtWo0fP14ZGRkaOXJkoMs7YwSpAEtJSZHdbj9u9CknJ+e4USogGI0dO1YLFizQF198oaZNmwa6HMtwOBxq06aNJKlHjx5atWqVnn/+eb300ksBriy0rVmzRjk5Oerevbtvm9vt1hdffKEXXnhBLpdLdrs9gBVaR2xsrDp37qytW7cGupSQ17hx4+P+EaVDhw565513AlSRNe3cuVOLFi3Su+++G+hSLOO+++7T73//e40YMUJS5T+y7Ny5U1OmTLFEkGKNVIA5HA51797d1yHGa+HCherVq1eAqgJOzTRN3X333Xr33Xe1ePFitWrVKtAlWZppmnK5XIEuI+T1799f69at09q1a323Hj166Je//KXWrl1LiKpDLpdLmzZtUuPGjQNdSsi75JJLjru8xJYtW9SiRYsAVWRNmZmZSk1N1VVXXRXoUiyjuLhYNpt/3LDb7ZZpf86IVBCYMGGCfvWrX6lHjx7q2bOnXn75Ze3atUu/+c1vAl1ayCssLNQPP/zgu799+3atXbtWSUlJat68eQArC31jxozRnDlz9N577yk+Pt43qpqYmKjo6OgAVxfaHnroIQ0ePFjNmjVTQUGB5s6dq88//1wff/xxoEsLefHx8cet44uNjVVycjLr+87QxIkTNXToUDVv3lw5OTl64oknlJ+fb4l/dQ60e+65R7169dLkyZN144036n//+59efvllvfzyy4EuzTI8Ho8yMzM1cuRIRUTw9biuDB06VH/+85/VvHlzdezYUd98842mTp2qUaNGBbq0umEiKPztb38zW7RoYTocDrNbt27m0qVLA12SJSxZssSUdNxt5MiRgS4t5J3ofZVkZmZmBrq0kDdq1Cjf/w8aNWpk9u/f3/z0008DXZZl9enTxxw3blygywh5N910k9m4cWMzMjLSzMjIMIcPH25u2LAh0GVZxvvvv2926tTJdDqd5rnnnmu+/PLLgS7JUj755BNTkrl58+ZAl2Ip+fn55rhx48zmzZubUVFR5jnnnGM+/PDDpsvlCnRpdYLrSAEAAABADbFGCgAAAABqiCAFAAAAADVEkAIAAACAGiJIAQAAAEANEaQAAAAAoIYIUgAAAABQQwQpAAAAAKghghQAAACAkPHFF19o6NChysjIkGEYmj9/fo2PYZqm/vKXv6hdu3ZyOp1q1qyZJk+eXKNjEKQAAEFj0qRJOv/88333b7vtNl177bUBqwcAEHyKiorUtWtXvfDCC7U+xrhx4/TPf/5Tf/nLX/T999/r/fff189+9rMaHSOi1mcHAISV5cuXq3fv3ho4cKA+/vjjs3LO559/XqZpnpVzefXt21fnn3++pk2bVif7AQDq1uDBgzV48OBqHy8rK9Mf/vAHvf766zp8+LA6deqkp556Sn379pUkbdq0STNmzND69evVvn37WtfBiBQA4LT861//0tixY7Vs2TLt2rXrrJwzMTFRDRo0OCvnAgBYw+23364vv/xSc+fO1Xfffaef//znuvLKK7V161ZJ0vvvv69zzjlHH3zwgVq1aqWWLVvqjjvu0KFDh2p0HoIUAOCUioqK9O9//1u//e1vdfXVV2vWrFl+j3/++ecyDEMffvihunbtqqioKF100UVat26db59Zs2apQYMGmj9/vtq1a6eoqCgNHDhQu3fvrva8x07t83g8euqpp9SmTRs5nU41b95cf/7zn32PP/DAA2rXrp1iYmJ0zjnn6JFHHlF5ebnvce/UwVdffVUtW7ZUYmKiRowYoYKCAt/5li5dqueff16GYcgwDO3YseO03qOWLVtq8uTJGjVqlOLj49W8eXO9/PLLfvvs2bNHI0aMUFJSkmJjY9WjRw999dVXvsdnzJih1q1by+FwqH379nr11Vf9nm8Yhl566SVdffXViomJUYcOHbRixQr98MMP6tu3r2JjY9WzZ09t27bN73nvv/++unfvrqioKJ1zzjl6/PHHVVFRcVqvCwBCybZt2/TGG2/orbfeUu/evdW6dWtNnDhRl156qTIzMyVJP/74o3bu3Km33npLs2fP1qxZs7RmzRrdcMMNNToXQQoAcEpvvvmm2rdvr/bt2+uWW25RZmbmCafc3XffffrLX/6iVatWKTU1VcOGDfMLMsXFxfrzn/+sV155RV9++aXy8/M1YsSI067jwQcf1FNPPaVHHnlEGzdu1Jw5c5SWluZ7PD4+XrNmzdLGjRv1/PPP6x//+Ieee+45v2Ns27ZN8+fP1wcffKAPPvhAS5cu1ZNPPimpciphz5499etf/1pZWVnKyspSs2bNTru+Z599Vj169NA333yju+66S7/97W/1/fffS5IKCwvVp08f7d27VwsWLNC3336r+++/Xx6PR5I0b948jRs3Tvfee6/Wr1+vO++8U7fffruWLFnid44//elPuvXWW7V27Vqde+65uvnmm3XnnXfqwQcf1OrVqyVJd999t2//Tz75RLfccot+97vfaePGjXrppZc0a9YsvwAKAFbx9ddfyzRNtWvXTnFxcb7b0qVLff/I5PF45HK5NHv2bPXu3Vt9+/bVzJkztWTJEm3evPn0T2YCAHAKvXr1MqdNm2aapmmWl5ebKSkp5sKFC32PL1myxJRkzp0717ft4MGDZnR0tPnmm2+apmmamZmZpiRz5cqVvn02bdpkSjK/+uor0zRN87HHHjO7du3qe3zkyJHmNddcY5qmaebn55tOp9P8xz/+cdp1P/3002b37t199x977DEzJibGzM/P92277777zIsuush3v0+fPua4ceNOeexj92vRooV5yy23+O57PB4zNTXVnDFjhmmapvnSSy+Z8fHx5sGDB094vF69epm//vWv/bb9/Oc/N4cMGeK7L8n8wx/+4Lu/YsUKU5I5c+ZM37Y33njDjIqK8t3v3bu3OXnyZL/jvvrqq2bjxo1P+RoBINhJMufNm+e7P3fuXNNut5vff/+9uXXrVr9bVlaWaZqm+eijj5oRERF+xykuLjYlmZ9++ulpn5sRKQDASW3evFn/+9//fCNHERERuummm/Svf/3ruH179uzp+zkpKUnt27fXpk2bfNsiIiLUo0cP3/1zzz1XDRo08NunOps2bZLL5VL//v2r3eftt9/WpZdeqvT0dMXFxemRRx45bj1Xy5YtFR8f77vfuHFj5eTknPL8p6NLly6+nw3DUHp6uu/Ya9eu1QUXXKCkpKQTPnfTpk265JJL/LZdcsklx703R5/DOxrXuXNnv22lpaXKz8+XJK1Zs0Z//OMf/f5l1jviVlxcfAavFgCCzwUXXCC3262cnBy1adPG75aeni6p8v+tFRUVftOgt2zZIklq0aLFaZ+Lrn0AgJOaOXOmKioq1KRJE9820zQVGRmp3NxcNWzY8KTPNwzjpPer23as6Ojokz6+cuVKjRgxQo8//riuuOIKJSYmau7cuXr22Wf99ouMjDzu3N7pdWfqZMc+Vf3e/Y9mmuZx244+h/exE23zntfj8ejxxx/X8OHDjztfVFTUKWsCgGBTWFioH374wXd/+/btWrt2rZKSktSuXTv98pe/1K233qpnn31WF1xwgQ4cOKDFixerc+fOGjJkiAYMGKBu3bpp1KhRmjZtmjwej8aMGaOBAweqXbt2p10HI1IAgGpVVFRo9uzZevbZZ7V27Vrf7dtvv1WLFi30+uuv++2/cuVK38+5ubnasmWLzj33XL/jedfxSJWjXYcPH/bbpzpt27ZVdHS0PvvssxM+/uWXX6pFixZ6+OGH1aNHD7Vt21Y7d+6s6UuWw+GQ2+2u8fNOpUuXLlq7dm21XaE6dOigZcuW+W1bvny5OnTocEbn7datmzZv3nzcv8y2adNGNhtfAwCEntWrV+uCCy7QBRdcIEmaMGGCLrjgAj366KOSpMzMTN16662699571b59ew0bNkxfffWVb82rzWbT+++/r5SUFF122WW66qqr1KFDB82dO7dGdTAiBQCo1gcffKDc3FyNHj1aiYmJfo/dcMMNmjlzpl9jgz/+8Y9KTk5WWlqaHn74YaWkpPh13YuMjNTYsWP117/+VZGRkbr77rt18cUXn9ZFEKOiovTAAw/o/vvvl8Ph0CWXXKL9+/drw4YNGj16tNq0aaNdu3Zp7ty5uvDCC/Xhhx9q3rx5NX7NLVu21FdffaUdO3YoLi5OSUlJdRI4fvGLX2jy5Mm69tprNWXKFDVu3FjffPONMjIy1LNnT91333268cYb1a1bN/Xv31/vv/++3n33XS1atOiMzvvoo4/q6quvVrNmzfTzn/9cNptN3333ndatW6cnnnjijF8XAJxtffv2Pek1BiMjI/X444/r8ccfr3afjIwMvfPOO2dUB/8UBQCo1syZMzVgwIDjQpQkXX/99Vq7dq2+/vpr37Ynn3xS48aNU/fu3ZWVlaUFCxbI4XD4Ho+JidEDDzygm2++WT179lR0dHSN/gXwkUce0b333qtHH31UHTp00E033eRbg3TNNdfonnvu0d13363zzz9fy5cv1yOPPFLj1zxx4kTZ7Xadd955atSoUZ1dM8vhcOjTTz9VamqqhgwZos6dO+vJJ5+U3W6XJF177bV6/vnn9cwzz6hjx4566aWXlJmZ6buAZG1dccUV+uCDD7Rw4UJdeOGFuvjiizV16tQarQMAABzPME8W5wAAOA2ff/65+vXrp9zc3GovoDtr1iyNHz9ehw8fPqu1AQBQHxiRAgAAAIAaIkgBAAAAQA0xtQ8AAAAAaogRKQAAAACoIYIUAAAAANQQQQoAAAAAaoggBQAAAAA1RJACAAAAgBoiSAEAAABADRGkAAAAAKCGCFIAAAAAUEP/D10hhY8LJcOZAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(loan_data['Applicant_Income'], bins=20, kde=True)\n",
    "plt.title('Distribution of Applicant Income')\n",
    "plt.xlabel('Applicant Income')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f8d3a1b1-9db4-4a97-a53b-d7af876b3705",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to calculate conditional probability\n",
    "def conditional_prob(data, condition_column, condition_value, target_column, target_value):\n",
    "    condition_count = len(data[data[condition_column] == condition_value])\n",
    "    target_count = len(data[(data[condition_column] == condition_value) & (data[target_column] == target_value)])\n",
    "    if condition_count == 0:\n",
    "        return 0\n",
    "    return \"{0:.4f}\".format(target_count / condition_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b6901722-14b3-49c0-8e96-9f7c465bc4b2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "22c6b997-1c83-4ca9-9b88-9daf945eab63",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to calculate marginal probability\n",
    "def marginal_prob(data, target_column, target_value):\n",
    "    target_count = len(data[data[target_column] == target_value])\n",
    "    total_count = len(data)\n",
    "    return \"{0:.4f}\".format(target_count / total_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5afa7ad2-a35b-44a4-b546-7e939b8ccfad",
   "metadata": {},
   "source": [
    "## Marginal Probability of Loan Approval:\n",
    "Calculate the overall probability of loan approval.<br>\n",
    "P(Status = Y) = (Number of approved loans) / (Total number of loans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "069c2802-6012-4554-85b2-28879bfefe28",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Marginal Probability of Loan Approval: 0.6834\n"
     ]
    }
   ],
   "source": [
    "# Marginal Probability of Loan Approval\n",
    "prob_loan_approval = marginal_prob(loan_data, 'Status', 'Y')\n",
    "print(\"Marginal Probability of Loan Approval:\", prob_loan_approval)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b211c774-8dbc-4afc-9dc3-ce51a0acc325",
   "metadata": {},
   "source": [
    "## Marginal Probability of Loan rejection:\n",
    "Calculate the overall probability of loan rejection.<br>\n",
    "P(Status = N) = (Number of rejected loans) / (Total number of loans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "adcfc6ab-8df5-47fd-82d2-34e02ba933bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Marginal Probability of Loan Rejection: 0.3166\n"
     ]
    }
   ],
   "source": [
    "# Marginal Probability of Loan Rejection\n",
    "prob_loan_rej = marginal_prob(loan_data, 'Status', 'N')\n",
    "print(\"Marginal Probability of Loan Rejection:\", prob_loan_rej)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9302ef3f-32ce-457e-848f-f85c6ff3c810",
   "metadata": {},
   "source": [
    "## Conditional Probability of Loan Approval Given Gender:\n",
    "\r\n",
    "Calculate the probability of loan approval given the gender of the applicant <br>.\r\n",
    "P(Status = Y | Gender = Male) = (Number of Male applicants with Status = Y) / (Total number of Male applicants)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a15ef4f7-09c1-4327-b2be-75769480638e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Conditional Probability of Loan Approval Given Gender (Male): 0.6959\n"
     ]
    }
   ],
   "source": [
    "# 1. Conditional Probability of Loan Approval Given Gender\n",
    "prob_gender_loan_approval = conditional_prob(loan_data, 'Gender', 'Male', 'Status', 'Y')\n",
    "print(\"Conditional Probability of Loan Approval Given Gender (Male):\", prob_gender_loan_approval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8d0eaa38-b746-4083-8f7d-cb340de1e920",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Conditional Probability of Loan Approval Given Gender (Female): 0.6250\n"
     ]
    }
   ],
   "source": [
    "prob_gender_loan_approval = conditional_prob(loan_data, 'Gender', 'Female', 'Status', 'Y')\n",
    "print(\"Conditional Probability of Loan Approval Given Gender (Female):\", prob_gender_loan_approval)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "470bc08c-1f8a-496f-ae6a-6da9f52e43b2",
   "metadata": {},
   "source": [
    "## Conditional Probability of Loan Approval Given Education:\r\n",
    "Calculate the probability of loan approval given the education level of the applicant <br>.\r\n",
    "P(Status = Y | Education = Graduate) = (Number of Graduate applicants with Status = Y) / (Total number of Graduate applicants)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "97f1fe5c-693c-4ff6-bebe-c637cb7d3d53",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Conditional Probability of Loan Approval Given Education (Graduate): 0.7038\n"
     ]
    }
   ],
   "source": [
    "# 2. Conditional Probability of Loan Approval Given Education\n",
    "prob_education_loan_approval = conditional_prob(loan_data, 'Education', 'Graduate', 'Status', 'Y')\n",
    "print(\"Conditional Probability of Loan Approval Given Education (Graduate):\", prob_education_loan_approval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "644528ee-3dd8-4f9e-9e37-9923cc1f989c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Conditional Probability of Loan Approval Given Education (Not Graduate): 0.6058\n"
     ]
    }
   ],
   "source": [
    "prob_education_loan_approval = conditional_prob(loan_data, 'Education', 'Not Graduate', 'Status', 'Y')\n",
    "print(\"Conditional Probability of Loan Approval Given Education (Not Graduate):\", prob_education_loan_approval)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51986313-898a-4adb-8210-d539efe54eb0",
   "metadata": {},
   "source": [
    "## Conditional Probability of Loan Approval Given Credit History:\r\n",
    "Calculate the probability of loan approval given the credit history of the applicant<br>.\r\n",
    "P(Status = Y | Credit_History = 1.0) = (Number of applicants with good credit history and Status = Y) / (Total number of applicants with good credit history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c97212cf-2ec1-4ec1-8ccd-268d7aad8300",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Conditional Probability of Loan Approval Given Credit History (Good): 0.7859\n"
     ]
    }
   ],
   "source": [
    "# 3. Conditional Probability of Loan Approval Given Credit History\n",
    "prob_credit_history_loan_approval = conditional_prob(loan_data, 'Credit_History', 1.0, 'Status', 'Y')\n",
    "print(\"Conditional Probability of Loan Approval Given Credit History (Good):\", prob_credit_history_loan_approval)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5ab0e368-9c01-4be7-8b8c-3c569c0419a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Conditional Probability of Loan Approval Given Credit History (Bad): 0.0946\n"
     ]
    }
   ],
   "source": [
    "# 3. Conditional Probability of Loan Approval Given Credit History\n",
    "prob_credit_history_loan_approval = conditional_prob(loan_data, 'Credit_History', 0.0, 'Status', 'Y')\n",
    "print(\"Conditional Probability of Loan Approval Given Credit History (Bad):\", prob_credit_history_loan_approval)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65c0e799-55a8-4422-95ef-d18872d271bc",
   "metadata": {},
   "source": [
    "## Conditional Probability of Loan Approval Given Area:\r\n",
    "Calculate the probability of loan approval given the area where the applicant resides<br>.\r\n",
    "P(Status = Y | Area = Urban) = (Number of Urban applicants with Status = Y) / (Total number of Urban applicants)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0af12bed-dde0-4576-b42f-ee4c66c60db7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Conditional Probability of Loan Approval Given Area (Urban): 0.6352\n"
     ]
    }
   ],
   "source": [
    "prob_area_loan_approval = conditional_prob(loan_data, 'Area', 'Urban', 'Status', 'Y')\n",
    "print(\"Conditional Probability of Loan Approval Given Area (Urban):\", prob_area_loan_approval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "7873fae7-f56f-496d-b3ac-2650ebacb33b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Conditional Probability of Loan Approval Given Area (Rural): 0.6069\n"
     ]
    }
   ],
   "source": [
    "prob_area_loan_approval = conditional_prob(loan_data, 'Area', 'Rural', 'Status', 'Y')\n",
    "print(\"Conditional Probability of Loan Approval Given Area (Rural):\", prob_area_loan_approval)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e042ff07-a9cc-437d-ae38-41f687f9c846",
   "metadata": {},
   "source": [
    "## Conditional Probability of Loan Approval Given if Candidate is self employed or not:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c73db83-cf88-436e-9ec2-b4a4b16f822e",
   "metadata": {},
   "source": [
    "Calculate the probability of loan approval given Given if Candidate is self employed or not:<br>\r\n",
    ". P(Status = Y | Self_Employeda =Yesn) = (Number of Urban applicants with Status = Y) / (Total number ofSelf Employedn applicants)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "5624b0ce-61c1-43d0-9dfd-dda39402c230",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Conditional Probability of Loan Approval Given if Candidate is self employed :yes: 0.1132\n"
     ]
    }
   ],
   "source": [
    "prob_approval = conditional_prob(loan_data, 'Area', 'Urban', 'Self_Employed', 'Yes')\n",
    "print(\"Conditional Probability of Loan Approval Given if Candidate is self employed :yes:\", prob_approval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "11d9bd1b-a00c-4407-940e-391ea87c9690",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Conditional Probability of Loan Approval Given if Candidate is self employed :No: 0.8868\n"
     ]
    }
   ],
   "source": [
    "prob_approval = conditional_prob(loan_data, 'Area', 'Urban', 'Self_Employed', 'No')\n",
    "print(\"Conditional Probability of Loan Approval Given if Candidate is self employed :No:\", prob_approval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a4b20b8-d6a2-4b6f-8e97-10a237f69b74",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "bb5436e9-e18c-43f7-b268-e3adfc35e8f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to perform normal distribution analysis\n",
    "def normal_distribution_analysis(data, column):\n",
    "    # Calculate basic statistics\n",
    "    mean = np.mean(data[column])\n",
    "    std_dev = np.std(data[column])\n",
    "    \n",
    "    print(\"Statistics for column '{}':\".format(column))\n",
    "    print(\"Mean:\", mean)\n",
    "    print(\"Standard Deviation:\", std_dev)\n",
    "    plt.figure(figsize=(8, 3))\n",
    "    sns.kdeplot(data[column], color='blue', fill=True)\n",
    "    plt.title('Density Plot of {}'.format(column))\n",
    "    plt.xlabel(column)\n",
    "    plt.ylabel('Density')\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "7b34540c-5dd2-40c2-a9f8-8a8695ed5894",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics for column 'Loan_Amount':\n",
      "Mean: 13952505.01002004\n",
      "Standard Deviation: 8336870.849941954\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\ProgramData\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "normal_distribution_analysis(loan_data, \"Loan_Amount\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "7016cd24-1382-46cb-9b3a-2d70385167fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics for column 'Applicant_Income':\n",
      "Mean: 533612.8256513026\n",
      "Standard Deviation: 561252.483697618\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\ProgramData\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "normal_distribution_analysis(loan_data, \"Applicant_Income\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "d56c98f5-c4d6-4c1b-b82f-6532766c1c66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics for column 'Coapplicant_Income':\n",
      "Mean: 156699.5831640561\n",
      "Standard Deviation: 257836.7173813811\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\ProgramData\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "normal_distribution_analysis(loan_data, \"Coapplicant_Income\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e11bc28-1fea-45a9-b942-c661431526d2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c472ab74-9530-4d23-a3a6-3c7ad770b146",
   "metadata": {},
   "source": [
    "## Probability of Loan Approval Given Multiple Conditions:\n",
    "\n",
    "Calculate the probability of loan approval given multiple conditions simultaneously, such as gender, education, and credit history."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "bfa98adb-12f2-46e5-a956-187ce5364492",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to calculate probability given multiple conditions\n",
    "def conditional_prob_multiple_conditions(data, conditions_dict, target_column, target_value):\n",
    "    condition_mask = pd.Series(True, index=data.index)\n",
    "    for column, value in conditions_dict.items():\n",
    "        condition_mask &= (data[column] == value)\n",
    "    condition_count = condition_mask.sum()\n",
    "    target_count = ((data[target_column] == target_value) & condition_mask).sum()\n",
    "    if condition_count == 0:\n",
    "        return 0\n",
    "    return \"{0:.4f}\".format(target_count / condition_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "feb92b52-4c79-477b-bc80-6e6edddccef1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "9ee57a16-f4fc-4bbc-bbd1-620401966c3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probability of Loan Approval Given Multiple Conditions: 0.7377\n"
     ]
    }
   ],
   "source": [
    "# Define multiple conditions\n",
    "multiple_conditions = {'Gender': 'Female', 'Education': 'Graduate', 'Credit_History': 1.0}\n",
    "\n",
    "# Calculate probability given multiple conditions\n",
    "prob_loan_approval_multiple_conditions = conditional_prob_multiple_conditions(loan_data, multiple_conditions, 'Status', 'Y')\n",
    "print(\"Probability of Loan Approval Given Multiple Conditions:\", prob_loan_approval_multiple_conditions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c09f06b5-e1ed-4ce9-a517-4c8870304a5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probability of Loan Approval Given Multiple Conditions: 0.7895\n"
     ]
    }
   ],
   "source": [
    "# Define multiple conditions\n",
    "multiple_conditions = {'Gender': 'Female', 'Education': 'Graduate', 'Credit_History': 1.0, 'Married':\"Yes\"}\n",
    "\n",
    "# Calculate probability given multiple conditions\n",
    "prob_loan_approval_multiple_conditions = conditional_prob_multiple_conditions(loan_data, multiple_conditions, 'Status', 'Y')\n",
    "print(\"Probability of Loan Approval Given Multiple Conditions:\", prob_loan_approval_multiple_conditions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "eed0c16e-bb30-4b05-a06d-801ab83c9d96",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probability of Loan Approval Given Multiple Conditions: 0.6364\n"
     ]
    }
   ],
   "source": [
    "# Define multiple conditions\n",
    "multiple_conditions = {'Gender': 'Female', 'Education': 'Not Graduate', 'Married':\"No\",}\n",
    "\n",
    "# Calculate probability given multiple conditions\n",
    "prob_loan_approval_multiple_conditions = conditional_prob_multiple_conditions(loan_data, multiple_conditions, 'Status', 'Y')\n",
    "print(\"Probability of Loan Approval Given Multiple Conditions:\", prob_loan_approval_multiple_conditions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "f7741a01-ebb1-4c57-869f-555a313eb73d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probability of Loan Approval Given Multiple Conditions: 0.6667\n"
     ]
    }
   ],
   "source": [
    "# Define multiple conditions\n",
    "multiple_conditions = {'Gender': 'Female', 'Education': 'Not Graduate', 'Married':\"Yes\",}\n",
    "\n",
    "# Calculate probability given multiple conditions\n",
    "prob_loan_approval_multiple_conditions = conditional_prob_multiple_conditions(loan_data, multiple_conditions, 'Status', 'Y')\n",
    "print(\"Probability of Loan Approval Given Multiple Conditions:\", prob_loan_approval_multiple_conditions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c869542e-2617-47a6-a3cd-180bdddc8634",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probability of Loan Approval Given Multiple Conditions: 0.6000\n"
     ]
    }
   ],
   "source": [
    "# Define multiple conditions\n",
    "multiple_conditions = {'Gender': 'Male', 'Education': 'Not Graduate', 'Married':\"No\",}\n",
    "\n",
    "# Calculate probability given multiple conditions\n",
    "prob_loan_approval_multiple_conditions = conditional_prob_multiple_conditions(loan_data, multiple_conditions, 'Status', 'Y')\n",
    "print(\"Probability of Loan Approval Given Multiple Conditions:\", prob_loan_approval_multiple_conditions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49ae7c11-6b5c-490d-85e8-a7f1e132dbc4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ad9f30d0-293a-4933-8f35-be3fa9e47430",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probability of Loan Approval Given Multiple Conditions: 0.5161\n"
     ]
    }
   ],
   "source": [
    "# Define multiple conditions\n",
    "multiple_conditions = {'Gender': 'Male', 'Education': 'Not Graduate',\"Area\":\"Rural\"}\n",
    "\n",
    "# Calculate probability given multiple conditions\n",
    "prob_loan_approval_multiple_conditions = conditional_prob_multiple_conditions(loan_data, multiple_conditions, 'Status', 'Y')\n",
    "print(\"Probability of Loan Approval Given Multiple Conditions:\", prob_loan_approval_multiple_conditions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "d6c8e0f7-a1c0-4a94-ac46-fca7d2a68aab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probability of Loan Approval Given Multiple Conditions: 0.6489\n"
     ]
    }
   ],
   "source": [
    "# Define multiple conditions\n",
    "multiple_conditions = {'Gender': 'Male', 'Education': 'Graduate',\"Area\":\"Rural\"}\n",
    "\n",
    "# Calculate probability given multiple conditions\n",
    "prob_loan_approval_multiple_conditions = conditional_prob_multiple_conditions(loan_data, multiple_conditions, 'Status', 'Y')\n",
    "print(\"Probability of Loan Approval Given Multiple Conditions:\", prob_loan_approval_multiple_conditions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "2899ba5c-e83e-4e22-b4e1-4690dd4b9efc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probability of Loan Approval Given Multiple Conditions: 0.5333\n"
     ]
    }
   ],
   "source": [
    "# Define multiple conditions\n",
    "multiple_conditions = {'Gender': 'Female', 'Education': 'Graduate',\"Area\":\"Rural\"}\n",
    "\n",
    "# Calculate probability given multiple conditions\n",
    "prob_loan_approval_multiple_conditions = conditional_prob_multiple_conditions(loan_data, multiple_conditions, 'Status', 'Y')\n",
    "print(\"Probability of Loan Approval Given Multiple Conditions:\", prob_loan_approval_multiple_conditions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "806a8b3d-b58d-4e1d-ab70-23d88a2700cd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "754924ca-4178-443d-8b51-dcde46129037",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probability of Loan Approval Given Multiple Conditions: 0.7048\n"
     ]
    }
   ],
   "source": [
    "# Define multiple conditions\n",
    "multiple_conditions = {'Gender': 'Male', 'Education': 'Graduate',\"Area\":\"Urban\"}\n",
    "\n",
    "# Calculate probability given multiple conditions\n",
    "prob_loan_approval_multiple_conditions = conditional_prob_multiple_conditions(loan_data, multiple_conditions, 'Status', 'Y')\n",
    "print(\"Probability of Loan Approval Given Multiple Conditions:\", prob_loan_approval_multiple_conditions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "3f6e9726-2364-4c16-b8ac-5702bf115efd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probability of Loan Approval Given Multiple Conditions: 0.5238\n"
     ]
    }
   ],
   "source": [
    "# Define multiple conditions\n",
    "multiple_conditions = {'Gender': 'Female', 'Education': 'Graduate',\"Area\":\"Urban\"}\n",
    "\n",
    "# Calculate probability given multiple conditions\n",
    "prob_loan_approval_multiple_conditions = conditional_prob_multiple_conditions(loan_data, multiple_conditions, 'Status', 'Y')\n",
    "print(\"Probability of Loan Approval Given Multiple Conditions:\", prob_loan_approval_multiple_conditions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d881b454-633b-46fc-a794-ca5532b0e2eb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c7a551cd-201b-48de-b6c0-8911801887a5",
   "metadata": {},
   "source": [
    "## Central Limit Theorem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "032ca082-ab4e-4b12-a5e3-3c0e1c28ff92",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to perform Central Limit Theorem analysis\n",
    "def central_limit_theorem(data, sample_size, num_samples):\n",
    "    sample_means = []\n",
    "    for _ in range(num_samples):\n",
    "        sample = np.random.choice(data['Loan_Amount'], size=sample_size, replace=False)\n",
    "        sample_mean = np.mean(sample)\n",
    "        sample_means.append(sample_mean)\n",
    "    \n",
    "    # Calculate mean and standard deviation of sample means\n",
    "    mean_of_sample_means = np.mean(sample_means)\n",
    "    std_dev_of_sample_means = np.std(sample_means)\n",
    "    \n",
    "    return sample_means, mean_of_sample_means, std_dev_of_sample_means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "13ee637d-6b3e-4b0f-b4e7-a1d2d97793c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean of Sample Means: 14004200.0\n",
      "Standard Deviation of Sample Means: 789163.2784158168\n"
     ]
    }
   ],
   "source": [
    "# Set parameters\n",
    "sample_size = 100  # Sample size\n",
    "num_samples = 50  # Number of samples\n",
    "\n",
    "# Perform Central Limit Theorem analysis\n",
    "sample_means, mean_of_sample_means, std_dev_of_sample_means = central_limit_theorem(loan_data, sample_size, num_samples)\n",
    "\n",
    "# Print results\n",
    "print(\"Mean of Sample Means:\", mean_of_sample_means)\n",
    "print(\"Standard Deviation of Sample Means:\", std_dev_of_sample_means)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "46fa7e02-3ee8-4e3d-8b0d-c8235ac62e9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[12233000.0,\n",
       " 12431000.0,\n",
       " 12611000.0,\n",
       " 12855000.0,\n",
       " 12924000.0,\n",
       " 12926000.0,\n",
       " 13086000.0,\n",
       " 13113000.0,\n",
       " 13316000.0,\n",
       " 13325000.0]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_means[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "ffc4cd9f-afc2-44b4-a237-17c8b91fd87b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\ProgramData\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_means.sort()\n",
    "# Plot histogram of sample means\n",
    "plt.figure(figsize=(8, 6))\n",
    "\n",
    "sns.histplot(sample_means, bins=50, kde=True)\n",
    "plt.title('Histogram of Sample Means')\n",
    "plt.xlabel('Sample Mean')\n",
    "plt.ylabel('Frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "c255e2d1-afdf-4c7f-9966-5ce834bea87e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\ProgramData\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Density')"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot density plot of sample means\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.kdeplot(sample_means, color='blue', fill=True)\n",
    "plt.title('Density Plot of Sample Means')\n",
    "plt.xlabel('Sample Mean')\n",
    "plt.ylabel('Density')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a949d2b-c384-42ba-8a7a-ebde9d7dd3a3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "41b519d0-d50d-4bb1-b290-e355759b5e91",
   "metadata": {},
   "source": [
    "## Confidence Interval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f0a1e3f0-989b-40d1-a8c5-c391a8c0627b",
   "metadata": {},
   "outputs": [],
   "source": [
    "loan= loan_data[\"Loan_Amount\"].values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c22fca51-e3f5-4ebc-ad62-af84a26d9cd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([15000000, 12800000,  6600000, 12000000, 14100000, 26700000,\n",
       "        9500000, 15800000, 16800000, 34900000], dtype=int64)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ca25f9dc-eab4-433f-9eb6-e5fcf4ed42d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_mean=np.mean(loan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "67453f20-854f-40c3-9499-3d05a0aa7373",
   "metadata": {},
   "outputs": [],
   "source": [
    "std=np.std(loan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d8000106-e1c7-4654-9529-ebefc593ec59",
   "metadata": {},
   "outputs": [],
   "source": [
    "n=60\n",
    "p=.95"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2c2e2385-722e-4eab-a328-cfee11057fe8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def critical_z_value(p):\n",
    " norm_dist = norm(loc=0.0, scale=1.0)\n",
    " left_tail_area = (1.0 - p) / 2.0\n",
    " upper_area = 1.0 - ((1.0 - p) / 2.0)\n",
    " return norm_dist.ppf(left_tail_area), norm_dist.ppf(upper_area)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8d8ea489-ce04-47a9-8cf7-a93b09feb1cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def confidence_interval(p, sample_mean, sample_std, n):\n",
    " # Sample size must be greater than 30\n",
    " lower, upper = critical_z_value(p)\n",
    " lower_ci = lower * (sample_std / sqrt(n))\n",
    " upper_ci = upper * (sample_std / sqrt(n))\n",
    " return sample_mean + lower_ci, sample_mean + upper_ci"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9fc5f69a-0a33-496d-b5be-fd9e03f6a974",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11843024.39146163, 16061985.62857845)\n"
     ]
    }
   ],
   "source": [
    "print(confidence_interval(p, sample_mean, std , n))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd46a48c-3171-47d0-b851-c6b479e4dcb8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d50121cb-d29b-40df-a3e8-c121f72df0b1",
   "metadata": {},
   "source": [
    "## Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33476232-3ac3-46f9-bbb6-421d777a06ca",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "7e3481c1-9d2d-41b3-b4bd-3b4757d1d7a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter cases where loan was given\n",
    "loan_given_data = loan_data[loan_data['Status'] == 'Y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "73e8b63b-8511-4517-89ed-415aa455da3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Slope (Coefficient): 8.29021408232351\n",
      "Intercept: 9477807.519647662\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# Extracting features and target variable\n",
    "X = loan_given_data['Applicant_Income'].values.reshape(-1, 1)  # Feature: Applicant Income\n",
    "y = loan_given_data['Loan_Amount'].values  # Target: Loan Amount\n",
    "\n",
    "# Create and fit the linear regression model\n",
    "model = LinearRegression()\n",
    "model.fit(X, y)\n",
    "\n",
    "# Get the coefficients\n",
    "slope = model.coef_[0]\n",
    "intercept = model.intercept_\n",
    "\n",
    "# Print the coefficients\n",
    "print(\"Slope (Coefficient):\", slope)\n",
    "print(\"Intercept:\", intercept)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "3c39ab00-e6d6-43a4-baf3-049a76db71a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction=model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "71a3ac98-cfb7-4d3d-af4d-59f73fc2e38b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regression Equation:\n",
      "Loan_Amount = 9477807.52 + (8.29 * Applicant_Income)\n"
     ]
    }
   ],
   "source": [
    "# Get the coefficients\n",
    "intercept = model.intercept_\n",
    "coefficient = model.coef_[0]\n",
    "\n",
    "# Define the linear regression equation as a string\n",
    "linear_regression_equation = f\"Loan_Amount = {intercept:.2f} + ({coefficient:.2f} * Applicant_Income)\"\n",
    "\n",
    "# Print the equation\n",
    "print(\"Linear Regression Equation:\")\n",
    "print(linear_regression_equation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "bfc308d4-afa0-472e-a763-72f045045ec7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the data points\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(X, y, color='blue', label='Data Points')\n",
    "\n",
    "# Plot the linear regression line\n",
    "plt.plot(X, prediction, color='red', label='Linear Regression Line')\n",
    "\n",
    "# Add labels and title\n",
    "plt.title('Relationship between Applicant Income and Loan Amount')\n",
    "plt.xlabel('Applicant Income')\n",
    "plt.ylabel('Loan Amount')\n",
    "plt.legend()\n",
    "\n",
    "# Show plot\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a3f2350-2e10-4e22-a50e-12c4df1a55cd",
   "metadata": {},
   "source": [
    "## Statistical Count Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "df3ac73e-8d47-4cba-8c89-ba84a6461ab1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Count loan approvals (yes and no) for applicant marriage column\n",
    "loan_approval_counts = loan_data.groupby(['Married', 'Status']).size().unstack()\n",
    "\n",
    "# Plot bar plot\n",
    "loan_approval_counts.plot(kind='bar', figsize=(6, 3))\n",
    "\n",
    "# Add labels and title\n",
    "plt.title('Loan Approval Count by Applicant Marriage Status')\n",
    "plt.xlabel('Applicant Marriage Status')\n",
    "plt.ylabel('Count of Loan Approvals')\n",
    "plt.xticks(rotation=0)  # Rotate x-axis labels for better readability\n",
    "\n",
    "# Show plot\n",
    "plt.legend(title='Loan Status')\n",
    "plt.grid(axis='y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7e3e186-42dd-4e0b-ae65-5ac668361bbd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "ab5868f6-3a3a-4768-814d-d52ac5816397",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Count loan approvals (yes and no) for applicant marriage column\n",
    "loan_approval_counts = loan_data.groupby(['Dependents', 'Status']).size().unstack()\n",
    "\n",
    "# Plot bar plot\n",
    "loan_approval_counts.plot(kind='bar', figsize=(6, 3))\n",
    "\n",
    "# Add labels and title\n",
    "plt.title('Loan Approval Count by Applicant Number of dependents')\n",
    "plt.xlabel('Dependents')\n",
    "plt.ylabel('Count of Loan Approvals')\n",
    "plt.xticks(rotation=0)  # Rotate x-axis labels for better readability\n",
    "\n",
    "# Show plot\n",
    "plt.legend(title='Loan Status')\n",
    "plt.grid(axis='y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "4f6c4531-f718-4fca-b44f-bd79eaab90a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Count loan approvals (yes and no) for applicant marriage column\n",
    "loan_approval_counts = loan_data.groupby(['Education', 'Status']).size().unstack()\n",
    "\n",
    "# Plot bar plot\n",
    "loan_approval_counts.plot(kind='bar', figsize=(6, 3))\n",
    "\n",
    "# Add labels and title\n",
    "plt.title('Loan Approval Count by Education level')\n",
    "plt.xlabel('Education')\n",
    "plt.ylabel('Count of Loan Approvals')\n",
    "plt.xticks(rotation=0)  # Rotate x-axis labels for better readability\n",
    "\n",
    "# Show plot\n",
    "plt.legend(title='Loan Status')\n",
    "plt.grid(axis='y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "f7b03a07-c63f-47ef-b138-bc0aaff55687",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Count loan approvals (yes and no) for applicant marriage column\n",
    "loan_approval_counts = loan_data.groupby(['Self_Employed', 'Status']).size().unstack()\n",
    "\n",
    "# Plot bar plot\n",
    "loan_approval_counts.plot(kind='bar', figsize=(6, 3))\n",
    "\n",
    "# Add labels and title\n",
    "plt.title('Loan Approval Count if applicant is Self Employed or not ')\n",
    "plt.xlabel('Self Employed')\n",
    "plt.ylabel('Count of Loan Approvals')\n",
    "plt.xticks(rotation=0)  # Rotate x-axis labels for better readability\n",
    "\n",
    "# Show plot\n",
    "plt.legend(title='Loan Status')\n",
    "plt.grid(axis='y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "8e6a3beb-350c-407f-884a-4bb5c27ce0b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Count loan approvals (yes and no) for applicant marriage column\n",
    "loan_approval_counts = loan_data.groupby(['Credit_History', 'Status']).size().unstack()\n",
    "\n",
    "# Plot bar plot\n",
    "loan_approval_counts.plot(kind='bar', figsize=(6, 3))\n",
    "\n",
    "# Add labels and title\n",
    "plt.title('Loan Approval Count by Applcant Credit_History')\n",
    "plt.xlabel('Credit_History (1:YES 0:NO)')\n",
    "plt.ylabel('Count of Loan Approvals')\n",
    "plt.xticks(rotation=0)  # Rotate x-axis labels for better readability\n",
    "\n",
    "# Show plot\n",
    "plt.legend(title='Loan Status')\n",
    "plt.grid(axis='y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "b25eb1cb-f22a-4952-a3ef-ea7e7d260e1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Count loan approvals (yes and no) for applicant marriage column\n",
    "loan_approval_counts = loan_data.groupby(['Area', 'Status']).size().unstack()\n",
    "\n",
    "# Plot bar plot\n",
    "loan_approval_counts.plot(kind='bar', figsize=(6, 3))\n",
    "\n",
    "# Add labels and title\n",
    "plt.title('Loan')\n",
    "plt.ylabel('Count of Loan Approvals')\n",
    "plt.xticks(rotation=0)  # Rotate x-axis labels for better readability\n",
    "\n",
    "# Show plot\n",
    "plt.legend(title='Loan Status')\n",
    "plt.grid(axis='y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3420b195-49de-46ed-a732-33490b968a53",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93ffa363-aa35-44bd-813b-2dc710081ba5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a482b5c4-57b1-4025-a2ee-8bc9204b5dbf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "570d5cbd-e73e-41d7-988c-e0bd2c8ba669",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}