Loan Prediction Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import datasist as ds\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing of Train, Test Data\n",
    "\n",
    "Pandas Module would be used in loading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.read_csv(\"C:\\\\Users\\\\Toyin Obasoro\\\\Downloads\\\\train_data.csv\")\n",
    "test_data = pd.read_csv(\"C:\\\\Users\\\\Toyin Obasoro\\\\Downloads\\\\test_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_copy = train_data.copy()\n",
    "test_data_copy = test_data.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Exploratory for Loan Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>LP001002</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>LP001003</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
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       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>LP001005</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>LP001006</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>LP001008</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
       "0  LP001002   Male      No          0      Graduate            No   \n",
       "1  LP001003   Male     Yes          1      Graduate            No   \n",
       "2  LP001005   Male     Yes          0      Graduate           Yes   \n",
       "3  LP001006   Male     Yes          0  Not Graduate            No   \n",
       "4  LP001008   Male      No          0      Graduate            No   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5849                0.0         NaN             360.0   \n",
       "1             4583             1508.0       128.0             360.0   \n",
       "2             3000                0.0        66.0             360.0   \n",
       "3             2583             2358.0       120.0             360.0   \n",
       "4             6000                0.0       141.0             360.0   \n",
       "\n",
       "   Credit_History Property_Area Loan_Status  \n",
       "0             1.0         Urban           Y  \n",
       "1             1.0         Rural           N  \n",
       "2             1.0         Urban           Y  \n",
       "3             1.0         Urban           Y  \n",
       "4             1.0         Urban           Y  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>LP001015</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5720</td>\n",
       "      <td>0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>LP001022</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3076</td>\n",
       "      <td>1500</td>\n",
       "      <td>126.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>LP001031</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5000</td>\n",
       "      <td>1800</td>\n",
       "      <td>208.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>LP001035</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2340</td>\n",
       "      <td>2546</td>\n",
       "      <td>100.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>LP001051</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3276</td>\n",
       "      <td>0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
       "0  LP001015   Male     Yes          0      Graduate            No   \n",
       "1  LP001022   Male     Yes          1      Graduate            No   \n",
       "2  LP001031   Male     Yes          2      Graduate            No   \n",
       "3  LP001035   Male     Yes          2      Graduate            No   \n",
       "4  LP001051   Male      No          0  Not Graduate            No   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5720                  0       110.0             360.0   \n",
       "1             3076               1500       126.0             360.0   \n",
       "2             5000               1800       208.0             360.0   \n",
       "3             2340               2546       100.0             360.0   \n",
       "4             3276                  0        78.0             360.0   \n",
       "\n",
       "   Credit_History Property_Area  \n",
       "0             1.0         Urban  \n",
       "1             1.0         Urban  \n",
       "2             1.0         Urban  \n",
       "3             NaN         Urban  \n",
       "4             1.0         Urban  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(614, 13)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(367, 12)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>592.000000</td>\n",
       "      <td>600.00000</td>\n",
       "      <td>564.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>5403.459283</td>\n",
       "      <td>1621.245798</td>\n",
       "      <td>146.412162</td>\n",
       "      <td>342.00000</td>\n",
       "      <td>0.842199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>6109.041673</td>\n",
       "      <td>2926.248369</td>\n",
       "      <td>85.587325</td>\n",
       "      <td>65.12041</td>\n",
       "      <td>0.364878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>12.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>2877.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>3812.500000</td>\n",
       "      <td>1188.500000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>5795.000000</td>\n",
       "      <td>2297.250000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>81000.000000</td>\n",
       "      <td>41667.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>480.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "count       614.000000         614.000000  592.000000         600.00000   \n",
       "mean       5403.459283        1621.245798  146.412162         342.00000   \n",
       "std        6109.041673        2926.248369   85.587325          65.12041   \n",
       "min         150.000000           0.000000    9.000000          12.00000   \n",
       "25%        2877.500000           0.000000  100.000000         360.00000   \n",
       "50%        3812.500000        1188.500000  128.000000         360.00000   \n",
       "75%        5795.000000        2297.250000  168.000000         360.00000   \n",
       "max       81000.000000       41667.000000  700.000000         480.00000   \n",
       "\n",
       "       Credit_History  \n",
       "count      564.000000  \n",
       "mean         0.842199  \n",
       "std          0.364878  \n",
       "min          0.000000  \n",
       "25%          1.000000  \n",
       "50%          1.000000  \n",
       "75%          1.000000  \n",
       "max          1.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First five data points\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>LP001002</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>LP001003</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>LP001005</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>LP001006</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>LP001008</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
       "0  LP001002   Male      No          0      Graduate            No   \n",
       "1  LP001003   Male     Yes          1      Graduate            No   \n",
       "2  LP001005   Male     Yes          0      Graduate           Yes   \n",
       "3  LP001006   Male     Yes          0  Not Graduate            No   \n",
       "4  LP001008   Male      No          0      Graduate            No   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5849                0.0         NaN             360.0   \n",
       "1             4583             1508.0       128.0             360.0   \n",
       "2             3000                0.0        66.0             360.0   \n",
       "3             2583             2358.0       120.0             360.0   \n",
       "4             6000                0.0       141.0             360.0   \n",
       "\n",
       "   Credit_History Property_Area Loan_Status  \n",
       "0             1.0         Urban           Y  \n",
       "1             1.0         Rural           N  \n",
       "2             1.0         Urban           Y  \n",
       "3             1.0         Urban           Y  \n",
       "4             1.0         Urban           Y  "
      ]
     },
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Random five data points\n"
     ]
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       "      <td>245</td>\n",
       "      <td>LP001813</td>\n",
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       "      <td>6050</td>\n",
       "      <td>4333.0</td>\n",
       "      <td>120.0</td>\n",
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       "      <td>LP001439</td>\n",
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       "      <td>No</td>\n",
       "      <td>4300</td>\n",
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       "      <td>106</td>\n",
       "      <td>LP001369</td>\n",
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       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>11417</td>\n",
       "      <td>1126.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>188</td>\n",
       "      <td>LP001644</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
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       "      <td>Yes</td>\n",
       "      <td>674</td>\n",
       "      <td>5296.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>468</td>\n",
       "      <td>LP002502</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>210</td>\n",
       "      <td>2917.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Loan_ID  Gender Married Dependents     Education Self_Employed  \\\n",
       "245  LP001813    Male      No          0      Graduate           Yes   \n",
       "124  LP001439    Male     Yes          0  Not Graduate            No   \n",
       "106  LP001369    Male     Yes          2      Graduate            No   \n",
       "188  LP001644     NaN     Yes          0      Graduate           Yes   \n",
       "468  LP002502  Female     Yes          2  Not Graduate           NaN   \n",
       "\n",
       "     ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "245             6050             4333.0       120.0             180.0   \n",
       "124             4300             2014.0       194.0             360.0   \n",
       "106            11417             1126.0       225.0             360.0   \n",
       "188              674             5296.0       168.0             360.0   \n",
       "468              210             2917.0        98.0             360.0   \n",
       "\n",
       "     Credit_History Property_Area Loan_Status  \n",
       "245             1.0         Urban           N  \n",
       "124             1.0         Rural           Y  \n",
       "106             1.0         Urban           Y  \n",
       "188             1.0         Rural           Y  \n",
       "468             1.0     Semiurban           Y  "
      ]
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     "text": [
      "\n",
      "\n",
      "Last five data points\n"
     ]
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       "      <td>LP002978</td>\n",
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       "      <td>No</td>\n",
       "      <td>8072</td>\n",
       "      <td>240.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>Y</td>\n",
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       "    <tr>\n",
       "      <td>612</td>\n",
       "      <td>LP002984</td>\n",
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       "      <td>2</td>\n",
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       "      <td>No</td>\n",
       "      <td>7583</td>\n",
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       "      <td>187.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>Y</td>\n",
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       "    <tr>\n",
       "      <td>613</td>\n",
       "      <td>LP002990</td>\n",
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       "      <td>No</td>\n",
       "      <td>0</td>\n",
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       "      <td>Yes</td>\n",
       "      <td>4583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Loan_ID  Gender Married Dependents Education Self_Employed  \\\n",
       "609  LP002978  Female      No          0  Graduate            No   \n",
       "610  LP002979    Male     Yes         3+  Graduate            No   \n",
       "611  LP002983    Male     Yes          1  Graduate            No   \n",
       "612  LP002984    Male     Yes          2  Graduate            No   \n",
       "613  LP002990  Female      No          0  Graduate           Yes   \n",
       "\n",
       "     ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "609             2900                0.0        71.0             360.0   \n",
       "610             4106                0.0        40.0             180.0   \n",
       "611             8072              240.0       253.0             360.0   \n",
       "612             7583                0.0       187.0             360.0   \n",
       "613             4583                0.0       133.0             360.0   \n",
       "\n",
       "     Credit_History Property_Area Loan_Status  \n",
       "609             1.0         Rural           Y  \n",
       "610             1.0         Rural           Y  \n",
       "611             1.0         Urban           Y  \n",
       "612             1.0         Urban           Y  \n",
       "613             0.0     Semiurban           N  "
      ]
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     "text": [
      "\n",
      "\n",
      "Shape of  data set: (614, 13)\n",
      "\n",
      "\n",
      "Size of  data set: 7982\n",
      "\n",
      "\n",
      "Data Types\n",
      "Note: All Non-numerical features are identified as objects in pandas\n"
     ]
    },
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      "text/plain": [
       "                  Data Type\n",
       "Loan_ID              object\n",
       "Gender               object\n",
       "Married              object\n",
       "Dependents           object\n",
       "Education            object\n",
       "Self_Employed        object\n",
       "ApplicantIncome       int64\n",
       "CoapplicantIncome   float64\n",
       "LoanAmount          float64\n",
       "Loan_Amount_Term    float64\n",
       "Credit_History      float64\n",
       "Property_Area        object\n",
       "Loan_Status          object"
      ]
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    },
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     "text": [
      "\n",
      "\n",
      "Numerical Features in Data set\n",
      "['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount', 'Loan_Amount_Term', 'Credit_History']\n",
      "\n",
      "\n",
      "Categorical Features in Data set\n"
     ]
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       "['Loan_ID',\n",
       " 'Gender',\n",
       " 'Married',\n",
       " 'Dependents',\n",
       " 'Education',\n",
       " 'Self_Employed',\n",
       " 'Property_Area',\n",
       " 'Loan_Status']"
      ]
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     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Statistical Description of Columns\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>592.000000</td>\n",
       "      <td>600.00000</td>\n",
       "      <td>564.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>5403.459283</td>\n",
       "      <td>1621.245798</td>\n",
       "      <td>146.412162</td>\n",
       "      <td>342.00000</td>\n",
       "      <td>0.842199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>6109.041673</td>\n",
       "      <td>2926.248369</td>\n",
       "      <td>85.587325</td>\n",
       "      <td>65.12041</td>\n",
       "      <td>0.364878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>12.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>2877.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>3812.500000</td>\n",
       "      <td>1188.500000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>5795.000000</td>\n",
       "      <td>2297.250000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>81000.000000</td>\n",
       "      <td>41667.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>480.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "count       614.000000         614.000000  592.000000         600.00000   \n",
       "mean       5403.459283        1621.245798  146.412162         342.00000   \n",
       "std        6109.041673        2926.248369   85.587325          65.12041   \n",
       "min         150.000000           0.000000    9.000000          12.00000   \n",
       "25%        2877.500000           0.000000  100.000000         360.00000   \n",
       "50%        3812.500000        1188.500000  128.000000         360.00000   \n",
       "75%        5795.000000        2297.250000  168.000000         360.00000   \n",
       "max       81000.000000       41667.000000  700.000000         480.00000   \n",
       "\n",
       "       Credit_History  \n",
       "count      564.000000  \n",
       "mean         0.842199  \n",
       "std          0.364878  \n",
       "min          0.000000  \n",
       "25%          1.000000  \n",
       "50%          1.000000  \n",
       "75%          1.000000  \n",
       "max          1.000000  "
      ]
     },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Description of Categorical Features\n"
     ]
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    {
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       "      <td>Self_Employed</td>\n",
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       "      <td>Loan_Status</td>\n",
       "      <td>614</td>\n",
       "      <td>2</td>\n",
       "      <td>Y</td>\n",
       "      <td>422</td>\n",
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      ],
      "text/plain": [
       "              count unique        top freq\n",
       "Loan_ID         614    614   LP001356    1\n",
       "Gender          601      2       Male  489\n",
       "Married         611      2        Yes  398\n",
       "Dependents      599      4          0  345\n",
       "Education       614      2   Graduate  480\n",
       "Self_Employed   582      2         No  500\n",
       "Property_Area   614      3  Semiurban  233\n",
       "Loan_Status     614      2          Y  422"
      ]
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Unique class Count of Categorical features\n"
     ]
    },
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       "                <tr>\n",
       "                        <th id=\"T_9f2f7a8c_702c_11ea_95c7_3464a9d2312elevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_9f2f7a8c_702c_11ea_95c7_3464a9d2312erow0_col0\" class=\"data row0 col0\" >Loan_ID</td>\n",
       "                        <td id=\"T_9f2f7a8c_702c_11ea_95c7_3464a9d2312erow0_col1\" class=\"data row0 col1\" >614</td>\n",
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       "                        <th id=\"T_9f2f7a8c_702c_11ea_95c7_3464a9d2312elevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_9f2f7a8c_702c_11ea_95c7_3464a9d2312erow1_col0\" class=\"data row1 col0\" >Gender</td>\n",
       "                        <td id=\"T_9f2f7a8c_702c_11ea_95c7_3464a9d2312erow1_col1\" class=\"data row1 col1\" >3</td>\n",
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       "                        <td id=\"T_9f2f7a8c_702c_11ea_95c7_3464a9d2312erow2_col1\" class=\"data row2 col1\" >3</td>\n",
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       "                        <th id=\"T_9f2f7a8c_702c_11ea_95c7_3464a9d2312elevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_9f2f7a8c_702c_11ea_95c7_3464a9d2312erow3_col0\" class=\"data row3 col0\" >Dependents</td>\n",
       "                        <td id=\"T_9f2f7a8c_702c_11ea_95c7_3464a9d2312erow3_col1\" class=\"data row3 col1\" >5</td>\n",
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       "                        <td id=\"T_9f2f7a8c_702c_11ea_95c7_3464a9d2312erow4_col1\" class=\"data row4 col1\" >2</td>\n",
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       "                        <td id=\"T_9f2f7a8c_702c_11ea_95c7_3464a9d2312erow6_col0\" class=\"data row6 col0\" >Property_Area</td>\n",
       "                        <td id=\"T_9f2f7a8c_702c_11ea_95c7_3464a9d2312erow6_col1\" class=\"data row6 col1\" >3</td>\n",
       "            </tr>\n",
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       "                        <th id=\"T_9f2f7a8c_702c_11ea_95c7_3464a9d2312elevel0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_9f2f7a8c_702c_11ea_95c7_3464a9d2312erow7_col0\" class=\"data row7 col0\" >Loan_Status</td>\n",
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    {
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     "text": [
      "\n",
      "\n",
      "Missing Values in Data\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>features</th>\n",
       "      <th>missing_counts</th>\n",
       "      <th>missing_percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Loan_ID</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Gender</td>\n",
       "      <td>13</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Married</td>\n",
       "      <td>3</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Dependents</td>\n",
       "      <td>15</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
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       "      <td>4</td>\n",
       "      <td>Education</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Self_Employed</td>\n",
       "      <td>32</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>ApplicantIncome</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>CoapplicantIncome</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>LoanAmount</td>\n",
       "      <td>22</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Loan_Amount_Term</td>\n",
       "      <td>14</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>Credit_History</td>\n",
       "      <td>50</td>\n",
       "      <td>8.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>Property_Area</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>Loan_Status</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             features  missing_counts  missing_percent\n",
       "0             Loan_ID               0              0.0\n",
       "1              Gender              13              2.1\n",
       "2             Married               3              0.5\n",
       "3          Dependents              15              2.4\n",
       "4           Education               0              0.0\n",
       "5       Self_Employed              32              5.2\n",
       "6     ApplicantIncome               0              0.0\n",
       "7   CoapplicantIncome               0              0.0\n",
       "8          LoanAmount              22              3.6\n",
       "9    Loan_Amount_Term              14              2.3\n",
       "10     Credit_History              50              8.1\n",
       "11      Property_Area               0              0.0\n",
       "12        Loan_Status               0              0.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.structdata.describe(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 614 training rows and 367 test rows.\n",
      "There are 13 training columns and 12 test columns.\n",
      "Id field is unique.\n",
      "Train and test sets have distinct Ids.\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.structdata.check_train_test_set(train_data, test_data,'Loan_ID', 'Dependents')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['Loan_Status'] = train_data['Loan_Status'].replace({\"Y\":1, \"N\":0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "ylabel = train_data.Loan_Status\n",
    "train = train_data.drop(columns=['Loan_Status'])\n",
    "\n",
    "all_data, ntrain, ntest = ds.structdata.join_train_and_test(train, test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(981, 12)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>features</th>\n",
       "      <th>missing_counts</th>\n",
       "      <th>missing_percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Loan_ID</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Gender</td>\n",
       "      <td>24</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Married</td>\n",
       "      <td>3</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Dependents</td>\n",
       "      <td>25</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Education</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Self_Employed</td>\n",
       "      <td>55</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>ApplicantIncome</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>CoapplicantIncome</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>LoanAmount</td>\n",
       "      <td>27</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Loan_Amount_Term</td>\n",
       "      <td>20</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>Credit_History</td>\n",
       "      <td>79</td>\n",
       "      <td>8.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>Property_Area</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             features  missing_counts  missing_percent\n",
       "0             Loan_ID               0              0.0\n",
       "1              Gender              24              2.4\n",
       "2             Married               3              0.3\n",
       "3          Dependents              25              2.5\n",
       "4           Education               0              0.0\n",
       "5       Self_Employed              55              5.6\n",
       "6     ApplicantIncome               0              0.0\n",
       "7   CoapplicantIncome               0              0.0\n",
       "8          LoanAmount              27              2.8\n",
       "9    Loan_Amount_Term              20              2.0\n",
       "10     Credit_History              79              8.1\n",
       "11      Property_Area               0              0.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.structdata.display_missing(all_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fill categorical dataset\n",
    "all_data = ds.feature_engineering.fill_missing_cats(all_data)\n",
    "all_data = ds.feature_engineering.fill_missing_num(all_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>features</th>\n",
       "      <th>missing_counts</th>\n",
       "      <th>missing_percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Loan_ID</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Gender</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Married</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Dependents</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Education</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Self_Employed</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>ApplicantIncome</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>CoapplicantIncome</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>LoanAmount</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Loan_Amount_Term</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>Credit_History</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>Property_Area</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             features  missing_counts  missing_percent\n",
       "0             Loan_ID               0              0.0\n",
       "1              Gender               0              0.0\n",
       "2             Married               0              0.0\n",
       "3          Dependents               0              0.0\n",
       "4           Education               0              0.0\n",
       "5       Self_Employed               0              0.0\n",
       "6     ApplicantIncome               0              0.0\n",
       "7   CoapplicantIncome               0              0.0\n",
       "8          LoanAmount               0              0.0\n",
       "9    Loan_Amount_Term               0              0.0\n",
       "10     Credit_History               0              0.0\n",
       "11      Property_Area               0              0.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.structdata.display_missing(all_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cI+MGcjIx0nsWkeaiQK6R7cPexughq2Qh0pQUyDWyfdjby2vIOqkn0pwUyDUy3rC3VEon9USalQK5RrLZPAnC5dKl0jqpJ9K0FMg1kssXSIwKYwg95lxeJQuRZqRArpFsrsAYeUwqlSSbK1AoqJcs0mwUyDWSyxdeVq6AcFIPIK9AFmk6CuQayefzJBNjlCziMDjVkUWajwK5RsbvIYevJKtAFmk6CuQayeUKO+8h68SeSNNRINdILj/+ST1QD1mkGSmQa2Sik3rqIYs0HwVyjeRy+TEDWSf1RJqXArlGQslCJ/VEZDsFco3k8gUSOqknIiUUyDWSy+dJjvHpp9RDFmlaCuQaGa9koR6ySPNSINdILjfOKAud1BNpWgrkGhm3h1wsWWhOZJGmo0CukVBD3lkPWSULkWajQK6R3DjTbyYSCZKJhE7qiTQhBXKNjFeygHBiTyf1RJqPArlGxrtjCISyhU7qiTQfBXKN5HfWQ04myemknkjTUSDXyHgXhkDoIWd1Uk+k6SiQayQ7znzIEHvIKlmINB0Fco2MN/0mhJN6WZ3UE2k66Upu3MzOBi4BWoAvuftXRy1fClwNzATuAC5096yZ7QdcA8wHHDjH3XvNbDbwPeAAYB1wpru/VLK9NwOfcPeT4vMWYAPwdMnbHunuuYoccJkKhcIENeQEmawCWaTZVKyHbGZ7A58B3ggsBd5vZoeMWu0a4CJ3PwhIABfE9iuBK939YGAFcGls/zSw3N2XAFcBl8f3SprZx4HrgFTJ9g8D/sfdl5b8qWkYAyMn7MbrIadTOqkn0owqWbI4GbjV3Te6ex9wPfDO4kIz2x/ocPc7Y9My4IzYqz0+rj/SHh+fSughA3wfOCWuvyT+KQZ60dFAj5mtMLM7zexN03h8u2wkkMfO41Cy0Ek9kaZTyZLFXsDqkuergWMmWL4PMA/Y6u7ZUe07vCaWNrYCPe7+CPA+Mzth1D4UgB8CnwUOBW4ys0PdfX25BzF3ble5q+5UT0/3yOO+gQwAHe2tdHe1j7S3tKTp7mqno62FfGGQzs42evbonJb3r6bSY20GOt7GVs3jrWQgJwmBWJQA8mUsH91OyetG9ylHb3MH7v6Nkqe/N7O7gGOBH02080UbNvSSn2L5oKenm3Xrto0839o/DEAmk2Vb7+BIe/F5Pp8nk83R3z/EulzNKyyTMvpYG52Ot7FV4niTycS4Hb1KlixeBBaWPF8ArCpj+VpglpkVa8ELS163Mq6HmaWBbsJJuzGZ2blm9sqSpgSQmfSRTLPikLadndTTsDeR5lPJQL4FOMnMesysEzgduLm40N2fAwbN7NjYdC5wk7tngOXAWbH9POCm+PjG+Jy4fHlcfzyHAx8HMDMDjojbrqniPBXjXzodTuoVCgplkWZSsUB295XAJ4HbgPuBa939bjO70cyOiqudA3zRzB4HuoArYvsHCaMyHgWOIwydgzDa4nVm9khc50MT7MZlwHwze5hwkvA8d6/571vlnNQD3cZJpNlUdByyu18LXDuq7W0ljx9gxxN9xfbngBPGaN8IvH0n73d76evcfSslIzvqxUjJYrwecrymeji7e9WPRWRqdKVeDWzvIY9/pR6gi0NEmowCuQaKY4zHvXQ6qUAWaUYK5BqYqIecToWvRYEs0lwUyDWQG+khj728WLJQDVmkuSiQa2DCHnJSPWSRZqRAroFyT+oNZxTIIs1EgVwDxWFv410YopN6Is1JgVwDxSv1xrswZPtJPdWQRZqJArkGshNcGKJxyCLNSYFcA9t7yOPfUw8UyCLNRoFcAxNeOj0y7E2BLNJMFMg1MOEoi5GTeqohizQTBXINbL+n3tjLE4kESd3oVKTpKJBrYORKvXF6yBDKFgpkkeZSViCb2TfHaLt+rHVlYtkJ7joN4cSeasgizWWn8yGb2deAvYHjzKynZFELcEAld6yRqYcsImOZaIL6bxLu1nw4cENJexa4s1I71eiKNeSd5DGpZEKTC4k0mZ0GsruvAFaY2S3u/mKV9qnh5fIFkskEiZ0kciqVVA9ZpMmUewunfc3su8AehDs3A+Duh1VkrxpcLlcYGdo2nrRGWYg0nXID+RvAMuA+QHfenKJsPj9hIKuHLNJ8yg3krLt/oaJ70kRy+TJ6yDqpJ9J0yh2H/LCZvbqie9JEcrk8qdTOP/pUMsFwRif1RJpJuT3kA4B7zew5YKDYqBryrimnhqyShUjzKTeQP1nRvWgy5ZQsUjqpJ9J0yg3khyq6F00mmy+MzHk8nnQqqXHIIk2m3EBeTxhdkWD7KIvVwD6V2KlGl8vlR+Y8Hk9LOkk2VyCby4/cQUREGltZgezuI4lgZq3A2YBVaqcaXTkli9Z0+MgHhrJ0d7ZWY7dEpMYm3fVy92F3Xwa8efp3pzmEURYTBHJL+Gr6h7LV2CURqQNl9ZDNbI+SpwngKGBORfaoCZTXQ04B0D+oQBZpFrtSQwZYC3ykInvUBLL5Qlk1ZFAPWaSZTLqGLFOXyxVoad35R9raEnrIA+ohizSNcksWSeBi4BTCXMi/AP6vuystdkEunyeV3PlH36oeskjTKbfn+1ngj4DLgS8AbwA+V6mdanS5fIHkhCf1VEMWaTbl1pDfChzl7hkAM/sZ8ADw15XasUaWzU1cQ06nEiQS6iGLNJNye8jJYhgDuPsQkNnJ+rITuVye9ASjLBKJBB2tadWQRZpIuT3k+83si8BXCKMtPgw8WLG9anC5Mi6dBuhoS9M/pJ97Is2i3B7yhwjjjn8H3AXMI4Sy7IJyxiEDdLSnGRjSfBYizWKiu063AlcBP3T382Pbz4AcsLXie9egcrk8yQlqyAAdbSn6B9VDFmkWE6XCZcBM4LclbRcAs4F/rNA+Nbyye8htaZ3UE2kiEwXyacDZ7r622ODuq4DzgD+t5I41srIDuVWBLNJMJgrkYXcfGN3o7luBocrsUuMLU2qW2UPWKAuRpjFRIOfMrHt0Y2xrqcwuNbZ8oUChQJk15DSDwznyed3oW6QZTJQK3weuNrMZxYb4+GrghkruWKPK5UK4ljPsrbMtnHMdGFYvWaQZTBTIXwK2AC+Z2Z1mdjfwErCJcMJPJimXD/fJK6eG3N6my6dFmslOh725ex54v5l9BjgSyAN3ufvqauxcI8rF8kO5oyxAgSzSLMqdfvM54LkK70tTGClZlFFDLpYsNNJCpDlonuMqy+bKL1mohyzSXMqdy2KXmNnZwCWEERlfcvevjlq+lHCCcCZwB3Chu2fNbD/gGmA+4MA57t5rZrOB7wEHAOuAM939pZLtvRn4hLufFJ8nCNOEnkYot1zg7qUXuVTdSMmizGFvEG50KiKNr2I9ZDPbG/gM8EZgKaEWfcio1a4BLnL3gwi3h7ogtl8JXOnuBwMrgEtj+6eB5e6+hHBJ9+XxvZJm9nHgOiBVsv3TgSXAIcA7gGVmVtEfQhOZXA05ntRTIIs0hUqWLE4GbnX3je7eB1wPvLO40Mz2Bzrc/c7YtAw4w8xagOPj+iPt8fGphB4yhCF5p8T1l8Q/xUCnZP3r3D3v7k8AzxMm16+Z3EjJYuKPvr21WLLQfBYizaCSgbwXUDoaYzWwTxnL5wFbS24PVfq6kdfE5VuBHnd/xN3fB2yc5D5U3WRKFslkIkwwpB6ySFOo5K/vScLcyUUJQh13ouWj2yl53egUG73Nye7DhObO7ZrM6uPq6QkXPG4aCOHa2dFKW9uOh9nSkqa7q33keWdnG12dreRJjLx+d7A77et00PE2tmoebyUD+UXguJLnC4BVo5YvHGP5WmCWmaXcPRfXKb5uZVzvxVgL7gY2TLAPY71H2TZs6J3ypcs9Pd2sW7ctbq8PgGwmS+/AjqWITCbLtt7Bkef9/UO0pVNs2jIw8vp6V3qszUDH29gqcbzJZGLcjl4lSxa3ACeZWY+ZdRJOsN1cXBjHNg+a2bGx6VzgpnirqOXAWbH9POCm+PjG+Jy4fHnpraXGcCNwjpmlzGwxcBBwz9QPbdeNDHtLlffRd7alNMpCpElULJDdfSXwSeA24H7gWne/28xuNLOj4mrnAF80s8eBLuCK2P5BwqiMRwm97Eti+6XA68zskbjOhybYjeuBRwi3m/oR8N6xZq+rpsmMsgDobG/ROGSRJlHRIWDufi1w7ai2t5U8fgA4ZozXPQecMEb7RuDtO3m/20tf5+4F4OL4py5MZi4L0CT1Is1EV+pV2fZLp8vtIWtOZJFmoUCusu3D3sr76Ge0pxkYympOZJEmoECusmwsWSTL7CF3dbRQAHp1cYhIw1MgV1mxZJEuM5C7O1sB6O1XIIs0OgVylU3mSj2Ars5wp6zRY5ZFpPEokKtsMnNZAHR3hEDe1j9csX0SkfqgQK6y7CTHIRdLFtvUQxZpeArkKivWkMs/qReGim9TDVmk4SmQq2zkwpAya8gt6RRtrSmd1BNpAgrkKpvMPfWKujta6B1QDVmk0SmQqyybL5AAyqxYANDd2aKShUgTUCBXWTaXpyWdJJEoP5G7O1t1Uk+kCSiQqyyTDYE8GV0dLfRq2JtIw1MgV1kmmye9C4GsHrJI41MgV1kmm6elzImFiro7WxjO5BnK5Cq0VyJSDxTIVZbJlV+ySCQT9A1laW1JAbB28wDZSd0RUER2JwrkKstOooc8lMlxz2NrWB3vw3f3o2sYymhuZJFGpUCuskw2N+mTeu2toYc8OKyShUgjUyBX2a6MsmhrCZdPq4Ys0tgUyFWWyU1+lEWxhzykHrJIQ1MgV9mujLJobUmSSMDgsOrHIo1MgVxlu1KySCQStLWkVLIQaXAK5CrLTmLYW6m21pRO6ok0OAVylYUecmrSr2tvSamGLNLgFMhVlslNvoYMsYeskoVIQ1MgV9mu1JAhjLRQD1mksSmQqyhfKJDNFXathhxLFvlCoQJ7JiL1QIFcRdk4EcWuBHJrS4oCGoss0sgUyFWUycVA3oUacnGCof5BjUUWaVQK5CrKTKGH3NYSXtM/pEAWaVQK5CqaSiBv7yFronqRRqVArqJiIKd3ZdhbsYeskoVIw1IgV9G09JBVshBpWArkKho5qbcrgRyv7htQD1mkYSmQq2ikh7wLJYt0KkEyAX0KZJGGpUCuoqmULBKJBK0tKQaGdFJPpFEpkKtoKoEM4Wo9ndQTaVwK5CrK5MJVdrsayK0tSZUsRBqYArmKplJDBmLJQoEs0qgUyFU0lbksQCULkUanQK6iqdaQW9NJ+nVST6RhKZCraCrjkKFYssiRz2sKTpFGpECuoqlcOg2hZAG6Wk+kUSmQqyiTy5NOJUkkErv0+tY4n0WfJhgSaUgK5Cra1ds3FWlOZJHGpkCuouwUA7lNPWSRhqZArqJMdtfuOF2kHrJIY1MgV1EmN8WSRZzxrW9APWSRRpSu5MbN7GzgEqAF+JK7f3XU8qXA1cBM4A7gQnfPmtl+wDXAfMCBc9y918xmA98DDgDWAWe6+0tm1gp8EzgKGADOdvfHzawF2AA8XfK2R7p7Te4Umsnmd3mEBZSWLNRDFmlEFeshm9newGeANwJLgfeb2SGjVrsGuMjdDwISwAWx/UrgSnc/GFgBXBrbPw0sd/clwFXA5bH9I0BfbP8YsCy2Hwb8j7svLflTs9s2T/WkXiqVpCWdVMlCpEFVsmRxMnCru2909z7geuCdxYVmtj/Q4e53xqZlwBmxV3t8XH+kPT4+ldBDBvg+cEpcf6Td3e8AemIv++j4eIWZ3Wlmb6rIkZZpqoEM0NmWplcn9UQaUiVLFnsBq0uerwaOmWD5PsA8YKu7Z0e17/CaWNrYCvTsZFsF4IfAZ4FDgZvM7FB3X1/uQcyd21XuqjvV09MNiQQz2tP09HRT2NhPd1f7Duu0tKR3aBv9HKCrs5VcIW6vTtXzvlWCjrexVfN4KxnISUIgFiWAfBnLR7dT8rrRV1SM95oEkHf3b5S0/d7M7gKOBX5U7kFs2NA75UuVe3q6WbduG/2DGTpaU+HxUJZtvYM7rJfJ7Ng2+jlAe2uSTVsGWLdu25T2qVKKx9osdLyNrRLHm0wmxu3oVbJk8SKwsOT5AmBVGcvXArPMLBXbF5a8bmVcDzNLA92Ek3ZjbsvMzjWzV5a0J4Ca/b4/Pf2Mw10AABQzSURBVCWLFnoHVEMWaUSVDORbgJPMrMfMOoHTgZuLC939OWDQzI6NTecCN7l7BlgOnBXbzwNuio9vjM+Jy5fH9UfazeyNwKC7Pw8cDnw8thtwRNx2TUxLILenNeObSIOqWCC7+0rgk8BtwP3Ate5+t5ndaGZHxdXOAb5oZo8DXcAVsf2DhFEZjwLHEYbOQRht8TozeySu86HY/mWgLbZfQQh3gMuA+Wb2MOEk4XnuXrPft6Y6DhlCIPephyzSkCo6DtndrwWuHdX2tpLHD7Djib5i+3PACWO0bwTePkb7IPDuMdq3UjKyo9ameqUewOyuNoYyOfoGM8xob5mmPROReqAr9apoOkoWc2eFURfrNg9Mxy6JSB1RIFdJoVAgOw0li3kjgTw4wZoisrtRIFdJdop3CylSD1mkcSmQqySTDWOZp1pDbm9N09XRwnoFskjDUSBXyVTvp1eqZ3aHesgiDUiBXCWZbJjTKD0tgdyuGrJIA1IgV0nxBqfT1UPesHVQd58WaTAK5CoZCeRUaoI1J9Yzu4NcvsDGbeolizQSBXKVbK8h79odp0tp6JtIY1IgV0l2pIc8PSUL0NA3kUajQK6S7TXkqZcs9pjZRjKRUCCLNBgFcpVM50m9VDLJ3FltrN+ikoVII1EgV0mxhjwdw94A5s3SWGSRRqNArpLp7CFDqCOv3TRAoaChbyKNQoFcJZlpPKkHsHjvWfQOZHhq1dZp2Z6I1J4CuUqmu4d8pPXQ2pLktw+tnnhlEdktKJCrZDrnsgDoaEtzlM3n7sfWMJzJTcs2RaS2FMhVMl0li0QyQd9Qlr6hLEcePJ+BoRwrfN107KKI1FhFb+Ek2/UNZGhvTZFMTu1KvaFMjgeeCAFcKBSY0Z7mNw+u4g2HLpiO3RSRGlIPuUq29g8zc0brtG4zkUiweJ9ZPP78Zl5Y2zut2xaR6lMgV8nWvukPZICD959DR1uKHy5/etq3LSLVpUCuki19w8yqQCC3taQ48TX78Ps/rOeZ1RoCJ7I7UyBXSaV6yAAnHrkPne1p/uu2J9k2mKFvKEs8hygiuxEFchVksnn6BrPM6qxMICeTCQ59xR48/vxmvvgf93PXIy8xlMlW5L1EpHI0yqIKtvQOAVSshwyhlpzN5bnvifXkcgWOOWTPir2XiFSGeshVsHlb5QMZ4NAD5rL0wHm8sLaXF9Zo1IXI7kaBXAWbYw+5Eif1Rjt4v9mkkgl+97AuqRbZ3SiQq2DT1jBvcaV7yACtLSkWLexmxePrGBxWHVlkd6JAroLNVaghlzpwn9kMZXLc/djaqryfiEwPBXIVbN42RFtriraWqd++qRw9s9tZsEcnv75/VVXeT0SmhwK5CjZvG6rYkLexJBIJXv/qBTyzeiur1vdV7X1FZGoUyFWwfssAXZ0tI7O09Q1lyVf4Rh9H2XwSCbjz0Zcq+0YiMm0UyFWwadsQ2Vyeex5bM/Inm6/spXQzZ7TyqkV7cOcja8jrNk8iuwUFchVs7RumvbW61+AkkgleYz2s3zLIw89s1OXUIrsBBXKFZXN5+gYydLRV54Re0VAmRyabJ51KcOP/PMs9j63R5dQidU6BXGHb+jMAdFS5hwzhdlH7zu/imdXbdJsnkd2AArnCtvYNA9Be5R5y0SGv2INMNs+jz26qyfuLSPkUyBW2JQZyLXrIAHNntrP/gm4efXYj2/qHa7IPIlIeBXKFbekLV+nVqocMsHTxXHK5Ar+854Wa7YOITEyBXGEjJYsa9ZABZnW1ccDeM7nj/lW6955IHVMgV9jTq7Yyb1Y7LenaftRHWg+d7Wmu+smjZHYy/u35Ndu47fcryeY0Rk6k2jRBfQXl8nkef34TRy9ZUOtdob01zbtOPoh/+/Ej/GD505x54uKXrbNu8wCfv+5+egcy/ObBVZz2hkWs2zxIJpvj1QfMZd/5XSQSiRrsvUhzUCBX0DOrtjEwlOOQA+bS3z9U693hsAPn8fpDF3DzXc+zuXeIM05cTHdnG+kkDAxlueKGB8nnC5x10oH89LfP8OUbHhp57Q2/fpr95nfxN3++lJlVnJdDpJkokCvokWc3kgCW7D+Hex+r/ZwSQ5kcr9xrJr0DGe58ZA2PPbuJc99qLJjTyb/9+BFWr+/nr886nEULZ1Io5Nm0bYjZXW0cYfN54Il1/PcdT/P56+7nw6cfxpzudmpchRFpOPovVUGPPLuR/Rd001VHPcpkMsERB87j5KP2IZcv8JUbHuJT37qbjduG+PDpr+ZVi/YAQolj4dwZdLSlw9ShrSmOP3wvVq3r5XPX3sdLG18+i9yLa3v59Lfu4rJl97B280C1D01kt6cecoX0D2Z5euVWTnndfrXelTHtNW8Gf/LGRWzpz7BmQz/vOP4AZs5o3elMdHv3zOD4pXvxmwdX88/X3Mv5bz2Y11gPmWye/7ztSW6/byXtbeGf1Ke/s4IL/vgQliyaq560SJkUyBXiz28iXyiM9DjrUSqV5MQj9+GBJ9bhz2+/ku/wg3rGfc1+e3Zz2hvaWOHruPKHD7Nwbif5AqzZ2M8JR+zNK/edzYZN/fzq3hf58g0P8rfnvIbFe82qxuGI7PYqGshmdjZwCdACfMndvzpq+VLgamAmcAdwobtnzWw/4BpgPuDAOe7ea2azge8BBwDrgDPd/SUzawW+CRwFDABnu/vjZpYAPgecBuSBC9z9t5U8ZoCh4Rw/WP40XR0tvHLvxgujmTNaufjsI7j3sbXc/vuVDA7nuOj0V3PgvnN44sUtzJzRyluO2Y+f/u5Zrv7Jo3zq/KPpaGuMn/2FQoG+wSzrNg+QTCSYs8eMWu+SNJCK/S8xs72BzwBHAkPA78zsNnd/tGS1a4D3ufudZvZN4ALga8CVwJXufp2ZXQpcCvwd8GlgubufambnApcDZwEfAfrcfYmZHQ8sA14HnA4sAQ4BFgM/M7Ml7l6xac8KhQLfvPExVq7v46/POJyWdJJGnI04ly+QSMCJr9kbCBfAlM7x3Nme5vile3HLPS/w1R88xLlvMfac0wmE+aEfeHI967YMMHtGGwvndnLQvrNprfAtrrb2D/PQUxtYt3mAzrY0PbM7WLJozpgX7RQKBZ59aRt3P7aGlev6yOby9A5kWb9lgMHh7RM1taaTLN5nFicduS+HL55LssrDAguFAqs29HP/H9bRN5Bl/pwO9tuzm0ULukkmq7sv2VyeP7ywmVUb+tnSN0R3ZytHLJ7HvNkdVd2P6ZTJ5njwqQ088OR6UqkES/abg+03m872loq8XyW7LScDt7r7RgAzux54J3BZfL4/0OHud8b1lwH/x8yuBo4H3lHS/mtCIJ8alwF8H/iqmbXE9n8AcPc7zKwn9rJPBa5z9zzwhJk9D7yB0BufSAoo+x/1lr5hfrXiBZ5ds42XNvTz7lMO5rDF8wAoJBMv+wLTqeSEbeWsU422ybyuoy1NLhvaD9hrFu85rZub7nyOy//rQfaZ38XWvuGRm74mE4zUq9PpJPvP72LOzHa62ltIJBKMZFti+1/FtkKB7T/oCiVthUJ4WoAC4XHfQIZVG/pZs7GP0XP1p1IJ9unpZt6sNjrbW8gXCmzZNsyza7bR2z9MKplg/pxO2tvSzJvdwREHzWNWVxtb+4bI5Qv0DmR5ZtUW/uPWP3Djnc+x7/wu5s5qJ0nY/2QyAYlwrCO7O8a+b28vxH0vOa54HKPX2dw7zAtrt41cDZpKJcjlwpqd7S3sv2cXs7raaG9NkWDnn2fpPozarR2etHe0MBBnMCzuR/9Qlk3bBnl+TS9DcVbBRCJs61f3vsgeM9tYOHcGs7vbSSfG/1zGff8xGgoln03p8pf9m4gPRm9vvNcX5fIF1mzq58W1vWSyeVpbUuQLBR58agOJBOw9r4sFe3TQ0d7CMUv2ZM855f/QKcmUl/VAKhnIewGrS56vBo6ZYPk+wDxga0kvtti+w2tiaWMr0LOTbY3XXo6FAHPmlPcr6dy5cMB+49eLTz3ulS9rO2CfORO2lbNONdqmsq13nHDgy9pEhIXAU6UNlQzkJDv+3EkQ6rgTLR/dTsnrRndXx3vNRO3luAc4jhDimkxYRKZLihDG94xeUMlAfpEQaEULgFWjli8cY/laYJaZpdw9F9cpvm5lXO9FM0sD3cCGkm09NWpb471HOYaA35S5rojIZDw1VmMlR4jeApwU67mdhBNsNxcXuvtzwKCZHRubzgVucvcMsJxwsg7gPOCm+PjG+Jy4fHlcf6TdzN4IDLr787H9HDNLmdli4CDG+KkkIlIPKhbI7r4S+CRwG3A/cK27321mN5rZUXG1c4AvmtnjQBdwRWz/IPB+M3uU0Mu+JLZfCrzOzB6J63wotn8ZaIvtVxDCHeB64BHgQeBHwHvdXZeQiUhdShR0i3gRkbqgi1pFROqEAllEpE4okEVE6oQCWUSkTiiQRUTqRGNMwVWnJprtrp6Z2aeAM+PTn7n735rZycAXgA7gP9z9krjutMzaV8XDG5eZfR6Y5+7nV3o2wqofXAkz+2PgU8AM4Bfu/tFG/n7N7C+A/y8+vcndL67H71c95Aopme3ujcBSwrjqQ2q7V+WJ/zH/F3AEYd+PNLN3Ad8C/oQwg97RZnZKfMk1wEXufhDh8vQLYntx1r6DgRWEceSwfda+JcBVhFn7as7MTgLeXdI0Xcc1Mhsh8DHChFk1Y2YHAF8nTOB1GPCa+F025PcbL0y7AngTcDhwXPw3XnffrwK5ckZmu3P3PsJFKu+s8T6VazXwcXcfjldCPka4yvEP7v5MnPjpGuCMcWbtOyPOwnc84bhH2uPjUwk9DQiz9p0S168ZM9uD8AP0/8bn03lcI+3ufgdQnI2wVv6U0AN+MX6/ZwH9NO73myJk3QzCb6stQIY6/H4VyJUzlZnmasrdHyn+QzWzAwmlizyTm1Gv7Fn7gOKsfbX0DcKVpcVbp0zncdXbv4XFQMrMfmxm9xOuep3sjIm7zffr7tsIvdzHCfPbPAsMU4ffrwK5cqYy01xdMLNXAb8E/jfwNJObUW9XZu2rCTN7H/CCu/+qpLkasxHWSprwG9x7gdcDryXURRv1+z0M+Etgf0J45gglubr7fhXIlTOVmeZqLk769CvgE+7+HcY/ngln7YvtY83ax6hZ+2rlLOB/xd7iZcDbgfcxfcdVb/8WXgJucfd1cW6XHxACulG/37cAv3L3te4+RChDnEAdfr8K5MrZ6Wx39czM9gV+SDhbfF1svissssXxH+vZhLPV0zlrX024+5vd/VB3X0q488yP3f09VH42wlr5KfAWM5sdv8tTCDXThvx+gQeAk81sRrzP5h8T7kJUd9+vhr1ViLuvNLPibHetwNXufneNd6tcFwPtwBfMrNj2deB84Ia47Ea2n/g4B7jKzGYC97HjrH3fMbNLgOeBd8X2S4FlcXa+zfH19Wi6juvLwDdi+xDbZyOsCXe/y8z+lTDfdwuhLPU1Qo214b5fd/+FmR0B3Es4mXc38M+E3wzq6vvVbG8iInVCJQsRkTqhQBYRqRMKZBGROqFAFhGpEwpkEZE6oWFvUvfifAHPA/e7+ykTrT/BtgqEy13fAJzs7h+Zhl0s3f4rgM+7++nx+e3AV9z9+p2+UAT1kGX38GeEO5cfZWZLpmOD7v7j6Q7jaH/AJlxLZAzqIcvu4APAdcBTwEeBC83sBOBfgOeAgwnz0J7v7o+Z2bL4fClhTttfAB8pvVrMzM4H3unup5nZAsKFLwcT5iD4urtfYWavA/4VaCNcGvtLd3+vmS0iXFZ+I2EeiDnA3wI/Jsyvu7eZ/dzd31J6EGb2LOGy3ZOA/YB/d/dL47K/BD5OmGdhPfBud3/BzN5PmN4xB6whTBf5RDzGfuDVwJ7xvTcQrkJbALzP3W+Nc/X+C2HqyRTw+/hZbJ3slyCVpx6y1LU4h/Trgf8CvgOcZ2Zz4+KjgC+7+2HAt4Hvlrz0tcCbgUPin7/aydtcCTwR5799PWHu6sWE8P8Hd39t3MbbzezI+JoDgJ+7+zHAJwg3IMgR5sB4anQYl+hy9+MIJZOLzewVZnY4ITTfGo/lx8AnzeyPCEF/orsfDlwL/DBe/gvwGuCPCNNFfhzodfc3EObp/URc5xNAFjgybmMV4So1qUMKZKl3HwB+6u4b3P0e4Bng/XHZA+6+PD7+FnBESVgvc/feOJnMvxMmmBnPycC/Abj7ljivxZOEyepnm9nfE0K7A+iKrynOYQDhsts9yjyeH8X3WUmYyGYPQo/55+7+Qlz2JXe/EHgrYd7idbF9GbA3sChu6yfunol34+hj+1wpT5Xsz2mESed/HydPegfhh4vUIZUspG6Z2QzCvABD8dd9CLfbuYhwJ4dsyerFXmMu/l26LFnSPpYsJdMnxjtqrCeUOh4kBN1/EnrdxfcZdvfiFIsFXj4143gGSh4XXzf6/TsItegUYd7eUgnC/BMQ5k0oNdYEPingo+5+U9x2F2GuCqlD6iFLPTuHUBfdy90XufsiQqmgizBSYmmc6xZCr/l37r45Pj/LzNrMrJ3Q0/3JTt7nFuA9AGY2i1AfPhA4Gvg7d/9vwoTjiwkBtzNZtgdmuW4jzEZWnMLxrwi165uBPzeznrhv7yF8Hk9OYts/By4ys1YzSxJuPfTZSe6fVIkCWerZB4AvxNosADFwrwD+mjCv72fM7CHCr+Kls2z1E6ZRfCj+/e2dvM9FwBIzexD4LfBZd7+XEFz3mdnDhFrsbwmhvDOPEqZ1vLuk1rtT7v4Q4SYAN5vZA4RSxYXu/kvgi8CtcSaxdwOnlfTMy/FPhDtk/D7uW4JQb5Y6pNneZLcUR1l8xd0PHWPZMuBhd/98tfdLZCrUQxYRqRPqIYuI1An1kEVE6oQCWUSkTiiQRUTqhAJZRKROKJBFROrE/wP/vQ9jTeBeaQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.visualizations.histogram(all_data[:ntrain])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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mIhERuR3ooW6wmBtUdQzuicELaTzuyAhfU94P+LXPoumYIyHc0K7X4WqEK3ABcyaNx0yoHzOifgyPPXFPcl6Ou2Ru+mRfW+VIX761cVCajg0RAmI+GP8DF5y/SXbGPw7RfD/qxy7ZEzegVhnu2FzHZow3ISKjReRyVV2jqv9S1R/hxg1P0aRNuR3HfGPbLgDwx2sP4Ie4Y3q/iFzcWhnNprEA3YmpG9RJgEm+aaJ+uMmhwOsZjDtSISLH+PeOw33R3ybz8RTaGsMjk/FPZgJH+fZecMNutqTcj1VRX971QP3AUH/G1T7TH5veZKq6xpf5O3573XC15n/jxnaY42u9s2g8BsymaGssGtgwVkVGxzwt3719+Q/1eW7WeB+mfewmoTkFN+DPByKyDvej/TAb2kTPAG7x4xIU4Mcd8TcJa3BjNFyHC3YnqmpCROrHU/glLmi3NJ5C+hgetbhgUj+Gx8dsGF/hpJbKASAiPwKeFpE1QGvBdTlwsohci2sXP9n/SKGqb4rIStJqz+IGnGrpJmG6oSLSdED87YHxwJ/FDZ5UgBvXYRruxtzJfsyKkD8GPX37d7upmxigfiyaHXDnZTV+LBq/2D9wPwankPkx/zFwm4hcyIZxM8CN9zEGN95HNW6UtvM3peymdfYkodkk2e5XvLWJyE64EctEN3MOPmOyxWrQptPzNf4LcCPT5WVwFpH7aXkOxXFpNWXTgVgN2hhj8pTdJDTGmDzVGZs4CnF3nJfS+pQ8xhjTHmFcT5dXaT411ibpjAF6FP5BCWOMyYGDcD2XNltnDNBLAVauXEsymXn7e3l5CZWVTXtSdWy2z52D7XN2hEIBPXp0BR9jsqEzBugEQDKZaleArl+ns7F97hxsn7Mqa02ndpPQGGPylAVoY4zJUxagjTEmT3XGNmhjtjmJRJyVK1cQj7c23PWmWb48RDKZzHq++Wxz9zkSKaBHjwrC4dyGUAvQxmwDVq5cQVFRF7p27UMQtDr6aLtFIiHi8c4VoDdnn1OpFGvXVrFy5Qp69eqb5ZI1Zk0cxmwD4vE6unYty3pwNu0XBAFdu5bl5GqmKQvQbQiFAkKREFVr64hGN2fIXmM2jwXn/LGlzoU1cbQiFA5RXZtg+pPvsaq6lmMPGMSwweWk4vaEuDEm9yxAtyIZBPzwpueoqXMB+f0FK7ls/J6MGFxOLGZB2hiTWzkN0CJyJvBT/3KGqv7Qz8I8BTftznO4MXjjIjIAmI6bbUKB8apa7ac3uhs3O/QK4FRVXSYiBcAduCl51gNnqOr72Sp7OBzi3YUrG4JzvRkvLuArO/ZsfZI4Yzq411+fw9Spf+WWW1qbozf71qxZww03XMfHH7uZynr12o7vf/9y+vcfwJIli7nzzjv46U9/0Woe3/3uhdx22+QtUdzNlrM2aBHpAtyEm415D+AgETkCF4QnquoQ3ISU9VPl3Arc6qeanwNM8unXArNVdSgwGbjRp18CrPXp38NNJZQ1qVSKHqWFzdJ7lhURtpZ7Y7aK22+/hcGDd+Lvf7+fv//9fsaMOZYrr3R1wGXLlrJ48aI283jjjdfaXCZf5DLUhH3+XXGTUEZx89MVq+rLfplpwFg/YenBwAPp6f7vY3E1aIB7gTF++YZ0VX0ON4HpgGwVPplM0btnF74yaMOM98WFEc44elfohOMWGJOJv/99KmeeOZazzx7HzTf/kUTCXYHefvufueCCCZx22klMnHgBX35ZCcAJJxzNH//4O8499wzOO+9slixZ3Gr+lZWV1NXVNfRhPvzwo/jmNy8E4MYb/4Dqe1x//XXE43Guu+5aLrzwXMaOPYGf/vQyamtr+NOffg/AN795NgAHHrh3Q95PPPEYv/rVVQDccsufOOec0zn33DOYOnXLXiWky1mA9rMaTwLeBxYBC4A6Go/0tBTYAegFVNVP4JmWDtCvfh3/fhVQkZ6+kXWyI5Hk8vF7cd13DuSKb+7Dny8/jC7RoFMOLGNMW1566QWef/45pky5i6lT72bx4s945JEHWbToMz79dAF/+ctU7rvvIXr37sNTT80AXMDda699+Nvf7mGPPUby4IP/1+o2zjnnWzz++KMcf/xR/OIXP+Xxxx9l1Kh9Abj00h8iMpTLLvsx77zzFpFIlNtv/xv33/8wa9as4aWXXuB737scgKlT/97iNpYtW8rLL7/InXfey223TWXBgvnU1mZleOd2y1kbtIgMB74J7IibYXg6cBSQHt0CIIn7oWga9ZJpy6RraZ0gbZ02lZdnPtdprx5dMl62o6mo2KSJprdp+bjPy5eHiERyd8Hb3rzD4RBBEDRa74035nD00cdQUuK+L8cffyKPP/4Y48adxqWXXsYTT/yThQsXMm/e2/Tv379h3dGjRxOJhNhll515443XWy3LsGG78dBDj/HWW2/y6quvcN9903n00YeYPHlaozLtvffe9OzZg0ce+QcLFixg8eLPqK2taZR3/d/1/4dCAUEQ0KdPb4qKCvn2t7/FgQcexMSJl9K1a3GzsoRCoZx/VnJ5k/Bo4GlVXQ4gItOAH+JmHKjXB1gCLAe6iUhYVRN+mfpp7hf75RaJSAQoBSpxtfK+uKni0/PKSGVldbtqwhUVpaxYsSbj5TsC2+f8kUwmc/a036Y8VZdIJEmlUo3WSyQSJBIb0uLxJPF4nHfemcdVV/2c0047g0MO+SpBEJBIbNifcDhKPJ4kmXRNiy2VJZVKcf31v+W73/0Bw4ePZPjwkZxzznmcdto3eO+99xqV6fnnZzFlyu2MHXsaY8Ycx8qVKxtts758ALFYgiAIqKuL4eZoDXH77dOYO/d1XnrpBc4//xxuvvmvDBiwY6PyJJPJRp+VUChoV8UvE7lsg34TOEJEuopIABwHzAJqRGS0X+YsXO+OGG6Wk3E+/Wxghv/7Cf8a//5sv3xDuogcCNSo6qc53B9jTCv23HMU//nPU9TW1hCPx3niiUfZc8+9mTv3NUaO3IsTTzyF/v0H8OKLz2/SOBhBELBgwXzuvfeuhvWXLFlMIpFg++13IByONLR5z5nzP7761SM49tjjKSkp4Y03XiOZdO+Fw2Hicdea2r17d+bP/5hUKsXzzz8HwAcfvM/EiRewxx4jmTjxewwcOJhPP12YjUPUbjmrQavqTBEZCbyGuzn4P+C3wMPAZBEpA17H9fQAuBi4U0SuAD4FTvfpk4BpIjIPWAWM9+k3A7f79FpcsDfGbCFvvTWXI488qOH1UUeN4YADDuRb3zqbRCLOPvvsx8knj2Plyi/52c8u5+yzXf1LZChLl2Z8sdvIVVf9mptvvp6xY4+nuLiYrl1LuPLKaykr68bAgQOprl7DL385iTPOOIerr/45//nPU0QiUXbffThLlrhtHnjgwZx11mlMmXIXF100kR/96Pv07FnO8OEjWL16FUOG7MqwYcM5++xxFBUVsfvue7Dffgds/gHbBIGr0ncqA4H51sTRNtvn/LFs2UL69Nmx7QU3gQ2WtGmanpO0Jo5BuE4Rm82eJDTG5JWrr76C+fM/aZZ+4IEHc955F22FEm09FqCNMXnlyiuv3dpFyBv2TJwxxuQpC9DGGJOnLEAbY0yesgBtjGm3mTOf5Mwzx3Laad9o8/Fss+nsJqExpl1WrFjO5Mm3cscddxGNFnDRRd9kzz33ZtCgwVu7aB2OBWhjOqiX5i3joVkfU1lVS3lZIScdshP779Zns/OdM+d/7Lnn3pSVdQPgsMMO59lnn7YAnQPWxGFMB/TSvGXcOeN9KqvcKGyVVbXcOeN9Xpq3bLPz/uKLFZSX92p4XV7ei+XLl292vqY5C9DGdEAPzfqYuiZPytXFkzw06+MW1shcMplsNGlqKpUiFLI5hnLBArQxHVB9zTnT9PbYbrveVFZ+0fD6yy8r6dWrYrPzNc1ZgDamAyovaz5dW2vp7bH33vvw2muvsnLlSmpqanj22f+y7777b3a+pjm7SWhMB3TSITtx54z3GzVzFERCnHTITpudd0XFdpx//sVccsmFxGJxjjvuBL7ylWGbna9pzgK0MR1QfW+NXPTiADjqqGM46qhjspKXaZkFaGM6qP1365O1gGy2DmuDNsaYPGUB2hhj8pQFaGOMyVMWoI0xJk9ZgDbGmDxlAdoYY/KUBWhjzCZZu7aas846laVLl2ztonRYOesHLSLnARPTkgYBdwGPADcAxcD9qnqFX34EMAUoA54DLlLVuIgMAKYD2wEKjFfVahHpDtwNDAZWAKeq6uYP1WVMB1H34YvUvfogqepKgpJyCkadTMEuB2Ql73nz3uF3v7uWzz77NCv5mY3LWQ1aVaeo6ghVHQGMB5YD1wFTgROAocAoERnjV5kOTFTVIUAAnO/TbwVuVdVdgTnAJJ9+LTBbVYcCk4Ebc7Uvxmxr6j58kdrZ00hVVwKQqq6kdvY06j58MSv5P/bYw/zgBz+2QZJybEs1cdwG/AxX2/1QVeerahwXlMeKyI5Asaq+7Jef5tOjwMHAA+np/u9jcTVogHuBMX55Yzq9ulcfhHhd48R4nUvPgp/8ZBJ77DEyK3mZluX8UW8ROQIXfP8hIqcDS9PeXgrsAPRrIb0XUOWDeXo66ev4ppAqoALIqEGsvLyk3ftSUVHa7nW2dbbP+WH58hCRSOb1qfqa88bSN5ZPe/JuKhxuX9nyxeaWORQK5fyzsiXG4rgQ1+YMrsaeSnsvAJLtSMen1y+TLkh7r02VldUkk02zbllFRSkrVqzJePmOwPY5fySTSeLxjD/eBCXlGw3SQUl5s3wikVC78m4qkWhf2fLB5u4zuHOS/lkJhYJNqvi1Jqc/eyJSABwCPOqTFgF90xbpg6vxtpS+HOgmImGf3pcNNeTFfjlEJAKUAhuvNhjTyRSMOhkiBY0TIwUu3Wwzcn1dMhz4QFXX+tevACIiO/ugewYwQ1UXAjUiMtovd5ZPjwGzgXE+/Wxghv/7Cf8a//5sv7wxnV7BLgdQeNAEgpJywNWcCw+akLVeHGbLyHUTx2Bc7RgAVa0RkQnAg0ARLsjW3wAcD0wWkTLgdeAmn34xcKeIXAF8Cpzu0ycB00RkHrDKr2+M8Qp2OSDnAfmBBx7Laf6dXZBKZd4O20EMBOZbG3TbbJ/zx7JlC+nTZ8ec5J2N9thtTTb2uek5SWuDHgQs2KzM6/PMRibGGGOyzwK0McbkKQvQxmwjOmFzZN7aUufCArQx24BIpIC1a6ssSOeBVCrF2rVVRJp2Y8wBmzTWmG1Ajx4VrFy5gurqVVnPOxQKkUx2rpuEm7vPkUgBPXrkfhwSC9DGbAPC4Qi9evVte8FNkK89V3JpW9lna+Iwxpg8ZQHaGGPylAVoY4zJUxagjTEmT1mANsaYPGUBug2hUEBxJEFiXRXRqB0uY8yWY93sWhGNQFFdJSufmc7qdasp2XMMXQeOYG3MDpsxJvesStiK4qCGpXf+lPUfv07t0o+pfPwW4p++tU1O72OM2fZYpGlBOByibslHpGK1jdLXvDGTglRtC2sZY0z2WIBuQSqVIlzSvVl6pKwXyVB4I2sYY0x2WYBuQTKZIlTWm6IdhzWkhQq70P2gcdQlLEAbY3LP7na1ojoepcfXL4W1laRqqon0GsDaRCHJhI0oZozJPQvQrUiloDoWJSjsS6/tS/zgKhacjTFbhjVxZMDG4DXGbA0WoI0xJk/ltIlDRI4DrgS6AjNV9VIROQK4ASgG7lfVK/yyI4ApQBnwHHCRqsZFZAAwHdgOUGC8qlaLSHfgbmAwsAI4VVWX5XJ/jDFmS8pZDVpEBgN/AU4EhgN7isgYYCpwAjAUGOXTwAXhiao6BAiA8336rcCtqrorMAeY5NOvBWar6lBgMnBjrvbFGGO2hlw2cXwDV0NepKoxYBywDvhQVeerahwXlMeKyI5Asaq+7Ned5tOjwMHAA+np/u9jcTVogHuBMX55Y4zpEHLZxLEzUCcijwIDgH8B84ClacssBXYA+rWQ3guo8sE8PZ30dXxTSBVQASzJyd4YY8wWlssAHcHVfg8FqoFHgfU07qcWAElcTT6TdHx6/TLpgrT32lReXpLpog0qKkrbvc62zva5c7B9zk+5DNDLgP+o6goAEXkY1zyRSFumD67Guwjou+GuuzkAACAASURBVJH05UA3EQmrasIvU19DXuyXWyQiEaAUqMy0cJWV1SSTmXef21Ymmcwm2+fOwfY5O0KhYJMqfq3mmdXcGvsXcLSIdBeRMDAG15YsIrKzTzsDmKGqC4EaERnt1z3Lp8eA2bj2a4CzgRn+7yf8a/z7s/3yxhjTIeQsQKvqK8DvgOeBd4GFwG3ABOBBn/Y+G24Ajgf+KCLvAyXATT79YuACEXkXOAi4wqdPAvYTkXl+me/kal+MMWZrCDrhU3IDgfnWxNE22+fOwfY5O9KaOAYBC7KSZzYyMcYYk30WoI0xJk9ZgDbGmDxlAdoYY/KUBWhjjMlTFqCNMSZPWYA2xpg8ZQHaGGPylAVoY4zJUxagjTEmT1mANsaYPGUB2hhj8lSb40GLSAlwHbArbjzn3wCXqWp1jstmjDGdWiY16JuAVUBvoAY36/Zfc1koY4wxmQXokar6cyCmqutw4zaPyG2xjDHGZBKgE01eh2nH3H/GGGM2TSYB+jkRuQ4oFpGjgYeAZ3JbrPwSCjWdn9YYY3IvkwD9Y9ys3KuBXwFvAZfnslD5IggCSgtiFFXNp3reC5RFa4iGt3apjDGdRZu9OPxErL/0/zqVkkgdlQ/9ntolHwAQRIvoO+G3JMI92jVdljHGbIpMutkdCvwE6Jmerqr75KhMeSEUCkisWtoQnAFSsRpWPXcvXQ+/kJqkdSE3xuRWmwEamILravdxjsuSV4IgILm++aSSyXVrCJHAnvExxuRaJgH6c1W9KeclyTOJRJKufXcmKOxCqnZdQ3rpqK8RC4po3rnFGGOyK5MA/ZiIXAw8BcTqE1X107ZWFJFngO3S1rsQ2Am4AogCf1LVP/tljwBuAIqB+1X1Cp8+AleLLwOeAy5S1biIDACm+/wVGJ/tpxvXpQrpN+E6Vj3/fySqV1K699cI9RlCTcyCszEm9zK5Ti8HbgHmAvP8v3faWklEAmAIsIeqjlDVEcAiXE+QA3EPu1wgIl8RkWJgKnACMBQYJSJjfFbTgYmqOgQIgPN9+q3Araq6KzAHmJTBvrRLPA7VQRldDv0mvU/5EfHew1gXy+Q3zRhjNl8m0eY4oK+qft7OvMX/P1NEyoHJwBrgv6r6JYCIPACcAswCPlTV+T59OjBWRN4FilX1ZZ/XNOBqEZkCHAycmJY+C9clMKuSyRQ1yYDS7l2Jr2neJm2MMbmSSQ16ObBiE/LuATwNfAM4HLgIGAAsTVtmKbAD0K+d6b2AKlWNN0k3xpgOI5Ma9NvA8yLyGFBbn6iqN7S2kqq+BLxU/1pE7sC1MV+btliAe2w8BKQ2Ix3a+fh5eXlJexYHoKKitN3rbOtsnzsH2+f8lEmA7oK7CTckLa3NpzRE5ECgUFWf9kkBsADom7ZYH2AJrm26PenLgW4iElbVhF9mSQb70qCysrpdD5tUVJSyYkXnauKwfe4cbJ+zIxQKNqni15pMniQ8F0BEdgSiqvpRhnl3B64RkQNwPTbOAc4EpotIBbAWOBm4APf4uIjIzsB84AxgqqouFJEaERmtqi8AZwEzVDUmIrOBccA9wNnAjIz32hhjtgFttkGLyM4iMg/Xi+M1EflYRHZtaz1V/RfwOPAG8Bou4L4A/Bw32NJc4B5V/Z+q1gATgAeBd4H3gQd8VuOBP4rI+0AJ7qEZgItxvUDeBQ7Cdd0zxpgOI0ilWr/MF5EngXtV9U7/+lzgLFX96hYoXy4MBOZbE0fbbJ87B9vn7Ehr4hiEa87d/DwzWKZ3fXAGUNW/ARXZ2LgxxpiWZRKgIyLSMFCSiPQig5uExhhjNk8mvThuBl4Wkftxgfk04I85LZUxxpi2a9Cq+lfcGBoFuC5331bV23JdMGOM6ewy6cWxPTBWVX+Me1z7uyLSJ+clM8aYTi6TNug7cd3eABYCz+IGNjLGGJNDmQToXvXjQatqjar+icZP9xljjMmBTHtx9Kt/ISK9cY9tG2OMyaFMenHcAMz1D6ykgCPoJLN6G2PM1pRJL46pwJG4R7bnAEer6j25LpgxxnR2mc58ugp3c/AFoEBE9sxZiYwxxgAZNHGIyDXAD4H0GVVSwOBcFcoYY0xmbdBnATurarvGWzbGGLN5Mmni+MyCszHGbHmZ1KCfFpHfAf8E1tcnqurrOSuVMcaYjAL0BP//2LQ0a4M2xpgcy2TKq0FboiDGGGMaazFAi8hJra2oqg9lvzjGGGPqtVaD/m4r76UAC9DGGJNDLQZoVT1sSxbEGGNMY5mMBz1jI2kv56Y4xhhj6rXWBv0AMATYSUTeSnsrCtTmumD5JBzO9Il4Y4zJntbaoH8IDMTPopKWHgfezXQDIvIH3JjSE0RkBDAFKAOeAy5S1biIDACmA9sBCoxX1WoR6Q7cjevStwI4VVWXiUgBcAewN65v9hmq+n6zjW+mUCigJFJH3bKPqFqwirJBI6ihiLq4jbZqjMm91tqgFwALRERUNbkpmYvI4cA5wOM+aTpwnqq+LCJ3AOcDtwG3Areq6n0iMgmYBPwYuBaYrarHishZwI3AOOASYK2qDhWRg4FpwH6bUsbWdI3UseL/rqVu+UIAgnCUvhN+Szzai2TSJjY3xuRWJtfux4nIAhFZLSJVIrJGRKraWklEegK/An7tX+8IFKtqffv1NGCsiESBg4EH0tP938fiatAA9wJj/PIN6ar6HFDha+FZEwoFJL5c3BCcAVKJGKueu4/CUDybm2omEgnTNVxH13At0ag1rxjTWWXyJOHvgB8Ar+O612XqduDnQH//uh+wNO39pcAOQC+gSlXjTdIbreObQqqAilby+jTTwpWXl7S5zNrKmmZpydp1FBdFKClue/1NkaxdT82SD/ny6b+TitXQbd/j6Tl0f8LFpTnZXlsqKrbOdrcm2+fOYVvY50wC9Kr2PpQiIufhBll6WkQm+OQQjQN8ACQ3ko5Pr18mXUvrBGnrZKSysrrNZopuvQcTKi4hub66Ia1s3+OpqgmIV69pz+YyEgRQShXL7rmG+t37YsbthEp6EqsYSjy+SS1Nm6yiopQVK7K/n/nM9rlzyMU+h0JBRhW/duWZwTKviMiYduY7DjhKROYC1wDHA+fReLLZPsASYDnQTUTCPr2vTwdY7JdDRCJAKVAJLGohr6xamyik74TfUTryKLrssje9T7+SoGKnnAXKSCTMuo9eo+nvVfWbTxMJEjnZpjEmf2USoL8GPC4iNZm2Qavqkao6TFVHAL8AHlXVc4EaERntFzsLmKGqMWA2LqgDnA3U971+wr/Gvz/bL9+QLiIHAjWqmnHzRqbiiRRrkl0oPvgstvvGZdR234l18UwuOjZNMpki2rNfs/Ro+Q4kA2uLNqazySTaHJ7F7Y0HJotIGa5N+yaffjFwp4hcgWtHPt2nTwKmicg83LRb4336zcDtPr0WF+yzrr6bXe2CuVStXUXXnUdREy6mLpGbYJlIJIn0HkzRjrtRs3AeAJHuvSnd62hW11mvEWM6myCVavuLLyIjgRJcW28YN8PK5ByXLVcGAvMzaYMuK4ix/L6riX2xyCWEIvSb8FvWFlTkrJtdEEDXSIygtppUIkbQpTvrkkUkElu2/RmsbbKzsH3OjrQ26EHAgqzk2dYCIjIZeArXl/mvwL/ZUJPtsEKhgHjlog3BGSAZz3k3u1QKqmNR1oR6sLZgO9bECrZKcDbGbH2ZXKsfiftFeBjX//gIYF0uC5UPgiAgGWv+RHsyVkP7ehtuugwubowxHVgmAXqpqq4F3gd2V9Vn2dBPucNKJJIU9BlMqEtZo/Ru+3+DGAVbqVTGmM4kk5uEdf5x6ndxT/I9g2uP7vDWJgrpd+7vqHr1cRJrKind+2skS/tu8f7IxpjOKZMA/WPcYEkTgJ8CXwC/yWGZ8kYikaIqWUTBqLGUlERZVVVHIm7tDsaYLaPNJg4/dsYk3//4SGBfVf1FzkuWJ1IpqI0lCUUKSCQsOBtjtpxMenF8F3jEv+wFPCAi38ppqYwxxmR0k/BC4EAAVZ0PjAQuzWWhjDHGZBagw6ra8Gi3qq5mS/UzM8aYTiyTm4Tvi8hvcQ+ppIBzgQ9zWqo8EgQQiYapiyUIhwNrhzbGbDGZ1KAvws1N+AYwx//97VwWKl+EwiESQYgHnv2YP977OktW1hDY/ITGmC2kzRq0qn4OnLQFypJ3kkHAD/40i6q1dQA8/+YSrj5/Pwb1LrG+0MaYnGttVu8/qer3ROQxNtLmrKrH57RkW1k4HOKDxasagnO9B5/5iO+NG7GVSmWM6Uxaq0E/7f9/oJVlOqxUKkVRtPnhKYyGCbC7pMaY3GstQL/gJ359bEsVJp8kkyl22K6E/r1L+exzNyxhJBww/phdCVIpC9DGmJxrLUB/wYaKYtO5AVO4caE7tCCZ5Jrz9+Pd+V/y5Zpa9t2tN9GAnI0FbYwx6VoM0Kra6bsrJJMpSCbYfVAPevToyooVa2wIUGPMFtNmLw4/meuFwNFAAje/4LQclyuvWI8NY8zWkEkt+WbgVOCfuMlavyUi1+a0VMYYYzJ6kvBI4Ct+NDtEZDrwJnBFLgtmjDGdXSY16BU0DuRJ3AzbxhhjciiTGvRcYLaITAPiwDjgCxH5AYCq3tDSiiJyDXAKrtfHHap6g4gcAdwAFAP3q+oVftkRwBSgDHgOuEhV4yIyAJgObAcoMF5Vq0WkO3A3MBj3I3Kqqi5r7wEwxph8lUkNuhh4G9gL2Bc3nfhyYHdgWEsricghwFeB4cDewHdFZA9gKnACMBQYJSJj/CrTgYmqOgTXre98n34rcKuq7oobC2SST78WmK2qQ4HJwI0Z7IsxxmwzMhmL41wAEekBJNKHHm1jvVkicpivBW/vt9Ud+NCPK13fnj1WRN4Fiv3sLQDTgKtFZApwMHBiWvos3DRcx/r3AO4F/iwi0fq2cmOM2dZlMqOKiMiruFpzpYjM8s0ObVLVmIhcjZtw9mmgH7A0bZGluBnCW0rvBVSparxJOunr+PergIpMymWMMduCTNqgp+HahqfiAvoFwB243h1tUtUrReQ63CPjQ2g8jEWAu+kYyjAdn16/TLog7b02lZe3f2LyiorSdq+zrbN97hxsn/NTJgG6i6renvb6ZhE5v8WlPRHZFShS1bmquk5EHsLdMEykLdYHWAIsAvpuJH050E1Ewqqa8Mss8css9sstEpEIUApUZrA/AFRWVrfrke2KilJWrFiT8fIdge1z52D7nB2hULBJFb9W88xgmfdF5ID6FyIyDJifwXqDgckiUigiBbgbg7e7LGRn/4TiGcAMVV0I1IjIaL/uWT49BszG9RwBOBuY4f9+wr/Gvz/b2p+NMR1JJjXoHYFZIvImrvY7AlgqIm8BqOrwja2kqk+IyD64mVgSwIOqep+IrAAeBIpwQbZ+ONPxuIBeBrwO3OTTLwbuFJErgE+B0336JGCaiMzD9csen/luG2NM/gtSbYz+47vL1YvibsR9H7gcXG+NnJUuNwYC862Jo222z52D7XN2pDVxDMJ1R95smXSzm+W72F0ATARKgJu2wcBsjDHblFYDtIgI8D1cm/AC3EMrA1V1de6LZowxnVuLNwlF5HHcI9cx4FBVHQasseBsjDFbRmu9OPYEXgPeAT7yaTZcvTHGbCGtBej+uIdUTsf12vgHronDGGPMFtBigFbVuKr+n6oehhsoaSlQJCIfishFW6yExhjTSWU076CqvquqlwDbA7/H9egwxhiTQ5k8qNJAVdcBf/X/jDHG5FCnn7nbGGPylQVoY4zJUxag2xCNhCiN1hGvXklR1HoZGmO2nHa1QXc2heEEoc/f4/OZU0iuq6Jk+GGUjT6Vqjo7bMaY3LMadCsKqWH5g78jsaaSVCLGmjdmsm7esxREm84VYIwx2WcBugXhcIjaJR/S9OHJdfoK4WTd1imUMaZTsQDdgmQyRbRX/2bpBX13IhlYE4cxJvcsQLcglUqRKupG2ajjqJ/+MFrRn277f4O6hDVxGGNyz6qCrVgbi1A86gRKRx1LiATxIMqaeCFtTXJgjDHZYDXoNqyPR6iKFxHt0YfqWEG7ZmExxpjNYQHaGGPylDVxZCAUyu8258IoRFO1pAiopYh4PLm1i2SMyQIL0K0IggDCIZavWs/Hy6oZ1K+MUCpJMpE/zRxlhXHWvPovVr7zLJHScnoedT51XXtTF8/vHxVjTNusiaM14RBPz/mM2liSrsVRXnx7KbXxVN7UqAuisHbuv6l6+RES1auoXfoxy+7+BUWB9dM2piPIaQ1aRK4ETvUvH1fVH4nIEcANuNlZ7lfVK/yyI4ApQBluLsSLVDUuIgOA6cB2gALjVbVaRLoDdwODgRXAqaq6LFtlD4UC1tbGKYiG+fW0/1FTF2fvXXuz+069KIxGYAveLIxEQoRCAbFYgvQOJNFkLVXvvdBo2VS8jljlIkLdd7YbmsZs43JWg/aB+ChgJDAC2EtETgemAicAQ4FRIjLGrzIdmKiqQ3Adj8/36bcCt6rqrsAcYJJPvxaYrapDgcnAjdksfxAEJJMpHnv+E04+qD/f/vpO1NQlmPX6IsLhLXPhEQoFBNEwb37yJU+88inr4ymCtNp7MogQ7dm32XqRsl7WFdCYDiCXkWYpcJmq1qlqDHgPGAJ8qKrzVTWOC8pjRWRHoFhVX/brTvPpUeBg4IH0dP/3sbgaNMC9wBi/fFaEwyGqqtdz3Zk7c/CaGQxfcA+XjKplxMAuJBOJbG2mValQiKunvML197zOPU8pE//wDEtXricScaetNhmhx1fPJty1W8M6pXsdQyLSFYvPxmz7ctbEoarz6v8WkV1wTR034wJ3vaXADkC/FtJ7AVU+mKenk76ObwqpAiqAJZmUr7y8pM1lJLWWJX+dRCpW4xIWKwOOvZTSLtsTLSzIZDOb5YNPV7JgaVXD62QKpj/5Pj8/d1969HDbT6W6sP1515NYu5pQYRdChV0IF5fQNUtlqKgozVJO2w7b585hW9jnnPfiEJHdgMeBy4E4rhZdLwCSuJp8KoN0fHr9MumCtPfaVFlZ3WobbTgcIvr5hxuCs1f71kzqdtydVVW1mW5qk4RCAbV1zWvqsXiSmpo6atelbz8CoXKIAbEUVK/JShkqKkpZsSI7eW0rbJ87h1zscygUZFTxa1eeWc2tCREZDTwN/ERV7wQWAemNpn1wNd6W0pcD3UQk7NP7sqGGvNgvh4hEgFKgMltlT6VShLt0b5YeLulBcgt0fkkmU/Qt70JFj+JG6ad8dRcKtlAbuDFm68rlTcL+wCPAGap6n09+xb0lO/ugewYwQ1UXAjU+oAOc5dNjwGxgnE8/G5jh/37Cv8a/P9svnxXJZIpwj94U9h/akBYUFNP94HHEUuFW1syeUCrFdd85kNOOHMIhI7fnNxePZsgO3YjFtkwbuDFm68plE8cPgSLgBhGpT/sLMAF40L/3BBtuAI4HJotIGfA6cJNPvxi4U0SuAD4FTvfpk4BpIjIPWOXXz5ogCEilUnTf7wTY93gS66soqBhAzaoviWxXTiIRbzuTzZRIuBabY/fbEYBkMtmQZozp+IJO2B1rIDC/rTbogoIwLHqTykf+QLi0J6GCYmKVSyjqP5Sy437A+njHfwjT2iY7B9vn7Ehrgx4ELMhGnh0/ymyieDxJl5IyABJrvqS+USHUpZRIJOJudxpjTA5ZgG5BEASES7pTftS3KOw7GFIpYqu/IFreD1IJgiBMQQRCqTjxoIBYbEPTQygU0CVURygVgyBEjCjr41um3doY03FYgG5BKBSQiiWJV69kzcy/ESooIpWIU3Hst0mlkpRF46x+5VFiyxfSZehoSncexRo/23dJpI4vHv4DtYsVghClex1D132/wdpY1p6jMcZ0AhagW5BMuhpxl8F7EC3rRWJdFcWDhlOz6H2Kd9mHZXddQaxyMQA1C9+h2wErKBx5PCkC1s79twvOAKkka+Y8QcluBxEq7mfjYxhjMmYdalsQDocJghBfPDmFL578Kyufu48l039BtEdfPyDR4kbLr3ljJgXUEk7FqV38frP8apd+Qihkh9sYkzmLGC1IpVLE13xB7IvPNiQmXLNGEDQ/bKGiUlIpiAdRinfZp9n7RQO+QmILjeFhjOkYLEC3IlVX0ywtWVdDihRdhx2alhrQ84gJ1FBELJakaMh+lI48CsIRQsUllH/t28QLSm0AI2NMu1gbdAuSyRQF2w0k1KWM5LoNAxZ12/c4EkQoO2Q8pXseTazyM4r6D6U26EI87iJwdaKAotGnUzb6FFIpqKWQ9TGLzsaY9rEA3YJUKkkQjdJn3M9Z8+Z/Sa5bTcmwgwmX9CAoKKJqTR2hLv0IlWxPVdocgKFomGlPvMdzry8iCAKO2X8gpxy2M2DNG8aY9rEmjhYUFkapWaQsvedqUvE6ImW9+PKZu/nyP3cS1LonkJLJVKMJWqPRMHM//IJnX1tEMgWJZIrHX5jPp5+v2WKD/BtjOg6rQbciVNSVVO06qt96piEtWt6PINzCQycBvPHBimbJb370BTv1LbNxNIwx7WIBugWxWIKSnn0pHrQHBX0GESrswvoFb9Pj4NNIsvEAnUqmGDV0O2a9vqhR+sghFdaDwxjTbnbd3YJ4PAnhKOVHnkt85ees/3guZSOPJFRcQm1y408ExuNJhg0u55j9diQcCoiEQ5x02M7sUFFCItExbhJGIqGGKbeMMbllNegWhMMhUrG1LJ72k4budjWfvUvFCd8jMmAPaptN6OIkYwnGHb4LYw/fxeUTQDLuas9BEBAOByQSyW2uy104HCIRBMz5YAXRSIhhg8sJpVLWbGNMDlmAbkEQBNQtm9+sL/Sa12fSs/8wWjt0qUSyIXw3hK9wiNXrYnyyZDW7DuhBcUGY1DYS3IIA6lLwvRueZe16NydCr+5F/H7iQVu5ZMZ0bBagWxAEKUJFJRRuP4TSEYe7NuiP3yBZW0MQbLz23GJe4RBPvLyQfzz9YUPaJaeOYO8hvRr1AslXkWiYR2Z90hCcAb5YVcOc9z5nv6/0thlejMkRa0xsQTKZItq9N91Hn0TVq09QOXMqoeJSeh5+JvFk+wJ0EnjomY8apU19bB7b0rMr1euazyZWXROjnb9Vxph2sADdgkQiBUGKzx/4PXXLF5KoXsnql//J2g/mQKQQgOJIgrJoDWXRGrpE4i0Gq2QyRaLJKHbrauMELbRj55tEPMnxBw5qtH/RSIjRw/tRt5GZx40x2WFNHC0oLAyz9tMPIdk4AK1993m6D9mfkiiseeF+qt/8L6RSdP3KaLp9dULDmNDpwqGAoQN78t6CLxvSDhm5PQEptoVKdDKZoqw4wvWXHMyDz3xIQTTM2K/uQkEoINlBeqcYk48sQLeisGffZmnRXv2JRAuILVGq5/6nIX3tu89TvNNIogP2adYmG0ql+NFZe/H48/N5b+FK9h66HYft2Z9U3M2bFQoFeT9OdCqZomfXKBecMMy9TiRJbiM3OY3ZVlkTRwtSqYD1kTIKh27oqRApqyCy14nUJUPULHyn2To1899iY0M+J5MpUrEEx40eyGWnjeDwkduTjMUhFJAMhfjk82pqEimCPH8cPJlMkYglSMQSef+DYkxHkPMatIiUAS8CX1fVBSJyBHADUAzcr6pX+OVGAFOAMuA54CJVjYvIAGA6sB2gwHhVrRaR7sDdwGBgBXCqqi7LVrnD4YD5lXF67XkKOxwyjmSshkS0K/+c8yXH7V9G8eCRVL3yaMPyoS5lFO51PLXxFJFIaKO9MxI+LZ50y8z/vJpr7niloX163BFDOGbfAdtM9ztjTG7ltMomIvsCzwND/OtiYCpwAjAUGCUiY/zi04GJqjoECIDzffqtwK2quiswB5jk068FZqvqUGAycGM2y55IpNh1+yIquiRY/epjrH7xEfhiASfv24vCcIJQz/50O+AkgkgB0T67UHja79EvkoRqVhJavYhuBXVEW5knNpaCWx98q9HNw388/QEWmo0x9XJdgz4f+A5wl3+9D/Chqs4HEJHpwFgReRcoVtWX/XLTgKtFZApwMHBiWvos4MfAsf49gHuBP4tIVFWb9wfbBKEQhBMxFt99VcN40Gvfe57tTr6cyI7DqY4lKBrxdfqNPJrqVBEvzV3IXql3WP34P4AUQUExfc+8hmRh740+bRcEAZWrGz8Ek0xBLJ6kIIudO0KhgIJwElJJYqmoPflnzDYkpzVoVT1PVWenJfUDlqa9Xgrs0Ep6L6BKVeNN0hvl5d+vAiqyVfZwOETtsk8aDdYPUDVnBqG6dQDUxENUxQqpWpdg1M7dWP+SC84Aqbr1VM74C0VB81lZAEKkOGTP7Rul9e3VlYIsjnMRDafomlzFumfvoHrmnylas4DiiHWLM2ZbsaV7cYSgUc+yAPccR6bpsOHp6ab1zCDtvTaVl5e0ucy6wi7N0kIFRQSRAiq6lW4o0JfrYNX6ZkWNrVxGQTRElx6lbMy5x+5Gz7IiPvzs/9s78/gqqrv/v2e5a5KbPSTs+wHZV7WAG2pFENuKUsWltYo+Vn2ep2pbty7+1NpVW1uttdZau+hTa91RixviAgiCgHhkDyQEsucmuevM/P6YISTEsBkkgfN+vXhx5zszZ853bu5nvnOW76lj5pQBDOqdTcBnEMlwx1mnm6PYVhpd1zEzsg/UtRbSDVVse/gmnHQSgNiGFfS89C4K+ww74DIKCz+77kczyudjg+7g8xct0NuB1mPXioHyfdh3AdlCCENKaXnHlHvHlHnHbRdCmEAWUH2gFamubtznSIRIJIgZKcBX0GfPwrGGSfbxs8FOU1kZbTlWN3QIZrZbHissTiCeNoi1Otbv09GdNJZmYlkOc6b1I5rsx31PfsjHm6oZ1DuHm+ZNIN8Xo+ql3xPf8hH+Hv0oOOd6Yr68lmW19ofPZ8CG5S3ivJv6Jc+TyupJPLX/dpTCwqw2fh4LKJ+PDQ6Hz2v8wwAAH8hJREFUz7quHVDgd1Bldmpp+2cJIIQQg4UQBnARsEBKuRWICyGmeMdd4tlTwNvAXM9+KbDA+/ySt423/+3Oan8GsCybdCJGwVlXUDDzGnrMvZXeV94LmoFj+Pc62MYMZ9Nj3h2EBk8g0FuQNeEsck+ai+GkMU0dTdOIBNLYH79K4yv3w/pFRMw4TTtL+enjH7BmYzW2A+u31bFxczlVL/yW+OaV4NgkKzaz8x8/JqQn9ltvTQO/30DXNfRQ+z8WPRzB+YxVyRUKRdfjC42gpZRxIcQ3gH8BQVyRfcrbPQ942BuWtwL4jWe/BnhMCHEbUApc6NlvB/4shFgL1HnndxrptE3KyCLxzr/Im3Y+TR+/Q+ULv8Wf35v8s+ZjGDnYtoOu666YpywIZJJ3yjzSdRX4CvtS//6zxDauIHf6ZYSKBlLz4gPENq0EILbxQ5LlnxI88QLWb/u0zbX7FIaI/6ftOGursQ5SCWCvh0MrAoZNwGmiafX7mNkFhPqNINh3BPHStQBogTCRE84l2p2SgCgUxzBfiEBLKfu3+vwaMOYzjlmFO8pjb/tW4JTPsNcAszuznq0xTYMmAuRO/yZWTSm+nCKKz/8+tYuepOJvP6Tkmz/FSkRJ1+0ko3ggjmYSfe9fRJe/7Bagm/T42g0kytez658/pc81v2sR5900rl1MZOoFFOWG2FUba7HXNCQoyO9Fqrqsxab5Ami+ALRtsWjBMHR8zRWU/eUWcNymeF9BH4ovvJ34jk04iRjB/iNJaUH8tkMy1f1yUisUxxrqXbdDHPICSWqeu4+dT95N1YKH2PH3H5Mz5TzQDezGWnb86SYqn/455Q9/B91J7RFnADtNzVv/IDLudMDBsVKgtx0Yrfn8OLEGvnOeICPoPisDfoNAVg65s65HD7mdGJrpJ2fGNST3ET37tSR1i/7eIs4AqaptJGsqsHqMQO83FjsWpemtR0ku/SdZRjOm0T2SNSkUxyoqF0cHaJqGXb+LRNknLTYnGadh6YtkjjwJd8SGG4IGTr6U5sZm/EX9CA0aj51opmndO1iNtehBV2Rt2yYyaRYNS55tKS/nxK/SuOo1chrq+PX8C0kH8/H7TDRs0noxRd/8BVY8hhkMkdICxDuIngE0x8FJt2+Cd9JJHMfBjFVT/uh3WwS8cdVr9LziXuqtwOe/WQqF4rCgBHofWHuNgXZt9UQmzqB5w3JC42diZ+Tj7z8e20mTM3UOjavfQg9lUTz3VuLlG4htXkl42Jdw/JmEJ84iPOwEEmWfEux7HGY4m9jWNQQch2BOhEbLHVXo85k4jkMsFUQPhklYNo7XHtFRYqWkFiBy4teIb7urxWZk5uIr7Ivj2NQveaZNdG0nmmne9CG+gVNUwn2FoouiBLoDdF3D7DkEzR9ss+xV1oSz8BX1o87M56nFZVRsSTA93MyY/plEnTDa2HPxxSpJ7ColY/iXcDSdxoRGk62j4+CEemKI3jSlbZykQ7j/BEwngQP4/DqfbK0nEY8zrHcWmX6bhBMk7WgEDJugniRVW4EZKSSlB4il93x96bRNoHAgxZfc6Ta1ZOYTHvdlmrUwpmOhme2bR3TTh6UaohWKLosS6A5wHLAti5LL7qHu7Sewm+rJmnAWRl4vGlIG3/vjKmqj7rC3jzZUccXskWytcHh/zRa+ctIgzhiTi91YgxbK5oFnNpBKW3xj5ggiIYNUysIwdEJGitiqhexa8hyabhA6YQ616YHc/+x6Ihl+fjl/DHnBKAF/CDtaSc17z+LLLSJRsZnQwHEEh59KPL2nG6HJ8fPzZ6sY3OsUojvTvPfLZZw7bRCzpvQjcsJXaFq7uGVctJGVR7DfKOqTauq3QtFVUZ2EHWAYkDKC6P4AmSOmEpl4Nr68EvRQFrtqYi3ivJuX39/CsP55RJtTPP7yJ3y8pZaqBQ9R/96/ueGrA9mwvZ6b7n8bGw3D0Ek5EN26jrpFT+Akmt0OvDceZXQPm8KcEA1NSV5eWUOiuozmD19B1zTCQyeSqq0gY+gk/IW9CWht25wbmpKs21LD8++U8uaH5SSSFguXlZJMO8T1CD2vvI/cUy8m76z5lFx2D41W8JDujWboOIaOYxjuJB2FQnFYUBH0PgiQouzR77WZHdjjgpspKDyu3bGZYR+xRLple9G6egb1HEb0gxfInnw2l54xgJeWVaAlohDM4PWl2zgtvqRdOc7WFQzqLaisi1HTkMDM60WqajsVT9xFj/NupPnTZTR/uozIhC/jL+rX5txQoP3XmRsJoAEpC1KE8Y/4Mo4D9UmLg5gZ34LuM/jry5LXPyhF1zXOnjKAr540CCet2rEVis5GhT/7IFG+fo84e7Pv6pc8T5YWY/zQgpbjTEPjgulDeW1ZaYttSHEQp34nAHa8mROG5/OTS4YSX3Av8boa1pXWYRUMbndNp3AQFdVuMqaZEwqoWfAggcLeBEoGYUTyKbnk/1H0tRuIl21g73QkPkNj+qQ+beo1/9xR+Fp9y8mkdcidgqZpsHpjNQuXlWI7kLYcnlu0idKdUQwVSSsUnY6KoDvAcUAPhAiedgVa79FuGtBkA8amxeiGzn9NC1M5ZTQ7mzSGDchn6doKNpe7Yj6kTw4nj8gl+vgKjIxsjIxswo5NqqaSHl+9gbhjsGVHA8nTx+Hvs4zkNnemX1gcTzK3P/nZpcyf0Z+sbe8S37YO48zLiUw4ix1/uR2rqQ5fXk8KZ30bR9/r67Ns5p0pmD1tIDtrmhnYMxtTcz5z8YBDQdPhg092tbOvXF/JoJKISmWqUHQySqA7QNMglTeYp5eleeWpDwAoyc/grqsvAk0j9tKvKB55Muv9U7nlgXe48iujuPd/TyKVdrAsG3PLG4QGjiVv+mXYiWZq1i3HGTCZytIGevYs4KdXH88PHlnO5WdczOBTQ2SG/Ri6Q90ny5k/JIb1zpPEK7fhK+iDEcwiuvJ1jKxcrKY6UjXlVL36CIVzbgbaTn7xaw49M2x6ZYVI2DYpS8M03enon3fAhmPDxGFFvLViexv7uKFFSpwVisOAei/dB1UNCV5Zume69Y7qJp58bQNJ26Fw1rdJ15QzoCDAz781koGpT8mIlpJqrKdsV5TQ8KmE+o1k5xN3Et22kQ/NsVz9m+U8u3QXVZU1ZJkp7rtqNH2LwtipOA3LF6CZAbKHjMHetIxU5TYCvYaSNfdO3tnQzN/qxrNt9HwyvnIL6CbJik3oe7Uhh800bFnCzj/fRMWfbiQZrWVnfZz31+0ibvG51zxMpy1GDcrnjMl93WGIhsa5Jw2kT1GmEmiF4jCgIuh9UFbV3M62pTxKytZw6ispmPVtcpsb2PHYnpzL+f1HM3TWdex45Ds4sSYix8/CP3A8rz22ihmTenLecTax/9zNrsY6QoPHU3Dy16n45y8o+vrtJGt2kNq1lcJzrkMPhmm2DB567mPeXuWuZfDaMjh3Sh9mTpyNvf2jNhGxrmvosVqqX3oAgODpV/PYmzt4c6W7TKOmwc2XTWJYn2zSqUMXUztlcdEZQ/n6GUPd62qoDkKF4jChBHofDO2dha65S1HtZsrIAkJ6GmPkNJJJi/rX/rIn57JhEuo1BMNO0vtbPwfNoHHDCuKL/sKNp0wiu28/Kv5wPVju8LjYhuXUZ+ZSfOnd1Lz+OLGP9yw+U/i1Gwn0GtYizrt5aUkZ53x7Grkjjiet+VrspqkTW/tBy7bW8zje/L89244Djzy3lp/81xQ+L45lt3RPqmkuCsXhQzVx7IMMO8oPLhlFr8JMMkI+vjq1L9OGZmDXlBPfsgZTs7Bie4bgFc26Fqu5gW0PXse23/4XNQsfJZBdQOOat4i9+Avia96gx5zvtiRBAohv/gjNTrcRZ4Da1x5DS8UZ0ienjV3XNMysPIysfOLWnvZny3LwlwxC8wUxc4pJW+2lsymWQks1keVPt9unUCi6Hkqg90EgHKbvjjf4wYxM7r2kLzMyVhMJmaSb6tj1zL3YzQ1kjTsTAF9+L9A0oh/+x8t54dC07l1SVdsI9h0BuEP09GAGxed/D81wo9/MEdPQTR9GVn6ba9uJGI5tMfvE3m3s507tS2Lp08RL15CpNZFl15LlSwIO/qL+lMz7EblTz6Moy+DSMwa2OXfG8T1JLX+G+jf/4rZXK1rw+XQyjARWUz2muY/l2BWKLxDVxLEP0okE4QGjQAOruQL/iBNo3LyGYP9RADR+9AaZo06h8JzrSFWXkdixsV0Z8fIN+Ap6ES9di6brOPEmmrd8RObY6XDcmazc6bD5jTKmnnU7WTs/JL7oMQAyxp2JE29kTE8/t3x9GKtLmzl+RDHF6e0E40XEt66l8pn7AAgPO4HwaVfSXFVG3bO/wo5FQdOZcf73iWSP4u1VFUwdnsP4Hika/70QHJucky7CNAP4SOBoOknL2OcSYEczGb40qc3LqX7/WTSfn5xTLsbMG9DmDUWhOBIogd4HifIN1L7wa4ysPHR/iFR1OYGeg/EPGAuAmVtMuqEaPSMXI53El5VP/ZLn2pQR7DOMxrWLAcg+fjaNHy8mVbOD8Ozvc/fjq/iktBaAZ96CG74+ipFfOg+yCskcOAZDh/grDzM4Usj4qWfSvH4xde89S+T871P9ysOARsbM7/Bxcx4Ln1hLUW6Q8+fchbXwflI71lO94CFOvewnjEqugu3/oXHxasABTUfTNczyj2j44EX0cIScaV8nbuZwrCW2Mwwdu3Iz1S892GLb9eRd9Jx/HwktWy1qcIgYho4F2I5DwNBVxsRDRAn0Pgj1HEQtYEVr2P3n5e87ClIJAiWDCBQPxMzKY/sfb8RJxSmcdS3Zk8+hYfnLOI5N5uhTCQ0YTapmBzknnEs6Wk3j6kVEJs8i2hhvEefd/GPhJu66ejYZpoVVtg47M4fwkEk0Foykubac2reewMzpQbq+EoDg4AmsqM/hgedkSxnL1u3il9+8itTfbsRqqnPrbDXSsOWjlmNyT7uYZMVGKp/+eYsttnElveb/+pjLD+3TbZrXLSY4fT5ayXA0HOxNS4mt/wDzuDOVsBwCmq5RGU3wp+fX0ticYuaU/kw+rliN9jkElEDvA9sXJveMb1H35t9wUnFCg8YTmTgDTXPIPe0SrGQSw0rhpNx0pJUvPkD2pJkUX3gbWlYhi9fV8SVMgn2HU/PGX0lVlxHqP5rsSTNorG8/1M12HJLbPobyVfiKB5LaupZUrJGF24oY1icHcfY1RD9YgL9HPzBMnEEn8tKbO9uUUd+YpKLBIiczl2D/USQdk4zxswj1H0Ns00qC/UfhK+xH9Qu/aXOek4oT3/4JZq9xnTbzsDtgo2McP5eHX9zE+//8EF3XmHn8QOZM64NzjDb5fF7SaHz/d4tbOqoffHo14aCPsYPy1QPvIFEC3QHptI3dXIc5aDKFgya6kRU60dJPCfYfgRYpoTGtk2cm3aaO2gpwbOqXPk+iYhO+6Vczqsim8q+3ER46iR4X3AyOg2OlqXzhQSJnXc/AXtlsKqtvueb50/qgffIs5BQQKBlM/TtPETznZhLLKhlQFMSJZlN07vVogQyKL/oR0apKMkPt+3kzwgEyx55O5rgvE03qOI6OkTMI36QhJC0bx7EwwtntzjMyskkdY6LkAO+tb+T9te4Udtt2eP697UwZ15finGPrXnQGpqnz0YbqdqOI/rO0lBH9845QrbovSqA7IJGwMEPZNLz6MIYOWiCDZMVGIrNvhMYa/KEQ/mQTSVuneO6tVC14iETFJkL9RpJ38oVULvgdie3uclnRFa+QOepkNN3AijWSd/JcUjvW8MPLJ/Lump1sKmvg1DGFFNR9THzzSvKv/CWJpibss25hY7XFxROCVDx+A9hpaoGsSTPJmPw1ItklXF5g893fvkPam8k3Zkghefk5mAUzaWiVEdWy7JbZfo6jkzNtLs0bluMk3cVqAyWDMfJ6YSePLVGyHY1V66vb2ddsqqHXpD7Ytor4DgbbdijKDbezF+eHMXSN7jTh1OczcJzOy2VzKCiB7gDLsrGNAKnJF2M6zdjxZoxxX8UO5RLS4qQbqgjrGno4j1RVGQVnX73n3OYoibJPW7YDJYMxM3NAN0nV7SJZWUqo73FUP3odE3oMYHJ2EWFtPOlwiLz592FrPtKRHLavr2BcsUX1Sw+DvWdYXHTZi2RPmkV9KkRepo/ff+801m6qpjA3TEl+GNuySOxDZ23bodnMoueV95EsX48ejmDkltCY8nOsTT3RcJg0vAfvr2k7IWjMkEI1ff0QsG2Hkvww44YW8uGnbl9JTlaA808bim11j4edpuukHId3VpWTHwkyfEAemmUfkVFO3VqghRAXAbcBPuA+KeXvOqts23bQDR0zI4JuZuPYYOoa8YSFPxTEMEyMcCZ2rAFfpADMAOCA7WBk5dFr/q+JbVyOGSkg0HMwqepyql95hECvweRMmcPOp36G3dxAbPMqAJrWvEXJVffTaIew0zZgMWlIhPSuLVjR2vb1SyVAC2FbNjowbnA+tu1gH2BHTDoNDQQwikfjOI4XOR9b4gxuU9aEYYWcMbkvr3+wDdPQOX/6EPIjAWwl0IeGZXH9BWNpaE7SHE/TIy+M4dh0B302DI2aphQ33b+opZmmb48sfnzlCXAE3qa6rUALIXoBdwETgATwrhDiDSnlx512Ecsm6DdYvaGaaHOS8cOKCJo6zXGLSEYutW//g1TVDvJOm0ezGWFXbYKAz6AxliI3kok58FQK/AkS29ex65l7AUjVlJE1Zjqpqm1tLuWkk2jpJLbTahRFOkVs00oyjptCdMUrLWYjUgD+MLRaUOVQX8NUlNg2v4hpGDi2fcAPOkV7HAdIW0QCBpGAiZO26C5300bjry+va9OGXrozSllVI73zwl94FN1tBRo4HXhdSlkDIIR4CpgD3NFZF7BtB2yLcYPzyckJU1UVxfEELZo0CX1pHtlaGkc3+OjTWn71jxVEMvwEfAaVdTEmDe/BTXNH0CTbrpySqNhEqP9oYq2GvhkZOThmoI3oOoaf2NY15J9xOXogRPOGFfjze5N76jyanCCHsiKK4rPZnV8kLy+Dysroka7OUYE7hrx7vZU5DiQ+Y6RJPGGhaZ9xwmGmOwt0T6B1w+EOYPKBnpyfn3nQFywoyOpwXzjkDrVraErusQVNjGAQf1Ffmta922KvX/IcJRffQfUrfyS2+SP8Rf0omn09vkgOQb3t7LXiC26m6tVHMbPyyDt1HoGSQZiZubRfo/vwUFjYsc9HK8rnY4OOfJ5z2hDWbNzTcZyd6WdI31zyIoe2hufnoTsLtE7bx7PGQYSU1dWNB/W6UliYtc/IalDPCL0KMymrbAQg4DOYe/pQog0JskadRuPqt0jVuM8TM1KEYwaJnHUteZqN5UCjE8Sqbp/eVNczyDrjKgzHIq37qYtrOLEvJsLbn89HI8rnY4N9+dyvKJOfXDOFFxZvpiA3xDlTB0A6vd97pOvaIQV++6I7C/R2YFqr7WKg/AjVBc22ufOqE1m3pYaG5iQTh/XA1BxsyyGaDlB44R04sTo03cQJZBJN+nDazCP+7GeLbTvEbBMw6TYNeQpFN8axbHrmhrjq3BFouAtVfEZyyC+E7izQC4EfCSEKgSbgPGD+karM7vbqkf1z0TSNVMpqkVzbdojaPjRfodsul4Lu1janUBxLWJbdJQKibptuVEpZBtwKvAGsBP4upVx6ZGvljqboaDqrSryjUCgOhu4cQSOl/Dvw9yNdD4VCoTgcdNsIWqFQKI52lEArFApFF0UJtEKhUHRRlEArFApFF0UJtEKhUHRRuvUojkPEAHfWz8FyKOd0d5TPxwbK504tr9NWG9acY29w7lTg7SNdCYVCcdQyDVjcGQUdiwIdACbhJlfqAnOFFArFUYIBlADLcFMgf26ORYFWKBSKboHqJFQoFIouihJohUKh6KIogVYoFIouihJohUKh6KIogVYoFIouihJohUKh6KIogVYoFIouyrE41fugEUJcBNwG+ID7pJS/O8JVOmiEEBHgXWCWlHKLEOJ04FdACHhSSnmbd9xY4I9ABFgEXC2lTAsh+gJ/BYoACcyTUjYKIXKAvwEDgUrgAillxRfsXjuEED8ELvA2X5RSfvcY8PkOYA7uemqPSCl/dbT7DCCE+AVQIKX8Rmf5JYTwA48AE4EYcJGU8pMv2jcVQe8HIUQv4C7cKeJjgflCiOOObK0ODiHE8bhTT4d62yHgT8C5wHBgkhBihnf4X4FrpZRDcVdKv9KzPwA8IKUcBnwA3O7Z7wTellIOBx4Gfn34Pdo3niidCYzD/c4mCCEu5Oj2+WTgNGA0rqhcJ4QYw1HsM4AQYjpwWStTZ/l1PdDk2f8H+PPh9KMjlEDvn9OB16WUNVLKJuAp3CilO3El8G32rHo+GVgvpdwspUzj/lGfL4ToB4SklO97x/3Zs/uAk3B9b7F7n2fiRiAA/wBmeMcfSXYAN0gpk1LKFLAO9+F01PospXwLONXzrQj37TiHo9hnIUQebvB0t7fdmX612KWUi4BCLwr/QlECvX964v7gd7MD6H2E6nJISCmvkFK2ThDVkU8d2QuABu9H3trepixvfwNQ2Nk+HAxSyrW7f6RCiCG4TR02R7HPXl1SQogfAx8Dr3GUf8/AQ7gLR9d6253pV5f43SuB3j86bpvebjTcH3t3piOfDtQOe+7B3jkbu8z9EUKMAP4D3ARs4hjwWUr5Q1yB6YP71nBU+iyEuALYJqV8rZW5M/3qEr97JdD7ZztuhqrdFLOnqaC70pFPHdl3AdlCiN15bkvYcw/KvOMQQphAFlB92Gp+gAghpuBGkd+XUj7GUe6zEGKY10GGlLIZeBo4haPX57nAmUKIlcAdwGzgCjrPry7xu1cCvX8WAtOFEIVCiDBwHvDyEa7T52UJIIQQg70/2ouABVLKrUDcEzeASzx7CjeH9lzPfimwwPv8kreNt/9t7/gjhhCiD/AMbs/7E575qPYZdxTCw0KIgDcC4VzcJoCj0mcp5RlSypFSyrHAD4DnpJTfpPP8arELIaYCcSll6eH2a2/UMLv9IKUsE0LcCrwB+IE/SimXHuFqfS6klHEhxDeAfwFB3D/G3R0o83B/6BFgBfAbz34N8JgQ4jagFLjQs98O/FkIsRao884/0tyI69evhBC7bb8HvsFR6rOU8iUhxGTgQ9w85/+SUj4hhKjkKPW5AzrLr/uBhzx7Alfsv3BUPmiFQqHooqgmDoVCoeiiKIFWKBSKLooSaIVCoeiiKIFWKBSKLooSaIVCoeiiqGF2isOGN/b2v3HH35q4wxSfB34gpeyUZekPoA79gTVSykwhxNVAjpTynk6+xiTgW1LKq73tLcAcKeUHnXkdxbGHEmjF4eRBIBeYLqWsF0Jk4Cag+SNHYFyplPL3h6noEXSz/CyK7oESaMVhwYtc5wElUsoGACllkxfFThFCZAO/w00H6uDO7LrFy917OXAVbsSdB9wjpXzQm1xzPm7TXD/cabqXSSnLhRBv4k5MmIqbHOdxLy9F6zr9CDdv8LVCiKG4M+2KcHMs3CmlfFIIMQu4xbt2EfCYlPJ2IcQpuJnTNgEjcXODX4U76eEO3KnEj3qz2VpfMw7cg5v+tAT4mZTyQW/fzbipMtPAeuAb3oPsdtyJFGngU9z0mRWej8uBE7y6/QF3CvLJQAZuLuPV3r39NTDKq+drwE2tkgUpugmqDVpxuJgArN0tzruRUlZIKf+FO8OrGldEJgJjgBuFEJm46VHPllKOw51++7NWRZwM/LeU8jhcsfpNq30CmAKMB+Z6YtsRTwD/lFKOAM4G7vaE7QZc0Z+IK4Q3CyEKvHOOB37p1etR4G4p5TbcqcZv7y3OHgGgSkr5Jdw0tfcKIYJCiNm4MxtPlFKOBDYD1wohvgnMACZJKUcDa2ibi7i/lHIKcLF3X9706voycJ13zL3AcinlBNyc2AXAd/ZxLxRdFBVBKw4XuzOCdcQMYIqU0gESQojfA/8jpbzHE9aZXqrQsUBmq/NelVJ+6n1+GFjZat9DXh6FOiHEP4Ev4wpcG7w8wmNwm1rwRHaQt+8cYJZwV9EZjpvFLMM7dauUcvf1VuAK7IHwbKtzAl55p+M+IGq9OnzHu/7/AY96ucfBjYRv9fJrgJsECWCj9//LrbZP8T7PAiYLIb7lbYcOsJ6KLoYSaMXhYgkwXAiRJaWM7jZ6K9T8ATBom85RB3xCiN7Ae94xi3FzR7SOhNN7nWMd4D4+47iW6ws3acd23FwW/8ZNrvMn4CvsSUkZa1WGQ/tUlR0RA5BSOl5uEM2rQ+vr5+Am2P+s+2K2ulabztUOEhYZwPlSynWtylY5HbohqolDcViQUpbjdgj+yUtcs3tdxAdwmzZexn2l14QQAWA+bu7mibhrw90JvIonzq1SRU73RB7gatxRIbu5WAihCyFycZP0t97Xum4NuM0jl3ll9wHewW1uiQC3SSmfx41IA7iCty/SuG29B8NC4Gu77w3wI9xmiJeBy70OVXCXXlp0kKNeXgH+t9W9fQ649iDrp+gCKIFWHE6uwV3d410vb+8Sb/sKXOEpAlZ7/yRuJ9yruJGsxF2qqi+uYA/2ytwOPC6EWAf0x10vbjchYCnwPu76c62Tue/NRcAFQohVuEJ+hXfuC8AnXvnnePUd3GEpLu8DA4UQT+/nuBaklC/htmO/I4RYjdvZdyvuQqULgaVeHcZz8JnjrsdtRlkNfOT9/7N9nqHokqhsdopugzeKY46Usl3nnzfC4bdSyqf23qdQdFdUBK1QKBRdFBVBKxQKRRdFRdAKhULRRVECrVAoFF0UJdAKhULRRVECrVAoFF0UJdAKhULRRVECrVAoFF2U/w8n4qfUNK4k6QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.visualizations.scatterplot(train_data, target='ApplicantIncome', separate_by='Loan_Status')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop Name and PassengerId\n",
    "all_data['TotalIncome'] = all_data['ApplicantIncome'] + all_data['CoapplicantIncome'] \n",
    "all_data.drop(columns=['Loan_ID'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_enc = ['Dependents']\n",
    "\n",
    "import category_encoders as ce\n",
    "\n",
    "enc = ce.OrdinalEncoder()\n",
    "all_data = enc.fit_transform(all_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>TotalIncome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>142.51153</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5849.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.00000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>6091.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.00000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.00000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4941.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.00000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Married  Dependents  Education  Self_Employed  ApplicantIncome  \\\n",
       "0       1        1           1          1              1             5849   \n",
       "1       1        2           2          1              1             4583   \n",
       "2       1        2           1          1              2             3000   \n",
       "3       1        2           1          2              1             2583   \n",
       "4       1        1           1          1              1             6000   \n",
       "\n",
       "   CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
       "0                0.0   142.51153             360.0             1.0   \n",
       "1             1508.0   128.00000             360.0             1.0   \n",
       "2                0.0    66.00000             360.0             1.0   \n",
       "3             2358.0   120.00000             360.0             1.0   \n",
       "4                0.0   141.00000             360.0             1.0   \n",
       "\n",
       "   Property_Area  TotalIncome  \n",
       "0              1       5849.0  \n",
       "1              2       6091.0  \n",
       "2              1       3000.0  \n",
       "3              1       4941.0  \n",
       "4              1       6000.0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of train (614, 12)\n",
      "Shape of test (367, 12)\n"
     ]
    }
   ],
   "source": [
    "train = all_data[:ntrain]\n",
    "test = all_data[ntrain:]\n",
    "\n",
    "print(f\"Shape of train {train.shape}\")\n",
    "print(f\"Shape of test {test.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "x_train,x_val,y_train,y_val=train_test_split(train,ylabel,test_size=0.3,random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import precision_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn=KNeighborsClassifier(n_neighbors=15)\n",
    "tree_gini=DecisionTreeClassifier(criterion=\"gini\")\n",
    "svm_lin=SVC(kernel=\"linear\")\n",
    "logr=LogisticRegression(C=100)\n",
    "rf=RandomForestClassifier(max_depth=4, n_estimators=100)\n",
    "models=[knn,tree_gini,svm_lin,logr,rf]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn.fit(x_train,y_train)\n",
    "knnpred=knn.predict(x_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree_gini.fit(x_train,y_train)\n",
    "tree_pred=tree_gini.predict(x_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_lin.fit(x_train,y_train)\n",
    "svm_pred=tree_gini.predict(x_val)\n",
    "logr.fit(x_train,y_train)\n",
    "logpred=logr.predict(x_val)\n",
    "rf.fit(x_train,y_train)\n",
    "rfpred=rf.predict(x_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'svm' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-34-03b88a848e39>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0maccuracysvm\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maccuracy_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_val\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msvm_pred\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mf1scoresvm\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mf1_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_val\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msvm\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0maucsvm\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mroc_auc_score\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_val\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msvm_pred\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mprecisionsvm\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mprecision_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_val\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msvm_pred\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'svm' is not defined"
     ]
    }
   ],
   "source": [
    "accuracysvm=accuracy_score(y_val,svm_pred)\n",
    "f1scoresvm=f1_score(y_val,svm)\n",
    "aucsvm=roc_auc_score=(y_val,svm_pred)\n",
    "precisionsvm=precision_score(y_val,svm_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "percentiles = df['arrival_delay_deciles'].apply(pd.Series)\n",
    "percentiles = percentiles.rename(columns = lambda x : str(x*10) + \"%\")\n",
    "df = pd.concat([df['departure_delay'], percentiles], axis=1)\n",
    "df.head()"
   ]
  }
 ],
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