ML Classification Projects.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0a939a53",
   "metadata": {},
   "source": [
    "# ML Classification Projects\n",
    "\n",
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "442b66b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pre-processing\n",
    "import urllib.request as req\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Visualisation\n",
    "import matplotlib.gridspec as gd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "# Classification pre-processing\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Metrics\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import jaccard_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import log_loss\n",
    "\n",
    "# K Nearest Neighborhood\n",
    "from sklearn.neighbors import KNeighborsClassifier as knn\n",
    "\n",
    "# Decision Tree Classifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# Support Vector Machine\n",
    "from sklearn import svm\n",
    "\n",
    "# Logistic Regression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68feea34",
   "metadata": {},
   "source": [
    "### Load Dataset\n",
    "This dataset is about past loans. The Loan_train.csv data set includes details of 346 customers whose loan are already paid off or defaulted. It includes following fields:\n",
    "\n",
    "| Field          | Description                                                                           |\n",
    "|----------------|---------------------------------------------------------------------------------------|\n",
    "| Loan_status    | Whether a loan is paid off on in collection                                           |\n",
    "| Principal      | Basic principal loan amount at the                                                    |\n",
    "| Terms          | Origination terms which can be weekly (7 days), biweekly, and monthly payoff schedule |\n",
    "| Effective_date | When the loan got originated and took effects                                         |\n",
    "| Due_date       | Since it’s one-time payoff schedule, each loan has one single due date                |\n",
    "| Age            | Age of applicant                                                                      |\n",
    "| Education      | Education of applicant                                                                |\n",
    "| Gender         | The gender of applicant                                                               |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e9b5d3f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('load_train.csv', <http.client.HTTPMessage at 0x1b1d5ce5dc0>)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = \"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_train.csv\"\n",
    "file = \"load_train.csv\"\n",
    "req.urlretrieve(url, file)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bdc8a8e",
   "metadata": {},
   "source": [
    "### Load dataset from CSV file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f3726dac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>Principal</th>\n",
       "      <th>terms</th>\n",
       "      <th>effective_date</th>\n",
       "      <th>due_date</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>9/8/2016</td>\n",
       "      <td>10/7/2016</td>\n",
       "      <td>45</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>9/8/2016</td>\n",
       "      <td>10/7/2016</td>\n",
       "      <td>33</td>\n",
       "      <td>Bechalor</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>9/8/2016</td>\n",
       "      <td>9/22/2016</td>\n",
       "      <td>27</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>9/9/2016</td>\n",
       "      <td>10/8/2016</td>\n",
       "      <td>28</td>\n",
       "      <td>college</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>9/9/2016</td>\n",
       "      <td>10/8/2016</td>\n",
       "      <td>29</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Unnamed: 0.1 loan_status  Principal  terms effective_date  \\\n",
       "0           0             0     PAIDOFF       1000     30       9/8/2016   \n",
       "1           2             2     PAIDOFF       1000     30       9/8/2016   \n",
       "2           3             3     PAIDOFF       1000     15       9/8/2016   \n",
       "3           4             4     PAIDOFF       1000     30       9/9/2016   \n",
       "4           6             6     PAIDOFF       1000     30       9/9/2016   \n",
       "\n",
       "    due_date  age             education  Gender  \n",
       "0  10/7/2016   45  High School or Below    male  \n",
       "1  10/7/2016   33              Bechalor  female  \n",
       "2  9/22/2016   27               college    male  \n",
       "3  10/8/2016   28               college  female  \n",
       "4  10/8/2016   29               college    male  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('load_train.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a9e2400c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(346, 10)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6884a3c",
   "metadata": {},
   "source": [
    "### Convert to date time object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a2c0a39f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>Principal</th>\n",
       "      <th>terms</th>\n",
       "      <th>effective_date</th>\n",
       "      <th>due_date</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>2016-10-07</td>\n",
       "      <td>45</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>2016-10-07</td>\n",
       "      <td>33</td>\n",
       "      <td>Bechalor</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>2016-09-22</td>\n",
       "      <td>27</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-09</td>\n",
       "      <td>2016-10-08</td>\n",
       "      <td>28</td>\n",
       "      <td>college</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-09</td>\n",
       "      <td>2016-10-08</td>\n",
       "      <td>29</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Unnamed: 0.1 loan_status  Principal  terms effective_date  \\\n",
       "0           0             0     PAIDOFF       1000     30     2016-09-08   \n",
       "1           2             2     PAIDOFF       1000     30     2016-09-08   \n",
       "2           3             3     PAIDOFF       1000     15     2016-09-08   \n",
       "3           4             4     PAIDOFF       1000     30     2016-09-09   \n",
       "4           6             6     PAIDOFF       1000     30     2016-09-09   \n",
       "\n",
       "    due_date  age             education  Gender  \n",
       "0 2016-10-07   45  High School or Below    male  \n",
       "1 2016-10-07   33              Bechalor  female  \n",
       "2 2016-09-22   27               college    male  \n",
       "3 2016-10-08   28               college  female  \n",
       "4 2016-10-08   29               college    male  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['due_date'] = pd.to_datetime(df['due_date'])\n",
    "df['effective_date'] = pd.to_datetime(df['effective_date'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98920c70",
   "metadata": {},
   "source": [
    "## Data Visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ab5a27a4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PAIDOFF       260\n",
       "COLLECTION     86\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['loan_status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5c76d9e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Principal of loan by gender\n",
    "\n",
    "bins = np.linspace(df.Principal.min(), df.Principal.max(), 10)\n",
    "g = sns.set_theme(style = 'white')\n",
    "g = sns.FacetGrid(df, col = \"Gender\", hue = \"loan_status\", palette = \"mako_r\", col_wrap = 2)\n",
    "g.map(sns.histplot, \"Principal\", bins = bins, multiple = \"stack\")\n",
    "\n",
    "g.axes[-1].legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b215ee1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Age of people taking out loan by gender\n",
    "\n",
    "bins = np.linspace(df.age.min(), df.age.max(), 10)\n",
    "g = sns.FacetGrid(df, col=\"Gender\", hue=\"loan_status\", palette=\"mako_r\", col_wrap=2)\n",
    "g.map(sns.histplot, \"age\", bins = bins)\n",
    "\n",
    "g.axes[-1].legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9a7c201e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Days of week people took out loan\n",
    "\n",
    "df['dayofweek'] = df['effective_date'].dt.dayofweek\n",
    "bins = np.linspace(df.dayofweek.min(), df.dayofweek.max(), 10)\n",
    "g = sns.FacetGrid(df, col=\"Gender\", hue=\"loan_status\", palette=\"mako_r\", col_wrap=2)\n",
    "g.map(sns.histplot, 'dayofweek', bins=bins)\n",
    "\n",
    "g.axes[-1].legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db36ec4b",
   "metadata": {},
   "source": [
    "We see that people who get the loan at the end of the week dont pay it off, so lets use Feature binarization to set a threshold values less then day 4 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9fb0a15",
   "metadata": {},
   "source": [
    "## Data Pre-processing: Feature selection/extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7e13657e",
   "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>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>Principal</th>\n",
       "      <th>terms</th>\n",
       "      <th>effective_date</th>\n",
       "      <th>due_date</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>Gender</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>weekend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>2016-10-07</td>\n",
       "      <td>45</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>2016-10-07</td>\n",
       "      <td>33</td>\n",
       "      <td>Bechalor</td>\n",
       "      <td>female</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>2016-09-22</td>\n",
       "      <td>27</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-09</td>\n",
       "      <td>2016-10-08</td>\n",
       "      <td>28</td>\n",
       "      <td>college</td>\n",
       "      <td>female</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-09</td>\n",
       "      <td>2016-10-08</td>\n",
       "      <td>29</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Unnamed: 0.1 loan_status  Principal  terms effective_date  \\\n",
       "0           0             0     PAIDOFF       1000     30     2016-09-08   \n",
       "1           2             2     PAIDOFF       1000     30     2016-09-08   \n",
       "2           3             3     PAIDOFF       1000     15     2016-09-08   \n",
       "3           4             4     PAIDOFF       1000     30     2016-09-09   \n",
       "4           6             6     PAIDOFF       1000     30     2016-09-09   \n",
       "\n",
       "    due_date  age             education  Gender  dayofweek  weekend  \n",
       "0 2016-10-07   45  High School or Below    male          3        0  \n",
       "1 2016-10-07   33              Bechalor  female          3        0  \n",
       "2 2016-09-22   27               college    male          3        0  \n",
       "3 2016-10-08   28               college  female          4        1  \n",
       "4 2016-10-08   29               college    male          4        1  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['weekend'] = df['dayofweek'].apply(lambda x: 1 if (x>3)  else 0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb7cf10b",
   "metadata": {},
   "source": [
    "### Convert Categorical features to numerical values\n",
    "Gender Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b7d6b473",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender  loan_status\n",
       "female  PAIDOFF        0.865385\n",
       "        COLLECTION     0.134615\n",
       "male    PAIDOFF        0.731293\n",
       "        COLLECTION     0.268707\n",
       "Name: loan_status, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Gender'])['loan_status'].value_counts(normalize = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b0aa873e-7e03-4aae-8bf3-b7b73119d4cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "        vertical-align: top;\n",
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       "\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>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>Principal</th>\n",
       "      <th>terms</th>\n",
       "      <th>effective_date</th>\n",
       "      <th>due_date</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>Gender</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>weekend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>2016-10-07</td>\n",
       "      <td>45</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>2016-10-07</td>\n",
       "      <td>33</td>\n",
       "      <td>Bechalor</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>2016-09-22</td>\n",
       "      <td>27</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-09</td>\n",
       "      <td>2016-10-08</td>\n",
       "      <td>28</td>\n",
       "      <td>college</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-09</td>\n",
       "      <td>2016-10-08</td>\n",
       "      <td>29</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Unnamed: 0.1 loan_status  Principal  terms effective_date  \\\n",
       "0           0             0     PAIDOFF       1000     30     2016-09-08   \n",
       "1           2             2     PAIDOFF       1000     30     2016-09-08   \n",
       "2           3             3     PAIDOFF       1000     15     2016-09-08   \n",
       "3           4             4     PAIDOFF       1000     30     2016-09-09   \n",
       "4           6             6     PAIDOFF       1000     30     2016-09-09   \n",
       "\n",
       "    due_date  age             education  Gender  dayofweek  weekend  \n",
       "0 2016-10-07   45  High School or Below       0          3        0  \n",
       "1 2016-10-07   33              Bechalor       1          3        0  \n",
       "2 2016-09-22   27               college       0          3        0  \n",
       "3 2016-10-08   28               college       1          4        1  \n",
       "4 2016-10-08   29               college       0          4        1  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gender'].replace(to_replace = ['male','female'], value = [0,1], inplace = True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a9d2f43-39f4-4db8-a65c-8b54c16b3b90",
   "metadata": {},
   "source": [
    "Onehot Encoding Education"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2f45f849-d9bb-4892-93d0-4f7f367d841b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "education             loan_status\n",
       "Bechalor              PAIDOFF        0.750000\n",
       "                      COLLECTION     0.250000\n",
       "High School or Below  PAIDOFF        0.741722\n",
       "                      COLLECTION     0.258278\n",
       "Master or Above       COLLECTION     0.500000\n",
       "                      PAIDOFF        0.500000\n",
       "college               PAIDOFF        0.765101\n",
       "                      COLLECTION     0.234899\n",
       "Name: loan_status, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['education'])['loan_status'].value_counts(normalize = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1ade6dca-2bf0-47e6-a3ef-1a52c0ea8589",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Principal</th>\n",
       "      <th>terms</th>\n",
       "      <th>age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>weekend</th>\n",
       "      <th>Bechalor</th>\n",
       "      <th>High School or Below</th>\n",
       "      <th>college</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Principal  terms  age  Gender  weekend  Bechalor  High School or Below  \\\n",
       "0       1000     30   45       0        0         0                     1   \n",
       "1       1000     30   33       1        0         1                     0   \n",
       "2       1000     15   27       0        0         0                     0   \n",
       "3       1000     30   28       1        1         0                     0   \n",
       "4       1000     30   29       0        1         0                     0   \n",
       "\n",
       "   college  \n",
       "0        0  \n",
       "1        0  \n",
       "2        1  \n",
       "3        1  \n",
       "4        1  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Feature = df[['Principal','terms','age','Gender','weekend']]\n",
    "Feature = pd.concat([Feature,pd.get_dummies(df['education'])], axis = 1)\n",
    "Feature.drop(['Master or Above'], axis = 1, inplace = True)\n",
    "Feature.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31873fc5-23a3-446c-a7b1-679ae16449f9",
   "metadata": {},
   "source": [
    "### Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b306bb27-e92a-4ca5-bb14-bc13f3313f15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Principal</th>\n",
       "      <th>terms</th>\n",
       "      <th>age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>weekend</th>\n",
       "      <th>Bechalor</th>\n",
       "      <th>High School or Below</th>\n",
       "      <th>college</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Principal  terms  age  Gender  weekend  Bechalor  High School or Below  \\\n",
       "0       1000     30   45       0        0         0                     1   \n",
       "1       1000     30   33       1        0         1                     0   \n",
       "2       1000     15   27       0        0         0                     0   \n",
       "3       1000     30   28       1        1         0                     0   \n",
       "4       1000     30   29       0        1         0                     0   \n",
       "\n",
       "   college  \n",
       "0        0  \n",
       "1        0  \n",
       "2        1  \n",
       "3        1  \n",
       "4        1  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = Feature\n",
    "X[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ea1c789d-c046-493c-9a23-7b21542a50df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df['loan_status'].values\n",
    "y[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a6a9e1f-d24c-4894-b9c5-385c90637440",
   "metadata": {},
   "source": [
    "### Normalize Dataset\n",
    "Data Standardization give data zero mean and unit variance (technically should be done after train test split )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6b50530d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.51578458,  0.92071769,  2.33152555, -0.42056004, -1.20577805,\n",
       "        -0.38170062,  1.13639374, -0.86968108],\n",
       "       [ 0.51578458,  0.92071769,  0.34170148,  2.37778177, -1.20577805,\n",
       "         2.61985426, -0.87997669, -0.86968108],\n",
       "       [ 0.51578458, -0.95911111, -0.65321055, -0.42056004, -1.20577805,\n",
       "        -0.38170062, -0.87997669,  1.14984679],\n",
       "       [ 0.51578458,  0.92071769, -0.48739188,  2.37778177,  0.82934003,\n",
       "        -0.38170062, -0.87997669,  1.14984679],\n",
       "       [ 0.51578458,  0.92071769, -0.3215732 , -0.42056004,  0.82934003,\n",
       "        -0.38170062, -0.87997669,  1.14984679]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = preprocessing.StandardScaler().fit(X).transform(X)\n",
    "X[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dd39101",
   "metadata": {},
   "source": [
    "# Classification\n",
    "\n",
    "Split Dataset into Training-Testing Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c94b161e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b21d39b5",
   "metadata": {},
   "source": [
    "## K Nearest Neighbourhood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f5345555",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit KNN Models\n",
    "\n",
    "mean_accuracy = []\n",
    "\n",
    "for n in range(1, 10):\n",
    "    knn_model = knn(n_neighbors = n)\n",
    "    knn_model.fit(X_train, y_train)\n",
    "    knn_yhat = knn_model.predict(X_test)\n",
    "    mean_accuracy.append(accuracy_score(y_test, knn_yhat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cbfcbd1d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k = 1 has a score = 0.7\n",
      "k = 2 has a score = 0.6428571428571429\n",
      "k = 3 has a score = 0.7285714285714285\n",
      "k = 4 has a score = 0.7142857142857143\n",
      "k = 5 has a score = 0.7285714285714285\n",
      "k = 6 has a score = 0.7142857142857143\n",
      "k = 7 has a score = 0.7857142857142857\n",
      "k = 8 has a score = 0.7571428571428571\n",
      "k = 9 has a score = 0.7428571428571429\n"
     ]
    },
    {
     "data": {
      "image/png": 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+KAyPVi34/LtUXlv9I4dSsuvUasgSTB5RODs7y4Q3IYRJiqJwIPkKnXxa0a5NS0vHsTrd/Fvz6sO9SEnPY/O+dD7+5jTbf8xgwuAAenXVoLbiy4hNFoqq+jrJVU9CiN87f+UG2XklPDJKhqarolKpCO3UhuAAD5JStcQcSOfDmBP4eTkzcUgAIQEeVjnvxGShcHNzM/65oqKCvXv30q9fP3NmEkI0QgeTr+BgZ9OoGwA2FLVKRd9unvTuoiHh5FViD17gva+SCfR1ZeLgALr5t7Z0xEpMFopnn3220vKcOXOYO3eu2QIJIRqfW+U6fjqd2yQaADYktfr2pMT+93hxIDmbuEMXWPKfY9zToTUTh3QioF0rS0cE6nDjImdnZ3Jzc82RRQjRSCWe0VJWrmdwDzmJXRe2NmqGhvkwMNibvccu801CBm9/lkhY5zaMHxxAe8+634ytXvKZesHvz1H82hY8ICDArKGEEI3LgeQreLs7EejTdBoAWoK9nQ2R/fwY0qMduxIvsePwJRZ9cpi+3T0ZPzgAb3fLNB6s1TkKgLFjxzJ27Fhz5RFCNDLZeTc5m1XIlPs6WeWJ2MbI0cGW6IEdGdrLl+8OZ/J94iUSz2iJCPFm7MAOtHF1bNA8tT5HIYQQv3cw5XYDwIjgut0TW1TN2dGOSfd2YkSf9nyTcJEfjl3mx5NXubeHD1ER/rg2UC+tKgtFWFjYXX8d/Hrv66NHj5o1mBDC+ukNBuJTrhLayaPBvrSaI9eW9kwf0YUH+vmx9dBF9h67zIHkKwzv48uo/v44O5q38WCVhWLbtm1m3bEQovFLOZ9P4c1yBstM7Abh3qoFj4zqxqgBfsQevMCOHzP54dhlRvb1Y2Tf9mbbb5WFwsfHx/jnU6dOUVJSgqIo6PV6MjMzmTp1qtlCCSEahwPJV2jV0p6QZtoA0FK8WjvxZHQQowf4E3PgArEHL7A7KYvXH+2HV6v6P7IzeY5i4cKF7N69m7KyMjw9PcnMzKR37941KhRxcXGsWrUKnU7H7NmzmTFjhvG506dPs2DBAuNyfn4+rq6ulY5kTp06xdSpUzlx4kRt35cQwswKi8s4fi6Pkf2kAaCl+GqceXZiCBeyb/D9kUuUVxjMsh+ThSI+Pp7du3fz17/+lWeeeYbs7Gz+9a9/mdxwTk4Oy5YtY/Pmzdjb2zNt2jT69+9PYGAgAN27dyc2NhaA0tJSpkyZwqJFi4zrl5aW8tZbb1FRUVHHtyaEMKf4k1cxKIoMO1mBjm1b8eTYIDQaF7TaonrfvsmfARqNBicnJwICAkhLS6N///5cvXrV5Ibj4+MZMGAAbm5uODk5ERkZyY4dO+762o8++oi+ffvSp08f42OLFy9m9uzZtXgrQoiGoigKB5OzCfR1pa2HNABs6kwWCjs7O44cOUKnTp3Yv38/RUVFlJSUmNxwbm4uGo3GuOzp6UlOTs4drysqKmLjxo2VLsPdvXs3t27d4oEHHqjp+xBCNKDzl283ABzchO+JLX5T5dBTfn4+7u7uvPTSS3z22WcsXryY1atXM2DAAJ588kmTGzYYDJUur/31str/tnXrVkaMGIGHx+2TYVqtllWrVrF27do6vJ3bPDzqPt1do3Gp87rmZq3ZJFftNIVc/9lzjhb2Nowa3MnsvZ2awufVkMyRq8q/4eHDhzN8+HAefvhh/vnPfwKwceNGioqKcHExHcTb25vExETjslarxdPzzq6Su3bt4qmnnjIu//DDD1y/fr3Sie9x48bxxRdf4OxcswKQl1dcpxuCmGt8rz5YazbJVTtNIdetch37f75M326eFN8opdhKcjWkppZLrVZV+wO7yqGnXbt20aVLF1566SUmTpzI119/TXl5eY2KBEBERAQJCQnk5+dTWlrKzp07GTJkSKXX/No7KiwszPjYlClT2LVrF7GxscaT3bGxsTUuEkII8zpyOpeycj1DQttZOopoIFUWCg8PD5588km+//57XnzxRfbv38+wYcNYsmQJly5dMrlhLy8v5s+fz6xZsxg/fjxRUVGEhoYyZ84cUlJSgNvDW3Z2djg4yIxOIRqLAynZeLs70cnHOlpgC/Or0eDiwIEDGThwIPn5+fzf//0fo0aNqtHchujoaKKjoys9tmbNGuOfPTw8OHToULXbSE1NrUlEIUQDyM67ybmsQqYMlQaAzUmNCkVpaSnffvstmzZtQqvVMn/+fHPnEkJYoYPJvzQADJIGgM1JtYXi2LFjbNq0iZ07d9K7d2+eeuophgwZIr8khGiGdHoDh05cpUegNABsbqosFKNGjeL69etMmjSJLVu24Ovr25C5hBBWJiU9jxs3yxkkM7GbnSoLxVNPPcXo0aOxt7dvyDxCCCt14Hg2ri3tCZUGgM1OlVc9jR8/XoqEEAK43QAw+XweESHe2KilAWBzI3/jQgiT4k/cbgA4SFp2NEtSKIQQ1VIUhQPJ2XSWBoDNlslC8dxzzxEfH98QWYQQVujc5UKu5pfISexmzGShuP/++/nwww+JjIzk448/5vr16w0QSwhhLQ4cz8bB3oa+3e7s1SaaB5OFYuzYsaxbt44PP/yQvLw8Jk+ezEsvvURycnJD5BNCWFBpmY4jZ3Lp182TFvbm7RIrrFeNzlEYDAYyMjK4ePEier0eDw8PFi1axIoVK8ydT1ip/Bu3eONfP/FJ3EmKSsotHceqVej07DycycJ//cSHm45TUFRm6Ug1duRMLmUVegb3kAaAzZnJnwi/3s60ffv2TJ8+neXLl2NnZ0dJSQlDhw5l3rx5DZFTWJnDp3O5fO0msfvO8W38BUb2bc/Ivn44tZBfnb/S6Q0cTM4mLv4iBUVl+Hu78P3hDHYdyWRomA+jw/1p5WTdl6AfTM6mrYcTndpJA8DmzOS/6vz8fNasWUO3bt0qPe7k5GS8T4VofpLScvH3cuGV2X35JDaFrYcusjspi1ED/BneyxcHextLR7QYg0Hhx1NXiT14Ae31WwT6uDIn6h66+bfGYGPD2q0n+D7xEvuOX+H+Pu15oF97nFrYWTr2Ha5cu8m5y4VMHRoobXuaOZNDT8888wzr168HID09nT/96U9otVoABg0aZN50wioVFJVx/vINenXV0N7LhT9NCOHNR/rSyceVTT+c55WPEtiVeIkKncHSURuUoigknsnlL58c5l/bTuNob8sLU0J59eFedPNvDYCXuxOPjenO20/0p0cnD7bFX+TlVQl8k3CRsnK9hd9BZQdTsrFRqwgPlgaAzZ3JI4oFCxYwbNgwAHx8fOjXrx+vvfZapXbhonk5dvb2D4XeXX67J7q/twsvTOnBuaxCNu8/z5e7zvLd4UyiB3ZkYBOfzasoCinp+WzZn05GThFtPZyYOz6Y3l01qKv4Jd7WoyVPjwtm9IAituxP5+t96Xx/5BJjwjtwX1g77Gwte0Sm0xuIT8kmtJMHri2te3hMmJ/JQlFQUMCsWbMAcHBw4JFHHiEmJsbcuYQVS0rV0tbDiXZt7px8FejryksPhXEqo4DN+9JZ++0Zvv0xg3GDO9Kvu1eVX5yNVWpmAZv3p3M2q5A2ri14fEx3woO8Uatr9j79vFx4fkoPzl8uZPP+dP6z+yw7DmcydmAHBoa0xdbGMgU25XweN0oq5CS2AGpQKPR6PTk5OXh5eQFw7do1FKX296MWTUNxaQWpmdcZNcCvyteoVCqCOrhzj39rfj53jS3701m99RTbEzKYMDiAnp3bNPox7wvZN9i87zwnLxbg5mzPzMiuDA6t+xd7J5/bBfb0xXw270/n3ztS+fbHTMYN7kj/7l41Ljz15UByNq7O9oQEuDfofoV1MlkoHnnkEcaPH8/gwYNRqVTEx8fz8ssvN0Q2YYWOndViUBR6d9WYfK1KpSKss4YegW04cjqXmAPprNycQse2rZg4JIB7OrRudAUjK7eYLQfSOXb2Gs6OdkwdGsiwXj7Y29XPUFH3Du685t+a4+fz2LI/nTVxtwvs+MEB9OrSMAX2+i8NAB/o79ekhwxFzZksFJMnTyY4OJgff/wRGxsbHn/8cbp06dIQ2YQVOpqqxaNVC/y9XGq8jlqlov89XvTppiE+5SpbD13gnxt+pmt7NybeG0BnXzfzBa4nOfklxBy8wOFTObRwsGH84I7c36c9jg71fzmwSqWiZ2AbQjt5kHgml5gDF/hgSwodvF2YOCSAoI7uZi0YxgaA0rJD/KJG/5d7e3sTGRmJoijo9XoOHTrEwIEDTa4XFxfHqlWr0Ol0zJ49mxkzZhifO336NAsWLDAu5+fn4+rqyrZt20hKSuLdd9+loqICNzc33nnnHXx8fOrw9kR9Ki3TcfJiPsN6+dbpi8pGrWZwj3YMCPJm//ErxMVf5N11RwkJ8GDikAD8vWtefBpKXuEtth66wKGUq9jaqhgd7k9kPz+cHc1/OatapaJfdy96d9WQcCKHrYcu8L8bj9PF15WJ93aiS3u3et/nrw0Au/i64u3uVO/bF42TyUKxfPlyVq9effvFtraUl5cTGBhIXFxctevl5OQYJ+vZ29szbdo0+vfvT2BgIADdu3cnNjYWuH1P7ilTprBo0SIAXnrpJT788EO6devGpk2bePvtt1m1atUfeZ+iHqSk56HTK/TqYnrYqTp2tmqG9/ZlUGhb9iRlsf3HDP669gi9u2oYPzgAn7ucJG9ohcVlbEvIYN/PlwEY1suHMREdLHIFkI1azaDQtgwI8jIW2MVfHCW4ozsThgTQsW39TYY7m1VITn4JYwZ0r7dtisbPZKGIjY1l7969LF68mJdffpkff/yRffv2mdxwfHw8AwYMwM3NDYDIyEh27NjBs88+e8drP/roI/r27UufPn0oLy/n+eefN07w69q1K+vWravl2xLmkJSqpVVLewJ9XOtlew52Nowa4M+9PX3YeSSTnUcucTRVy4Agb8YN7oinm2O97Kc2iksr+PanDHYnZaHTKQwK9SY6oiMeri0aPMt/s7VRM6yXLwND2rL36GW2/5jBW/9OpFcXDRMGd8RH4/yH93Eg+QotpAGg+C8mC4W7uzuenp4EBARw5swZxo8fX6M5FLm5uWg0v/3y9PT0vGsjwaKiIjZu3Gg8QrG3t2fcuHHA7R5T77//PiNGjKjxGxLmUaHTk3w+j/Cg+r8Cx6mFLeMHBzC8ty/f/pTJ7qQsDp/OYXBoW6IiOuDeyvxf0qVlOr4/convjmRyq0xP/3u8GDeoI15WOPziYGfDA/39uLdnO2PmY2la+gf9krl13TL/2gBwwD3ezXpmvbiTyUJha2tLZmYmAQEBJCYmMmjQIMrKTDc1MxgMlcaxFUW567j21q1bGTFiBB4ele/DW15ezoIFC9DpdDz11FM1eS9GHh51/2Wl0VjfOPmvLJnt8MmrlFXoGdrP/44c9ZVLAzzj78G0yG58tfss3/14kUMnrjI6oiOTh3XGzcWhdturQa6yCj3fHLzApj1nKSopZ0CwNw8/0B3/ehzOqUuumnrctzVTRnZj896zxB28wOHTudzfz48HR3RF07p2R2Sns25QXmEg+t5OVvXvwJqy/F5zymWyUDz99NO88cYbrFq1iuXLlxMTE8N9991ncsPe3t4kJiYal7VaLZ6edx7O7tq1645CcPPmTebOnYubmxurVq3Czq52Jw7z8ooxGGo/10OjcUGrLar1eg3B0tn2HM7AycGWtq4OlXKYK9ekwR25N8Sb2EMX2HrgPDsSLnJ/X18e6OdXo75IpnLp9AbjeH9hcTlBHd2Z+LvxfnN91ub6vMb092NQkBfbEjLYfSST3UcyuS/MhzHhNTuvotG48G18Ou3atMTd0dZq/h1Y+v/7qjS1XGq1qtof2CYLhU6n49///jcAMTExZGRk0LVrV5M7joiIYOXKleTn5+Po6MjOnTt56623Kr1GURROnjxJWFhYpcdfeukl/P39+etf/4paruO2OJ3ewM/nrtEjsE2DzhRu4+bI42PuYfQAf2IOXGBbfAZ7ki7zQH8/RvTxrdP9EfQGg/EKomuFt+js68rTY4Po6tfaDO+gYbk6OzDj/i5E9mtP3KGL7Em6zP5fGw/296NlNQX2Uk4R5y/fkAaA4q5q1Gb813MEjo6Od3SRrYqXlxfz589n1qxZVFRUMHnyZEJDQ5kzZw7z5s0jJCSE/Px87OzscHD4bUjh1KlT7N69m8DAQCZMmADcPr8hvaUsJ/XSdW7e0tVokp05tPVoydzxwYzJud0XafP+dHYlXmJ0eAeG1rAvkuGXhn2xBy+QnVeCv7cLMyO7EmzmOQmW0MbVkUdHd2fUAH9iD15ge0IGe45e5oF+7RlRxdyP7w9nYqNWESENAMVdqBQT/Tjmz59Ply5d6NOnD05Ov50kCwoKMnu4upKhp/r1+XepHDqRzYp5g++YgWyJXL/2RTqdUUBrFweiB3Zg0H/1Rfo1l6IoHD+fR8z+dDJzi2nXpiUTBnekVxeNRQqEJT6v/55NPibcn6Fhv80m1+kNvLwqgYB2rXh2YkiDZjPFWv9NNrVcf3jo6fjx4xw/fpyvvvrK+JhKpWL37t21DiMaH4OicDRNS2iAR721qfij/rsv0mc7UtnxYybjBnWk/z2/XZV1+mI+mw+kc/7yDTRuLZgTdU+l55sLX09nnpsUSvqVG2zZf54Ne84ZO/sODm3L8XN5XC8uY7DMxBZVMFko9uzZ0xA5hJVKv3yDwpvl9LLQsFN17uiLtO0U3/yYwYjevvx8Po/kc9do7eLArAe63nHE0RwFtGvFi9PCSM0s4Ov96Xz+XSrf/phBC3tb3Fs5ECwNAEUVTBaKTz/99K6PP/roo/UeRlifpLRcbG1U9OjUxtJR7upufZE++y4VV2d7pg3vXONzGM1JV7/WvDqjFynp+Wzef57MnGKmDO8sDQBFlUwWirS0NOOfy8vLOXLkCOHh4WYNJayDoigkpWq5p4O7WZrf1aff90VKv3KDsHvaUnyj1NKxrJZKpSK0kwfBAe6kX75B7+C2FF4vsXQsYaVM/ut/9913Ky3n5OTw+uuvmy2QsB6ZOcVcK7xFVEQHS0epMRu1ms6+bjg62FJs6TCNgFqlItDX1WrOPwnrVOtjTS8vLy5fvmyOLMLKJKVpUamgZ2frHHYSQjSMWp2jUBSFEydO3NFuQzRNR9O0dG3vRisnuWeyEM1Zrc5RALRt21bucNcMZOfd5Mq1mwy9X25SJURzV6NzFEeOHKFv375cv36dxMREvL1l9mZTdzRNC0CYDDsJ0eyZPEexbNkyVqxYAcCtW7dYvXo1H374odmDCctKStUS0K5Vg7T4FkJYN5OFYvfu3XzyySfA7Y6w69atY/v27WYPJiwnr/AWF68W0fsP3slOCNE0mCwUFRUVldp829nZNbkmaqKyX4ed/ugtT4UQTYPJcxS9evXixRdfZPLkyahUKmJiYujRo0dDZBMWkpSai6+mpVXe3U0I0fBMHlG88cYbaDQa3n33XZYsWUKbNm1kwl0TVniznLNZhXI0IYQwMnlE4eTkxPDhw1mwYIHxqidHx4a/6b1oGMfOalGA3l3vvBuhEKJ5kqueRCVHU7V4ujniq2lp6ShCCCshVz0Jo5JbFZzOKKB3V8vc1EcIYZ3kqidhdPxcHnqDYpX3nhBCWI5c9SSMktK0tHZxoGPbVpaOIoSwIiYLxRtvvMGKFSt49913sbW1JTw8nOeee65GG4+Li2PVqlXodDpmz57NjBkzjM+dPn2aBQsWGJfz8/NxdXVl27ZtXLlyhZdeeom8vDw6duzI0qVLadlSxszNqaxcz4n0PAaHtkMtR4xCiN+p0VVPv/9CLy0tZfPmzUyfPr3a9XJycli2bBmbN2/G3t6eadOm0b9/fwIDAwHo3r07sbGxxm1OmTKFRYsWAfDXv/6V6dOnM2bMGD744AM+/PBDXnrppbq+R1EDKel5lOsMMuwkhLhDje9HkZOTw9KlS7nvvvuqvD3q78XHxzNgwADc3NxwcnIiMjKSHTt23PW1H330EX379qVPnz5UVFRw5MgRIiMjAZg4cWKV69Unnd7A8TQtBkUx+76s0dE0Lc6OdnRp72rpKEIIK2PyiCI5OZm1a9eyc+dOVCoVixYtYvz48SY3nJubi0bz269TT09PkpOT73hdUVERGzduJC4uDoCCggKcnZ2xtb0dTaPRkJOTU9P3A4CHh3OtXg+QkX2DhR/F88K0MIb39av1+g1Bo3Exy3YrdHqS0/MYGNoOb6/aFwpz5fqjJFftSK7aaU65qiwU3377Lf/+97+5cOECUVFRfPnll7zwwgtMmjSpRhs2GAyVro5SFOWuV0tt3bqVESNGGG+GdLfX1fYqq7y8YgyG2h0ZONpAx3atWL8zlWA/N9Rq6xqn12hc0GqLzLLt5PN5lNzScY+fW633Yc5cf4Tkqh3JVTtNLZdarar2B3aVQ0/z58/Hz8+Pb7/9ljfeeIPQ0NBafWF7e3uj1WqNy1qtFk/PO2f77tq1i9GjRxuX3d3dKSoqQq/XV7tefVOpVEwZ3oWr+SUkpWlNr9CEHE3LpYW9Dfd0cLd0FCGEFaqyUHz44YcUFBQwbNgwXnjhBX744QeUWozfR0REkJCQQH5+PqWlpezcuZMhQ4ZUeo2iKJw8eZKwsDDjY3Z2dvTp08c4qS8mJuaO9cwlIrQd3u5ObIu/WKv32pgZDApH067RI7ANdra1voW6EKIZqPKbYdiwYaxZs4a4uDh8fHx49dVXycnJ4f333+f69esmN+zl5cX8+fOZNWsW48ePJyoqitDQUObMmUNKSgpw+5JYOzs7HBwcKq375ptvsnHjRkaPHk1iYiIvvPDCH3qTNWWjVjEm3J9LucUkn89rkH1a2tms6xSXVsi9J4QQVVIpNfzpXF5ezvbt21m/fj2pqakcO3bM3NnqrC7nKOD2+F721UJeW/0jri3teW1mb6uZhW6uMdEvv09j3/ErLJ83iBb2Jq9taLBcf5Tkqh3JVTtNLVedz1H8N3t7e8aPH8/69ev5z3/+U+sgjYWtjZpR/f04f+UGZzIKLB3HrBRFISlNS3BH9zoVCSFE81CnQelu3brVdw6rMii0La7O9mxLyLB0FLO6kF1EQVGZ3HtCCFEtOXt5F3a2NkT29eN0RgHnLxdaOo7ZJKXlYqNW0SOwjaWjCCGsmMlCceXKlUr/ZWdnU1DQtIdkAO4La4ezox3b4i9aOopZKIpCUqqWbn5uODvamV5BCNFsmRyYfuihh8jNzaVly5ao1WqKioqwsbGhdevWLF++nF69ejVEzgbXwt6W+/v4suXABTJzivDzss5ZmHV1+dpNcgtKiexnnbPQhRDWw+QRRUREBO+++y6JiYkcPnyY9957j4kTJ/LRRx/x7rvvNkRGixne2xdHB5smea7iaKoWFdCrsww7CSGqZ7JQnDlzplJvp8jISE6cOME999xDRUWFObNZnFMLO4b18iXpTC7ZeTctHadeJaVpCfR1xdXZwfSLhRDNmslCodPpSEtLMy6npaVhMBgoKytDp9OZNZw1uL9ve+xs1WxvQkcVuQUlXMotlkl2QogaMXmO4s9//jMzZ86kc+fOGAwGMjIyWLp0KStWrGDEiBENkdGiWjnZM6RnO/YkXWbcoI60cXO0dKQ/7GjaNQC5LFYIUSMmC8W9997Ld999R2JiIjY2NvTq1QtXV1dCQkJwdq59O+/G6IF+fvxw7DLf/pTJzMiulo7zhyWl5uLv5dIkip4QwvxMForS0lL27t1LYWEhiqJw8eJFAB599FFzZ7Ma7q1aMDCkLQeSrxAV0YHWLo13XL+gqIzzV24wYUiApaMIIRoJk4Xi5Zdf5vLly3Tp0sVq+h5ZwqgB/hw4ns13hzOZNryzpePU2dFfWqjL+QkhRE2ZLBSpqals377deMe55srTzZH+93jyw8+XGRPuj4uTvaUj1cnRNC1tPZxo16alpaMIIRoJk1c9eXt7N0SORmF0eAfKKwx8n5hl6Sh1UlRSTmrmdXp3laMJIUTNmTxM6NKlC7NmzWLw4MG0aNHC+HhzOkfxK582LendVcPupCwe6OeHU4vGdZT187lrGBSF3l3Mf8dAIUTTYfKb7ubNm/j7+5OZmdkQeaxeVHgHklK17DmaRVREB0vHqZWjqVo8WrXAz6t5XK0mhKgfJgtFU2/TUVv+3i6EBHiw88gl7u/THgd7G0tHqpHSMh0nL+YzrJdvs74oQQhRe1UWiueff57ly5cTHR191+fj4uLMFsraRUX48+66o+w7foWRfdtbOk6NJJ/PQ6dXZJKdEKLWqiwUc+bMAeCNN96o88bj4uJYtWoVOp2O2bNnM2PGjErPp6en8+abb1JYWIhGo+F///d/cXV1JSsri1deeYXi4mJatWrF4sWL8fHxqXOO+tbZ142u7d3Y8VMGQ8N8sLO1/tt6JKVpadXSnkAfV0tHEUI0MlV+wwUHBwMQExNDv379Kv23bt06kxvOyclh2bJlfPnll8TExLBhwwbOnTtnfF5RFObOncucOXPYunUr3bt3Z/Xq1QAsX76cMWPGEBsby8iRI1m2bNkffZ/1LiqiA9eLyzl0ItvSUUwqr9CTcj6PXp3boFbLsJMQonaqPKJ48803ycnJISkpifz8fOPjOp2OS5cumdxwfHw8AwYMwM3NDbjddXbHjh08++yzAJw8eRInJyeGDBkCwNNPP82NGzcAMBgMFBcXA7dnhv/+aitrcU+H1nRs24rtCRkMDm2Ljdp6jypOXsynrEJPL7ksVghRB1UWismTJ3P27FlSU1OJjIw0Pm5jY0PPnj1Nbjg3NxeN5rcvJk9PT5KTk43LmZmZtGnThtdee43Tp08TEBBgHOZ6/vnnmTZtGp9//jkVFRVs2LChLu/NrFQqFVER/qz8OoWfTuUQEdzW0pGqdDRVi5ODLd38Wls6ihCiEaqyUISEhBASEkJERIRx0l15eTnXrl2jXbt2JjdsMBgqXV2jKEqlZZ1Ox+HDh1m3bh0hISG89957LF68mMWLF/PKK6/wt7/9jREjRvDdd9/x7LPPsnXr1hpfrePhUffLPzWamt/JboSHM3HxGXx35BLR93Y2+7BObbL9Sqc3cPx8HgNC2tLW2zznJ+qSqyFIrtqRXLXTnHKZvDw2JSWFNWvWMH/+fMaOHUtRURHPPvsss2fPrnY9b29vEhMTjctarRZPz98memk0Gvz9/QkJCQEgKiqKefPmkZ+fT3p6urGFeWRkJG+++SYFBQW4u7vX6E3l5RVjMCg1eu3vaTQuaLVFtVonsm97Ptp6ku8OpdOnm/kmstUlG9wediourSDIz61O65srl7lJrtqRXLXT1HKp1apqf2CbHFj/6KOPmDp1Kjt37qRnz57s3buX2NhYkzuOiIggISGB/Px8SktL2blzp/F8BEBYWBj5+fmcOXMGgD179hAUFETr1q1xcHAwFpmkpCRatmxZ4yLR0Pp288SrtSPbEi6iKLUvTuZ2NFWLvZ2aoI7W+fkJIayfySMKRVHo2rUra9asYciQITg7O9foC9HLy4v58+cza9YsKioqmDx5MqGhocyZM4d58+YREhLCBx98wMKFCyktLcXb25slS5agUql4//33eeutt7h16xYtW7Zk5cqV9fJmzUGtVjE63J9Pt58hJT2f0E4elo5kZFAUjqZpCQ3wwN6ucUwMFEJYH5OFQq1Ws337dg4ePMgrr7zCvn37anyuIDo6+o4Je2vWrDH+uUePHmzatOmO9UJDQ/nqq69qtA9rEB7kzdaDF9gWf5GQAHermfl8/nIhhTfL5WonIcQfYnLo6ZVXXmHjxo3Mnz8fjUbDqlWrWLhwYUNkazRsbdQ80N+fc5cLSc28buk4RkmpWmxtVPTo1MbSUYQQjZjJI4o+ffqwdu1a4xyH9evXmz1UYzQ4tC1x8RfZlnCRbv6WvwxV+WXY6Z4O7jg6NK4ut0II62LyiCI9PZ3Ro0czZswYcnJyGDVqFOfPn2+IbI2KvZ0Nkf3ac+piAelXblg6Dpk5xVwrvCW9nYQQf5jJQvH222/z+uuv4+HhgZeXFw8//DB/+ctfGiJbo3NfTx9atrBlW/xFS0chKU2LSgVhnWXYSQjxx5gsFNevX2fgwIHG5RkzZhjba4jKHB1sub9Pe34+d41LuZb9jI6maena3q3R3rJVCGE9atSgqKyszHglj1arxWAwmDVUYza8jy8t7G34JuGixTJk593kyrWb9O4qd7ITQvxxVRaK8vJyAKZPn87jjz9OXl4e//znP3nwwQd56KGHGixgY9OyhR1De/lw5HQuV/NLLJIhKVULyLCTEKJ+VFkoHnzwQeB2c8Dnn3+e6OhodDodb731FtOnT2+wgI3RyL5+2Nqq2Z6QYZH9J6VpCWjXCvdW1td1VwjR+FR53eTvZ1/37duXvn37NkigpsC1pT1DerTjh2OXGTuoA21cHRts39cKS8m4WsSU+zo12D6FEE1blYWirKyMU6dOVdmuIygoyGyhmoJR/f344dhldvyUycMjuzbYfo+mXQOQy2KFEPWmykJx6dIlnnvuubsWCpVKxe7du80arLFzb9WCgSHe7D+eTVREB9ycHRpkv0dTc/HVtMTL3alB9ieEaPqqLBSBgYHExMQ0YJSmZ9QAfw4kZ7Pz8CWmDgs0+/4Kb5ZzNquQ6IEdzL4vIUTzYb3372wCvFo70b+7F3uPXaa4tMLs+zt2VosC9JHLYoUQ9ajKQtGnT5+GzNFkjQ73p6xCz65E0/cZ/6OOpmrxbO2Ij6al2fclhGg+qiwU0iG2fvhqnAnr3IZdiVmUlunMtp+btyo4nVFA7y4aq2lzLoRoGmToqQFERXSgpEzHnqNZZtvH8XPX0BsUufeEEKLeSaFoAB3btiK4ozs7j1yirEJvln0kpWpp7eJAx7atzLJ9IUTzJYWigURFdKCopIL9x6/U+7bLyvWcuJBPr84a1DLsJISoZ1IoGkiX9m508XVlx0+Z6PT121QxJT2PCp1Bhp2EEGZh1kIRFxfH6NGjGTlyJF988cUdz6enpzNz5kzGjh3L448/TmFhIQC5ubk8+eSTjB8/nmnTppGVZb6x/YYUFdGBgqIy4k9crdftHk3T4uxoR5f2rvW6XSGEADMWipycHJYtW8aXX35JTEwMGzZs4Ny5c8bnFUVh7ty5zJkzh61bt9K9e3dWr14NwMsvv8zQoUOJiYlh3LhxLF261FwxG1RQR3c6eLuwPSEDfT21aq/QGTh+/hphndtgo5YDRCFE/TPbN0t8fDwDBgzAzc0NJycnIiMj2bFjh/H5kydP4uTkxJAhQwB4+umnmTFjBvn5+Zw5c4Zp06YBMGnSJF544QVzxWxQKpWKqIgO5F4v5fDp3HrZ5umMAkrL9PSWYSchhJmYrVDk5uai0fz25eXp6UlOTo5xOTMzkzZt2vDaa68xYcIE3nzzTZycnLh06RLt2rVj8eLFTJo0iXnz5mFnZ2eumA2uZ+c2+LRpyTcJGRiqaLhYG0mpubSwt6G7v3s9pBNCiDtV2evpjzIYDJUmfimKUmlZp9Nx+PBh1q1bR0hICO+99x6LFy9mypQpnDp1iueee45XX32Vr776igULFvD555/XeN8eHs51zq3RuNR53ZqaFtmNf36RRHpOMeEh7Wq83n9n0+sNHD+fR78gb9q1tdz5iYb4zOpCctWO5Kqd5pTLbIXC29ubxMRE47JWq8XT87ceRBqNBn9/f0JCQgCIiopi3rx5/OlPf6Jly5YMHTrU+Pjbb79dq33n5RVjMNT+17pG44JWW1Tr9Wqrm48Lnm6OfLHjDJ28nGs0k/pu2c5kFHDjZjnB/q0bJHdNc1kDyVU7kqt2mloutVpV7Q9ssw09RUREkJCQQH5+PqWlpezcudN4PgIgLCzMeD4CYM+ePQQFBeHn54e3tzf79u0DYO/evU3u3hc2ajWjw/3JuFrEyQv5dd5OUpoWO1s1wQEy7CSEMB+zHVF4eXkxf/58Zs2aRUVFBZMnTyY0NJQ5c+Ywb948QkJC+OCDD1i4cCGlpaV4e3uzZMkSAFauXMmbb77JP/7xD5ydnVm8eLG5YlpMRLA3sQcvEBd/keAAj1qvb1AUjqZpCe7oTgt7s/01CiGE+QoFQHR0NNHR0ZUeW7NmjfHPPXr0YNOmTXesFxAQUKtzEo2RrY2aUf39+HLXWVIzC+jq17pW61/MLqKgqIyJQwLMlFAIIW6TC+8taEiPdrRysmNbQkat101Ky8VGraJn5zZmSCaEEL+RQmFB9nY2jOznx8kL+VzIvlHj9RRFISlVSzf/1rRs0XQuHRZCWCcpFBY2NMwHJwdbtsVfrPE6l7U3yS0opXcXmWQnhDA/KRQW5uhgy4g+vhw7e40sbXGN1klK06ICwmTYSQjRAKRQWIERfdrjYG/DNzU8V5GUqiXQ1xVXZwczJxNCCCkUVsHZ0Y6hYT4cPp1DTkFJta/NKSghS1ssw05CiAYjhcJKRPZtj41azXYTRxVH07QA9JJCIYRoIFIorISrswNDerQl/sRV8m/cqvJ1R1O1+Hu50MbNsQHTCSGaMykUVmRUf38Avv0p867PFxSVcf7KDbmTnRCiQUmhsCIeri0ID/Zm//ErFN4sv+P5X4ed+kihEEI0ICkUVmbMAH90egM7D995VHE0TUtbDyfaerS0QDIhRHMlhcLKeLk70bebJ3uOXaa4tML4eGFxGamZ1+VOdkKIBieFwgpFhXegrFzP7qQs42OHT17FoCj07uJZzZpCCFH/pFBYIV9PZ3oGtmFX4iVKy3QAxKdk49GqBX5edb97nxBC1IUUCisVFdGBm7d0/HDsMqVlOn5O09K7q6ZGd8MTQoj6JHe8sVIB7VoR1KE13x25hLOTHTq9QSbZCSEsQo4orFhURAdu3Cxn/e6zuLk4EOjjaulIQohmSAqFFevS3o1AX1dKy/QMCG6LWi3DTkKIhieFwoqpVCrGDeyICrg3zMfScYQQzZRZC0VcXByjR49m5MiRfPHFF3c8n56ezsyZMxk7diyPP/44hYWFlZ4/deoUwcHB5oxo9YI6urP8+cEEd5J7TwghLMNshSInJ4dly5bx5ZdfEhMTw4YNGzh37pzxeUVRmDt3LnPmzGHr1q10796d1atXG58vLS3lrbfeoqKi4m6bb1acHeV2p0IIyzFboYiPj2fAgAG4ubnh5OREZGQkO3bsMD5/8uRJnJycGDJkCABPP/00M2bMMD6/ePFiZs+eba54Qgghashsl8fm5uai0fx2OaenpyfJycnG5czMTNq0acNrr73G6dOnCQgI4I033gBg9+7d3Lp1iwceeKBO+/bwqPukNI3Gpc7rmpu1ZpNctSO5akdy1Y45cpmtUBgMhkqTwxRFqbSs0+k4fPgw69atIyQkhPfee4/Fixfz4osvsmrVKtauXVvnfeflFWMwKLVeT6NxQastqvN+zclas0mu2pFctSO5aqeuudRqVbU/sM029OTt7Y1WqzUua7VaPD1/61Ok0Wjw9/cnJCQEgKioKJKTk/nhhx+4fv06M2bMYNy4cQCMGzeO4uJic0UVQghRDbMVioiICBISEsjPz6e0tJSdO3caz0cAhIWFkZ+fz5kzZwDYs2cPQUFBTJkyhV27dhEbG0tsbCwAsbGxODtLjyMhhLAEsw09eXl5MX/+fGbNmkVFRQWTJ08mNDSUOXPmMG/ePEJCQvjggw9YuHAhpaWleHt7s2TJEnPFEUIIUUcqRVFqP5hv5QoKbtbpHIWHhzN5edY5xGWt2SRX7Uiu2pFctVPXXGq1itatq74hWpMsFEIIIeqPtPAQQghRLSkUQgghqiWFQgghRLWkUAghhKiWFAohhBDVkkIhhBCiWlIohBBCVEsKhRBCiGpJoRBCCFEtKRS/U1xcTFRUFFlZWZaOYvT+++8zZswYxowZY1W9sJYvX87o0aMZM2YMn376qaXj3OHvf/87CxYssHQMo5kzZzJmzBjGjRvHuHHjOH78uKUjAbebcU6cOJFRo0bx9ttvWzoOAF999ZXxcxo3bhy9e/fmb3/7m6VjAbcblP767/Hvf/+7peMYrV69msjISKKjo1m1alX970ARiqIoys8//6xERUUpQUFByqVLlywdR1EURTl06JDy4IMPKmVlZUp5ebkya9YsZefOnZaOpfz000/KtGnTlIqKCqW0tFQZOnSocv78eUvHMoqPj1f69++vvPLKK5aOoiiKohgMBmXQoEFKRUWFpaNUkpmZqQwaNEjJzs5WysvLlYceekj54YcfLB2rkrS0NOX+++9X8vLyLB1FKSkpUfr27avk5eUpFRUVyuTJk5VDhw5ZOpZy6NAhJSoqSikqKlJ0Op3y1FNPKd9991297kOOKH6xceNG3nzzzUr3zLA0jUbDggULsLe3x87Ojk6dOnHlyhVLx6Jfv3589tln2NrakpeXh16vx8nJydKxALh+/TrLli3j6aeftnQUo/T0dAAee+wxxo4dy7p16yyc6Lbvv/+e0aNH4+3tjZ2dHcuWLaNHjx6WjlXJokWLmD9/Pu7u7paOgl6vx2AwUFpaik6nQ6fT4eDgYOlYnDp1ikGDBuHs7IyNjQ2DBw9m165d9boPKRS/+J//+R/69Olj6RiVdO7cmZ49ewJw8eJFvv32W+69917LhvqFnZ0dK1asYMyYMYSHh+Pl5WXpSAD85S9/Yf78+bRq1crSUYxu3LhBeHg4H3zwAWvXrmX9+vUcOnTI0rHIyMhAr9fz9NNPM27cOL788ktcXV0tHcsoPj6eW7duMWrUKEtHAcDZ2Znnn3+eUaNGce+99+Lj40OvXr0sHYugoCAOHjzI9evXKSsrY8+ePVy7dq1e9yGFohE4e/Ysjz32GC+//DIdOnSwdByjefPmkZCQQHZ2Nhs3brR0HL766ivatm1LeHi4paNUEhYWxpIlS3BxccHd3Z3Jkyezb98+S8dCr9eTkJDAO++8w4YNG0hOTmbLli2WjmW0fv16Hn30UUvHMDpz5gxff/01e/fu5cCBA6jVaj7++GNLxyI8PJyJEycyc+ZMnnjiCXr37o2dnV297kMKhZVLSkrikUce4cUXX2TChAmWjgPA+fPnOX36NACOjo6MHDmS1NRUC6eC7du3c+jQIcaNG8eKFSvYs2cP77zzjqVjkZiYSEJCgnFZURRsbc12z7Aaa9OmDeHh4bi7u9OiRQtGjBhBcnKypWMBUF5ezpEjRxg2bJiloxgdPHiQ8PBwPDw8sLe3Z+LEiRw+fNjSsSguLmbkyJHExcXx+eefY29vT/v27et1H1IorFh2djbPPPMMS5cuZcyYMZaOY5SVlcXChQspLy+nvLyc3bt307t3b0vH4tNPP2Xbtm3ExsYyb948hg0bxmuvvWbpWBQVFbFkyRLKysooLi5my5Yt3H///ZaOxdChQzl48CA3btxAr9dz4MABgoKCLB0LgNTUVDp06GA1574AunXrRnx8PCUlJSiKwp49ewgJCbF0LLKysvjTn/6ETqejqKiITZs21ftwneV/1ogqffzxx5SVlbF48WLjY9OmTeOhhx6yYCq49957SU5OZvz48djY2DBy5EirKmTWZujQoRw/fpzx48djMBiYPn06YWFhlo5Fjx49eOKJJ5g+fToVFRUMHDiQSZMmWToWAJcuXcLb29vSMSoZNGgQp06dYuLEidjZ2RESEsKTTz5p6Vh069aNkSNHMnbsWPR6PY888ki9/3CTO9wJIYSolgw9CSGEqJYUCiGEENWSQiGEEKJaUiiEEEJUSwqFEEKIakmhEM3OzJkzWb169R2Pf/LJJ8ydO7fK9VauXGnRLqZ6vZ65c+cSGRl5R7+o/85WXl7OvHnzmD59OoWFhQ0dVTQxUihEszN9+nS+/vrrOx7fuHEjM2bMsECimsnJyeHgwYNs376dhx9+uMrXlZSUMHfuXPR6PZ9++qlV9W8SjZMUCtHs3H///ZSUlJCYmGh87PDhwyiKwsCBA/m///s/pkyZQnR0NCNGjOD777+/YxvDhg0jJSXlrstHjx5l+vTpTJgwgUmTJrF3714AtFotjz32GBMmTGDChAm89957d82XmJjI1KlTiY6OZuLEiezfv5/i4mKeeOIJdDodEydOJDMz867rFhYW8thjj9G+fXtWrlxpFd1NReMnM7NFs2Nra8vUqVPZtGmTsWPwhg0bmD59OleuXCE+Pp7PP/+cFi1a8M0337BixYoat9woLCzk1Vdf5eOPP8bX15ecnBymTp1K165d2bJlC76+vnzyySeUlJTw+uuvU1RUhIuLi3H9goIC5s2bx6pVq+jRowdnz57l4YcfZtOmTaxevZro6GhiY2Pvuu9r164xc+ZMsrKyWLlyJWq1/A4U9UMKhWiWpk6dypgxYyguLkan03Hw4EEWLVqEi4sLS5YsIS4ujoyMDI4fP87NmzdrvN2ff/4ZrVbLM888Y3xMpVKRmprK4MGDefLJJ8nOziYiIoIXX3yxUpEASE5Oxs/Pz3hfiM6dO9OrVy8OHz5M//79q933rl27WLRoESkpKbzwwgusXbu23ruIiuZJfnKIZsnLy4uIiAi2b99OTEwMkZGRuLi4cPLkSR588EGKi4sZOHAgTzzxRJXb+H33m/LycuD2CedOnToRGxtr/G/Dhg0MGjSI0NBQdu/ezYMPPsjly5eZMmUKJ06cqLRNvV6PSqW6Yz86nc7ke5o4cSJTp05l4cKFFBcXW9WtOkXjJoVCNFszZswgLi6OmJgY40nsI0eOEBwczKOPPkq/fv3YvXs3er3+jnXd3d2NX/I//fQTWq0WgJ49e5KRkcGRI0cAOH36NJGRkeTk5LB06VI+/PBDRowYweuvv05gYCBnz56ttN2ePXuSnp5ubPd99uxZjhw5Qr9+/Uy+H3t7ewAcHBxYvnw5W7ZsqXKYSojakKEn0Wz179+ft99+G1dXV7p27QpAVFQUO3fuZNSoURgMBoYOHUphYSHFxcWV1v3zn//MokWL2LBhA0FBQcb23O7u7qxYscLYVlxRFJYsWYKvry+zZ89mwYIFREVFYW9vT9euXe/ouuvu7s7y5ct56623uHXrFiqVinfffZeOHTuSlZVV4/fWoUMH3n77bV599VW6dOlC9+7d/+CnJZoz6R4rhBCiWjL0JIQQolpSKIQQQlRLCoUQQohqSaEQQghRLSkUQgghqiWFQgghRLWkUAghhKiWFAohhBDV+v8OrMVCfyU9EAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# KNN Model Evaluation\n",
    "\n",
    "for i in range(0, len(mean_accuracy)):\n",
    "    print(\"k = {} has a score = {}\".format(i+1, mean_accuracy[i]))\n",
    "\n",
    "sns.set_style(\"darkgrid\")\n",
    "plt.plot(range(1, 10), mean_accuracy)\n",
    "plt.xlabel('Values of K', fontsize = 12)\n",
    "plt.ylabel('Testing Accuracy Values', fontsize = 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "241ee518",
   "metadata": {},
   "source": [
    "For K = 2 and K = 8 has highest value of Testing Accuracy Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2063ad47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=8)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Training KNN Model with 8 Neighborhoods\n",
    "\n",
    "neigh_ = knn(n_neighbors = 8)\n",
    "neigh_.fit(X_train, y_train)\n",
    "yhat_knn = neigh_.predict(X_test)\n",
    "\n",
    "neigh_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b20d576e",
   "metadata": {},
   "source": [
    "## Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cab7d474",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Fit Decision Tree Models\n",
    "\n",
    "mean_accuracy = []\n",
    "\n",
    "for d in range(1, 10):\n",
    "    dt = DecisionTreeClassifier(criterion = 'gini', max_depth = d)\n",
    "    dt.fit(X_train, y_train)\n",
    "    dt_yhat = dt.predict(X_test)\n",
    "    mean_accuracy.append(f1_score(y_test, dt_yhat, average = 'weighted', pos_label = 'PAIDOFF'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e7e96c8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "d = 0 has a f1 score = 0.6524970963995355\n",
      "d = 1 has a f1 score = 0.6524970963995355\n",
      "d = 2 has a f1 score = 0.6524970963995355\n",
      "d = 3 has a f1 score = 0.6763313241782142\n",
      "d = 4 has a f1 score = 0.6577391304347826\n",
      "d = 5 has a f1 score = 0.6858023332599603\n",
      "d = 6 has a f1 score = 0.6857142857142857\n",
      "d = 7 has a f1 score = 0.7028571428571428\n",
      "d = 8 has a f1 score = 0.6857142857142857\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Decision Tree Model Evaluation\n",
    "\n",
    "for i in range(0, len(mean_accuracy)):\n",
    "    print('d = {} has a f1 score = {}'.format(i+0, mean_accuracy[i]))\n",
    "\n",
    "sns.set_style(\"darkgrid\")\n",
    "sns.lineplot(x = range(0, 9), y = mean_accuracy, linewidth = 2.5)\n",
    "plt.ylabel('f1_score', fontsize = 12)\n",
    "plt.xlabel('max depth', fontsize = 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf1fa6c6",
   "metadata": {},
   "source": [
    "Around max depth = 5 f1_score is highest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b99cb611",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=4)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Training Decision Tree with max depth = 5\n",
    "\n",
    "tree = DecisionTreeClassifier(criterion = 'gini', max_depth = 4)\n",
    "tree.fit(X_train, y_train)\n",
    "tree"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9806a6f5",
   "metadata": {},
   "source": [
    "## Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "86a431dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit different SVM Model\n",
    "\n",
    "kernel_func = ['sigmoid', 'poly', 'rbf', 'linear']\n",
    "mean_accuracy = []\n",
    "\n",
    "for k in kernel_func:\n",
    "    svc_model = svm.SVC(C = 0.01, gamma = 'auto', kernel = k)\n",
    "    svc_model.fit(X_train, y_train)\n",
    "    svc_yhat = svc_model.predict(X_test)\n",
    "    mean_accuracy.append(f1_score(y_test, svc_yhat, pos_label = 'PAIDOFF'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4c5ddee7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Model Visualisation\n",
    "\n",
    "plots = sns.barplot(x = kernel_func, y = mean_accuracy, palette = 'rocket')\n",
    "for bar in plots.patches:\n",
    "    plots.annotate(format(bar.get_height(), '.6f'), (bar.get_x() + bar.get_width() / 2, bar.get_height()),\n",
    "                  ha = 'center', va = 'center', size = 12, xytext = (0, 8), textcoords = 'offset points')\n",
    "sns.despine(left = True, bottom = True)\n",
    "sns.set_style('white')\n",
    "plt.tick_params(labelleft = False, left = False)\n",
    "plt.ylim([0, 1])\n",
    "plt.xlabel(\"\\nKernel Function\", fontsize = 14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0517422",
   "metadata": {},
   "source": [
    "Accuracy of different kernel function is same. So we will use 'linear' function to train model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "026492d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=0.01, gamma='auto', kernel='linear')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Training SVM with linear kernel function\n",
    "\n",
    "clf = svm.SVC(C = 0.01, gamma = 'auto', kernel = 'linear')\n",
    "clf.fit(X_train, y_train)\n",
    "clf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78840847",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "bac93fe2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 0.45909710044785085    for C = 0.1 and Solver = liblinear\n",
      "Accuracy = 0.44720066709682477    for C = 0.1 and Solver = newton-cg\n",
      "Accuracy = 0.44720122397561085    for C = 0.1 and Solver = lbfgs\n",
      "Accuracy = 0.4471956665088851    for C = 0.1 and Solver = sag\n",
      "Accuracy = 0.4471990481917436    for C = 0.1 and Solver = saga\n",
      "\n",
      "\n",
      "Accuracy = 0.55845787057088    for C = 0.01 and Solver = liblinear\n",
      "Accuracy = 0.49605255036359563    for C = 0.01 and Solver = newton-cg\n",
      "Accuracy = 0.4960526443270092    for C = 0.01 and Solver = lbfgs\n",
      "Accuracy = 0.4960468223295355    for C = 0.01 and Solver = sag\n",
      "Accuracy = 0.49605474676107214    for C = 0.01 and Solver = saga\n",
      "\n",
      "\n",
      "Accuracy = 0.6676081588694727    for C = 0.001 and Solver = liblinear\n",
      "Accuracy = 0.5442860144094683    for C = 0.001 and Solver = newton-cg\n",
      "Accuracy = 0.5442860122054438    for C = 0.001 and Solver = lbfgs\n",
      "Accuracy = 0.5442850510294623    for C = 0.001 and Solver = sag\n",
      "Accuracy = 0.5442860665092751    for C = 0.001 and Solver = saga\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Model Training\n",
    "\n",
    "solvers = ['liblinear', 'newton-cg', 'lbfgs', 'sag', 'saga']\n",
    "regularization_val = [0.1, 0.01, 0.001]\n",
    "mean_logloss = []\n",
    "\n",
    "for c in regularization_val:\n",
    "    for solver in solvers:\n",
    "        lr_model = LogisticRegression(C = c, solver = solver)\n",
    "        lr_model.fit(X_train, y_train)\n",
    "        lr_yhat = lr_model.predict_proba(X_test)\n",
    "        score = log_loss(y_test, lr_yhat)\n",
    "        print('Accuracy = {}    for C = {} and Solver = {}'.format(score, c, solver))\n",
    "        mean_logloss.append(score)\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9aa4b929",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Model Evaluation\n",
    "\n",
    "dictionary = {0.1: mean_logloss[0:5],\n",
    "             0.01: mean_logloss[5:10],\n",
    "             0.001: mean_logloss[10:15]}\n",
    "\n",
    "sns.set_style(\"darkgrid\")\n",
    "sns.lineplot(data = pd.DataFrame.from_dict(dictionary), palette = \"rocket\", linewidth = 2.5)\n",
    "plt.xticks([0.0, 1.0, 2.0, 3.0, 4.0], labels = solvers)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65359459",
   "metadata": {},
   "source": [
    "Using 'liblinear' function for Logistic Regression yeilds highest accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3ec9f75f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=0.001, solver='liblinear')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Training Logistic Regression Model with 'liblinear' function\n",
    "\n",
    "LR = LogisticRegression(C = 0.001, solver = 'liblinear')\n",
    "LR.fit(X_train, y_train)\n",
    "LR"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "342f0cc8",
   "metadata": {},
   "source": [
    "# Model Evaluation using Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f8cf07a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('load_test.csv', <http.client.HTTPMessage at 0x1b1db86cfd0>)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Download Test Dataset\n",
    "\n",
    "url = \"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_test.csv\"\n",
    "file = \"load_test.csv\"\n",
    "req.urlretrieve(url, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7892e520",
   "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>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>Principal</th>\n",
       "      <th>terms</th>\n",
       "      <th>effective_date</th>\n",
       "      <th>due_date</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>9/8/2016</td>\n",
       "      <td>10/7/2016</td>\n",
       "      <td>50</td>\n",
       "      <td>Bechalor</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>300</td>\n",
       "      <td>7</td>\n",
       "      <td>9/9/2016</td>\n",
       "      <td>9/15/2016</td>\n",
       "      <td>35</td>\n",
       "      <td>Master or Above</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>9/10/2016</td>\n",
       "      <td>10/9/2016</td>\n",
       "      <td>43</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>9/10/2016</td>\n",
       "      <td>10/9/2016</td>\n",
       "      <td>26</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>800</td>\n",
       "      <td>15</td>\n",
       "      <td>9/11/2016</td>\n",
       "      <td>9/25/2016</td>\n",
       "      <td>29</td>\n",
       "      <td>Bechalor</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Unnamed: 0.1 loan_status  Principal  terms effective_date  \\\n",
       "0           1             1     PAIDOFF       1000     30       9/8/2016   \n",
       "1           5             5     PAIDOFF        300      7       9/9/2016   \n",
       "2          21            21     PAIDOFF       1000     30      9/10/2016   \n",
       "3          24            24     PAIDOFF       1000     30      9/10/2016   \n",
       "4          35            35     PAIDOFF        800     15      9/11/2016   \n",
       "\n",
       "    due_date  age             education  Gender  \n",
       "0  10/7/2016   50              Bechalor  female  \n",
       "1  9/15/2016   35       Master or Above    male  \n",
       "2  10/9/2016   43  High School or Below  female  \n",
       "3  10/9/2016   26               college    male  \n",
       "4  9/25/2016   29              Bechalor    male  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load Model for Evaluation\n",
    "\n",
    "test_df = pd.read_csv('load_test.csv')\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5be04730",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.49362588  0.92844966  3.05981865  1.97714211 -1.30384048  2.39791576\n",
      "  -0.79772404 -0.86135677]\n",
      " [-3.56269116 -1.70427745  0.53336288 -0.50578054  0.76696499 -0.41702883\n",
      "  -0.79772404 -0.86135677]\n",
      " [ 0.49362588  0.92844966  1.88080596  1.97714211  0.76696499 -0.41702883\n",
      "   1.25356634 -0.86135677]\n",
      " [ 0.49362588  0.92844966 -0.98251057 -0.50578054  0.76696499 -0.41702883\n",
      "  -0.79772404  1.16095912]\n",
      " [-0.66532184 -0.78854628 -0.47721942 -0.50578054  0.76696499  2.39791576\n",
      "  -0.79772404 -0.86135677]]\n",
      "['PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF']\n"
     ]
    }
   ],
   "source": [
    "# Data Pre-processing\n",
    "\n",
    "test_df['due_date'] = pd.to_datetime(test_df['due_date'])\n",
    "test_df['effective_date'] = pd.to_datetime(test_df['effective_date'])\n",
    "test_df['dayofweek'] = test_df['effective_date'].dt.dayofweek\n",
    "test_df['weekend'] = test_df['dayofweek'].apply(lambda x: 1 if (x>3)  else 0)\n",
    "test_df['Gender'].replace(to_replace=['male','female'], value=[0,1],inplace=True)\n",
    "\n",
    "X_test_ = test_df[['Principal','terms','age','Gender', 'weekend']]\n",
    "X_test_ = pd.concat([X_test_,pd.get_dummies(test_df['education'])], axis=1)\n",
    "X_test_.drop(['Master or Above'], axis = 1,inplace=True)\n",
    "\n",
    "X_test_ = preprocessing.StandardScaler().fit(X_test_).transform(X_test_)\n",
    "\n",
    "y_test_ = test_df['loan_status'].values\n",
    "\n",
    "print(X_test_[0:5])\n",
    "print(y_test_[0:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18113f92",
   "metadata": {},
   "source": [
    "### Model Evaluation for Different Classifications"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "89427334",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "jaccard score for KNN =  0.6938775510204082\n",
      "F1 score for KNN =  0.8192771084337349\n"
     ]
    }
   ],
   "source": [
    "# For KNN Algorithm\n",
    "\n",
    "ytest_knn = neigh_.predict(X_test_)\n",
    "\n",
    "jaccard_knn = jaccard_score(y_test_, ytest_knn, pos_label = 'PAIDOFF')\n",
    "f1_knn = f1_score(y_test_, ytest_knn, pos_label = 'PAIDOFF')\n",
    "\n",
    "print(\"jaccard score for KNN = \", jaccard_knn)\n",
    "print(\"F1 score for KNN = \", f1_knn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7180d880",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "jaccard score for Decision Tree =  0.7647058823529411\n",
      "F1 score for Decision Tree =  0.8666666666666667\n"
     ]
    }
   ],
   "source": [
    "# For Decision Tree Algorithm\n",
    "\n",
    "ytest_tree = tree.predict(X_test_)\n",
    "\n",
    "jaccard_tree = jaccard_score(y_test_, ytest_tree, pos_label = 'PAIDOFF')\n",
    "f1_tree = f1_score(y_test_, ytest_tree, pos_label = 'PAIDOFF')\n",
    "\n",
    "print(\"jaccard score for Decision Tree = \", jaccard_tree)\n",
    "print(\"F1 score for Decision Tree = \", f1_tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "282d5649",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "jaccard score for SVM =  0.7407407407407407\n",
      "F1 score for SVM =  0.851063829787234\n"
     ]
    }
   ],
   "source": [
    "# For SVM Algorithm\n",
    "\n",
    "ytest_svm = clf.predict(X_test_)\n",
    "\n",
    "jaccard_svm = jaccard_score(y_test_, ytest_svm, pos_label = 'PAIDOFF')\n",
    "f1_svm = f1_score(y_test_, ytest_svm, pos_label = 'PAIDOFF')\n",
    "\n",
    "print(\"jaccard score for SVM = \", jaccard_svm)\n",
    "print(\"F1 score for SVM = \", f1_svm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "7e2ffb77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "jaccard score for LR =  0.7843137254901961\n",
      "F1 score for LR =  0.8791208791208792\n",
      "Log-Loss for LR =  0.6691574976376283\n"
     ]
    }
   ],
   "source": [
    "# For Logistic Regression Algorithm\n",
    "\n",
    "ytest_lr = LR.predict(X_test_)\n",
    "ytest_lr_ = LR.predict_proba(X_test_)\n",
    "\n",
    "jaccard_lr = jaccard_score(y_test_, ytest_lr, pos_label = 'PAIDOFF')\n",
    "f1_lr = f1_score(y_test_, ytest_lr, pos_label = 'PAIDOFF')\n",
    "logloss = log_loss(y_test_, ytest_lr_)\n",
    "\n",
    "print(\"jaccard score for LR = \", jaccard_lr)\n",
    "print(\"F1 score for LR = \", f1_lr)\n",
    "print(\"Log-Loss for LR = \", logloss)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94220c03",
   "metadata": {},
   "source": [
    "### Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "24457dc0",
   "metadata": {
    "scrolled": true
   },
   "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>Algorithm</th>\n",
       "      <th>Jaccard</th>\n",
       "      <th>F1-score</th>\n",
       "      <th>LogLoss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KNN</td>\n",
       "      <td>0.693878</td>\n",
       "      <td>0.819277</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.764706</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SVM</td>\n",
       "      <td>0.740741</td>\n",
       "      <td>0.851064</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.784314</td>\n",
       "      <td>0.879121</td>\n",
       "      <td>0.669157</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Algorithm   Jaccard  F1-score   LogLoss\n",
       "0                  KNN  0.693878  0.819277        NA\n",
       "1        Decision Tree  0.764706  0.866667        NA\n",
       "2                  SVM  0.740741  0.851064        NA\n",
       "3  Logistic Regression  0.784314  0.879121  0.669157"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Report = {'Algorithm': ['KNN', 'Decision Tree', 'SVM', 'Logistic Regression'],\n",
    "         'Jaccard': [jaccard_knn, jaccard_tree, jaccard_svm, jaccard_lr],\n",
    "         'F1-score': [f1_knn, f1_tree, f1_svm, f1_lr],\n",
    "         'LogLoss': ['NA', 'NA', 'NA', logloss]}\n",
    "\n",
    "Report_df = pd.DataFrame.from_dict(Report)\n",
    "\n",
    "Report_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "6606d965",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(constrained_layout = True, figsize = (10, 4))\n",
    "grid = gd.GridSpec(ncols = 2, nrows = 1, figure = fig)\n",
    "sns.set_theme(style = \"darkgrid\")\n",
    "\n",
    "ax1 = fig.add_subplot(grid[0, :1])\n",
    "sns.despine(fig, left = True, bottom = True, ax = ax1)\n",
    "g = sns.barplot(x = \"Algorithm\", y = \"Jaccard\", data = Report_df, label=\"Total\", palette = \"rocket_r\", ax = ax1)\n",
    "for bar in g.patches:\n",
    "    g.annotate(format(bar.get_height(), '.6f'), (bar.get_x() + bar.get_width() / 2, bar.get_height()), ha = 'center',\n",
    "               va = 'center', size = 12, xytext = (0, 8), textcoords = 'offset points')\n",
    "sns.despine(left = True, bottom = True, ax = ax1)\n",
    "plt.ylim([0, 1])\n",
    "plt.xlabel(\"\\nKernel Function\", fontsize = 14)\n",
    "\n",
    "ax2 = fig.add_subplot(grid[0, 1:])\n",
    "sns.despine(fig, left = True, bottom = True, ax = ax2)\n",
    "g2 = sns.barplot(x = \"Algorithm\", y = \"F1-score\", data = Report_df, label=\"Total\", palette = \"rocket_r\", ax = ax2)\n",
    "for bar in g2.patches:\n",
    "    g2.annotate(format(bar.get_height(), '.6f'), (bar.get_x() + bar.get_width() / 2, bar.get_height()), ha = 'center',\n",
    "               va = 'center', size = 12, xytext = (0, 8), textcoords = 'offset points')\n",
    "sns.despine(left = True, bottom = True, ax = ax2)\n",
    "plt.ylim([0, 1])\n",
    "plt.xlabel(\"\\nKernel Function\", fontsize = 14)\n",
    "\n",
    "plt.show()"
   ]
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    "___"
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