From e5d4b58eb40df839e27419a935afef9fd2012c83 Mon Sep 17 00:00:00 2001
From: fcnwanganga <48266502+fcnwanganga@users.noreply.github.com>
Date: Tue, 20 Sep 2022 12:18:10 -0400
Subject: [PATCH 01/10] Create temp.txt

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From 90c0456909835b4d77f05e43b262986cbd753818 Mon Sep 17 00:00:00 2001
From: fcnwanganga <48266502+fcnwanganga@users.noreply.github.com>
Date: Tue, 20 Sep 2022 12:21:53 -0400
Subject: [PATCH 02/10] Add files via upload

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "11963cc7",
+   "metadata": {},
+   "source": [
+    "# Classifying Data with Logistic Regression in Python\n",
+    "\n",
+    "## Learning Objectives\n",
+    "Logistic Regression is one of the simplest and most commonly used classification approaches in machine learning. Logistic regression allows us to model the relationship between independent variables and the probability of a categorical response (such as True or False, Yes or No). By the end of this tutorial, you will have learned:\n",
+    "\n",
+    "+ How to import, explore and prepare data\n",
+    "+ How to build a Logistic Regression model\n",
+    "+ How to evaluate a Logistic Regression model\n",
+    "+ How to interpret the coefficients of a Logistic Regression model "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2887bb87",
+   "metadata": {},
+   "source": [
+    "## 1. Collect the Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7c96c17b",
+   "metadata": {},
+   "source": [
+    "Before we import our data, we must first import the `pandas` package."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "de4b4a56",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eff3ec92",
+   "metadata": {},
+   "source": [
+    "Now, we can import our data into a dataframe called `loan`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b53e3b3f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loan = pd.read_csv(\"loan.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "836693c2",
+   "metadata": {},
+   "source": [
+    "To verify that the import worked as expected, let’s use the `head()` method of the pandas dataframe to preview the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "046f5901",
+   "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>Income</th>\n",
+       "      <th>Loan Amount</th>\n",
+       "      <th>Default</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>30</td>\n",
+       "      <td>8</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>22</td>\n",
+       "      <td>10</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>33</td>\n",
+       "      <td>12</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>28</td>\n",
+       "      <td>20</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>23</td>\n",
+       "      <td>32</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Income  Loan Amount Default\n",
+       "0      30            8      No\n",
+       "1      22           10      No\n",
+       "2      33           12      No\n",
+       "3      28           20      No\n",
+       "4      23           32      No"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "loan.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "758f2183",
+   "metadata": {},
+   "source": [
+    "Our dataset has three columns. The first two - `Income` and `Loan Amount` - are the predictors (or independent variables), while the last one - `Default` - is the response (or dependent variable).\n",
+    "\n",
+    "In this exercise, we’ll use this `loan` data to train a logistic regression model to predict whether a borrower will default or not default on a new loan based on their income and the amount of money they intend to borrow. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6095cae",
+   "metadata": {},
+   "source": [
+    "## 2. Explore the Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c1c66cc1",
+   "metadata": {},
+   "source": [
+    "Now that we have our data, let's try to understand it.\n",
+    "\n",
+    "First, let's get a concise summary of the structure of the data by calling the `info()` method of the `loan` dataframe."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "67de73e2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 30 entries, 0 to 29\n",
+      "Data columns (total 3 columns):\n",
+      " #   Column       Non-Null Count  Dtype \n",
+      "---  ------       --------------  ----- \n",
+      " 0   Income       30 non-null     int64 \n",
+      " 1   Loan Amount  30 non-null     int64 \n",
+      " 2   Default      30 non-null     object\n",
+      "dtypes: int64(2), object(1)\n",
+      "memory usage: 848.0+ bytes\n"
+     ]
+    }
+   ],
+   "source": [
+    "loan.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7f146df",
+   "metadata": {},
+   "source": [
+    "By looking at the `RangeIndex` value from the summary, we can tell that there are 30 instances (or rows) in the dataset. \n",
+    "\n",
+    "The `Data columns` value shows that the dataset consists of 3 features (or columns). Looking at the `Dtype` column within this section, we see that the `Income` and `Loan Amount` columns hold integer values, while the `Default` column holds text (aka object)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c4602524",
+   "metadata": {},
+   "source": [
+    "Next, let's get summary statistics for the numeric features in the data by calling the `describe()` method of the dataframe."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "acdf6ca5",
+   "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>Income</th>\n",
+       "      <th>Loan Amount</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>30.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>20.966667</td>\n",
+       "      <td>54.233333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>6.195011</td>\n",
+       "      <td>28.231412</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>12.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>16.250000</td>\n",
+       "      <td>32.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>20.500000</td>\n",
+       "      <td>54.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>24.750000</td>\n",
+       "      <td>71.750000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>34.000000</td>\n",
+       "      <td>110.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          Income  Loan Amount\n",
+       "count  30.000000    30.000000\n",
+       "mean   20.966667    54.233333\n",
+       "std     6.195011    28.231412\n",
+       "min    12.000000     8.000000\n",
+       "25%    16.250000    32.000000\n",
+       "50%    20.500000    54.500000\n",
+       "75%    24.750000    71.750000\n",
+       "max    34.000000   110.000000"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "loan.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8889a842",
+   "metadata": {},
+   "source": [
+    "From the statistics, we can see the average, standard deviation, minimum, and maximum values for both the `Income` and `Loan Amount` variables. We also get the 25th, 50th and 75th percentile values for both variables.\n",
+    "\n",
+    "Note that the values are in the thousands, so the minimum and maximum income values are \\\\$12,000 and \\\\$34,000, respectively. \n",
+    "\n",
+    "Now that we've described our data structurally and numerically, let’s describe it visually as well."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1ed89ece",
+   "metadata": {},
+   "source": [
+    "### Boxplot\n",
+    "Before we create the plots we need, we must first import a couple of packages. The first is the `matplotlib` package and the second is the `seaborn` package."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7239372b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib import pyplot as plt\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5013146d",
+   "metadata": {},
+   "source": [
+    "Let's start by creating a boxplot that highlights the difference in annual income between those that did not default on their loan (No) and those that did default (Yes). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "9308d55a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = sns.boxplot(data = loan, x = 'Default', y = 'Income')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6da0bae9",
+   "metadata": {},
+   "source": [
+    "The chart shows that those that did not default on their loans tend to have a higher annual income than those that did default on their loans. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fd1574a4",
+   "metadata": {},
+   "source": [
+    "Next, let's create another box plot to highlight the difference in amount borrowed between those that did not default on their loans and those that did."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "bcb7b490",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = sns.boxplot(data = loan, x = 'Default', y = 'Loan Amount')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7158408e",
+   "metadata": {},
+   "source": [
+    "This chart shows that those that defaulted on their loans tend to have borrowed more money than those that did not default."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9e51544a",
+   "metadata": {},
+   "source": [
+    "### Scatterplot\n",
+    "If we recode the `Default` feature values 'No' and 'Yes' to '0' and '1', we can also use a scatterplot to get a slightly different perspective of our data. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bbdc98b1",
+   "metadata": {},
+   "source": [
+    "However, before we do so, we must first import the `numpy` package."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "04b9e4bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "88188d4c",
+   "metadata": {},
+   "source": [
+    "Now, we can create a scatterplot that describes the relationship between the annual income of borrowers and loan outcomes. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "f0e2438f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = sns.scatterplot(x = loan['Income'], \n",
+    "                     y = np.where(loan['Default'] == 'No', 0, 1), \n",
+    "                     s = 150)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dbcc993e",
+   "metadata": {},
+   "source": [
+    "We can also describe the relationship between the amount borrowed and loan outcomes. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "1387c926",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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G5317+8C/Pc54amhGnfXM2ex1m0z2pV6t8dpbXUAWOwaGeHHLdm557AVWPruF4ZFgTlcH37nsBAaHg1sf76u6PdcmzjxiLtecuZhDu2fS1Tn+1kvVU2n+WsfXatuuQV7s385tJfr9wukf5tDumeTaVLUGoOF11tN7s9dtMtmXerXmUUS05IF7enqit7e36rgdA0Pc+4tXuf7+NYyW2t4mvn3ZCazb/Ftu+sG6qtuLSXDj0qP54+PmM3Na7b8gpeqpNH+t42u1bdcg9/3iVW54YG3Z+W84/yj+4KMHcvJ/X8nOwZG9xnTkxMovn8YTz/c3tM56em/2uk0m+1KvVj9JqyOip9R9mU65SDpX0nOS+iRdW+J+Sbq5cP/Tkj5Wb9GjXtzy7l47+jlHfQhgr9Aut71YBFx//xo2vrG9YfVUmr/W8TXX07+9bJiPzn/DA2t5+c0d/MezDy855uwlH2LzOzsbXmc9vTd73SaTfalXa66qgS4pB9wKnAcsAS6UtGTMsPOAxYWvy4FvNKK4bbsGueWxvr129ItPOoTlP9mYeftYEXDLYy/UfH6yXD3l5t+6Y6Cm8bXW886OAW57PNv8t/2oj/OPObDk/c1Yt1rXqnjOen52qtmXerXmy3KEfjzQFxEbImIAuAdYOmbMUuBbkfcUMEfSvHqL62xvY+WzW/bY1tWZ4+j5szNvL+fR9VvobK/tb8Kl6innyRffZHpnrqn1zKhx/vfP7OQDXZ17bG/WutWyVmPnrOdnp5p9qVdrvix7x3zglaLbmwrbah2DpMsl9Urq7e/vr/rAOweGGR7Z89Bl1rR2tu4czLy9nOGRYOfAcKaxleopZ0ZHjh27s48fTz21zr9j9zBzZ0/bY3uz1q2WtRo7Zz0/O9XsS71a82UJdJXYNnYPzDKGiFgWET0R0dPd3V31gWd05si17Tn1u7uHmD2jI/P2cnJtYkZnLtPYSvWUs3NwmK5p2cePp55a5++almPL1t17bG/WutWyVmPnrOdnp5p9qVdrviyBvgk4qOj2AmDzOMbUbGBohDOPmLvHth0Dw6x5dWvm7eWcdeRcBob2fsVHrfWUc/Jh+7NrYLip9eyscf63tw/w1pjXpjdr3WpZq7Fz1vOzU82+1Ks1X5ZAXwUslrRIUidwAbBizJgVwOcKr3Y5EdgaEa/VW9x+0zu4+owPozEHMHf99CUuOXVR5u1jSXD1GYvZb3pHQ+opN//srs6axtdaz5yuTq48Pdv8V572YR54uvT/sc1Yt1rXqnjOen52qtmXerXmqxroETEEXAU8DKwHvhcRayVdIemKwrCHgA1AH/BN4MpGFXjY3FncuPToPXb4h9e+DsB1n1qSaXux0df0LjpgZsPqqTR/reNrrqd7Jjecf1TF+W84/ygO3r+Lrz3yfMkxj6x7nQPnzGh4nfX03ux1m0z2pV6tuSb9G4sAtu8eYuMb+XfRPbr+vXeE3n3ZCQyNBLc93ld1e65NnHXkXK4+YzGLDphZ1xs0StVTaf5ax9dq265BNvRv57Yf7d3vlafl3ynaJlWtAWh4nfX03ux1m0z2pV6tPpXeWDQlAn3Utl2DdLa3sXNgmBmdOQaGRthvekfN2xul1vmbXc87OwaY0Zljx8AwXZ05dg4MM2fMyxSz1NCMOuuZs9nrNpnsS73a+CQT6GZm+7q63/pvZmaTnwPdzCwRLTvlIqkfeKklD954BwBvtLqICeR+07Uv9QpTs99DIqLkOzNbFugpkdRb7pxWitxvuvalXiG9fn3KxcwsEQ50M7NEONAbY1mrC5hg7jdd+1KvkFi/PoduZpYIH6GbmSXCgW5mlggHeo0kHSTpcUnrJa2V9MXC9g9I+idJLxT+fX+ra20USTlJ/yrpwcLtlHudI+kfJD1beI5PSrzf/1DYj9dI+q6k6Sn1K2m5pC2S1hRtK9ufpK8WPuz+OUnntKbq8XOg124I+HJEHAmcCHyh8KHZ1wIrI2IxsLJwOxVfJH/p5FEp9/q/gX+MiCOAY8n3nWS/kuYD1wA9EXE0kCP/eQcp9XsncO6YbSX7K/weXwAcVfiZ2yRNrY+Iigh/1fEF3A98EngOmFfYNg94rtW1Nai/BeR3+jOABwvbUu31fcBGCi8WKNqear+jnwX8AaAdeBA4O7V+gYXAmmrPJ/BV4KtF4x4GTmp1/bV8+Qi9DpIWAscBPwM+GIVPaSr8m+1zxSa/vwX+Eij+7LNUez0U6Af+rnCK6Q5JM0m034h4FfifwMvAa+Q/aewREu23SLn+Mn3Y/WTmQB8nSbOAe4EvRcRvW11PM0j6NLAlIla3upYJ0g58DPhGRBwHbGdqn26oqHDueCmwCDgQmCnpT1tbVUtl+rD7ycyBPg6SOsiH+d0RcV9h828kzSvcPw/Y0qr6GugU4A8k/Rq4BzhD0rdJs1fIH5FtioifFW7/A/mAT7Xfs4CNEdEfEYPAfcDJpNvvqHL9NeXD7ieSA71GkgT8H2B9RHyt6K4VwJ8Vvv8z8ufWp7SI+GpELIiIheT/WPRYRPwpCfYKEBGvA69I+khh05nAOhLtl/yplhMldRX26zPJ/xE41X5HletvBXCBpGmSFgGLgZ+3oL5x8ztFayTpVODHwDO8d175v5A/j/494GDyvyj/PiLeakmRTSDpNOA/RcSnJe1Por1K+ihwB9BJ/oPPP0/+wCfVfv8b8CfkX731r8BlwCwS6VfSd4HTyF8m9zfAXwHfp0x/kv4rcAn59fhSRPxw4qsePwe6mVkifMrFzCwRDnQzs0Q40M3MEuFANzNLhAPdzCwRDnSblCS9O8GP90eSQtIRE/m4Jer4kqSuVtZgU5cD3SzvQuAn5N9A1UpfAhzoNi4OdJsyJH1U0lOSnpb0/0avYy3pLyStkvQrSfeOHuFKulPSzZKelLRB0mfKzDuL/GUOLqUo0CWdJukJSd+T9Lykv5Z0kaSfS3pG0mGFcYdIWlmoa6Wkg4se/zNF871bNO+Piq67frfyriF/TZXHJT3elEW0pDnQbSr5FvCViDiG/Dt1/6qw/b6I+HhEjF6//NKin5kHnAp8GvjrMvP+IflroD8PvCXpY0X3HUv+evC/A1wMHB4Rx5N/N+nVhTFfB75VqOtu4OYMvRxH/mh8CfmrPJ4SETeTv3bI6RFxeoY5zPbgQLcpQdJsYE5EPFHY9H+B3yt8f7SkH0t6BriI/AcUjPp+RIxExDrgg2Wmv5D8xcco/Hth0X2rIuK1iNgNvAg8Utj+DPnrbAOcBHyn8P1d5P8DqebnEbEpIkaAXxbNZTZu7a0uwKwB7gT+MCJ+JenPyV+7Y9Tuou/3ujxq4bo0Z5D/TyHIf2pPSPrLEj8/UnR7hPK/P6PX0xiicNBUuPhVZ5m6hivMZZaZj9BtSoiIrcDbkj5R2HQxMHq0vh/wWuGyxhfVOPVnyJ8uOSQiFkbEQeQ/tSjLUfaoJ3nv3PtF5P+4CvBr4HcL3y8FOjLMtY18P2Y181GBTVZdkjYV3f4a+Uud3l74o+folRABriN/tcuXyJ8KqSUQL2Tvc+v3Ap8F/j7jHNcAyyX9Z/KfeDRa1zeB+yX9nPzH+G3PMNcy4IeSXvN5dKuVr7ZoZpYIn3IxM0uEA93MLBEOdDOzRDjQzcwS4UA3M0uEA93MLBEOdDOzRPx/pVT7k4XA3C0AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = sns.scatterplot(x = loan['Loan Amount'], \n",
+    "                     y = np.where(loan['Default'] == 'No', 0, 1), \n",
+    "                     s = 150)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b063af6",
+   "metadata": {},
+   "source": [
+    "Looking at these two charts, we can easily imagine a sigmoid curve that fits the data. This tells us that a logistic regression function would model the relationship between the predictors (`Income` and `Loan Amount`) and the response (`Default`) well."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d6d214f4",
+   "metadata": {},
+   "source": [
+    "## 3. Prepare the Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c7e68c4",
+   "metadata": {},
+   "source": [
+    "Our primary objective in this step is to split our data into training and test sets. The training set will be used to train the model, while the test set will be used to evaluate the model.\n",
+    "\n",
+    "Before we split the data, we first need to separate the dependent variable from the independent variables."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9e239ee4",
+   "metadata": {},
+   "source": [
+    "Let's start by creating a pandas Series called `y` for the dependent variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "a570e61b",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "y = loan['Default']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "408be828",
+   "metadata": {},
+   "source": [
+    "Then we create a pandas DataFrame called `X` for the independent variables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "1fda732e",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "X = loan[['Income', 'Loan Amount']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89042894",
+   "metadata": {},
+   "source": [
+    "Next, we import the `train_test_split()` function from the `sklearn.model_selection` subpackage. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "cd01aaca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67a48b50",
+   "metadata": {},
+   "source": [
+    "Using the `train_test_split()` function, we can split `X` and `y` into `X_train`, `X_test`, `y_train` and `y_test`.\n",
+    "\n",
+    "Note that within the `train_test_split()` function, we will set:\n",
+    "\n",
+    "* `train_size` to `0.7`. This means we want $70\\%$ of the original data to be assigned to the training data while $30\\%$ is assigned to the test data. \n",
+    "\n",
+    "* `stratify` as `y`, which means that we want the data split using a stratified random sampling approach based on the values of `y`. \n",
+    "\n",
+    "* `random_state` to `123`, so we get the same results every time we do this split. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "346cdb9d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X, y,\n",
+    "                                                    train_size = 0.7,\n",
+    "                                                    stratify = y,\n",
+    "                                                    random_state = 123) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "394ac187",
+   "metadata": {},
+   "source": [
+    "After the data is split, the newly created `X_train` and `X_test` data sets hold the independent variables for the training and test sets, respectively. While the `y_train` and `y_test` data sets hold the dependent variable for the training and test sets respectively.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbc90e5d",
+   "metadata": {},
+   "source": [
+    "We can refer to the `shape` attribute of any of the newly created data sets to know how many instances or records are in each. Let's look at the training data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "66162c9a",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(21, 2)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cf46e048",
+   "metadata": {},
+   "source": [
+    "The result is a tuple that holds the number of rows and columns in the `X_train` dataframe. It tells us that $21$ out of the $30$ instances in the `loans` data were assigned to the training set.\n",
+    "\n",
+    "Let's look at the test set as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "20c26601",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(9, 2)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8dfa25f9",
+   "metadata": {},
+   "source": [
+    "The result tells us that $9$ out of the $30$ instances in the `loans` data were assigned to the test set."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7e67d2b0",
+   "metadata": {},
+   "source": [
+    "## 4. Train and Evaluate the Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "36c65e26",
+   "metadata": {},
+   "source": [
+    "We are going to use the `LogisticRegression` class from the `sklearn.linear_model` subpackage to train our model. Let's import it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "69e53d51",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d42be18c",
+   "metadata": {},
+   "source": [
+    "We can now instantiate a new object called `classifier` from the `LogisticRegression` class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "263345cf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "classifier = LogisticRegression()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b138191",
+   "metadata": {},
+   "source": [
+    "To train a model, we pass the training data (`X_train` and `y_train`) to the `fit()` method of the classifier object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "eafcb43f",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "model = classifier.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be18f2c1",
+   "metadata": {},
+   "source": [
+    "Recall that there are $9$ instances (or rows) in the test set. To predict labels for the test instances, we pass the independent variables of the test set (`X_test`) to the `predict()` method of the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "7332d1bc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Yes', 'Yes', 'Yes', 'Yes', 'Yes', 'No', 'No', 'No', 'Yes'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f6ae05fc",
+   "metadata": {},
+   "source": [
+    "The output lists the predictions made by the model in the order in which the instances appear in the test set.\n",
+    "\n",
+    "To evaluate how accurate our model is, we pass the test data (`X_test` and `y_test`) to the `score()` method of the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "20fa1a33",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8888888888888888"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.score(X_test, y_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17cb769b",
+   "metadata": {},
+   "source": [
+    "The result tells us that our Logistic Regression model is able to correctly predict $8$ out of $9$ (or $89\\%$) of the labels in the test set."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d887aaa1",
+   "metadata": {},
+   "source": [
+    "The accuracy of a model only gives us a one-dimensional perspective of performance. To get a broader perspective, we need to generate a confusion (or error) matrix of the model's performance. \n",
+    "\n",
+    "To do this, we need to import the `confusion_matrix` function from the `sklearn.metrics` subpackage."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "c1b722a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import confusion_matrix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2fa1d34b",
+   "metadata": {},
+   "source": [
+    "Then we pass the dependent variable from the test set (which are the actual labels) and the model's predicted labels to the `confusion_matrix()` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "3cba5859",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[3, 1],\n",
+       "       [0, 5]])"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "confusion_matrix(y_test, model.predict(X_test))"
+   ]
+  },
+  {
+   "attachments": {
+    "image-2.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "fc57c822",
+   "metadata": {},
+   "source": [
+    "The result is a $ 2\\times 2$ array that shows how many instances the model predicted correctly or incorrectly as either `Yes` or `No`. This confusion matrix can be illustrated as follows:\n",
+    "\n",
+    "<div>\n",
+    "<img src=\"attachment:image-2.png\" width=\"240\"/>\n",
+    "</div>\n",
+    "\n",
+    "The first row of the matrix shows that of the $4$ instances that were actually `No`, the model predicted $3$ of them as `No` but $1$ of them as `Yes`. The second row of the matrix shows that of the $5$ instances that were actually `Yes`, the model predicted all $5$ correctly as `Yes`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "de7143cf",
+   "metadata": {},
+   "source": [
+    "## 5. Interpret the Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad2c87a8",
+   "metadata": {},
+   "source": [
+    "Now that we've built a Logistic Regression model and evaluated the performance of the model on the test data, we can now interpret the model's output. Specifically, the model coefficients."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c4f3dfc1",
+   "metadata": {},
+   "source": [
+    "The relatonship between the dependent and independent variables in a Logistic Regression model is generally represented as follows:\n",
+    "\n",
+    "$$ log(\\frac{P}{1 - P}) = \\beta_{0} + \\beta_{1}X_{1} + ...+ \\beta_{n}X_{n}$$\n",
+    "\n",
+    "In this representation, the left hand side of the equaton is known as the **logit** or the log-odds of the probability of an outcome or class $P$. $\\beta_{0}$ is the intercept. $\\beta_{1}$ to $\\beta_{n}$ are the coefficients of the independent variables $X_{1}$ to $X_{n}$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "54b5daba",
+   "metadata": {},
+   "source": [
+    "To get the intercept (or $\\beta_{0}$), we refer to the `intercept_` attribute of our model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "1dc049cf",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([15.4670632])"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.intercept_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "46073f5a",
+   "metadata": {},
+   "source": [
+    "To get the other model coefficients ($\\beta_{1}$ and $\\beta_{2}$), we refer to the `coef_` attribute of our model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "a5273286",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-1.0178107 ,  0.14656096]])"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.coef_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a47e48aa",
+   "metadata": {},
+   "source": [
+    "The model coefficients correspond to the order in which the independent variables are listed in the training data. This means that the equation for our Logistic Regression model can be written as:\n",
+    "\n",
+    "$$ log(\\frac{P}{1 - P}) = 15.4670632 -1.0178107 \\times \\text{Income} + 0.14656096 \\times \\text{Loan Amount} $$\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "594a64b8",
+   "metadata": {},
+   "source": [
+    "To make our coefficients easier to work with, let's convert the coefficients from a two-dimenionsal array to a one-dimensional array and round the values to two decimal places."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "c89d0a08",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-1.02,  0.15])"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "log_odds = np.round(model.coef_[0], 2)\n",
+    "log_odds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f09002c7",
+   "metadata": {},
+   "source": [
+    "Next, let's create a pandas DataFrame using the coefficient values and the column names from the training data as row indexes:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "556a5e49",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>log odds</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Income</th>\n",
+       "      <td>-1.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Loan Amount</th>\n",
+       "      <td>0.15</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             log odds\n",
+       "Income          -1.02\n",
+       "Loan Amount      0.15"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame({'log odds': log_odds}, \n",
+    "              index = X.columns)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ea1dc32f",
+   "metadata": {},
+   "source": [
+    "The first coefficient tells us that, when all other variables are held constant, a $\\$1$ increase in a borrowers income decreases the log odds that they will default on their loan by $1.02$. \n",
+    "\n",
+    "Likewise, the second coefficent tells us that a $\\$1$ increase in the amount a customer borrows, increases the log odds that they will default on their loan by $0.15$ when all other variables are held constant.\n",
+    "\n",
+    "Understandably, interpreting the coefficients in terms of log odds is a bit confusing. A more intuitive approach would be to look at them in terms of odds. Let's exponentiate the coefficients so we can interpret them in terms of odds rather than log odds:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "f5bd37c6",
+   "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>odds</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Income</th>\n",
+       "      <td>0.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Loan Amount</th>\n",
+       "      <td>1.16</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             odds\n",
+       "Income       0.36\n",
+       "Loan Amount  1.16"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "odds = np.round(np.exp(log_odds), 2)\n",
+    "pd.DataFrame({'odds': odds}, \n",
+    "              index = X.columns)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "91d11fda",
+   "metadata": {},
+   "source": [
+    "The first coefficent tells us that for every $\\$1$ increase in a borrower's income, the odds that they will default on their loan reduces by $64\\%$ ($1 - 0.36$) when all other variables are held constant. Earning more money decreases the odds of default.\n",
+    "\n",
+    "The second coefficent tells us that, assuming all other variables are held constant, for every $\\$1$ increase in the amount borrowed, the odds that a borrower will default on their loan increases by $16\\%$ ($1.16 - 1$). Borrowing more money increases the odds of default.\n",
+    "\n",
+    "We can also interpret the second coefficent as saying that for every $\\$1$ increase in the amount borrowed, the odds that a borrower will default on their loan increases by a factor of $1.16$, assuming all other variables are held constant."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 28d50d862ffac4d89e9a4b66451014f18a580cd1 Mon Sep 17 00:00:00 2001
From: fcnwanganga <48266502+fcnwanganga@users.noreply.github.com>
Date: Tue, 20 Sep 2022 12:25:58 -0400
Subject: [PATCH 03/10] Delete temp.txt

---
 src/temp.txt | 1 -
 1 file changed, 1 deletion(-)
 delete mode 100644 src/temp.txt

diff --git a/src/temp.txt b/src/temp.txt
deleted file mode 100644
index 8b13789..0000000
--- a/src/temp.txt
+++ /dev/null
@@ -1 +0,0 @@
-

From 3557468eba49009332046d13696b32832da25a5b Mon Sep 17 00:00:00 2001
From: fcnwanganga <48266502+fcnwanganga@users.noreply.github.com>
Date: Tue, 20 Sep 2022 12:41:25 -0400
Subject: [PATCH 04/10] Add files via upload

---
 src/loan.csv | 31 +++++++++++++++++++++++++++++++
 1 file changed, 31 insertions(+)
 create mode 100644 src/loan.csv

diff --git a/src/loan.csv b/src/loan.csv
new file mode 100644
index 0000000..721c5f9
--- /dev/null
+++ b/src/loan.csv
@@ -0,0 +1,31 @@
+Income,Loan Amount,Default
+30,8,No
+22,10,No
+33,12,No
+28,20,No
+23,32,No
+34,32,No
+23,35,No
+26,38,No
+30,50,No
+26,70,No
+23,45,No
+24,32,No
+18,24,No
+25,32,No
+17,63,Yes
+12,71,Yes
+24,62,Yes
+19,40,Yes
+18,68,Yes
+13,54,Yes
+13,55,Yes
+15,58,Yes
+18,72,Yes
+17,76,Yes
+13,78,Yes
+15,85,Yes
+18,90,Yes
+16,100,Yes
+22,105,Yes
+14,110,Yes
\ No newline at end of file

From 0be6e50409dc63a5482f0462d8fe6e7ec1627c66 Mon Sep 17 00:00:00 2001
From: fcnwanganga <48266502+fcnwanganga@users.noreply.github.com>
Date: Tue, 20 Sep 2022 14:05:48 -0400
Subject: [PATCH 05/10] Add files via upload

---
 src/03e.ipynb | 28 ++++++++++++++++++++++++++++
 1 file changed, 28 insertions(+)

diff --git a/src/03e.ipynb b/src/03e.ipynb
index 85f3070..0e5c6dc 100644
--- a/src/03e.ipynb
+++ b/src/03e.ipynb
@@ -920,9 +920,37 @@
     "<img src=\"attachment:image-2.png\" width=\"240\"/>\n",
     "</div>\n",
     "\n",
+    "\n",
+    "\n",
     "The first row of the matrix shows that of the $4$ instances that were actually `No`, the model predicted $3$ of them as `No` but $1$ of them as `Yes`. The second row of the matrix shows that of the $5$ instances that were actually `Yes`, the model predicted all $5$ correctly as `Yes`."
    ]
   },
+  {
+   "attachments": {
+    "conf_matrix.png": {
+     "image/png": 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+    }
+   },
+   "cell_type": "markdown",
+   "id": "358fd32f",
+   "metadata": {},
+   "source": [
+    "![conf_matrix.png](attachment:conf_matrix.png)"
+   ]
+  },
+  {
+   "attachments": {
+    "conf_matrix.jpg": {
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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "72ec9e05",
+   "metadata": {},
+   "source": [
+    "![conf_matrix.jpg](attachment:conf_matrix.jpg)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "de7143cf",

From a207c16317682c9c4693ab79f8b4b8c43644c605 Mon Sep 17 00:00:00 2001
From: fcnwanganga <48266502+fcnwanganga@users.noreply.github.com>
Date: Tue, 20 Sep 2022 17:38:59 -0400
Subject: [PATCH 06/10] Add files via upload

---
 src/03b.ipynb | 310 ++++++++++++++++
 src/03e.ipynb | 965 +-------------------------------------------------
 2 files changed, 328 insertions(+), 947 deletions(-)
 create mode 100644 src/03b.ipynb

diff --git a/src/03b.ipynb b/src/03b.ipynb
new file mode 100644
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--- /dev/null
+++ b/src/03b.ipynb
@@ -0,0 +1,310 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "11963cc7",
+   "metadata": {},
+   "source": [
+    "# Classifying Data with Logistic Regression in Python\n",
+    "\n",
+    "## Learning Objectives\n",
+    "Logistic Regression is one of the simplest and most commonly used classification approaches in machine learning. Logistic regression allows us to model the relationship between independent variables and the probability of a categorical response (such as True or False, Yes or No). By the end of this tutorial, you will have learned:\n",
+    "\n",
+    "+ How to import, explore and prepare data\n",
+    "+ How to build a Logistic Regression model\n",
+    "+ How to evaluate a Logistic Regression model\n",
+    "+ How to interpret the coefficients of a Logistic Regression model "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2887bb87",
+   "metadata": {},
+   "source": [
+    "## 1. Collect the Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7c96c17b",
+   "metadata": {},
+   "source": [
+    "Before we import our data, we must first import the `pandas` package."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "de4b4a56",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eff3ec92",
+   "metadata": {},
+   "source": [
+    "Now, we can import our data into a dataframe called `loan`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b53e3b3f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "836693c2",
+   "metadata": {},
+   "source": [
+    "To verify that the import worked as expected, let’s use the `head()` method of the pandas dataframe to preview the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "046f5901",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "758f2183",
+   "metadata": {},
+   "source": [
+    "Our dataset has three columns. The first two - `Income` and `Loan Amount` - are the predictors (or independent variables), while the last one - `Default` - is the response (or dependent variable).\n",
+    "\n",
+    "In this exercise, we’ll use this `loan` data to train a logistic regression model to predict whether a borrower will default or not default on a new loan based on their income and the amount of money they intend to borrow. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6095cae",
+   "metadata": {},
+   "source": [
+    "## 2. Explore the Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c1c66cc1",
+   "metadata": {},
+   "source": [
+    "Now that we have our data, let's try to understand it.\n",
+    "\n",
+    "First, let's get a concise summary of the structure of the data by calling the `info()` method of the `loan` dataframe."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "67de73e2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7f146df",
+   "metadata": {},
+   "source": [
+    "By looking at the `RangeIndex` value from the summary, we can tell that there are 30 instances (or rows) in the dataset. \n",
+    "\n",
+    "The `Data columns` value shows that the dataset consists of 3 features (or columns). Looking at the `Dtype` column within this section, we see that the `Income` and `Loan Amount` columns hold integer values, while the `Default` column holds text (aka object)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c4602524",
+   "metadata": {},
+   "source": [
+    "Next, let's get summary statistics for the numeric features in the data by calling the `describe()` method of the dataframe."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "acdf6ca5",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8889a842",
+   "metadata": {},
+   "source": [
+    "From the statistics, we can see the average, standard deviation, minimum, and maximum values for both the `Income` and `Loan Amount` variables. We also get the 25th, 50th and 75th percentile values for both variables.\n",
+    "\n",
+    "Note that the values are in the thousands, so the minimum and maximum income values are \\\\$12,000 and \\\\$34,000, respectively. \n",
+    "\n",
+    "Now that we've described our data structurally and numerically, let’s describe it visually as well."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1ed89ece",
+   "metadata": {},
+   "source": [
+    "### Boxplot\n",
+    "Before we create the plots we need, we must first import a couple of packages. The first is the `matplotlib` package and the second is the `seaborn` package."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7239372b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib import pyplot as plt\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5013146d",
+   "metadata": {},
+   "source": [
+    "Let's start by creating a boxplot that highlights the difference in annual income between those that did not default on their loan (No) and those that did default (Yes). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9308d55a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6da0bae9",
+   "metadata": {},
+   "source": [
+    "The chart shows that those that did not default on their loans tend to have a higher annual income than those that did default on their loans. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fd1574a4",
+   "metadata": {},
+   "source": [
+    "Next, let's create another box plot to highlight the difference in amount borrowed between those that did not default on their loans and those that did."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bcb7b490",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7158408e",
+   "metadata": {},
+   "source": [
+    "This chart shows that those that defaulted on their loans tend to have borrowed more money than those that did not default."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9e51544a",
+   "metadata": {},
+   "source": [
+    "### Scatterplot\n",
+    "If we recode the `Default` feature values 'No' and 'Yes' to '0' and '1', we can also use a scatterplot to get a slightly different perspective of our data. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bbdc98b1",
+   "metadata": {},
+   "source": [
+    "However, before we do so, we must first import the `numpy` package."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "04b9e4bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "88188d4c",
+   "metadata": {},
+   "source": [
+    "Now, we can create a scatterplot that describes the relationship between the annual income of borrowers and loan outcomes. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f0e2438f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dbcc993e",
+   "metadata": {},
+   "source": [
+    "We can also describe the relationship between the amount borrowed and loan outcomes. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1387c926",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b063af6",
+   "metadata": {},
+   "source": [
+    "Looking at these two charts, we can easily imagine a sigmoid curve that fits the data. This tells us that a logistic regression function would model the relationship between the predictors (`Income` and `Loan Amount`) and the response (`Default`) well."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/src/03e.ipynb b/src/03e.ipynb
index 0e5c6dc..19ec61b 100644
--- a/src/03e.ipynb
+++ b/src/03e.ipynb
@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "de4b4a56",
    "metadata": {},
    "outputs": [],
@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "b53e3b3f",
    "metadata": {},
    "outputs": [],
@@ -70,85 +70,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "046f5901",
    "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>Income</th>\n",
-       "      <th>Loan Amount</th>\n",
-       "      <th>Default</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>30</td>\n",
-       "      <td>8</td>\n",
-       "      <td>No</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>22</td>\n",
-       "      <td>10</td>\n",
-       "      <td>No</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>33</td>\n",
-       "      <td>12</td>\n",
-       "      <td>No</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>28</td>\n",
-       "      <td>20</td>\n",
-       "      <td>No</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>23</td>\n",
-       "      <td>32</td>\n",
-       "      <td>No</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Income  Loan Amount Default\n",
-       "0      30            8      No\n",
-       "1      22           10      No\n",
-       "2      33           12      No\n",
-       "3      28           20      No\n",
-       "4      23           32      No"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "loan.head()"
    ]
@@ -183,27 +108,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "67de73e2",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 30 entries, 0 to 29\n",
-      "Data columns (total 3 columns):\n",
-      " #   Column       Non-Null Count  Dtype \n",
-      "---  ------       --------------  ----- \n",
-      " 0   Income       30 non-null     int64 \n",
-      " 1   Loan Amount  30 non-null     int64 \n",
-      " 2   Default      30 non-null     object\n",
-      "dtypes: int64(2), object(1)\n",
-      "memory usage: 848.0+ bytes\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "loan.info()"
    ]
@@ -228,97 +136,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "acdf6ca5",
    "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>Income</th>\n",
-       "      <th>Loan Amount</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>30.000000</td>\n",
-       "      <td>30.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>20.966667</td>\n",
-       "      <td>54.233333</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>6.195011</td>\n",
-       "      <td>28.231412</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>12.000000</td>\n",
-       "      <td>8.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>16.250000</td>\n",
-       "      <td>32.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>20.500000</td>\n",
-       "      <td>54.500000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>24.750000</td>\n",
-       "      <td>71.750000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>34.000000</td>\n",
-       "      <td>110.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          Income  Loan Amount\n",
-       "count  30.000000    30.000000\n",
-       "mean   20.966667    54.233333\n",
-       "std     6.195011    28.231412\n",
-       "min    12.000000     8.000000\n",
-       "25%    16.250000    32.000000\n",
-       "50%    20.500000    54.500000\n",
-       "75%    24.750000    71.750000\n",
-       "max    34.000000   110.000000"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "loan.describe()"
    ]
@@ -346,7 +167,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "7239372b",
    "metadata": {},
    "outputs": [],
@@ -365,23 +186,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "9308d55a",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ax = sns.boxplot(data = loan, x = 'Default', y = 'Income')"
    ]
@@ -404,23 +212,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "bcb7b490",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ax = sns.boxplot(data = loan, x = 'Default', y = 'Loan Amount')"
    ]
@@ -452,7 +247,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "04b9e4bd",
    "metadata": {},
    "outputs": [],
@@ -470,23 +265,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "f0e2438f",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ax = sns.scatterplot(x = loan['Income'], \n",
     "                     y = np.where(loan['Default'] == 'No', 0, 1), \n",
@@ -503,25 +285,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "1387c926",
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ax = sns.scatterplot(x = loan['Loan Amount'], \n",
     "                     y = np.where(loan['Default'] == 'No', 0, 1), \n",
@@ -535,704 +304,6 @@
    "source": [
     "Looking at these two charts, we can easily imagine a sigmoid curve that fits the data. This tells us that a logistic regression function would model the relationship between the predictors (`Income` and `Loan Amount`) and the response (`Default`) well."
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d6d214f4",
-   "metadata": {},
-   "source": [
-    "## 3. Prepare the Data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1c7e68c4",
-   "metadata": {},
-   "source": [
-    "Our primary objective in this step is to split our data into training and test sets. The training set will be used to train the model, while the test set will be used to evaluate the model.\n",
-    "\n",
-    "Before we split the data, we first need to separate the dependent variable from the independent variables."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9e239ee4",
-   "metadata": {},
-   "source": [
-    "Let's start by creating a pandas Series called `y` for the dependent variable."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "a570e61b",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "y = loan['Default']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "408be828",
-   "metadata": {},
-   "source": [
-    "Then we create a pandas DataFrame called `X` for the independent variables."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "1fda732e",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "X = loan[['Income', 'Loan Amount']]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "89042894",
-   "metadata": {},
-   "source": [
-    "Next, we import the `train_test_split()` function from the `sklearn.model_selection` subpackage. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "cd01aaca",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import train_test_split"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "67a48b50",
-   "metadata": {},
-   "source": [
-    "Using the `train_test_split()` function, we can split `X` and `y` into `X_train`, `X_test`, `y_train` and `y_test`.\n",
-    "\n",
-    "Note that within the `train_test_split()` function, we will set:\n",
-    "\n",
-    "* `train_size` to `0.7`. This means we want $70\\%$ of the original data to be assigned to the training data while $30\\%$ is assigned to the test data. \n",
-    "\n",
-    "* `stratify` as `y`, which means that we want the data split using a stratified random sampling approach based on the values of `y`. \n",
-    "\n",
-    "* `random_state` to `123`, so we get the same results every time we do this split. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "346cdb9d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X_train, X_test, y_train, y_test = train_test_split(X, y,\n",
-    "                                                    train_size = 0.7,\n",
-    "                                                    stratify = y,\n",
-    "                                                    random_state = 123) "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "394ac187",
-   "metadata": {},
-   "source": [
-    "After the data is split, the newly created `X_train` and `X_test` data sets hold the independent variables for the training and test sets, respectively. While the `y_train` and `y_test` data sets hold the dependent variable for the training and test sets respectively.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cbc90e5d",
-   "metadata": {},
-   "source": [
-    "We can refer to the `shape` attribute of any of the newly created data sets to know how many instances or records are in each. Let's look at the training data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "66162c9a",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(21, 2)"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "X_train.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cf46e048",
-   "metadata": {},
-   "source": [
-    "The result is a tuple that holds the number of rows and columns in the `X_train` dataframe. It tells us that $21$ out of the $30$ instances in the `loans` data were assigned to the training set.\n",
-    "\n",
-    "Let's look at the test set as well."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "20c26601",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(9, 2)"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "X_test.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8dfa25f9",
-   "metadata": {},
-   "source": [
-    "The result tells us that $9$ out of the $30$ instances in the `loans` data were assigned to the test set."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7e67d2b0",
-   "metadata": {},
-   "source": [
-    "## 4. Train and Evaluate the Model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "36c65e26",
-   "metadata": {},
-   "source": [
-    "We are going to use the `LogisticRegression` class from the `sklearn.linear_model` subpackage to train our model. Let's import it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "69e53d51",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.linear_model import LogisticRegression"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d42be18c",
-   "metadata": {},
-   "source": [
-    "We can now instantiate a new object called `classifier` from the `LogisticRegression` class."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "263345cf",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "classifier = LogisticRegression()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6b138191",
-   "metadata": {},
-   "source": [
-    "To train a model, we pass the training data (`X_train` and `y_train`) to the `fit()` method of the classifier object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "eafcb43f",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "model = classifier.fit(X_train, y_train)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "be18f2c1",
-   "metadata": {},
-   "source": [
-    "Recall that there are $9$ instances (or rows) in the test set. To predict labels for the test instances, we pass the independent variables of the test set (`X_test`) to the `predict()` method of the model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "7332d1bc",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['Yes', 'Yes', 'Yes', 'Yes', 'Yes', 'No', 'No', 'No', 'Yes'],\n",
-       "      dtype=object)"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.predict(X_test)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f6ae05fc",
-   "metadata": {},
-   "source": [
-    "The output lists the predictions made by the model in the order in which the instances appear in the test set.\n",
-    "\n",
-    "To evaluate how accurate our model is, we pass the test data (`X_test` and `y_test`) to the `score()` method of the model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "20fa1a33",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.8888888888888888"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.score(X_test, y_test)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "17cb769b",
-   "metadata": {},
-   "source": [
-    "The result tells us that our Logistic Regression model is able to correctly predict $8$ out of $9$ (or $89\\%$) of the labels in the test set."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d887aaa1",
-   "metadata": {},
-   "source": [
-    "The accuracy of a model only gives us a one-dimensional perspective of performance. To get a broader perspective, we need to generate a confusion (or error) matrix of the model's performance. \n",
-    "\n",
-    "To do this, we need to import the `confusion_matrix` function from the `sklearn.metrics` subpackage."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "c1b722a1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.metrics import confusion_matrix"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2fa1d34b",
-   "metadata": {},
-   "source": [
-    "Then we pass the dependent variable from the test set (which are the actual labels) and the model's predicted labels to the `confusion_matrix()` function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "3cba5859",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[3, 1],\n",
-       "       [0, 5]])"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "confusion_matrix(y_test, model.predict(X_test))"
-   ]
-  },
-  {
-   "attachments": {
-    "image-2.png": {
-     "image/png": 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"
-    }
-   },
-   "cell_type": "markdown",
-   "id": "fc57c822",
-   "metadata": {},
-   "source": [
-    "The result is a $ 2\\times 2$ array that shows how many instances the model predicted correctly or incorrectly as either `Yes` or `No`. This confusion matrix can be illustrated as follows:\n",
-    "\n",
-    "<div>\n",
-    "<img src=\"attachment:image-2.png\" width=\"240\"/>\n",
-    "</div>\n",
-    "\n",
-    "\n",
-    "\n",
-    "The first row of the matrix shows that of the $4$ instances that were actually `No`, the model predicted $3$ of them as `No` but $1$ of them as `Yes`. The second row of the matrix shows that of the $5$ instances that were actually `Yes`, the model predicted all $5$ correctly as `Yes`."
-   ]
-  },
-  {
-   "attachments": {
-    "conf_matrix.png": {
-     "image/png": 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-    }
-   },
-   "cell_type": "markdown",
-   "id": "358fd32f",
-   "metadata": {},
-   "source": [
-    "![conf_matrix.png](attachment:conf_matrix.png)"
-   ]
-  },
-  {
-   "attachments": {
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T/tIft1+Ok/aK/wCCn/jC+8Ya/L+8tPDEUxh0jSYnO4QRxREIoQYDJHhW/jaRsuW/BXxP8Rf25/jt4s/4Kg/tMK0vibxzfTL4etJGZ4dH0iImKKOBXACBUHlowGSgL5JkbP200KRupOdv94DIx9O2a+JzzPKntHh8O7Jbvrfsv6/4PwHEHEFT2ksNhnZLRtb37L0OL8H/AA7+HvgLSxongTQ7DSbRD8kFpbpEpyevA6k8nrXU32l2t87Wt/aRXSsDkSRhhj0wwxj86vIWNwlvH/rJzsijxuZ2AzhVGSxA/hUE96t3iXOk3UWnarHNYzyZaKO4iktpWx1KpKqOwHqAcCvk3OTfM3qfGSnJtyb1Phn4qfsK/C3xPrMXjr4QvP8ADfxnZSfabPXfD8jWskVyMlZGSNkBYMc7lKOOzCv3V/4JG/8ABe740fDz406R/wAE9/8AgrReQNq+seXbeDviLxHbaqxISO3v2wqiVjhFuMK24hZgSRK3wDJJIuEVcocknP3R6+9eFftLfAbwt+0R8ML3wHrUYa7dDLpt4Y8Na3aA7XAPPP3XGQGUkV72VZ5Vw81Cq7w8+np/kfRZPxDWw01CtLmpvvuvNf5H+lerBhkU6v54f+DcH/god4x/bS/Y2vfhD8dblpvij8Fr1PDOvPMczXdsqn7FdPklmZ0RonYkl2iLk5av6Hq/Q4yTV0fp8ZKSUlsFfw3/APB7t/ybt8Cf+xl1f/0kir+5Cv4b/wDg92/5N2+BP/Yy6v8A+kkVMZ9Rf8GXX/KLHxj/ANlM1T/026ZX9d1fyI/8GXX/ACix8Y/9lM1T/wBNumV/XdQAUhOBmlr+Yv8A4Odv+Civjb9lf9lTRf2Tf2frxrb4mfHe5l0O1ngcrNY6Om1b6dWTLI0nmLAr4+VWkdSGQUpSSTb2LpwlOShFXb0Pzu/4K8f8HAXxq+M3xa1b9hT/AIJPaounwaPK9r4r+JURDLAVJWSDTHGQoBBQ3K5kkbPkbEXzj/P34D/ZI+FXhXUZfFfjhJfGnie7f7Rdavrjm6mmuGO5nAckK2ed3Lk8kk16t8Gfgz4V+B/gOz+H3hdMeQA9zMRh7u4YAPKx6k54GfuqAOleooiRhgQwPOM+nchvevz3NM7q4ibjTdoeXX1/yP3zhvg3C4ClGpiIKdZ731S8l6d9+2hFa2NtDEYbGOONVQfcQKAPTgVzHiL4feCvHWlvpfjTQ7PUbWTqlxCrqSvuRnryDxzXpXhHw54q+IM9va/DzSNT8StOwRDomnXWqAvkAAtaxSovJ/iYZ596z7yO5trq4069R4bm1neCeCQFGimhcpIjKeQyMpVlPKsCDyK8VOcbTX3n2M4QknTmk11T/wAj5m+Do/a0/wCCdvxEf4+/8E3PF9z4fuVO/UPCN7I11pGrRgAmKSF22tkAqoYh1z+7kjODX983/BHv/gsJ8Fv+Cr/wTn13RLb/AIRb4jeFtlt4s8KXD5nsZzlRNCWAaS2kYEK5AZGBRwCAW/inhl82U/acxZyBxv3fl0+tfP8AD8Z/H3/BOf8Aar8I/wDBRv4EwOk+gXaWXi3T04TVdJnKpOkoyBl1GMnIVwj9UFfV5Jnc3NYfEO99n19Gfl/GXBlFUZY/Ax5XHWUVtbuu1uq2trpbX/VforgfhV8SvB/xl+Gnh/4t/D27W/0HxPp1tqunXK9JbW8jWWJup5KsMjsa76vsj8fI5Og+or/Jz/4Ix/8AKzl4V/7Hvxr/AOkmqV/rGSdB9RX+Tn/wRj/5WcvCv/Y9+Nf/AEk1SgD/1P7+KKKKACiiigCKeVYImmcEhQSQoLHj0AySfYDmvm3wp+2H+zZ468MeCvGng3xXbappXxDv5tL8P3VrHLLHeXtv5glhyqHynjMModZdhVkYHBBFfR92zpbtJHGZWX5gi4yxXnAyQMnoMkCvw0+En7FX7UvgrQ/hhJ4Qs4fCSXZ1C88YWE17HJLpevRabf2dnqtu0BeKdrnz4o7tFIJKRTD94km8A/c6KeKbPlkHaSpwc8jqOKWX/VmvzW/YV+AHxK+EXifUNX17wqPAukt4a0XSbjTRqKah/aWtWJmN1qeY2cDzFdEEshWefbulRSq5/SmX/VmgD/J41T/lbPX/ALOIh/8ATstf6xK9PxNf5O2qf8rZ6/8AZxEP/p2Wv9Ylen4mgB1fzA/8Hafxe17wP/wStk+FPhacx3nxO8W6N4adVJUtAC944yOcF4IgR3Br+n6v5KP+DvvRLy0/Yb+F/wAUNpay8L/E/Srm7x/DHJbXADH0GUxn3qZtqLa3IqNqLcdz8e/BfhDS/AnhLS/BehosNrpVnBaRRqMKEhQKMdRniupthEzBGG0BsNjgkfjxSCS0lhDwyERyDeuOm1sHr+NRJPEzE7sY4y54I6delfj0m27vc/D5Nttvc/aX9iP9rj9hr9jn9ly/8dX09rrvxkure5uJtOkheO6eRXYW1jHcPGYoYwu0sQwBJLNk4r9Dv2Pfj/4W/wCCs3wJ8a+Ff2hfAul2R0a+j09zY3RvoD58CzRywTvFDJDcQk8gAFflYHDYH81vwR8W6V8LPir4Y+KPxD8OXPijwto149xf6cLffFewPbyxKoM4WA4ldH+ZsfJgc1/TN+zf8evhT+3d+zf45+Ev7JC3/wAF9Z05VtpljsbWOW1a8UlJ4wnmQyJKEZGdT5ikH7rAGvsslxUqqVGTSik1yWV56b3fzv8AO59zkOMlWjGjKUVGKa9nZXnpe931et+9nfTU/k8kBsNYvNCErXH9n3l7Y+eyjEwsrmW3835QF/eeXuIAAycDioZ8K20YOV+9g4zxxjrmludB1Twrq994O1SP7Pc6FeXOlzRj5lWawleCQKx5KbkJVjyQQTyaRUSMkL93rnqM/l/k18a0fC2tozv/APgiF47vvgf/AMF9dZ+G+l/utK+MXgK4uLiAH5GvtOKzo4HZgkMg/wCBn1r+/uv887/glzpU/i7/AIOKfhkuloWXwp4G1rULxkHCrPbzQLn23TJ19a/0MRwMV+o5Q28HScux+v5JKUsDRct7IK/hv/4Pdv8Ak3b4E/8AYy6v/wCkkVf3IV/Df/we7f8AJu3wJ/7GXV//AEkir0j1T6i/4Muv+UWPjH/spmqf+m3TK/rur+RH/gy6/wCUWPjH/spmqf8Apt0yv67qAGu4jQyN0UZ/Kv8AOV/4LReOb749f8F9/E2jao/2nS/g94P07SrIHlY7i7iW5dgOzFrqUE9TgV/ozXKGS3kjHVlI/MV/mu/8FI9Gm8Ef8F/P2hfD+sAq/iLTdF1e0fs0Qs7b7vY4LYP0NeZnMpLBVXHt/wAOfS8IU4TzjDqpte/zWq/E4lkKR5PzgdiOf846U+dgkQmhJDJJG6YGRhWDY55+bGG9qJpIEPmB2YjjbwO9MLRtGBG6KeoDHk59uDX5kf0ef2s/8ElP29vGX7bnhXxtb+LPCWi+Ej4Ju9NsIodFnlnikF1bLOSxlhhxgMAAqke9fxp/E+cp8Z/iECGb/itPFHC8sSdXu6/o8/4NwR/xKfjmev8AxPdC59f+JbDX83fxWeQfGL4hEAjb408UEELzgaved+a+nzarOrgKE6ju3c+E4bwVHCZxmGHw8eWC9nZXbteN3q7vd9Wc5bSi5t9zHABON6nPH0//AFVwvxC8MWXjfwHrPhDUIleHUbKeBkfqNynBz2xwQa7GKJnjxOck446HcPTFQahcQWmlz3V0dqRwSO+RnhVJPoOK+ag2pJrc+4qwjKEoz2a19D+sD/g1i+NeufGD/gjx4K0XxLKZ7zwHqmreFy7HLeVaT+dCp/3Ip0Qeyiv6LK/lt/4ND/Dl9pv/AASp1DxXcqyQ+JfH2vahbZGAYVEEGR7b4mH4V/UlX67HbU/lGpbmdiOToPqK/wAnP/gjH/ys5eFf+x78a/8ApJqlf6xknQfUV/k5/wDBGP8A5WcvCv8A2PfjX/0k1SmQf//V/v4oor8DP2/v+Djj9gL/AIJu/tH337Ln7QWneMLjxJp9lZ38kmj6bb3FoYr1PMjCvJdRMWC/e+QAHoTQB++dFfycf8RlX/BJX/oEfEP/AME1p/8AJ9fob/wRt/4LC6T/AMFdLj4weKvBfhZ/DvhXwLr1rp2iT3Mpa8vrO6ikkWW4i5SGT5BlEdwM4ycZIB+3VGBRRQAYA6VHL/qzUlRy/wCrNAH+Txqn/K2ev/ZxEP8A6dlr/WJXp+Jr/J21T/lbPX/s4iH/ANOy1/rEr0/E0AOr8xf+Cxn7Flz+37/wTk+KH7NmhQrNr+p6X9t0PJwf7V05xc2qA9F8108knsshr9OqQjIxQB/mHfsOfGu5+MHwG0dNc8yLxF4bT+xdat51Kzx3Np8gLqQCpdQG6cNkdRX1+Z8gyZ+XPHdcCvpn/guf/wAEuvih+xj+0Jrf/BVb9inw9JrXgzxKTcfE3wvYqd8Eud0mpxRrnMTnMkzAEwSlpDmKRzH8F/BP45/Dj9oDwlH4z+HGpR31sflki4W4t5B/yzmj+8rj6bT1BIIr83zrK54as6kV7j1Xl5f5H5Xn+UTwteVSK/dyd0+1+n+R/QJ+x7+35+y1cfspyfsS/tywSaboENpJp9vqrRSvYXFg7F40mlh3PbXEOQFdtqthWVt2VHtvgH9tf/gmB+wf8ONftf2NdSuPiH4l110leO2lmvHlmiQrCLq7kVYreCMFmwMEksVV3bB/nqRWRvlkZD1yODnvgiqtwZ3JjZ3bP3d7lh9fT9amlnlaEY+5FyirKTXvJet/09b9YpcQV6cYJQi5RVlJx95L1v8Ap631J9Q1HWtc1i/8ReIpRNqGqXd1f3MwO3dcXkrzylV6qu92CAkkKAKztQ12z0/RbjVdWljgtbeN555JG2KscYy5bOOABUOqahpWkWEuq6rcx29tbIXlkndUSNQOSzNgDGOpNfKnwO+Cvxz/AOC2f7QQ/ZT/AGU/tGk/CbRLiF/HnjsxH7OsG7d9nty2A7OARFGPmnb5jtgVnPLgMvq4uryQ26vt/wAE5Mty2rjayhBadX2/4PY/bj/g1d/Z/wBb+KvxN+NP/BU3xdaNBY+L518IeEfNU5bTbJ1kupU3chWdIFGONyuOoNf2f141+z18Bfhl+y/8FPDP7P8A8G9OGleGPCdhFp2n2wO5lijHLO38ckjEvI55d2LHk17LX6hSpxpwjCOyVj9do0o0qcacdkrBX8N//B7t/wAm7fAn/sZdX/8ASSKv7kK/hv8A+D3b/k3b4E/9jLq//pJFWhqfUX/Bl1/yix8Y/wDZTNU/9NumV/XdX8iP/Bl1/wAosfGP/ZTNU/8ATbplf13UAFfwv/8AB1H+z1rPwD/ag+Dn/BT3wzZyP4fljPgfxc0KkiNSXls5nwCfmR5V3cDdAi9WFf3QV89ftW/sw/CH9sz9nrxV+zN8ddOGp+F/F1i9ldxggSRnIaOaJiDtmhkVZYmwdrqDg9KzrUo1ISpy2asdGExM8PWhXpu0otNeq1P86Szv4LmyW6tZFlhlAaF16Mj8qQec5ByKWaaWPIjBZiUBDddoYFyD2+XOPevEfjp8Efjp/wAEfPjpJ+yB+12kknhOaVz4K8aBG/s/UNP3YVWbny2j3ASRE7rdjg5iKO3uFvNb31nHeW8gkjkAYPGQ6svYqQcEd+DX5hj8BUwtRwmtOj7n9KZLneHzPDqtQevVdYv/AC7Pqf06/shf8FDf+CL37EuhaxpfwPvPGNnJ4lktbnVJLzQNdvHmntohEjZktiFwoC4QAHGfevx+/by8Sf8ABOjxnq2neM/2Cl8Qf2zrOs6jqHidNZtdTtbfZerNceZAuoIihnu3BZYsgITwBjHwY8ZdS7OzN/dU7Tkf57VVUbiDIzbVBBbPr9ccV1YnN5VqKoOlFJbWW3procmB4ZoYTFPGQr1ZTe/NNNS0sub3U3bprpoIsr26gcDcdrAHdya+aP2u/H2s6J8Mz4K8HQm+8T+NJk0LR7O3O6eW4uiEIjUc9DgYHLEDqa734wfG74c/AvwzL4o8d3ojXaxhto8Nc3LdQkacEk9Nxwq9SQK/b/8A4N8v+CR3xc+L/wAYdM/4Kw/t1aNJokVogm+G3hO5Qq0Eb5KalOjAMMA5tt4DSOfPwqLFu2yTK54iqqsl7if3+X+Z5vGPElLAYaWHpyvWmrJLon1fby89dkf1I/8ABMX9kW2/YT/YK+F/7KwCG98KaJDHqTxgBZNSuSbi8YYzkG4kfB9MV940iqFG0UtfoZ+At31I5Og+or/Jz/4Ix/8AKzl4V/7Hvxr/AOkmqV/rGSdB9RX+Tn/wRj/5WcvCv/Y9+Nf/AEk1SgR//9b+/ivz68Zf8E5f2H9R/ab8Uft0/FXwfp2t+LNX0CDSdRu9cRL6yhsLDDhkt7hXiicLGN0ijcVGM4Jz+gtcH8U/BEHxN+GXiL4b3Upgi8QaZeaa8o6ot3E0RYYweN2etAH8IXj3/g5Q/wCCb0Pju40L4D/sQ2Xjbw/LqE2n6Rqz6Zp9o2pyRHrDAlhMcsCGEe4yBSCyqcgfqR/wbD/HPwh+0/P+03+0N8OfhHZ/Cbw/4p8c2txa2ViZ2idhbuZI2Z8W/mQlgXW3jiRTJgoOCfyw8If8EvP+C7vwN/YX+HGgeHvAPhiPxp+yf48HiTwDa2Fza3E/iOw1Bpzf/aAtx5bNG/llFcwSyQuyjMiLX69f8G0nwQ/ah+Hlh+0d8Xf2uZNE0Pxp8SvHn9tal4M0i9t5m0G8kWWSY3FrbyzCye5My7IZG87yolL9qAP6hqKKKACo5f8AVmpKjl/1ZoA/yeNU/wCVs9f+ziIf/Tstf6xK9PxNf5O2qf8AK2ev/ZxEP/p2Wv8AWJXp+JoAdRRRQBFNDHPGYpQCp4INfy+ft5/8Gw37Nnxy+IF5+0N+xJ4hufgN8Q7lnll/siPfol3I7Bm32alTBvI+YREw9SYSxJr+oiiplFSXLJXRM4RknGSuj/PZ8Yf8EoP+C/8A8DbiTTovCPg74w6fbDZHqOlajHZXEuP4mScwnJ7gRDmuV0P9gv8A4OAfiDIdI0D4AaH4WckAXesa1btDEM8nasys3HYd/Wv9E1o435dQfrSeVFnO0flXmvJcE5c3s1/XlseTLIMA5czpK/8AXTY/ic+A/wDwa5fH/wCPmtWfiv8A4KlfGD+0dHjdZm8HeCwYLRyCDtmupEUYBABxHI2M7XU81/Xr+zr+zX8C/wBk34U6d8Ef2dfC9h4R8LaVuNvYafHsTe/3pHYkvLK/V5ZGaRzyxNe5UV6FKjClHlpqy8j06NCnSjyU4pLyCiiitDUK/hv/AOD3b/k3b4E/9jLq/wD6SRV/chX8N/8Awe7f8m7fAn/sZdX/APSSKgD6i/4Muv8AlFj4x/7KZqn/AKbdMr+u6v5Ef+DLr/lFj4x/7KZqn/pt0yv67qACiiigD52/ai/ZP/Z8/bN+EWofA39pbwtZeLfDWpYZ7S8U5jlUELNDKpEkEyZO2WJldcnnGRX8d37Qf/Bqz+0h8CtWu/EP/BMn4vRvoLu80fhHxqpKR7mJ2RXcSFG5PGY4Tx8zE81/c1RWdWjCrHkqRTXmdOFxlbDTVShNxl3Tt+R/mv69/wAEy/8Agvb4DuRpWq/s/wCl+IZVyBdaTrdr5Lc8MAZiV/HmvRvAX/BFX/gvT8dLyGw1vw94Q+EGl3ICS3up6jHeXKA9SI7cznPTAKDnvX+it5UWc7R+VOVET7igfSuCOTYNS5lTX9eR7s+MM3nDkeIdvLR/etT+ZD/gnj/wbG/spfsueNrP4/ftWatcfHT4k2zJPDd65EF0iymRt6tBZMXEjI33GnZkU4ZI1YA1/TgFA6d6WivSjFRVorQ+cqVJTk5zd2wooopkEcnQfUV/k5/8EY/+VnLwr/2PfjX/ANJNUr/WMk6D6iv8nP8A4Ix/8rOXhX/se/Gv/pJqlAH/1/7+K4D4r6TqmvfC7xJoehzrbXt5pd5BbzNIYljlkhdUYuOUCsQSw6da7+vnT9qr4ifCr4f/AAM8Sn4s+JtO8MWOp6Vf2cdxqFxFDvd7eTKxLI6ebJtyRGpycUAfw0/BD/g3p/aT8WfCrSvEH7QP7ez+D/E9ysh1DSdO1yTVba2VXITbcnU4BIWjAdvkUKTtBOM1+gn/AAaO+GvDvgK0/aq+G7eLT458S6B8QYbTU9dt5Bc2GpwxJcRQXtvcFmklNyyTO+9mwNpBO41+TP8AwTm/4IG/8ESv2sP2SPCfxz+Jf7Res2mtau12l3DLqGk+Hzut7h4gFsrtbieMbVGGaVt+d4wCBX9MH/BvzN/wS/8AA3hn4y/sw/8ABNLTtbktfhr4njsfEviHXJYbqTXbx1liiuYLmFikluq27rFsjiTb86p85ZgD+iOiiigAqOX/AFZqSo5f9WaAP8njVP8AlbPX/s4iH/07LX+sSvT8TX+Ttqn/ACtnr/2cRD/6dlr/AFiV6fiaAHUVl63Nqtvo13PoUcc16kMjW8czFI3lCnYrMASqlsAnBwOa/L9f2w/2k/hZJ468Y/tBaT4eu/Cnwxmgg8RL4Xivp9TH2qxt7pZbWKbCzKks/lunDsi+YoyQlAH6qUV8bL+2v8P7S6u9N8TaB4g0a807UdB068t721iD2x8RMUtZZCkzqIlYETMGJjPUcVofAD9tb4I/tK+LNQ8GfDO5uGu7Wy/tO1e4jVY77T/tElr9qg2OzCPz4mXbKI5MbW2bWUkA+uaK/H74a/8ABRn4k+LfC/hr/hNNH0Xw5ry+KpdO8RRzS3Elo3h0NqHk6tYyAKdkq2Eiss3+qlV435Cl/dPCH/BSz4B+P7O3i8F2GuaprF/c2ENho1taxSX95DqdvPd29xGom8tYTb2s8jmWRHj8oq6K5RWAP0Nor8+/C/8AwUc+DXieLwNfyaF4k0qy8fNYx2FzqNjHbLDNqlxLa2cUyNN5oeaWEqRGjiHennGPdXHeFv8AgofDrnw38O/FzxT4Q1zw/b6lovirWZdFFpHfXs8fhqQJJHDLHcIizFAZEiZCZRlQQVG8A/TWivz68Yf8FJPgL4N1O50e9sdanni+zfZ/Kt4VS887T01SX7PJLMiOba0liecEghpY0TezAV9t+BfGvhr4k+CtI+Ifgu6F7o+vWVvqNjcqrKJra6jWWJwGAYBkYHBAIzzQB1Vfw3/8Hu3/ACbt8Cf+xl1f/wBJIq/uQr+G/wD4Pdv+TdvgT/2Mur/+kkVAH1F/wZdf8osfGP8A2UzVP/Tbplf13V/Ij/wZdf8AKLHxj/2UzVP/AE26ZX9d1ABRXh37SPxjm/Z/+B/iX4yQaV/bZ8OWT3zWX2lLPzUjI3jzpAUTCknLccYrwPTf24vDnhnSbvxB8etOg8L6d97TrzSr7/hJIL2KGEz3j/6BE0kK2SjNw8qLGqkMGINAH3bRXw7L+3F8OfC/jvxX4b+I00Flp2lXmm2+i3enefqcurJf6e2oEx29vC0haKGOSV/L8xRCvmFgMget6X+1X8A9b8eWHw20jxFDcarqccL2qJHKYpHuLb7ZFEJ9nkieS1/fpAX80xfOF280AfQ9FfAeoftt6roHxg1bwF4k8HCDQ9J8Wab4QbVYtXtpbh7rV4oZLaUWJCymItOiyBWZ0UPJtKRuy99Z/t4fsrajpF3rOm+LI7lLeWzhijitbl5r1tRlaG0NlEIvMvFuJUZIXt1kV2U7SaAPr6ivg7xB/wAFBvgd4chk143cur6PLd+GLG1fSrS6ubvzPFErxW8s0HkqUhyh+ZWdsq6MquArd5pn7ZHwWj8T3nhHxTrtpZ3yajrllaxRJcuWXw9DHPe+cWhRYpYY5N5TLBoyrIzAnAB9a0V8xfD39sf9nP4peI9H8LeB/EiXt5r9vHcWCm2uIUmMtsLxYg8kaotwbUic27ETCL5ygXmvp2gCOToPqK/yc/8AgjH/AMrOXhX/ALHvxr/6SapX+sZJ0H1Ff5Of/BGP/lZy8K/9j341/wDSTVKAP//Q/v4r8xv+CpP/AASz/Z4/4KrfBXTPhJ+0Je6rp9t4dvpdY06fS7hLfy7028kCtNvjffEocsyjaTj7wr9Oa84+MXg3UfiL8JPFHw+0e6Fjd67pF9p0Fyc4hkuoXiV+OflLA8c8UAf53P8AwTn/AOCOn/BAT9pnwZ4y+H2ufGHxX8SfH/wr0nU9e8V6l4Zgl0nSXsLOZ1D2a3dtKZQqALlZD5pO5QFIr+sz/gg38Jv+CVHw8/ZN1DxF/wAEo9WfxB4X17U9+tajfTSy6q2oQRKBBeJOkTwtDGw2RCNE2vvXdvLH+UL/AIJ4/wDBWrRP+CEvwf1r9gT9sX9lHU1+ImkXd/aXmsaXbwRSa/b3ErOi3Us8W6eHB2RyRvLE8JUhAc5/cf8A4NYf2dPjP4C+GPxz/an+I/w//wCFU+HvjV4wTWfC/hPyZLZLDTYBOwMMMgVktz9oEUJZVLpFuChCuQD+reiiigAqOX/VmpKjl/1ZoA/yeNU/5Wz1/wCziIf/AE7LX+sSvT8TX+Ttqn/K2ev/AGcPD/6dlr/WFWQY6N37UASsAylT3r458dfsbeGvH/h/4peHNW8Ua/DD8Vbqzur54JbdXsDZxQwqtkTAQiskCBvMEh6kEE19h+YPQ/lR5g9D+VAHxf8AFv8AYk8G/Fvx8/j+88TeINHmvJNFuNSttNmt47bULjw/O09m86yQOcAu6TRoypMhAYfKpHe/s/fsxeFf2cob/SvBGp30ulTKIrKxuUtfLsYVd3EcckUEc0oUuQrXEkrBAq54yfpPzB6H8qPMHofyoA+CbP8A4J0fAiXRfA2neLLjVdeuvAV3q9xaXt5PGk93Dra3K3dreeTHGk9s32p2WMqNjBGUhlyeX8SfsS6z4F+GGk6T8F9Z1HUNf8LahBdeH7i7u7PS5LGJLWSyZPOt9NlWcNbysshuYZXk4berqrD9HvMHofyo8weh/KgD8qvh7/wS38JaLoHgS58ZeL9al1/wnY+HluJ7GSEw3N/4dumvIJQ1zDLMqNNI4nRHjS4GGlUvzX0T4J/Yo8EeBvEfhbVbDXtZudP8Hy6+dP0q5e3ks/s/iFw89vLmHzJI4myIdz7lU7WLACvszzB6H8qPMHofyoA/PvTf+CbnwM8LeA9C8HeBb3VtLuvDmp6pqdjqsj29/eK2sH/SYnF7BPBJDsEccStETEsMQQjZX3vo+mpo+k22kxyPMtrEkQeTG9gihcttAGTjJwAM9AKu+YPQ/lR5g9D+VAElfw3/APB7t/ybt8Cf+xl1f/0kir+4zzB6H8q/hz/4PdNzfs6fAptpA/4SXV+o/wCnSKgD6i/4Muv+UWPjH/spmqf+m3TK/rur+RD/AIMvCV/4JYeMWKnB+Jmq84/6humV/XX5g9D+VAHhn7Tfwbvv2gfgP4p+DOm6pHos3iSwew+2zWgvo4VlI3lrdnjEgKgjaWA55z0r53/aS/YdX42eCtE8C+CNetPBum2FvqVveWFrpatp90+p2wtzc/ZopYALi3ILwF2kiBdhJHJ8pH335g9D+VHmD0P5UAfkvqv/AATY8dX3hzUPD4+IGnypeW2i28Xn+H+bd9H0w6alxFJHeJNFcDJnikikj2N+7YPGSK9f+GX7A+jfDf42r8WLjVbPxGZrm31W7n1jTBcau+qwafFp7XMd4syxxGZYllfFsXV2dUdY32j9CfMHofyo8weh/KgD4+8Hfse+C9N+LPxE+LPjyPTNf1Dxvfpc2s/9mJb32nWwsUsGgW73vI+6NWIdfLI3sOQa+eZ/+Cd/jC/8N6do3iHx1Za4/hrR7Lw3okeo6Cj2q6TZypNtukiuI5JLmRobf/SYJLcxGENEqF3z+o/mD0P5UeYPQ/lQB+Z2ifsD/EXQ9En0+T4nTarexWXg2OzvtR00S3H2zwfePeJLcss6+fHcs5jePCMiBSJC4LHS8ef8E/7/AOJvg3xd4Z8SeOJ7O78S+M38VW+oaZYrbT2drcwxWd3p/wA8sgkjubNJLeSU7W2ykgZVa/R/zB6H8qPMHofyoA+FvBX7EHhbwJ+0nqXxy0ltKns9S1WXXhb3el+bqFrfy2a2TfZrzzwkUWxcgfZzIAzIJBG22vuwAAYHQUzzB6H8qPMHofyoAJOg+or/ACc/+CMf/Kzl4V/7Hvxr/wCkmqV/rEu+cAA9R2r/ACd/+CMisv8Awc5+FlYEEePPGuQev/HpqlAH/9H+/ivgD9uj/gqF+w5/wTh8PWmtftceO7Xw3c6nFJLp2mRxyXeo3oiIVjDbQK8hUMQpdgqA9WFff9fhN/wWM/4IS/BH/grvJ4b8X+LfF+s+C/F3g+0uLTS76xSK6szHcOJStxayANJ+8AIKSoR3zQB+Cv7WP/B2N8X/AIs/2X4e/wCCc37P91c2niHWIvD2ieLfHUGbabU7tgkMNvBERAsrE7h5l4cDlkABr9k/+CA/gn/gqn4V1f8AaA1b/gqqNZPijWfFFjc6c99cLPprRfZ5BMNNWJmgjt1OxdsIVBgDqDX8+H7d/wDwTa/4OMPhJ4Q+HnhbxPfWH7TPw4+EHirTvFuhJo0UUWqQy6YB5cMlvshumiKAqUjM5UtkGv6Pv+CFH/BV74/f8FR/+F1al8evAth8O7jwD4hs9LtNFhSdb+1SeGRpIr4z7WaZJIyMiKLHIKZGaAP6AaKKKACo5f8AVmpKjl/1ZoA/xp/+Cl3wu+Knxu/4Ls/GH4P/AAOge68Y+J/ivqWmaJDHcJaO9/c3pSBVmkdEjJcjDs6hepIr7Q/4h2/+DjL/AKE7Vf8AwtNN/wDljS6p/wArZ6/9nEQ/+nZa/wBYhQuOnc0Af5O3/EO3/wAHGX/Qnar/AOFppv8A8saP+Idr/g4y/wChO1X/AMLTTf8A5Y1/rF4WjC0Af5On/EO3/wAHGXT/AIQ7Vf8AwtNN/wDljR/xDt/8HGQ/5k7Vf/C003/5Y1/rA3t3Z6dayXt66xRRKXd3IVVVRkkk4AAHJJ4A61/Kh+3l/wAHSPwI+E/jq/8AgJ/wT/8AC0vx08bWTtDcX9rIYfDtpIuM/wCkr81yBzkxlIT/AAyseKmc4xTlJ2RpSozqzUKcW5PZI/kt/wCIdv8A4OMun/CHar/4Wmm//LGj/iHc/wCDjIf8ydqv/haab/8ALGv0L8e/8FYv+C9Hxznlvb74qeH/AIV2jvui0/w7pUMzRqexlmWSQ46f6w1xPh/9v7/gux8P9QGs+G/2mRr0kWcW2s6PavbyN1wcxMcfSvMed4JPldRfj+ex9JDgzOJQ51h3b5J/c3f8D4r/AOIdv/g4y/6E7Vf/AAtNN/8AljSf8Q7X/Bxl/wBCdqv/AIWmm/8Ayxr96f2dv+DpT9qX4A6raeHv+CnXwog1Dw9JIsT+LvBXymIMQPMltJGKP15w0PTgE8V/Y1+y9+1b+z5+2Z8INN+On7NXiiy8WeGdTysd3ZscxyqAXhmjYCSGZMjfFIquuRxjmvQo1qdWPNTkmvI8DF4OvhpuniIOMuzVj/L8/wCIdv8A4OMv+hO1X/wtNN/+WNH/ABDtf8HGX/Qnar/4Wmm//LGv9YvC0YWtTmP8nT/iHa/4OMv+hO1X/wALTTf/AJY1+dv/AAUO/wCCaH/BTj9hHwn4b8S/t66JeaVpfiC8uLXSWuddtdXD3EKK8oCW91OY/kI+YgA9Mmv9p3C1/Df/AMHuwA/Z2+BOP+hl1f8A9JIqAP5Iv2AP+CUn/BWX9uD4MX/xb/YY0C+1TwhZavNpdzLbeIbTSUGoQxQyyL5M93A7ERyxHeFIOcZJBA+4v+Idv/g4y6/8Idqv/haab/8ALGv6nf8Agy7AP/BLHxjn/opmqf8Apt0yv67sLQB/k6f8Q7X/AAcZf9Cdqv8A4Wmm/wDyxo/4h2/+DjL/AKE7Vf8AwtNN/wDljX+sXhaMLQB/k6f8Q7f/AAcZdP8AhDtV/wDC003/AOWNL/xDt/8ABxl/0J2q/wDhaab/APLGv9Oj9rn9sr9m39hn4P33xy/ae8U2fhbw/ZAqstwS01zNgkQ20CZknmYAkRxqTgFjhQTX8iPxt/4OXv24P2m9QutN/wCCaPwktPDfhdXZLfxb44O+WcA8PDbRkRJkYIAM4P8AeB4rKtXp0o81SSS8zGviaVGPPVkkvM/AT/iHc/4OMjz/AMIdqv8A4Wmm/wDyxo/4h2/+DjLoPB2q/wDhaab/APLGv0i1v9q//gux45u21zxF+1ONElk+Y2ukaLbJAh9Fwi8A+1dl4K/4KY/8F/fgDIl9YfEnwz8YbGBt8um+INMitZ5QMEKskKxMCRnrIuDjrXnRzzBOXKqi/H/hjy48Q5fKXKqq/Ffi9D8q/wDiHb/4OMj/AMydqv8A4Wmm/wDyxpf+Idv/AIOMv+hO1X/wtNN/+WNf2BfsF/8ABzf+z38bfiDZ/s7ftzeG5/gR8RLl0hgfU5d+g3srsVGy7YD7PuP3fN3RdB5u4gV/UJFJFMgkjwQa9SE4ySlF3R7EJxmlKLuj/J4/4h2v+DjL/oTtV/8AC003/wCWNL/xDt/8HGX/AEJ2q/8Ahaab/wDLGv8AWKwtGFqij/J2X/g3c/4OMwwI8HaqDkf8zrpv/wAsa8W/4N3PDHijwV/wX2+D3g/xtG0Ws6TrniKzv0aQSlbm30vUI5QXUsGIdSNwJB65PWv9e6QDA+or/J0/4Ix/8rOXhX/se/Gv/pJqlAH/0v7+KKKKAEKg9a5Dw58PfAPg7V9W8QeEdE0/S7/X5xdanc2ltHBNezqu0SXDooaVwvAZyTjjNdhRQAUUUUAFRy/6s1JUcv8AqzQB/k8ap/ytnr/2cRD/AOnZa/1iV6fia/ydtU/5Wz1/7OIh/wDTstf6xK9PxNADqQnAyaWvyz/4LQ/tq3/7An/BNn4o/tFeGrgW/iOz0wadoLdWXVNTcWtvIo7+SXM5B6iM0Afy0/8ABez/AIKpfEj9t/4+69/wS6/Y98QNpHw38JObb4j+IrF/3mo3SMVk06NlP/HvEwMci5/fShg2Y4yJPy8+G/ww8C/CTwynhP4f6fHYWwwzsBmSaQDG+R8ZdmxyT26cV5h+yR8MW+FnwT0201Pe2t6wP7U1SaU7pJbi5G4h25JKghfrk9819MsHbEqAoc8dCpJ9v5V+c5zmc8TWcIv3Ft5+f+R/QnCHDlLLsLGrOP76Su31V/srtbr3fyIkRtzK5AJIJPOffB/+tipPItnm87cUU5XCDOPTIH/66+rP2Tf2LPj3+2p4pv8Aw38DtNguYNJ8r+09Sv5jbWFmZQCsckoSRmmZfnWGNGbbgsUUqT9R/tL/APBHv9sj9mjwJdfFO9h0nxXoOmQtPqMmhyzNdWsKcl2tZY90kaDJd43LqBkRkZx58MDiJ03VjTbj3t/X4Hu1s5wNHELC1a0VUfRvXXb0v0va/S5+Ut7ZWd9E8d7GssTja8cg3Iy9DkHIP0rx79n749/Gv/gjj8fY/wBrj9k77Re+BL2aJPHHgxZGNjeWRbmSNefLePJMMigtAx7xF0Ps3kRzMkscnONy4b5Wz0OehB7diKh1S0h1i0k07U0EkM6PHLHtVUdWGCMdM4J+tXgMfUwtRTg9Oq7oxzzJMPmeHdGstfsy6p/5d11+5n+jp+zR+0V8K/2s/gR4W/aM+Cmo/wBqeGPF1hHqFhORsk2Pw0ci5OyWJw0cqfwupHavdK/iV/4NQP2idc+GHxR+M3/BMDxdeNNY6AyeM/CiyvkpZXLpDdxrkk7QzwPgcbi5xk1/bVX6fSqRqQU47NXP5sxWGnh606FRWlFtP1WgV/Df/wAHu3/Ju3wJ/wCxl1f/ANJIq/uQr+G//g92/wCTdvgT/wBjLq//AKSRVoc59Rf8GXX/ACix8Y/9lM1T/wBNumV/XdX8iP8AwZdf8osfGP8A2UzVP/Tbplf13UAFfNX7X/7V3wf/AGIv2cPFf7UHx1vxYeG/CVk11PtI82eQkJDbwgkBpp5WWKMEgFmGSBkj6Vr+G3/g58+Oer/tJ/tlfBf/AIJi6HcufDGmwt478XxQsQs2C8dpFJyPuRq/Azzcq38IrKtVjSpyqS2SuZV60aNOVWeyTf3H5DeKvFvxr/4K1fHN/wBuz9ug/aNEuQ6+CvBYZn0zTNMZsxnYcB94AZ2KgzsN7/KERPsiGJEiFvbKI4kQIEUYAVemAMfpVqBLeCJLeyjCwKqokagKEVeAqjGAAOKSZmt0ZmY4JUFT13OQoGee54r8ux2Oq4qq6k36Lsfj+YZhVxlV1Kj9F0SJEEe3dJtwQAPT3J/xoSCKBCmWZgcY/hwfQ1+0tj/wQx/aNvrOK/i8feF4vNRXCNp94xQMAdpIuACRnkgDPpXzP+1l/wAE1/i7+x38NrT4lfEHxXomuWV3qVvpkdvptpcQTLJcbiGLSyyKVG3kYB9KqplONpwdSpRait3eP+d/wLq5Nj6cHUqUGorVu8dvlK/4H5HfGj4B/C74+eEJfA/xN09by3f54Zwdlzbyr0eGT7yMD2+6wyCCCRX6Kf8ABBn/AIKgfFj9lT4+6V/wSb/bX1q41jw7rShPhj4pvck46Jpk0hJ+RvuW4Y7oZMRDMckYT5Ntka3zLbuGV/vBhuwD0wD0+tfGn7cfwo1X4hfBS58WeEJpbXxR4LmXXtGuo2KTRTWh3EKVIIJUcc/eAPau3JM0nh6ypyfuN/d5/wCZ38P5vPC140pP93J2a7X6/wCfkf6gSsGUMO9LXwD/AMEtf2vI/wBu39gD4XftSysjah4n0WI6oqEYXUrQm3uxgdB58bkDsCK+/q/Rz9TI5Og+or/Jz/4Ix/8AKzl4V/7Hvxr/AOkmqV/rGSdB9RX+Tn/wRj/5WcvCv/Y9+Nf/AEk1SgD/0/7+KKKKACiiigDxP9ojT/jRqPwc12P9nnU4NK8ZRwCfSpLqCO4glnhYOLeRZCqqtwFMPmZzEXD4bbtP5q2//BRnxlYiP40NpF/e+GviBNcWvgrw3e2w066hi8PWjzatd3cyJPMHluFa2toDHglFfdsmBT9k3QSIUbPPpwa+W7b9jT4B2Hwz0T4UaXpt7ZaZ4aubm70qW21S9ivrOW8MnnmK9WYXKiUTSK6+ZtZWKkYxgA8z+GX7a998Sfjvp3whk8FXvh+21SyivLefWbhbK/kjlsluzJHYyIDLCjMbaQxytLHMp3xCP56+75f9Wa+eNK/ZU+CWj/FW2+Mlrpc765ZSyXVs099dT28F3Nbi0luY7eWVoUuJLYeS8yoHdCcklmJ+h5f9WaAP8njVP+Vs9f8As4iH/wBOy1/rEr0/E1/k7ap/ytnr/wBnEQ/+nZa/1iV6fiaAHV/I3/weE67eS/sLfC/4cElbHxH8TtMhu8fxJFa3BAPtl846ZFf1yV/MN/wdp/BbXviP/wAEorz4l+F4DLefDPxTo/iVmUZZYNz2chwOytcRsx7AE1M03FpGtCUVUi5LS5/M9+6ij2bQqx4XbjkbeB09OMVJZ5WdQCcM2d2MEZ6j8fcVheEfFNr408K6V4s0iVZLbVbOG7ibpkSqG4PXIrajMkEvmTMHwykhsgDn1/8ArV+RtNOzP6tjJSipRejP6k/2KfEepfs5f8EGfFPxx+HE32TxDNpfibX/ALYoHmLevNKkcgJB/wBUoUJnOAornf8Ag3m+LHxD8Z6N8Vfg78RvEGr+KbDTn0rUbdtcvp9Rlja/jmguEWS5eSQJIYBIY920OzEAbiK6b9iTw1e/tL/8EJvFfwC+HqLceIYtN8S+HmtEIDfbfNkkjQA8jzUZChOMhgehrN/4N/fgn8UPhrpHxU+MXxa8Pan4TstUOmWFsms2klhK405Z5biTyplSQJG0wj3kBWZW25AyftcJ7X22CUL8ns9e3w6/jY/G8yVD6lnDq29r7bS+/wAeluu3Nt0ufzIeNND07wf8TvFng3SGEdhoHibX9JtYQoCx21jqVzBCigAAKkSKgHYAAVz7Hzm+7twNpTGARxzj8K3fF/iHTvGHxF8XeOtIfzbDxB4l1/V7V1IZWtb/AFG5ngcEdQ8bq4I6hgazWiIRpgwJPygdif8APrXxte3tJcu1z9jp35VzbnsH/BHjU7rwn/wcKfDJdOyieJfBut2F2qn7yQ2s0wJ9t8Scewr/AEeByM1/nrf8G/8A8OdQ+Nn/AAXR174qWCFtJ+EPga4gmmx8q32pFYEjB6ZKSzH/AIAa/wBCmv0rKYtYOkpdkfzfxXUhPNsTKG3M/vWj/EK/hv8A+D3b/k3b4E/9jLq//pJFX9yFfw3/APB7t/ybt8Cf+xl1f/0kir0T54+ov+DLr/lFj4x/7KZqn/pt0yv67q/kR/4Muv8AlFj4x/7KZqn/AKbdMr+u6gCG5cx20ki9VUn8hX+dL+21qEvjT/g4G/aP8TawC0vh/StE0m1BAwkDWlsDgenyAnHXNf6LzoJEMbdGGPzr/Pb/AOCs/gu/+AH/AAcJa7q+qKbXS/jX4LsL+yYg7JLuyjW3dd2fvA2kpI/2h615edRlLBVVHt/w55GfQlLAVVHe35b/AIGfLtJ8vOGOOQK6rwPolz4r8e+F/CkA+bVNf0m22AZ3CS7i3DHP3hke1c03nzw/K4Hrjgj649q7n4T+HNR8afFbwv4N0rWYfD17quqWlpa6vLvxZXMr4hmwrKflfbgh1OSORX5lSV5xVr3aPyair1Iq17tfmf1af8FBf2MvHf7Wuv8Agm18P/Ej/hX2l6GL5GjhluYp765uQiRKvk3FuCsaqTgljkjAFfhX+3P8F/2yv2VvAnhn4EfGPxzN4r+Gz3rT6FPhXIvYvMmMc0k6vd+aqs8iK88iFQ2MBMU7/goD+wX+2l4b1nwzF491bX/jZcz21zFpOpWtvcStpl8WXbEmHdoJZGw6XLFQCv31wK/T7/gsDc3ui/8ABPjwP4Q+IV2j+LrnWvD0RYsC8t1aL5t66nuBEkpYjqM+uD9hjrV44qo6cqc4pfafvafDZe706X3vvdH2+YWxEMZUlSlTnCK3k/e0+Gybjra2jervvofzM203lwc4nI56A9R1/AelUdTsYr7S7m0uF82OSGVGB6lXU5GfxrStgvlhDxxxn0GPwz9a4X4t+MLD4Z/DPX/G+qSBbfSNOuLh26kFUO0DPXJPbrXx0IuUlFbs+GhFykox3Z+8v/Borrd7df8ABLDVPCVwzNB4b+IGv6fbEnIEJFvNge2+Rj+Nf1H1/PF/wa4/BHXvg3/wR68C6t4oiMN/471HVfFToww3l3s5jhJGB9+GFHHs1f0O1+wx21P3CN7K5HJ0H1Ff5Of/AARj/wCVnLwr/wBj341/9JNUr/WMk6D6iv8AJz/4Ix/8rOXhX/se/Gv/AKSapTKP/9T+/iiiigAooooAKKKKACo5f9WakqOX/VmgD/J41T/lbPX/ALOIh/8ATstf6xK9PxNf5O2qf8rZ6/8AZxEP/p2Wv9Ylen4mgB1eU/HT4M+A/wBon4NeKfgT8ULQXvh7xhpV3o+ow8Za2vI2ifaSDhgG3K2MqwBHSvVqKAP8pPRvhZ8SP+Cf/wC0t4p/4Js/tFOV1rwjeSyeGr51KRavpE5MkM0DMcEMnzqoJKnfGQGjYV9I8svyurEnOByv1x14/nX9p3/BYX/gjv8ABj/gq58HLbTtUuf+EU+JfhYPP4U8WW6Ez2c2d/kThSGktncAkAh4n+eM53K/8E3xek/ax/4JxfED/hR//BSXwVeaDcA7LHxbYRtdaRqyDJ82OWNdrkgAnbh1ziSOM8V8bnWR1HN4jDq9911v3X9f8D9g4O4zoqjHA4+XK46Rk9rdn2t0e1t7W1/RH9mT9rr9oj9jfxdfeNf2ePEEemyaqkceo6dfwfbtMvfK4R5rcPEwkQZVJYpEfadrb1Cqv0p+0d/wVv8A23v2nfAl78LPGOsaX4f8OalCYb+28O2Mtrc3kTZDRyXU08jLC44ZIkRm6GTGQfys8K/FT4d+OLNdT8G61ZapbnjdBOjkH0wDkEd8it7Uda0Syie81O9ggjUFpDLKo2j6lsYr5+OLxVODoKTS7H3tTLMtxFVY2VOEp/zWT22d9nbo3t0LoWJG/dqeR0JxwBwAMYHpgV4t8dvjLo3wL+Hlx4n1NRc3kgMOn2qtta5uT9xV/wBleCx7AfhXnXif9rrwrqfiS1+GPwB068+J3jHVJFt9P0rw/DJdNNK2do3RKxPI+7GGb271/TR/wRw/4N8filD8VdG/b8/4Krx2954u0zy7rwr4DQrNZ6LIpDx3F5gtG9whw0cKlljYB5GaQBY/RyvI6teanWVoLvuz5ziXjTC4KlKlhZqdZ6K2qj5t7X8vvP0i/wCDcv8A4Jw+K/2D/wBiqbx18bbVoPij8XrtPEviNJQBNaROp+x2b8DDxI7SSKQCkkrIfu1/QbTVUIu0U6v0BJJWR+Dzm5ycpPVhX8N//B7t/wAm7fAn/sZdX/8ASSKv7kK/hv8A+D3b/k3b4E/9jLq//pJFTJPqL/gy6/5RY+Mf+ymap/6bdMr+u6v5Ef8Agy6/5RY+Mf8Aspmqf+m3TK/ruoAK/mv/AODlj/gnX46/az/ZV0X9p/8AZ3tWm+KvwIvJPEGlxQIWnvtMO1r61VUwzuBGk6JkltjooJkr+lCkIyKUkmmnsKUVJOL2Z/mq/s5fHrwf+0Z8LrL4ieFJo1efEd/b5+e0uwAZImHBGOqEgBlINe33UCXEZjlJKN3BKtxyMMuCNp5yDkHkHNffH/BWn/ggZ8aPhn8ZNW/b7/4JK2MD3+sM9z4v+HGRFbakcmR59PXKpvLZc2+VYMWMBO7yT+F3w2/b5+Ems6pJ8PvjNDd/DLxnp7eRfaN4ija1aC4XG9BJKqDILY2uEfHVa/PM0yKrh5udFOUPLVr1/wAz8wzfh6vhpudGLlT8t15P/M/fr4b/APBXD9un4ZeGYvCP9t6P4ntrWMR211renSS3yqBx5k1vPEs+OxaNXI+8zHmvkL48ftG/Gv8Aag8XQ+Ofjzrp1e+t4nhtIbaAWdjZxPgutvBucrvIG92d3cgZbACjwqy8S6Jqdr5tjeQSxNz5kMyEbfXKnBrg/HPxm+EnwtsDrHj7xNpmkx8hDcXKKXOM7VXcWJ4zgDPpXnVMZi60VRlKTXbV/wDDnl1Mbja8VQlOUl21f/D/ADPRSuQFiGArcZO4n257mvknW/hV46/4KVfta+E/+CaPwL3GLULpNR8b6rEwaLS9ItGDyh8gjcoIOCRmRo485eqPwj1r9sr/AIKcePX+CH/BMzwdeT2ZkEGq+O9Wiez0nTInUEv5jqdrAHKghpmwfLibqP7nv+CSP/BI74H/APBKf4ITeEPCE7eJvHfiQpdeK/Fl3HtutTulyQqgljFbRszGKLcxyS7lnYmvosjyOcZrEYhWtsuvqz6jh/h6pGosTila2y637vtbt/T/AEz+Gvw+8J/Cb4e6H8L/AAHaLYaJ4csLbTNPtk6RWtpGsUSfgqiu3oor7M+8I5Og+or/ACc/+CMf/Kzl4V/7Hvxr/wCkmqV/rGSdB9RX+Tn/AMEY/wDlZy8K/wDY9+Nf/STVKAP/1f7+KKKKACiiigAooooAKjl/1ZqSo5f9WaAP8njVP+Vs9f8As4iH/wBOy1/rEr0/E1/k7ap/ytnr/wBnEQ/+nZa/1iV6fiaAHUUUUAFcD8SPhZ8N/jB4Su/AXxU0HT/EmiXwxc6fqlrHeWso/wBuKVWQ49cZ9676igD+ef41/wDBrv8A8Ec/jRrEmuL8OLjwlcTEl28OanPZoSe4icyxL9FUAeleR+EP+DR//gjz4X1OPUtQ0HxPr6IwP2fU9cfymx2PkRxNj/gQr+nSilZFc8u58ffst/sB/sbfsV6S2kfsufDnQ/BayLsln0+2H2yZOPllun33Eg46NIR7V9gKqqAqjAHQClopkt9wooooAK/hv/4Pdv8Ak3b4E/8AYy6v/wCkkVf3IV/Df/we7f8AJu3wJ/7GXV//AEkioA+ov+DLr/lFj4x/7KZqn/pt0yv67q/kR/4Muv8AlFj4x/7KZqn/AKbdMr+u6gAooooARlVhtYZB9a+Lf2qf+Cdv7FH7bFkLX9qH4a6H4xlVPLS7vLcJfRpjAVLuIpcKvsHx7V9p0UAfzD+Kv+DR3/gj34l1N9RsdF8U6IjsSLfTtcYQr7Dzo5Gx9WNe5fA3/g2K/wCCPHwO1aPXIvho/iu7gIaOTxJqE98oI9YVMcT+4dSK/oGopWQuVHG+BPh34E+F/hi08E/DjRrHQNGsF2WthptvHaWsC+kcUSqij6CuyoopjCiiigCOToPqK/yc/wDgjH/ys5eFf+x78a/+kmqV/rGSdB9RX+Tn/wAEY/8AlZy8K/8AY9+Nf/STVKAP/9b+/iiiigAooooAKKKKACo5f9WakqOX/VmgD/J41T/lbPX/ALOIh/8ATstf6xK9PxNf5O2qf8rZ6/8AZxEP/p2Wv9Ylen4mgB1JkHpUF1E08D26SNEZAV3pjcuQeRkEZHUZBr8TvgL8b/2iPDf7P1j+0V478ReJb+8MMemjT/FkdhFpmq3uo6itpbzWg0y1e+LwopdLdIjJc+YEVdxDAA/bmivxh0P9sf44+KdXtvjN4Y8N3+oXw8GeJLeTwlDcyJZ3Or6D4hg0vz4lmiSUOQ0riMqJ3QiHYZQBW/N/wUl+JV3LpPhvwJ4CPi/xBFZXupavaaKl7MTFZ6k+nfZ4Ue2SW2vGaKV5Yb5IVtmQRSPvYEAH6/0V+ZP7SPxy+M3wd/ay0jUdN1Gaf4a2vhSW58VaYtsrvaxSX0duNWtpljaRWsVcvcxMWR7fc6qGj+bwb9n/APb0+MbeFvh/4Kt9C1T4mXZ8PeH9b8R63DC0s00PiSa48uWA2lutoqW8MDTM0rRLImEjLSBqAP2vor8c7b/gon8cri6s7288J+G9P0rxJoNrruhSPq11c3TrqWpJY2kL21vaSSSzGMmbyrcO0rHyowSrMKlp+3B+0L8TdNtvEHh7RtK03R5Ph7421XUbB7m5hvxrHhjUl00m3m8gtFC5DtHvj81d/wA67k2kA/ZeivymP7efj3w74xsfhleeGrRbu6k0jUbJLq9me7n8KzaXJf3+osohy01rJby22xRskmMYZkMoFe5fsY/tU/Eb9pvSJ/EXjDwNeeGdMu9N0zWdKvXjnW3nt9UjaQW++eOLzJ7ZdnmyQh7d948tzg0Afc9fw3/8Hu3/ACbt8Cf+xl1f/wBJIq/uQr+G/wD4Pdv+TdvgT/2Mur/+kkVAH1F/wZdf8osfGP8A2UzVP/Tbplf13V/Ij/wZdf8AKLHxj/2UzVP/AE26ZX9d1ABSZFZus21/eaVc2ulXP2O5kidYpwgk8p2UhX2tw204ODwcYr8eNU8YftNfASw+MvxQTx7rXxA/4U69u8WiawljaWmswy6NbXUyNNbWSyQSfaJmkjkUsqYEbDaSwAP2aor835v2wfi/4U8XX/w+8f8Ah3Rf7V0jxX4W0K7bT7+Z4WtfEg/1sXmQI7SwHHyFQJACQQeK2P2Pv2wfH/7VV7PqF94H1DQPDGqaX/auj6u0UyR+W07wrbyvPGkb3JjCXANsZYgrFS25fmAP0HyD0pa/Cb4EftOftVeFPhrp/wAXfELeIvFulyWk+n6gfFcNjplnLrV7rEWmaSLKeCKKRYmMjNdSOjxrCFdR5h2t7v40/b7+MngC9n8P+JvB2kW9x4X1e50zxPqp1Cd9GgeOKwuLcLPHbySWwuYL4N593FHbwSQtHI+XjJAP1ior8q/BX7Rn7RGt/HLRPh3oNnpyaXez/ERNVj1C6uZ52m8P3ttBaPayLCDHAftAzGynaGIVmKpuxfCP7cfxTsvDfgbwevh6wOt+OvDvgrUPCkOo6lcTT6gNXITV/tEotlJfTI8Tu6ITKjqzCPOAAfrdRX59fsc/tleOf2p9Zm1SfwNf6P4S1LTm1XRtXkinSIxrctAtvO8yJG9xJGFuFNs0saoSjMHXn9BaAI5Og+or/Jz/AOCMf/Kzl4V/7Hvxr/6SapX+sZJ0H1Ff5Of/AARj/wCVnLwr/wBj341/9JNUoA//1/7+KKKKACiiigAooooAKjl/1ZqSo5f9WaAP8njVP+Vs9f8As4iH/wBOy1/rEr0/E1/j5/ti/tBaT+yf/wAHGnjz9prxBp02r2PgH403Ovz2Vu6xy3EdhqPnNGjNlVZguATxX9RY/wCD3H9mYDH/AAo7xT/4NLP/AOIoA/t9uLeG7ge2uBuSRSrDpkEYP6V4VD+y9+z9bfBi3/Z5tfCdhF4LtNpt9JRCsELJN9oRo8HcjpN+8V1IZX5BBr+Pj/iNy/Zm/wCiG+Kf/BnZ/wDxFH/Ebj+zN/0Q3xT/AODOz/8AiaAP6yrP9gz9jrToZbex+HWiwxzW1zZuqQYXybydLmdQAcDzLiNJmYYPmqHzu5rpn/ZB/Zpk0rRNGbwbp/keHZp7iw+Vt8cl1KJ52Z92+QzTASy+YzeY4DNlhmv5D/8AiNx/Zn/6Ib4p/wDBnZ//ABNH/Ebj+zN/0Q3xT/4M7P8A+JoA/s6vfhB8M9R+Ja/GK/0W2m8Tppj6MNRdd039nyP5jW/PHls/zFcYJrgNP/ZN/Zv0a48K3Oh+DNLsG8EW0FloQtYfISxtrVt8EMax7V8qJvmiRgVjYkqATX8gn/Ebj+zN/wBEN8U/+DOz/wDiaP8AiNx/Zm/6Ib4p/wDBnZ//ABNAH9OPgv8A4JrfAPRvHHivxn41sLHW08SxRWqWMOnw6dbW0EF5JfxkC32s1wtxIXE4ZSuPlCksW9uX9in9lNPDVt4Og8CaTDpdnHqkUNrFEY4kj1vnUEAUj5Lo/NMh+V2+YjdzX8kH/Ebl+zN/0Q3xT/4M7P8A+Io/4jcv2Zv+iG+Kf/BnZ/8AxFAH9mOgfBP4SeGNb0bxLoPh2wttS8O6O3h/TLtYVM9rpbmNmtY5DllhLQxkpnBKL6VX+FvwL+EfwTjv4PhToNroUepyLJcR2oKoxTdtCqSQiLubaiAKuTgDNfxrf8RuX7M3/RDfFP8A4M7P/wCIo/4jcf2Zv+iG+Kf/AAZ2f/xNAH9wVfw3/wDB7t/ybt8Cf+xl1f8A9JIqvf8AEbj+zN/0Q3xT/wCDOz/+Jr8BP+C+H/Beb4Uf8Ff/AIX/AA98B/DzwBrHg6fwZqt7qE0upXcFwkyXUKRBVEQBBBXJzxigD+o//gy6/wCUWPjH/spmqf8Apt0yv67q/wAtz/ghZ/wcW/B3/gkp+yFrf7N3j/4ba34uvtV8V3fiFb3TryC3iSK5tbS3EZWVS24G3JJ6EMPSv2h/4jcf2Z+n/CjfFP8A4M7P/wCJoA/uBIBGD0NeA+If2WP2evFmn+NdK8SeEtPvbf4jPBJ4mjlQsuqNbKqRG4Gfm2IiqP8AZAHQYr+Pz/iNy/Zm/wCiG+Kf/BnZ/wDxFH/Ebj+zN/0Q3xT/AODOz/8AiaAP6+fGv7JX7NvxF8Y6f8QfHHg3TdU1vSvsZtL2eLdNE2nSebauGzzJA+WikOXTJ2kZOeq8B/AP4O/DDWdY8QfD/wAP2ukXevMzXz2wKCTe7SNhc7UDO7OQgUFmLHk1/G5/xG5fszf9EN8U/wDgzs//AIij/iNx/Zm/6Ib4p/8ABnZ//E0Af2OaD+zx8EPDHwh/4UFofhfT4PBfkTW39jGEPaeVcOZJE2NkYZ2LH/aOetcDL+xP+ylcaXpOjT+BNJe20SSeW1RoifmuZY55hKc5mWWWKOSRZS6uyKWBKjH8kP8AxG5fszf9EN8U/wDgzs//AIij/iNx/Zn/AOiG+Kf/AAZ2f/xNAH9e9l+yR+zdprxS6f4O0+CSDU9R1lJI0KyLfaupS9lDghs3KnEoztfgkZANdF4V/Zz+Bngi08H2XhTwrptlH8Pree08NbIFLaVBcxiKWO1Y5aJXjARgp5UAdBX8c3/Ebl+zN/0Q3xT/AODOz/8AiKP+I3H9mcf80N8U/wDgzs//AImgD+yb4f8AwG+D/wAK/EOq+Kvh34ftNHvtaYveSWylBIWdpDhclUBdmdggUFiWPJr1yv4ff+I3H9mY/wDNDfFP/gzs/wD4mj/iNx/Zn/6Ib4p/8Gdn/wDE0Af2/SdB9RX+Tn/wRj/5WcvCv/Y9+Nf/AEk1Sv6Dv+I3D9mVmUN8DvFWMgn/AImdn/8AEV/Nr/wQQ8e2fxV/4OJfhn8TtOtpLO38ReJ/FOqRwSsGeJLzTtRmVGYAAlQ2CRwSKAP/0P7+KKKKACiiigAooooAKQgEYNLRQB/Oj+0V/wAEE/8Agk38efjv4u+NHxX+E41bxN4p1W51PVLz+3NYg8+6uHLyP5cN8kabmJO1EVR2Arxj/iG6/wCCLf8A0Rdf/Cg1z/5Y1+83iz/kZb//AK7vXPUAfh1/xDdf8EW/+iLr/wCFBrn/AMsaP+Ibr/gi3/0Rdf8AwoNc/wDljX7i0UAfh1/xDdf8EW/+iLr/AOFBrn/yxo/4huv+CLf/AERdf/Cg1z/5Y1+4tFAH4df8Q3X/AARb/wCiLr/4UGuf/LGj/iG6/wCCLf8A0Rdf/Cg1z/5Y1+4tFAH4df8AEN1/wRb/AOiLr/4UGuf/ACxo/wCIbr/gi3/0Rdf/AAoNc/8AljX7i0UAfh1/xDdf8EW/+iLr/wCFBrn/AMsaP+Ibr/gi3/0Rdf8AwoNc/wDljX7i0UAfh1/xDdf8EW/+iLr/AOFBrn/yxo/4huv+CLf/AERdf/Cg1z/5Y1+4tFAH4df8Q3X/AARb/wCiLr/4UGuf/LGj/iG6/wCCLf8A0Rdf/Cg1z/5Y1+4tFAH4df8AEN1/wRb/AOiLr/4UGuf/ACxo/wCIbr/gi3/0Rdf/AAoNc/8AljX7i0UAfh1/xDdf8EW/+iLr/wCFBrn/AMsaP+Ibr/gi3/0Rdf8AwoNc/wDljX7i0UAfh1/xDdf8EW/+iLr/AOFBrn/yxo/4huv+CLf/AERdf/Cg1z/5Y1+4tFAH4df8Q3X/AARb/wCiLr/4UGuf/LGj/iG6/wCCLf8A0Rdf/Cg1z/5Y1+4tFAH4df8AEN1/wRb/AOiLr/4UGuf/ACxo/wCIbr/gi3/0Rdf/AAoNc/8AljX7i0UAfh1/xDdf8EW/+iLr/wCFBrn/AMsa+mf2O/8Agh//AMEvP2Uv2kfDH7QPwC+F40Dxf4dluJNOvxrOrXXktPbyQOfKuL2WJsxyOvzocZyMEA1+lldj4B/5G2y/3n/9ANAH/9k="
-    }
-   },
-   "cell_type": "markdown",
-   "id": "72ec9e05",
-   "metadata": {},
-   "source": [
-    "![conf_matrix.jpg](attachment:conf_matrix.jpg)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "de7143cf",
-   "metadata": {},
-   "source": [
-    "## 5. Interpret the Model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ad2c87a8",
-   "metadata": {},
-   "source": [
-    "Now that we've built a Logistic Regression model and evaluated the performance of the model on the test data, we can now interpret the model's output. Specifically, the model coefficients."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c4f3dfc1",
-   "metadata": {},
-   "source": [
-    "The relatonship between the dependent and independent variables in a Logistic Regression model is generally represented as follows:\n",
-    "\n",
-    "$$ log(\\frac{P}{1 - P}) = \\beta_{0} + \\beta_{1}X_{1} + ...+ \\beta_{n}X_{n}$$\n",
-    "\n",
-    "In this representation, the left hand side of the equaton is known as the **logit** or the log-odds of the probability of an outcome or class $P$. $\\beta_{0}$ is the intercept. $\\beta_{1}$ to $\\beta_{n}$ are the coefficients of the independent variables $X_{1}$ to $X_{n}$."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "54b5daba",
-   "metadata": {},
-   "source": [
-    "To get the intercept (or $\\beta_{0}$), we refer to the `intercept_` attribute of our model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "1dc049cf",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([15.4670632])"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.intercept_"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "46073f5a",
-   "metadata": {},
-   "source": [
-    "To get the other model coefficients ($\\beta_{1}$ and $\\beta_{2}$), we refer to the `coef_` attribute of our model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "a5273286",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[-1.0178107 ,  0.14656096]])"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.coef_"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a47e48aa",
-   "metadata": {},
-   "source": [
-    "The model coefficients correspond to the order in which the independent variables are listed in the training data. This means that the equation for our Logistic Regression model can be written as:\n",
-    "\n",
-    "$$ log(\\frac{P}{1 - P}) = 15.4670632 -1.0178107 \\times \\text{Income} + 0.14656096 \\times \\text{Loan Amount} $$\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "594a64b8",
-   "metadata": {},
-   "source": [
-    "To make our coefficients easier to work with, let's convert the coefficients from a two-dimenionsal array to a one-dimensional array and round the values to two decimal places."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "c89d0a08",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([-1.02,  0.15])"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "log_odds = np.round(model.coef_[0], 2)\n",
-    "log_odds"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f09002c7",
-   "metadata": {},
-   "source": [
-    "Next, let's create a pandas DataFrame using the coefficient values and the column names from the training data as row indexes:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "556a5e49",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>log odds</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Income</th>\n",
-       "      <td>-1.02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Loan Amount</th>\n",
-       "      <td>0.15</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             log odds\n",
-       "Income          -1.02\n",
-       "Loan Amount      0.15"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.DataFrame({'log odds': log_odds}, \n",
-    "              index = X.columns)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ea1dc32f",
-   "metadata": {},
-   "source": [
-    "The first coefficient tells us that, when all other variables are held constant, a $\\$1$ increase in a borrowers income decreases the log odds that they will default on their loan by $1.02$. \n",
-    "\n",
-    "Likewise, the second coefficent tells us that a $\\$1$ increase in the amount a customer borrows, increases the log odds that they will default on their loan by $0.15$ when all other variables are held constant.\n",
-    "\n",
-    "Understandably, interpreting the coefficients in terms of log odds is a bit confusing. A more intuitive approach would be to look at them in terms of odds. Let's exponentiate the coefficients so we can interpret them in terms of odds rather than log odds:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "f5bd37c6",
-   "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>odds</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Income</th>\n",
-       "      <td>0.36</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Loan Amount</th>\n",
-       "      <td>1.16</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             odds\n",
-       "Income       0.36\n",
-       "Loan Amount  1.16"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "odds = np.round(np.exp(log_odds), 2)\n",
-    "pd.DataFrame({'odds': odds}, \n",
-    "              index = X.columns)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "91d11fda",
-   "metadata": {},
-   "source": [
-    "The first coefficent tells us that for every $\\$1$ increase in a borrower's income, the odds that they will default on their loan reduces by $64\\%$ ($1 - 0.36$) when all other variables are held constant. Earning more money decreases the odds of default.\n",
-    "\n",
-    "The second coefficent tells us that, assuming all other variables are held constant, for every $\\$1$ increase in the amount borrowed, the odds that a borrower will default on their loan increases by $16\\%$ ($1.16 - 1$). Borrowing more money increases the odds of default.\n",
-    "\n",
-    "We can also interpret the second coefficent as saying that for every $\\$1$ increase in the amount borrowed, the odds that a borrower will default on their loan increases by a factor of $1.16$, assuming all other variables are held constant."
-   ]
   }
  ],
  "metadata": {

From c4074f16120cfdf06e4ca0ea6ef63e20b7311675 Mon Sep 17 00:00:00 2001
From: smoser-LiL <smoser-LiL@localhost>
Date: Mon, 7 Nov 2022 16:19:16 +0000
Subject: [PATCH 07/10] Moving files  using main

---
 NOTICE    |  3 ---
 README.md | 23 ++++++++++++-----------
 2 files changed, 12 insertions(+), 14 deletions(-)

diff --git a/NOTICE b/NOTICE
index 547595f..4851a73 100644
--- a/NOTICE
+++ b/NOTICE
@@ -4,9 +4,6 @@ All Rights Reserved.
 Licensed under the LinkedIn Learning Exercise File License (the "License").
 See LICENSE in the project root for license information.
 
-ATTRIBUTIONS:
-[PLEASE PROVIDE ATTRIBUTIONS OR DELETE THIS AND THE ABOVE LINE “ATTRIBUTIONS”]
-
 Please note, this project may automatically load third party code from external 
 repositories (for example, NPM modules, Composer packages, or other dependencies). 
 If so, such third party code may be subject to other license terms than as set 
diff --git a/README.md b/README.md
index 95d74e5..c9c8a9a 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,10 @@
-# Machine Learning with Python: Logistic Regeression
-This is the repository for the LinkedIn Learning course `Machine Learning with Python: Logistic Regeression`. The full course is available from [LinkedIn Learning][lil-course-url].
+# Machine Learning with Python: Logistic Regression 
+This is the repository for the LinkedIn Learning course Machine Learning with Python: Logistic Regression . The full course is available from [LinkedIn Learning][lil-course-url].
+
+![Machine Learning with Python: Logistic Regression ][lil-thumbnail-url]
+
+Are you looking for a practical way to use machine learning to solve complex real-world problems? Logistic regression is an approach to supervised machine learning that models selected values to predict possible outcomes. In this course, Notre Dame professor Frederick Nwanganga provides you with a step-by-step guide on how to build a logistic regression model using Python. Learn hands-on tips for collecting, exploring, and transforming your data before you even get started. By the end of this course, you’ll have the technical skills to know when and how to design, build, evaluate, and effectively manage a logistic regression model all on your own.<br><br>This course is integrated with GitHub Codespaces, an instant cloud developer environment that offers all the functionality of your favorite IDE without the need for any local machine setup. With GitHub Codespaces, you can get hands-on practice from any machine, at any time—all while using a tool that you’ll likely encounter in the workplace. Check out the [Using GitHub Codespaces with this course][gcs-video-url] video to learn how to get started.
 
-_See the readme file in the main branch for updated instructions and information._
 ## Instructions
 This repository has branches for each of the videos in the course. You can use the branch pop up menu in github to switch to a specific branch and take a look at the course at that stage, or you can add `/tree/BRANCH_NAME` to the URL to go to the branch you want to access.
 
@@ -20,15 +23,13 @@ To resolve this issue:
     Add changes to git using this command: git add .
 	Commit changes using this command: git commit -m "some message"
 
-## Installing
-1. To use these exercise files, you must have the following installed:
-	- [list of requirements for course]
-2. Clone this repository into your local machine using the terminal (Mac), CMD (Windows), or a GUI tool like SourceTree.
-3. [Course-specific instructions]
+### Instructor
 
+Frederick Nwanganga
 
-[0]: # (Replace these placeholder URLs with actual course URLs)
+Check out my other courses on [LinkedIn Learning](https://www.linkedin.com/learning/instructors/frederick-nwanganga?u=104).
 
-[lil-course-url]: https://www.linkedin.com/learning/
-[lil-thumbnail-url]: http://
+[lil-course-url]: https://www.linkedin.com/learning/machine-learning-with-python-logistic-regression
+[lil-thumbnail-url]: https://media.licdn.com/dms/image/D560DAQFoo2MgD4SlGw/learning-public-crop_675_1200/0/1666987773227?e=1667955600&v=beta&t=l1zM021mP2to79JuY-ZCtCT4ixziL9zEvsxniekbFQQ
+[gcs-video-url]: https://www.linkedin.com/learning/machine-learning-with-python-logistic-regression/using-github-codespaces-with-this-course
 

From a9490244b6b7bc98b74776f0fce1e01389feaee0 Mon Sep 17 00:00:00 2001
From: smoser-LiL <smoser-LiL@localhost>
Date: Tue, 8 Nov 2022 22:53:50 +0000
Subject: [PATCH 08/10] Moving files  using main

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index c9c8a9a..30c7ced 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,5 @@
 # Machine Learning with Python: Logistic Regression 
-This is the repository for the LinkedIn Learning course Machine Learning with Python: Logistic Regression . The full course is available from [LinkedIn Learning][lil-course-url].
+This is the repository for the LinkedIn Learning course Machine Learning with Python: Logistic Regression. The full course is available from [LinkedIn Learning][lil-course-url].
 
 ![Machine Learning with Python: Logistic Regression ][lil-thumbnail-url]
 

From baac65c38b1d4f3a8b2f6f7e9eedcdcda094741a Mon Sep 17 00:00:00 2001
From: pcstevens <pcstevens@localhost>
Date: Wed, 9 Nov 2022 05:18:10 +0000
Subject: [PATCH 09/10] Moving files  using main

---
 README.md | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 30c7ced..c0f1fbc 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,7 @@
 # Machine Learning with Python: Logistic Regression 
 This is the repository for the LinkedIn Learning course Machine Learning with Python: Logistic Regression. The full course is available from [LinkedIn Learning][lil-course-url].
 
-![Machine Learning with Python: Logistic Regression ][lil-thumbnail-url]
+![1666987773227](https://user-images.githubusercontent.com/25848438/200744832-b3c06517-af4a-4dea-9131-fc05d33e866f.jpeg)
 
 Are you looking for a practical way to use machine learning to solve complex real-world problems? Logistic regression is an approach to supervised machine learning that models selected values to predict possible outcomes. In this course, Notre Dame professor Frederick Nwanganga provides you with a step-by-step guide on how to build a logistic regression model using Python. Learn hands-on tips for collecting, exploring, and transforming your data before you even get started. By the end of this course, you’ll have the technical skills to know when and how to design, build, evaluate, and effectively manage a logistic regression model all on your own.<br><br>This course is integrated with GitHub Codespaces, an instant cloud developer environment that offers all the functionality of your favorite IDE without the need for any local machine setup. With GitHub Codespaces, you can get hands-on practice from any machine, at any time—all while using a tool that you’ll likely encounter in the workplace. Check out the [Using GitHub Codespaces with this course][gcs-video-url] video to learn how to get started.
 
@@ -30,6 +30,5 @@ Frederick Nwanganga
 Check out my other courses on [LinkedIn Learning](https://www.linkedin.com/learning/instructors/frederick-nwanganga?u=104).
 
 [lil-course-url]: https://www.linkedin.com/learning/machine-learning-with-python-logistic-regression
-[lil-thumbnail-url]: https://media.licdn.com/dms/image/D560DAQFoo2MgD4SlGw/learning-public-crop_675_1200/0/1666987773227?e=1667955600&v=beta&t=l1zM021mP2to79JuY-ZCtCT4ixziL9zEvsxniekbFQQ
 [gcs-video-url]: https://www.linkedin.com/learning/machine-learning-with-python-logistic-regression/using-github-codespaces-with-this-course
 

From 7298b32bb210495260f612b9071415764d933833 Mon Sep 17 00:00:00 2001
From: anirudh-DE <saggamanirudh@gmail.com>
Date: Tue, 29 Apr 2025 00:15:06 +0000
Subject: [PATCH 10/10] Implemented the functions that explores tha data
 structurally, graphically

---
 src/03b.ipynb | 313 ++++++++++++++++++++++++++++++++++++++++++++------
 1 file changed, 280 insertions(+), 33 deletions(-)

diff --git a/src/03b.ipynb b/src/03b.ipynb
index 0356599..9b9fb7c 100644
--- a/src/03b.ipynb
+++ b/src/03b.ipynb
@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "de4b4a56",
    "metadata": {},
    "outputs": [],
@@ -52,27 +52,106 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "b53e3b3f",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "loan = pd.read_csv(\"loan.csv\")"
+   ]
   },
   {
-   "cell_type": "markdown",
-   "id": "836693c2",
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "c190dc61",
    "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>Income</th>\n",
+       "      <th>Loan Amount</th>\n",
+       "      <th>Default</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>30</td>\n",
+       "      <td>8</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>22</td>\n",
+       "      <td>10</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>33</td>\n",
+       "      <td>12</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>28</td>\n",
+       "      <td>20</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>23</td>\n",
+       "      <td>32</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Income  Loan Amount Default\n",
+       "0      30            8      No\n",
+       "1      22           10      No\n",
+       "2      33           12      No\n",
+       "3      28           20      No\n",
+       "4      23           32      No"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "To verify that the import worked as expected, let’s use the `head()` method of the pandas dataframe to preview the data."
+    "loan.head()"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "046f5901",
+   "cell_type": "markdown",
+   "id": "836693c2",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "To verify that the import worked as expected, let’s use the `head()` method of the pandas dataframe to preview the data."
+   ]
   },
   {
    "cell_type": "markdown",
@@ -104,11 +183,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "67de73e2",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 30 entries, 0 to 29\n",
+      "Data columns (total 3 columns):\n",
+      " #   Column       Non-Null Count  Dtype \n",
+      "---  ------       --------------  ----- \n",
+      " 0   Income       30 non-null     int64 \n",
+      " 1   Loan Amount  30 non-null     int64 \n",
+      " 2   Default      30 non-null     object\n",
+      "dtypes: int64(2), object(1)\n",
+      "memory usage: 852.0+ bytes\n"
+     ]
+    }
+   ],
+   "source": [
+    "loan.info()"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -130,11 +228,100 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "acdf6ca5",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "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>Income</th>\n",
+       "      <th>Loan Amount</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>30.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>20.966667</td>\n",
+       "      <td>54.233333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>6.195011</td>\n",
+       "      <td>28.231412</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>12.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>16.250000</td>\n",
+       "      <td>32.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>20.500000</td>\n",
+       "      <td>54.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>24.750000</td>\n",
+       "      <td>71.750000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>34.000000</td>\n",
+       "      <td>110.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          Income  Loan Amount\n",
+       "count  30.000000    30.000000\n",
+       "mean   20.966667    54.233333\n",
+       "std     6.195011    28.231412\n",
+       "min    12.000000     8.000000\n",
+       "25%    16.250000    32.000000\n",
+       "50%    20.500000    54.500000\n",
+       "75%    24.750000    71.750000\n",
+       "max    34.000000   110.000000"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "loan.describe()"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -159,7 +346,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "7239372b",
    "metadata": {},
    "outputs": [],
@@ -178,11 +365,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "9308d55a",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = sns.boxplot(data = loan,x= 'Default',y='Income',hue = 'Default')\n",
+    "plt.show()"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -202,11 +403,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "bcb7b490",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = sns.boxplot(data =loan,x ='Default',y ='Loan Amount',hue = 'Default')\n",
+    "plt.show()"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -235,7 +450,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "04b9e4bd",
    "metadata": {},
    "outputs": [],
@@ -253,11 +468,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "id": "f0e2438f",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = sns.scatterplot(x = loan['Income'],\n",
+    "                     y = np.where(loan['Default'] == 'No',0,1),\n",
+    "                     s = 100)\n",
+    "plt.show()"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -269,13 +500,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "id": "1387c926",
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = sns.scatterplot(x = loan['Loan Amount'],\n",
+    "                     y = np.where(loan['Default'] == 'No',0,1),\n",
+    "                     s = 100)\n",
+    "plt.show()"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -288,7 +535,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -302,7 +549,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.12.1"
   }
  },
  "nbformat": 4,