L06-EnsembleModels-AnaJaved.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "responsible-origin",
   "metadata": {},
   "source": [
    "## Lesson 6 - Targeted Marketing Campaign\n",
    "### Author: Ana Javed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "suburban-passenger",
   "metadata": {},
   "source": [
    "### Workplace Scenario\n",
    "\n",
    "Cypress from New Ventures Department has found a banking client who wants to do a targeted marketing campaign for a specialty investment product to some of its customers. Cypress stresses that the bank only wants to spend the effort on customers who are likely to say yes; last year they used a different company to generate an algorithm and ended up spamming many of their customers with products they didn't want.\n",
    "\n",
    "Now that you know a few ensemble models to improve a classification model let's try it out in this assignment.\n",
    "\n",
    "### Instructions\n",
    "\n",
    "It is recommended you complete the lab exercises for this lesson before beginning the assignment.\n",
    "\n",
    "Using the Bank Data csv file, create a new notebook to train, test, and evaluate an ensemble model. Perform each of the following tasks and answer the related questions:\n",
    "\n",
    "- Read in, parse, and pre-process the data\n",
    "- Perform a brief exploratory analysis\n",
    "- Compare the performance between a decision tree and a random forest approach by assessing the accuracy of each model\n",
    "- Graph the structure of your decision tree\n",
    "- Graph the confusion matrix of your random forest model\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "complicated-panic",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Importing Necessary Libraries & Packages \n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "import csv\n",
    "import sklearn \n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Declaring inline visualizations \n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "elementary-ratio",
   "metadata": {},
   "source": [
    "### 1. Read in, parse, and pre-process the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "damaged-antique",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ## Reading data file into Dataframe \n",
    "url = 'https://library.startlearninglabs.uw.edu/DATASCI420/2019/Datasets/Bank%20Data.csv'\n",
    "df = pd.read_csv(url, sep=\",\")\n",
    "\n",
    "## First 5 Rows from Dataframe\n",
    "# df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "unlikely-agenda",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(600, 11)\n",
      "age              int64\n",
      "sex             object\n",
      "region          object\n",
      "income         float64\n",
      "married         object\n",
      "children         int64\n",
      "car             object\n",
      "save_act        object\n",
      "current_act     object\n",
      "mortgage        object\n",
      "pep             object\n",
      "dtype: object\n",
      "              age        income    children\n",
      "count  600.000000    600.000000  600.000000\n",
      "mean    42.395000  27524.031217    1.011667\n",
      "std     14.424947  12899.468246    1.056752\n",
      "min     18.000000   5014.210000    0.000000\n",
      "25%     30.000000  17264.500000    0.000000\n",
      "50%     42.000000  24925.300000    1.000000\n",
      "75%     55.250000  36172.675000    2.000000\n",
      "max     67.000000  63130.100000    3.000000\n"
     ]
    }
   ],
   "source": [
    "## Conducting Exploratory Data Analysis: \n",
    "print(df.shape)  # (600, 11)\n",
    "print(df.dtypes) \n",
    "print(df.describe()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "frank-thriller",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Changing Binary Category Columns to 0 and 1: \n",
    "df['sex'] = df['sex'].map({'MALE': 0, 'FEMALE': 1})\n",
    "df['married'] = df['married'].map({'NO': 0, 'YES': 1})\n",
    "df['save_act'] = df['save_act'].map({'NO': 0, 'YES': 1})\n",
    "df['current_act'] = df['current_act'].map({'NO': 0, 'YES': 1})\n",
    "df['mortgage'] = df['mortgage'].map({'NO': 0, 'YES': 1})\n",
    "df['pep'] = df['pep'].map({'NO': 0, 'YES': 1})\n",
    "df['car'] = df['car'].map({'NO': 0, 'YES': 1})\n",
    "df['children'] = df['children'].map({0: 'None', 1: 'One', 2:'Two', 3:'Three'})\n",
    "\n",
    "\n",
    "## Making region and children One-hot encoded columns \n",
    "df_expanded = pd.get_dummies(df, columns = [\"children\", \"region\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "major-education",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(600, 17)\n",
      "age                    int64\n",
      "sex                    int64\n",
      "income               float64\n",
      "married                int64\n",
      "car                    int64\n",
      "save_act               int64\n",
      "current_act            int64\n",
      "mortgage               int64\n",
      "pep                    int64\n",
      "children_None          uint8\n",
      "children_One           uint8\n",
      "children_Three         uint8\n",
      "children_Two           uint8\n",
      "region_INNER_CITY      uint8\n",
      "region_RURAL           uint8\n",
      "region_SUBURBAN        uint8\n",
      "region_TOWN            uint8\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "## Columns after One-Hot Encoding: \n",
    "print(df_expanded.shape)  # (600, 17)\n",
    "print(df_expanded.dtypes) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "prospective-ancient",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of missing values removed in age column: 0\n",
      "Number of missing values removed in sex column: 0\n",
      "Number of missing values removed in income column: 0\n",
      "Number of missing values removed in married column: 0\n",
      "Number of missing values removed in car column: 0\n",
      "Number of missing values removed in save_act column: 0\n",
      "Number of missing values removed in current_act column: 0\n",
      "Number of missing values removed in mortgage column: 0\n",
      "Number of missing values removed in pep column: 0\n"
     ]
    }
   ],
   "source": [
    "## Function to Find Missing Values (if any)\n",
    "def replace_missing_values(df):\n",
    "    for each in df.columns:\n",
    "        if df.loc[:, each].dtype in ('float64', 'int64'):\n",
    "            df.loc[:, each] = pd.to_numeric(df.loc[:, each], errors='coerce')\n",
    "            HasNan1 = np.isnan(df.loc[:, each] )\n",
    "            print(\"Number of missing values removed in \" + each + \" column:\", sum(HasNan1)) \n",
    "            # df.loc[HasNan1, each] = np.nanmedian(df.loc[:, each] )\n",
    "\n",
    "replace_missing_values(df_expanded)\n",
    "\n",
    "## No missing values in the columns, can proceed with the analysis! "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "toxic-apparatus",
   "metadata": {},
   "source": [
    "### 2. Perform a brief exploratory analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "renewable-membership",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Values in Each Age: \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([60., 66., 49., 62., 66., 71., 57., 42., 59., 68.]),\n",
       " array([18. , 22.9, 27.8, 32.7, 37.6, 42.5, 47.4, 52.3, 57.2, 62.1, 67. ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Distribution of The Age Categories \n",
    "print(\"Number of Values in Each Age: \")\n",
    "\n",
    "plt.hist(df.loc[:, \"age\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "stopped-stroke",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distinct Values in the Pep Target Column: \n",
      "0    326\n",
      "1    274\n",
      "Name: pep, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([326.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0., 274.]),\n",
       " array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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e40nyi8BHgNdX1beXqbZx6TPmSWB/F+zrgBuSnKqqv12WCkev77/tb1XVD4EfJrkfuApYreHeZ8xvAd5bcxPS00keB34eOLw8JS67kefXapmW6fM4g4PAm7u/Ol8N/HdVnVjuQkdo0TEneRlwD/CmVXwWN2jRMVfV5qraVFWbgL8Bfn8VBzv0+7d9L/DrSdYk+XHmnrB6bJnrHKU+Y36Suf+pkORy4OeAbyxrlctr5Pm1Ks7ca4HHGST5vW77h5i7cuIGYBr4H+Z+869aPcf8h8BPAXd1Z7KnahU/Ua/nmJvSZ8xVdSzJZ4CHgGeBj1TVvJfUrQY9f85/BHwsycPMTVm8q6pW7aOAk3wCuAZYl2QGuAO4GMaXXz5+QJIatFqmZSRJ58Fwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ36P81MIAqMNNNSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plotting Pep Column (Target Variable) \n",
    "print(\"Distinct Values in the Pep Target Column: \")\n",
    "print(df.loc[:, \"pep\"].value_counts())\n",
    "\n",
    "plt.hist(df.loc[:, \"pep\"], color = 'green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "south-advocacy",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Income Plot: \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([ 32., 106., 110., 108.,  76.,  62.,  35.,  36.,  21.,  14.]),\n",
       " array([ 5014.21 , 10825.799, 16637.388, 22448.977, 28260.566, 34072.155,\n",
       "        39883.744, 45695.333, 51506.922, 57318.511, 63130.1  ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/D8/uEXS8qa13AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plotting Income\n",
    "print(\"Income Plot: \")\n",
    "plt.hist(df.loc[:, \"income\"], color = 'orange')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "improved-banking",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Standardizing / Normalizing Values with StandardScaler -- only the Age and Income Columns \n",
    "for each in ['age', 'income']:\n",
    "    column_df = pd.DataFrame(df_expanded.loc[:, each])\n",
    "   # print(column_df)\n",
    "    standardization_scale = StandardScaler().fit(column_df)\n",
    "    column_df = pd.DataFrame(standardization_scale.transform(column_df))\n",
    "    df_expanded.loc[:, each] = column_df[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "plastic-munich",
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>income</th>\n",
       "      <th>married</th>\n",
       "      <th>car</th>\n",
       "      <th>save_act</th>\n",
       "      <th>current_act</th>\n",
       "      <th>mortgage</th>\n",
       "      <th>pep</th>\n",
       "      <th>children_None</th>\n",
       "      <th>children_One</th>\n",
       "      <th>children_Three</th>\n",
       "      <th>children_Two</th>\n",
       "      <th>region_INNER_CITY</th>\n",
       "      <th>region_RURAL</th>\n",
       "      <th>region_SUBURBAN</th>\n",
       "      <th>region_TOWN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.000000e+02</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>6.000000e+02</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-2.279658e-16</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>2.590520e-17</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>0.493333</td>\n",
       "      <td>0.690000</td>\n",
       "      <td>0.758333</td>\n",
       "      <td>0.348333</td>\n",
       "      <td>0.456667</td>\n",
       "      <td>0.438333</td>\n",
       "      <td>0.225000</td>\n",
       "      <td>0.113333</td>\n",
       "      <td>0.223333</td>\n",
       "      <td>0.448333</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>0.103333</td>\n",
       "      <td>0.288333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.000834e+00</td>\n",
       "      <td>0.500417</td>\n",
       "      <td>1.000834e+00</td>\n",
       "      <td>0.474104</td>\n",
       "      <td>0.500373</td>\n",
       "      <td>0.462879</td>\n",
       "      <td>0.428451</td>\n",
       "      <td>0.476840</td>\n",
       "      <td>0.498534</td>\n",
       "      <td>0.496597</td>\n",
       "      <td>0.417931</td>\n",
       "      <td>0.317264</td>\n",
       "      <td>0.416827</td>\n",
       "      <td>0.497738</td>\n",
       "      <td>0.366912</td>\n",
       "      <td>0.304648</td>\n",
       "      <td>0.453365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.692578e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.746475e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-8.599922e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-7.960089e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-2.740596e-02</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>-2.016284e-01</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.919080e-01</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.710246e-01</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.707149e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.762577e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age         sex        income     married         car  \\\n",
       "count  6.000000e+02  600.000000  6.000000e+02  600.000000  600.000000   \n",
       "mean  -2.279658e-16    0.500000  2.590520e-17    0.660000    0.493333   \n",
       "std    1.000834e+00    0.500417  1.000834e+00    0.474104    0.500373   \n",
       "min   -1.692578e+00    0.000000 -1.746475e+00    0.000000    0.000000   \n",
       "25%   -8.599922e-01    0.000000 -7.960089e-01    0.000000    0.000000   \n",
       "50%   -2.740596e-02    0.500000 -2.016284e-01    1.000000    0.000000   \n",
       "75%    8.919080e-01    1.000000  6.710246e-01    1.000000    1.000000   \n",
       "max    1.707149e+00    1.000000  2.762577e+00    1.000000    1.000000   \n",
       "\n",
       "         save_act  current_act    mortgage         pep  children_None  \\\n",
       "count  600.000000   600.000000  600.000000  600.000000     600.000000   \n",
       "mean     0.690000     0.758333    0.348333    0.456667       0.438333   \n",
       "std      0.462879     0.428451    0.476840    0.498534       0.496597   \n",
       "min      0.000000     0.000000    0.000000    0.000000       0.000000   \n",
       "25%      0.000000     1.000000    0.000000    0.000000       0.000000   \n",
       "50%      1.000000     1.000000    0.000000    0.000000       0.000000   \n",
       "75%      1.000000     1.000000    1.000000    1.000000       1.000000   \n",
       "max      1.000000     1.000000    1.000000    1.000000       1.000000   \n",
       "\n",
       "       children_One  children_Three  children_Two  region_INNER_CITY  \\\n",
       "count    600.000000      600.000000    600.000000         600.000000   \n",
       "mean       0.225000        0.113333      0.223333           0.448333   \n",
       "std        0.417931        0.317264      0.416827           0.497738   \n",
       "min        0.000000        0.000000      0.000000           0.000000   \n",
       "25%        0.000000        0.000000      0.000000           0.000000   \n",
       "50%        0.000000        0.000000      0.000000           0.000000   \n",
       "75%        0.000000        0.000000      0.000000           1.000000   \n",
       "max        1.000000        1.000000      1.000000           1.000000   \n",
       "\n",
       "       region_RURAL  region_SUBURBAN  region_TOWN  \n",
       "count    600.000000       600.000000   600.000000  \n",
       "mean       0.160000         0.103333     0.288333  \n",
       "std        0.366912         0.304648     0.453365  \n",
       "min        0.000000         0.000000     0.000000  \n",
       "25%        0.000000         0.000000     0.000000  \n",
       "50%        0.000000         0.000000     0.000000  \n",
       "75%        0.000000         0.000000     1.000000  \n",
       "max        1.000000         1.000000     1.000000  "
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Updated Data frame:\n",
    "df_expanded.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "useful-audit",
   "metadata": {},
   "source": [
    "### 3. Compare the performance between a decision tree and a random forest approach by assessing the accuracy of each model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "excessive-blade",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Separating out Target Variable & Test/Train Sets \n",
    "\n",
    "col_names_list = list(df_expanded.columns)\n",
    "col_names_list.remove(\"pep\")\n",
    "\n",
    "X = df_expanded.loc[:, col_names_list]\n",
    "Y = df_expanded.loc[:, \"pep\"]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, \n",
    "                    test_size = 0.25, random_state = 99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "moved-benefit",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree Method Accuracy is : 86.0%\n"
     ]
    }
   ],
   "source": [
    "## Decision Tree Approach:\n",
    "from sklearn import tree\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "np.random.seed(123)\n",
    "\n",
    "## Generating Decision Tree Model\n",
    "dec_tree_ent = DecisionTreeClassifier(min_samples_leaf=4, max_depth = 6)\n",
    "model = dec_tree_ent.fit(X_train,y_train)\n",
    "\n",
    "# Validate the model\n",
    "y_predict = model.predict(X_test)\n",
    "\n",
    "#def measure_performance(X_train, y_train)\n",
    "acc = accuracy_score(y_test, y_predict) * 100\n",
    "print(\"Decision Tree Method Accuracy is : {}%\".format(acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "demographic-remark",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ensemble Method Accuracy on Testing Data = 86.67%\n"
     ]
    }
   ],
   "source": [
    "## Ensemble Model Approach:\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "## Defining Model Parameters: \n",
    "nTrees = 100\n",
    "max_depth = 6\n",
    "min_node_size = 4\n",
    "verbose = 0\n",
    "\n",
    "## Generating Random Forest Classifier \n",
    "clf = RandomForestClassifier(n_estimators=nTrees, \n",
    "                             max_depth=max_depth,\n",
    "                             random_state=0, \n",
    "                             verbose=verbose, \n",
    "                             min_samples_leaf=min_node_size)\n",
    "clf.fit(X_train,y_train)\n",
    "\n",
    "\n",
    "## Validating the model\n",
    "y_predict2 = clf.predict(X_test)\n",
    "Accuracy = [1 for i in range(len(y_predict2)) \n",
    "            if y_test.iloc[i] == y_predict2[i]]\n",
    "Accuracy = round(float(np.sum(Accuracy))/len(y_predict2)*100,2)\n",
    "print(\"Ensemble Method Accuracy on Testing Data = %.2f%%\"%Accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hungry-judges",
   "metadata": {},
   "source": [
    "We can see that the Ensemble model approach increased the accuracy by a small amount, but still increased the overall accuracy. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "official-cornwall",
   "metadata": {},
   "source": [
    "### 4. Graph the structure of your decision tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "collect-cooperative",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1800x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Graphing Gini Decision Tree Model: \n",
    "fig = plt.figure(figsize=(25,20)) \n",
    "tree.plot_tree(model,\n",
    "                   feature_names=col_names_list,  \n",
    "                   class_names=col_names_list,\n",
    "                   filled=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "positive-capability",
   "metadata": {},
   "source": [
    "Top attributes in the decision tree model is \"children_One\", \"Income\", \"Married\", and then \"children_None\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "considered-controversy",
   "metadata": {},
   "source": [
    "### 5. Graph the confusion matrix of your random forest model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "initial-engineering",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Defining Function to Plot Confusion Matrix (via Sklearn)\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "affecting-photography",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             Predicted No Pep  Predicted Pep\n",
      "True No Pep                78              7\n",
      "True Pep                   13             52\n"
     ]
    },
    {
     "data": {
      "image/png": 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2OEjS1Nz8uPRbbzYCmANcIGljYBrwbWBoRMwCiIhZkoZUe5KOGCU/DXgAuKDKNq0lrssj4ojmGUkfBx6NiG0KltOpE6JZt9a2i2/MjYjNq6zvCWwKfCsi7pX0O2o0v1vS7jfMiIhXgYnAwbnFdwNfTtP7AXcWLO5JYLCkbQAk9ZK0YW79Pmn59sDrEeGDEM06KSGamoo9CngReDEi7k3zV5Il0Fdy4yDDgNnVCumoOwydCuRHy48EDpT0MLA/WdW4ptTv8EXgl5IeAqYD2+Y2eU3S3cDZLJmgzawTkoo9aomIl4EXJI1Miz4FPAZcB4xNy8YC11Yrp92a5BHRLzf9CtA3N/8csFON/S8ELmxh+XSyztuWXBURx7U5WDMrpTofY/kt4BJJywHPAAeSVRonSjoYmAlUPTjWZ/qYWTkVrD0WlSpbLfVzfqpoGV0mYUbE6EbHYGb1I6BHj3KdG9llEqaZdT2++IaZWRF1bpLXgxOmmZWScA3TzKwg36LCzKywggeldxgnTDMrJ/dhmpkV4z5MM7M2KFm+dMI0s/JyDdPMrAh50MfMrJCsD7PRUSzJCdPMSsrHYZqZFVayfOmEaWbl5RqmmVkB8qCPmVlxrmGamRVUsnzphGlm5eUapplZEb74hplZMc33JS8TJ0wzK62mOlYxJT0HvAksBhZFxOaSBgKXA2sCzwFfiojXWo2nbtGYmdWZVOzRBp+MiFER0Xy73WOBKRGxDjAlzbfKCdPMSilLhir0WAZ7AuPT9Hhgr2obt9okl/QHIFpbHxFHLkVwZmaF9SjehzlI0tTc/LiIGFexTQA3SwrgnLR+aETMAoiIWZKGVHuSan2YU6usMzNrd22oPM7NNbNbs11EvJSS4mRJT7Q1nlYTZkSMz89LWiEi3m7rE5iZLQ2RjZTXS0S8lP6fLekaYEvgFUnDUu1yGDC7Whk1+zAlbSPpMeDxNL+xpDOXPXwzs+qaVOxRi6QVJK3YPA18BngEuA4YmzYbC1xbrZwihxWdBuySCiYiHpK0Y4H9zMyW3rIP6OQNBa5J5fUELo2ISZLuByZKOhiYCYypVkih4zAj4oWKwBcvVchmZgWJNg36VBURzwAbt7B8HvCpouUUSZgvSNoWCEnLAUeSmudmZu2pbKdGFjkO8zDgcGBV4F/AqDRvZtauOuA4zDapWcOMiLnAfh0Qi5nZB5biLJ52V2SUfISk6yXNkTRb0rWSRnREcGbWvTVJhR4dFk+BbS4FJgLDgFWAK4AJ7RmUmRl0zoSpiLg4Ihalx5+ocsqkmVk9iPodh1kv1c4lH5gmb5V0LHAZWaLcB7ixA2Izs+6sgwd0iqg26DONLEE2R3xobl0AJ7dXUGZmUL5Bn2rnkq/VkYGYmVXqTDXMD0jaCNgA6N28LCIuaq+gzMzqeaZPvdRMmJJOAEaTJcy/ALsBdwJOmGbWrsqVLouNkn+R7FzLlyPiQLLzMZdv16jMrNuTyndYUZEm+cKIeF/SIkn9ya4X5wPXzazdlawLs1DCnCppZeCPZCPnbwH3tWdQZmbQCQd9IuKbafJsSZOA/hHxcPuGZWbdnVDnGfSRtGm1dRHxQPuEZGYGlPDiG9VqmKdWWRfATnWOpcN9fOTqTLrtN40Ow2rY9fS7Gh2CNUinaZJHxCc7MhAzs0pFDuPpSIUOXDcz62id8sB1M7NGKVm+dMI0s3LKrrheroxZ5IrrkvQ/kn6S5teQtGX7h2Zm3V09r4cpqYekByXdkOYHSposaUb6f0DNeAo8z5nANsBX0vybwBnFQjQzW3rN9/Wp9Sjo2yx5x9tjgSkRsQ4wJc1XVSRhbhURhwP/BoiI14DlCodoZrYUBPSUCj1qliWtBnwWODe3eE9gfJoeD+xVq5wifZjvSepBui2FpMHA+wX2MzNbJm2oPQ6SNDU3Py4ixuXmTwOOAVbMLRsaEbMAImKWpCG1nqRIwvw9cA0wRNIpZFcv+lGB/czMlpradiWiuRGxeSvlfA6YHRHTJI1elpiKnEt+iaRpZJd4E7BXRDxeYzczs2VWp0Hy7YA9JO1OdhH0/pL+BLwiaViqXQ4juxJbVUVGydcAFgDXA9cBb6dlZmbtqh6j5BFxXESsFhFrAl8GbomI/yHLZ2PTZmOBa2vFU6RJfiMf3gytN7AW8CSwYYF9zcyWSgec6fMLYKKkg4GZwJhaOxRpkn88P5+uYnRoK5ubmdVHO9xzPCJuA25L0/PIuhoLa/OZPhHxgKQt2rqfmVlbqWR39SlyE7Tv5GabgE2BOe0WkZkZWZO8M55Lnj9uaRFZn+ZV7ROOmdmHOlXCTAes94uI73dQPGZmQCe7vJuknhGxqNqtKszM2k0nu0XFfWT9ldMlXQdcAbzdvDIirm7n2Mysm+vIe44XUaQPcyAwj+wePs3HYwbghGlm7aazDfoMSSPkj/BhomwW7RqVmRmdq0neA+gHLR4I5YRpZu1KiB4ly5jVEuasiDipwyIxM8trhzN9llW1hFmyUM2su+lMgz5tOsfSzKyeRCfqw4yIVzsyEDOzSp3mwHUzs0YSxW461pGcMM2snEp4X3InTDMrrXKlSydMMyup7EyfcqVMJ0wzK62Sjfk4YZpZWcl9mGZmRZRxlLxs8ZiZfUBSoUeBcnpLuk/SQ5IelfTTtHygpMmSZqT/B1QrxwnTzEpLBR8FvAPsFBEbA6OAXSVtDRwLTImIdYApab5VTphmVkoS9JAKPWqJzFtptld6BLAnMD4tHw/sVa0cJ0wzK616NclTWT0kTQdmA5Mj4l5gaETMAkj/D6lWhgd9zKy02jBGPkjS1Nz8uIgYl98gIhYDoyStDFwjaaO2xuOEaWal1YajiuZGxOZFNoyI+ZJuA3YFXpE0LCJmSRpGVvtslZvkZlZK2WFFKvSoWZY0ONUskdQH2Bl4ArgOGJs2GwtcW60c1zDNrKRUz1MjhwHjJfUgqyhOjIgbJN0DTJR0MDATGFOtECdMMyuteuXLiHgY2KSF5fNow8XSnTDNrJSam+Rl4oRpZuWkTnSLCjOzRnPCtLo5+vBD+NtNf2HQ4MHces+DAPzqZydy01+uR01NDBo8mNPOPJePDlulwZF2b5cdtBkL3l3M+xEsfh8OnfAQh22/JtuOGMB7i4OXXv83v5w8g7feWdzoUEtFULr7kvuwok5sn33355Irr19i2TeO/A5T7p7G3+68n5132Z3f/uqUBkVneUdf+Qhfu+QhDp3wEABTZ87nwIsf5OBLpvPC/IXsu8VqDY6wnFTwX0dxwuzEtt5uBwYMWPLiKiv27//B9MIFC0p3PUHLTJ05n8WRTT82600G91u+sQGVlFTs0VHcJO+CfnHyT7jiskvo378/V15/c6PD6fYi4H+/sCERcP0/XuaGR15ZYv3uGw7l1qfmNii6cuvI2mMRpa5hSlosabqkRyRdIalvo2PqDI798UlMe/RpvjDmK5w/7qxGh9PtHTHxHxxy6UP84M+PsdfGw/ivVT9sBfzPFqux+P1g8hNzGhhhOYliVyrqyH7OUidMYGFEjIqIjYB3gcMaHVBnsvcX9+Ev11/T6DC6vXlvvwvA/IXvcefT81h/aD8Adll/MNuMGMDPJj3VyPDKq2BzvCOb5GVPmHl3AGtLWkHS+ZLul/SgpD0BJB0g6VpJkyQ9KemEBsfbEM88PeOD6Zv+egNrrzOygdFY755N9OnV44PpzddYmWfnLWDL4Svzlc1X4/jrHuedRe83OMryquMFhOuiU/RhSuoJ7AZMAn4I3BIRB6WT6e+T9Le06ZbARsAC4H5JN0bE1IqyDgEOAVh19TU66BW0j28cvD/33Hk7r86by2YbjOC7x/6YWyZP4ul/PkWTmlh19TX45W9Pb3SY3dqAvr04+fPrA9CjSUx5Yg73PT+fSw7YlF49mjj1CxsC8Nist/jNLU83MtTS8W12265PuuAnZDXM84C7gT0kfS8t7w00Z77J6dxQJF0NbA8skTDTNfLGAWy8yWbRrtG3s7POu/g/lu371QMbEIm1ZtYb7/C1S6b/x/L9Lnyg44PphMqVLsufMBdGxKj8AmXHyfx3RDxZsXwrskvO53XqhGjW3ZXtsLjO1IfZ7CbgWylxIil/BZJPp7vA9SG7N8ddDYjPzOrEgz7L7mSyGxg9LOmRNN/sTuBiYDpwVWX/pZl1Lh70aYOI6NfCsoXAoa3sMjsijmjfqMysw5SrRV7uhGlm3VdWeyxXxuwyCTMiLgQubHAYZlYvgqZy5cuukzDNrAtywjQzK6JjL91WhBOmmZVWyQ7D7JSHFZlZN1D0kKIiOVXS6pJulfS4pEclfTstHyhpsqQZ6f8B1cpxwjSz0pJU6FHAIuC7EbE+sDVwuKQNgGOBKRGxDjAlzbfKCdPMSqteZ/pExKyIeCBNvwk8DqwK7AmMT5uNJztDsFXuwzSz0mpDF+YgSfkz+8alC+38Z5nSmsAmwL3A0IiYBVlSlTSk2pM4YZpZObXtvMe5EbF5zSKlfsBVwFER8UZbL+7hJrmZlVY97xopqRdZsrwkIq5Oi1+RNCytHwbMrlaGE6aZlVJ2AeFij5plZVXJ84DHI+I3uVXXAWPT9Fjg2mrluEluZuVVv+MwtwP2B/6Ruyj58cAvgImSDgZmAmOqFeKEaWalVa8zfSLiTlpPv58qWo4TppmVVtnO9HHCNLPScsI0MyvA18M0Myuqg+/XU4QTppmVVsnypROmmZVYyTKmE6aZlZRoKlmb3AnTzEqpo2+hW4QTppmVV8kyphOmmZWWDysyMyuoZF2YTphmVlK+L7mZWVuUK2M6YZpZKQk3yc3MCitZvnTCNLPycg3TzKygtt6krL05YZpZaZUrXTphmllJyZd3MzMrzmf6mJkVVa586fuSm1l51fG+5OdLmi3pkdyygZImS5qR/h9QM55lezlmZu1Fhf8VcCGwa8WyY4EpEbEOMCXNV+WEaWal1HymT5FHLRFxO/BqxeI9gfFpejywV61y3IdpZl3BIElTc/PjImJcjX2GRsQsgIiYJWlIrSdxwjSz0mrDLSrmRsTm7RkLuEluZmVVsDm+DMdqviJpGED6f3atHZwwzayU1IbHUroOGJumxwLX1trBCdPMyqtOGVPSBOAeYKSkFyUdDPwC+LSkGcCn03xV7sM0s9Kq15k+EfGVVlZ9qi3lOGGaWWn5FhVmZkU5YZqZFVO2i28oIhodQ8NImgM83+g46mwQMLfRQVhNXfFzGh4Rg+tVmKRJZO9TEXMjovLUx7rr1gmzK5I0tSMO4LVl48+pc/JhRWZmBTlhmpkV5ITZ9dS64ICVgz+nTsh9mGZmBbmGaWZWkBOmmVlBTphmZgU5YZqZFeSE2U1I/3mZ1ZaWWWP4s+gcPErejUjaCRgO9CpwvxNrR5IUEZGu9P1eRHS10yS7JNcwu7jmmoukLYCzgCHANyWdX7mNdZyULD8LXAP8VdIPJG3b6LisOtcwuwFJW5Jdgv/2iLg8JchbgH9GxNcbG133JGkUcApwNDCA7IrfvYGzI+LFBoZmVbiG2T1sTHYT+40krRjZX8kxwKqSVmpsaN2PpEHAIcCawIyIuBe4GtgC2KSBoVkNTphdUK4Zvrak/sB5wJHAtsDWklYGRgCrAr0aFWd3UtHt8SowEZgJnCCpd0Q8RnbPmY83Ij4rxhcQ7mIkNUXE+5J2A04GbgS2AT4PrJ6WvQAsBH7swYb2lxvg2Q3YDFgMnAn8HvgccKmkc4G9ge80LlKrxTXMLqK5BpOS5Uiy/rExwOvASsDyEXE2cBqwCvDniLiuQeF2G7lk2XxXwhvIkuL3gdvImuIrAV8Hjo6IKZJ6NCpeq841zC4gJcgxknoCvyGrwUwARgL7APtGxBuSdoiIyyT1A46RNBe4IzzyV3eSPgr0Bf4NvATsBHybLDk+DYyLiIXAlPTHbhdgO0l3RcQ7DQrbanDC7OQkrQdcAlwK7EHWN3k82aj4IGBERCyQtCNwnKSDIuJcSQE852RZf+kzuYisdv+ipKuBGWSfyTrAfhExU9LX0i7nAf2ArdP/Tpgl5cOKOjFJA4E7gL9GxPfSsqnAOcAzwAnAtWR9lscBJ0bEtQ0Kt1uQtAHZH7DvkCXJPcmS5IPAicCREXF9OqzoIuCYiJiUWgfLR8TbDQncCnENs3PrA1wJrCjpExHxd+A6YBFwK9lo7PeBgcBx6YfZ3Nfpv5TtYyCwcUTcCiDpFuCzZAm0N3CIpLFkRyj8KH0mTRGxiOxzsxJzDbOTk7Q2WS1mDSDIjuX7UkT8q6GBdWOSdgXOjIgRkvYGjgD2jIi3JI0ga3L3joinmweFGhqwFeaE2QWkpPnfwKFkTbwr00hrRMT7jY2ue5K0O9mxlk8AO6Z+ZCfHTs4JsxPL/wAlrQF8ieyQoRsi4paGBmfNFzu5KCJWS/M9U9PbOikfh9mJVF4kIx3f1zNNzwT+DMwF9k1n81gDpT9aX5M0W9IAJ8vOzzXMTiJ3APRoYANAwAWpqdcrIt5L260NLI6IZxsXreWl5vmCiLit0bHYsnHC7ETS2SK/Bi4nS5qbAFulwYQeEbG4oQFaVe7D7PycMEssXdVmlYh4OM3/AXggIi5I878nO5vns27umbU/92GWlKTlgIPJjtvbLC1eQHacX7MfAf8Clu/g8My6JSfMkoqId8kuzjAH2Cf1TV4GHC9pz7TZf6VH/4YEadbNuEleMpJWB9aPiJvT/JpkV7JZjqz/cm3gj8BdZJdt+0FE3NiYaM26FyfMEknN8Blk1638IzCb7PJfvYEdyK5081uylkFfoG9EPObBBLOO4YRZMpI2Jjsf/EHgfOCHwH1ko+LzyPoxT4mIGQ0L0qybch9myUTEQ2SXafsE2R+0rcgu+vtPsptlfRUP8pg1hGuYJZXu9Hgz2VWGzsrdemItH5Ru1hi+vFtJRcR9knYG/iJp+Yg4La16DnwQtFkjuIZZcpK2Av4GbAi84CRp1jhOmJ2ApP4R8Uaj4zDr7jzo0zm8Cf95tSIz61iuYZqZFeQapplZQU6YZmYFOWGamRXkhGn/QdJiSdMlPSLpCkl9l6GsCyV9MU2fm+7b3dq2oyVtuxTP8Vy6dmih5RXbvNXG5zpR0vfaGqN1DU6Y1pKFETEqIjYC3gUOy69Md6Rss4j4WkQ8VmWT0UCbE6ZZR3HCtFruANZOtb9bJV0K/ENSD0n/K+l+SQ9LOhSyQ58knS7pMUk3AkOaC5J0m6TN0/Sukh6Q9JCkKekydocBR6fa7Q6SBku6Kj3H/ZK2S/t+RNLNkh6UdA7Z/Y2qkvRnSdMkPSrpkIp1p6ZYpkganJZ9TNKktM8dktary7tpnZpPjbRWpTtS7gZMSou2BDaKiGdT0nk9IraQtDxwl6Sbye4zNBL4ODAUeIzsqkv5cgeTXb5ux1TWwIh4VdLZwFsR8eu03aXAbyPiznQb4ZuA9YETgDsj4iRJnwWWSICtOCg9Rx/gfklXRcQ8YAWy2358V9JPUtlHAOOAwyJiRjrb6kxgp6V4G60LccK0lvSRND1N3wGcR9ZUvi934Y/PAP/V3D9Jdq3OdYAdgQnphmwvSWrp/uhbA7c3lxURr7YSx87ABrnj9ftLWjE9xxfSvjdKeq3AazpS0t5pevUU6zzgfbKbygH8CbhaUr/0eq/IPbevEGVOmNaihRExKr8gJY6384uAb0XETRXb7Q7UOhtCBbaBrMtom4hY2EIshc+4SLcm3jmVtUDSbWQXZW5JpOedX/kemLkP05bWTcA3JPUCkLSupBWA24Evpz7OYcAnW9j3HuATktZK+zbf2O1NYMXcdjeTNY9J241Kk7cD+6Vlu5FdJ7SalYDXUrJcj6yG26wJaK4l70vW1H8DeFbSmPQcShd2tm7OCdOW1rlk/ZMPSHoEOIesxXIN2W02/gGcBfy9cseImEPW73i1pIf4sEl8PbB386APcCSweRpUeowPR+t/Cuwo6QGyroGZNWKdBPSU9DBwMvB/uXVvAxtKmkbWR3lSWr4fcHCK71FgT6zb87nkZmYFuYZpZlaQE6aZWUFOmGZmBTlhmpkV5IRpZlaQE6aZWUFOmGZmBf1/ne8xPRiiDvUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Printing and Plotting the Confusion Matrix for the Random Forest Ensemble Model: \n",
    "from sklearn.metrics import confusion_matrix\n",
    "import itertools\n",
    "\n",
    "print(pd.DataFrame(\n",
    "    confusion_matrix(y_test, y_predict2),\n",
    "    columns=['Predicted No Pep', 'Predicted Pep'],\n",
    "    index=['True No Pep', 'True Pep']\n",
    "))\n",
    "\n",
    "cm = confusion_matrix(y_test, y_predict2)\n",
    "plot_confusion_matrix(cm, classes=[\"No Pep\", \"Pep\"],\n",
    "                      title='Confusion matrix of Ensemble Model')\n",
    "plt.figure()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "distinct-paragraph",
   "metadata": {},
   "source": [
    "### Summary\n",
    "\n",
    "When comparing the performance of the decision tree model and the random forest ensemble model, there was approximately 0.67% increase in the accuracy. I attempted to keep the parameters similar between the models to better compare performance, however the models can be adjusted individually to increase performance and accuracy to be greater than 86.67%. In the Random Forest confusion matrix, we see that the model had more trouble with false negatives than false positives (13 vs 7). Even though the two models were similar in performance, the Random Forest classifier ultimately predicted more accurately. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "derived-princeton",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}