CA04.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Data Source and Contents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pip install pandas numpy matplotlib scikit-learn xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.tree import export_graphviz\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from xgboost import XGBClassifier\n",
    "import graphviz\n",
    "from six import StringIO\n",
    "from IPython.display import Image\n",
    "import pydotplus\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn import tree\n",
    "from sklearn import datasets, metrics, model_selection, svm\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score, classification_report, roc_curve, auc, roc_auc_score, RocCurveDisplay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#loading in data\n",
    "df = pd.read_csv(\"https://github.com/ArinB/MSBA-CA-03-Decision-Trees/blob/master/census_data.csv?raw=true\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>hours_per_week_bin</th>\n",
       "      <th>occupation_bin</th>\n",
       "      <th>msr_bin</th>\n",
       "      <th>capital_gl_bin</th>\n",
       "      <th>race_sex_bin</th>\n",
       "      <th>education_num_bin</th>\n",
       "      <th>education_bin</th>\n",
       "      <th>workclass_bin</th>\n",
       "      <th>age_bin</th>\n",
       "      <th>flag</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>31-40</td>\n",
       "      <td>Mid - Low</td>\n",
       "      <td>Mid</td>\n",
       "      <td>&gt; 0</td>\n",
       "      <td>High</td>\n",
       "      <td>13</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>income</td>\n",
       "      <td>36-40 &amp; 56-60</td>\n",
       "      <td>train</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0-30</td>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "      <td>= 0</td>\n",
       "      <td>High</td>\n",
       "      <td>13</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>income</td>\n",
       "      <td>40-55</td>\n",
       "      <td>train</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31-40</td>\n",
       "      <td>Low</td>\n",
       "      <td>Mid</td>\n",
       "      <td>= 0</td>\n",
       "      <td>High</td>\n",
       "      <td>9-12</td>\n",
       "      <td>Mid</td>\n",
       "      <td>income</td>\n",
       "      <td>36-40 &amp; 56-60</td>\n",
       "      <td>train</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31-40</td>\n",
       "      <td>Low</td>\n",
       "      <td>High</td>\n",
       "      <td>= 0</td>\n",
       "      <td>Mid</td>\n",
       "      <td>0-8</td>\n",
       "      <td>Low</td>\n",
       "      <td>income</td>\n",
       "      <td>40-55</td>\n",
       "      <td>train</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31-40</td>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "      <td>= 0</td>\n",
       "      <td>Low</td>\n",
       "      <td>13</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>income</td>\n",
       "      <td>26-30 &amp; 71-100</td>\n",
       "      <td>train</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  hours_per_week_bin occupation_bin msr_bin capital_gl_bin race_sex_bin  \\\n",
       "0              31-40      Mid - Low     Mid            > 0         High   \n",
       "1               0-30           High    High            = 0         High   \n",
       "2              31-40            Low     Mid            = 0         High   \n",
       "3              31-40            Low    High            = 0          Mid   \n",
       "4              31-40           High    High            = 0          Low   \n",
       "\n",
       "  education_num_bin education_bin workclass_bin          age_bin   flag  y  \n",
       "0                13     Bachelors        income    36-40 & 56-60  train  0  \n",
       "1                13     Bachelors        income            40-55  train  0  \n",
       "2              9-12           Mid        income    36-40 & 56-60  train  0  \n",
       "3               0-8           Low        income            40-55  train  0  \n",
       "4                13     Bachelors        income   26-30 & 71-100  train  0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# removing first letter and the period from hours_per_week_bin,\toccupation_bin,\tmsr_bin, capital_gl_bin, race_sex_bin, education_num_bin, education_bin, workclass_bin, age_bin\n",
    "df['hours_per_week_bin'] = df['hours_per_week_bin'].str[2:]\n",
    "df['occupation_bin'] = df['occupation_bin'].str[2:]\n",
    "df['msr_bin'] = df['msr_bin'].str[2:]\n",
    "df['capital_gl_bin'] = df['capital_gl_bin'].str[2:]\n",
    "df['race_sex_bin'] = df['race_sex_bin'].str[2:]\n",
    "df['education_num_bin'] = df['education_num_bin'].str[2:]\n",
    "df['education_bin'] = df['education_bin'].str[2:]\n",
    "df['workclass_bin'] = df['workclass_bin'].str[2:]\n",
    "df['age_bin'] = df['age_bin'].str[2:]\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Finding Optimal Value of a key Ensemble Method Hyperparameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating train and test datasets\n",
    "df_train = df[df['flag'] == 'train']\n",
    "df_test = df[df['flag'] == 'test']\n",
    "\n",
    "features_cols = ['hours_per_week_bin',\t'occupation_bin',\t'msr_bin', 'capital_gl_bin', 'race_sex_bin', 'education_num_bin', 'education_bin', 'workclass_bin', 'age_bin']\n",
    "x_train = df_train[features_cols]\n",
    "y_train = df_train['y']\n",
    "\n",
    "x_test = df_test[features_cols]\n",
    "y_test = df_test['y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# encoding the dataset\n",
    "x_train = pd.get_dummies(x_train, columns = features_cols, drop_first=True)\n",
    "x_test = pd.get_dummies(x_test, columns = features_cols, drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = []\n",
    "max_depth_options = [2, 4, 6, 8, 10, 12, 14, 16, 18, 20]\n",
    "for trees in max_depth_options:\n",
    "    model = DecisionTreeClassifier(max_depth=trees, random_state=101)\n",
    "    model.fit(x_train, y_train)\n",
    "    y_pred = model.predict(x_test)\n",
    "    accuracy = np.mean(y_test == y_pred)\n",
    "    results.append(accuracy)\n",
    "\n",
    "# plot \n",
    "plt.figure(figsize=(8,4))\n",
    "pd.Series(results, max_depth_options).plot(color=\"darkgoldenrod\", marker=\"o\")\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Building a Random Forest Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# finding optimal # of estimators for random forest\n",
    "n_estimators = [50, 100, 150, 200, 250, 300, 350, 400, 450, 500]\n",
    "\n",
    "accuracy_results = []\n",
    "auc_results = []\n",
    "\n",
    "for trees in n_estimators:\n",
    "    rf = RandomForestClassifier(n_estimators=trees, random_state=101)\n",
    "    rf.fit(x_train, y_train)\n",
    "    y_pred = rf.predict(x_test)\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    auc = roc_auc_score(y_test, y_pred)\n",
    "\n",
    "    accuracy_results.append(accuracy)\n",
    "    auc_results.append(auc)\n",
    "\n",
    "results = ([accuracy_results, auc_results])\n",
    "\n",
    "\n",
    "# plot accuracy\n",
    "plt.figure(figsize=(8,4))\n",
    "pd.Series(accuracy_results, n_estimators).plot(color=\"mediumspringgreen\", marker=\"o\")\n",
    "plt.title('Random Forest Accuracy')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot auc\n",
    "plt.figure(figsize=(8,4))\n",
    "pd.Series(auc_results, n_estimators).plot(color=\"mediumspringgreen\", marker=\"o\")\n",
    "plt.title('Random Forest AUC')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max Accuracy: 0.8384005896443707 Max AUC: 0.7482791326128317\n"
     ]
    },
    {
     "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>50</th>\n",
       "      <th>100</th>\n",
       "      <th>150</th>\n",
       "      <th>200</th>\n",
       "      <th>250</th>\n",
       "      <th>300</th>\n",
       "      <th>350</th>\n",
       "      <th>400</th>\n",
       "      <th>450</th>\n",
       "      <th>500</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.837971</td>\n",
       "      <td>0.838401</td>\n",
       "      <td>0.837909</td>\n",
       "      <td>0.837786</td>\n",
       "      <td>0.836988</td>\n",
       "      <td>0.837664</td>\n",
       "      <td>0.837418</td>\n",
       "      <td>0.837725</td>\n",
       "      <td>0.838093</td>\n",
       "      <td>0.838401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUC</th>\n",
       "      <td>0.748279</td>\n",
       "      <td>0.748201</td>\n",
       "      <td>0.747431</td>\n",
       "      <td>0.747081</td>\n",
       "      <td>0.744852</td>\n",
       "      <td>0.746372</td>\n",
       "      <td>0.747558</td>\n",
       "      <td>0.746771</td>\n",
       "      <td>0.747013</td>\n",
       "      <td>0.746765</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               50        100       150       200       250       300  \\\n",
       "Accuracy  0.837971  0.838401  0.837909  0.837786  0.836988  0.837664   \n",
       "AUC       0.748279  0.748201  0.747431  0.747081  0.744852  0.746372   \n",
       "\n",
       "               350       400       450       500  \n",
       "Accuracy  0.837418  0.837725  0.838093  0.838401  \n",
       "AUC       0.747558  0.746771  0.747013  0.746765  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#storing max accuracy and auc\n",
    "max_rf = [max(accuracy_results), max(auc_results)]\n",
    "print(\"Max Accuracy:\", max(accuracy_results), \"Max AUC:\", max(auc_results))\n",
    "\n",
    "#print results\n",
    "rf_results = pd.DataFrame(results, columns = n_estimators, index = ['Accuracy', 'AUC'])\n",
    "rf_results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Write your observations about the Classifier’s behavior with respect to the number of estimators. <br>\n",
    "The accuracy increases to 100 initially but drops sharply as it reaches 250. Then again, it rises steadily to 500 with a minor drop from at 350. As the number of models (trees) increases the diversification raises the accuracy score until a certain point is reached. <br>\n",
    "2. Is there an optimal value of the estimator within the given range? <br>\n",
    "There seems to be 2 optimal value points (100 and 500) that share the highest accuracy range. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Building AdaBoost, Gradient Boost, and XGB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# finding optimal # of estimators for adaboost\n",
    "n_estimators = [50, 100, 150, 200, 250, 300, 350, 400, 450, 500]\n",
    "\n",
    "accuracy_results = []\n",
    "auc_results = []\n",
    "\n",
    "for trees in n_estimators:\n",
    "    ab = AdaBoostClassifier(n_estimators=trees, random_state=101)\n",
    "    ab.fit(x_train, y_train)\n",
    "    y_pred = ab.predict(x_test)\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    auc = roc_auc_score(y_test, y_pred)\n",
    "\n",
    "    accuracy_results.append(accuracy)\n",
    "    auc_results.append(auc)\n",
    "\n",
    "results = ([accuracy_results, auc_results])\n",
    "\n",
    "\n",
    "# plot accuracy\n",
    "plt.figure(figsize=(8,4))\n",
    "pd.Series(accuracy_results, n_estimators).plot(color=\"cornflowerblue\", marker=\"o\")\n",
    "plt.title('AdaBoost Accuracy')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot auc\n",
    "plt.figure(figsize=(8,4))\n",
    "pd.Series(auc_results, n_estimators).plot(color=\"cornflowerblue\", marker=\"o\")\n",
    "plt.title('AdaBoost AUC')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max Accuracy: 0.8449112462379461 Max AUC: 0.7463668172782401\n"
     ]
    },
    {
     "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>50</th>\n",
       "      <th>100</th>\n",
       "      <th>150</th>\n",
       "      <th>200</th>\n",
       "      <th>250</th>\n",
       "      <th>300</th>\n",
       "      <th>350</th>\n",
       "      <th>400</th>\n",
       "      <th>450</th>\n",
       "      <th>500</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.844604</td>\n",
       "      <td>0.844727</td>\n",
       "      <td>0.844788</td>\n",
       "      <td>0.844788</td>\n",
       "      <td>0.844850</td>\n",
       "      <td>0.844911</td>\n",
       "      <td>0.844788</td>\n",
       "      <td>0.844788</td>\n",
       "      <td>0.844788</td>\n",
       "      <td>0.844788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUC</th>\n",
       "      <td>0.739781</td>\n",
       "      <td>0.739233</td>\n",
       "      <td>0.746367</td>\n",
       "      <td>0.746277</td>\n",
       "      <td>0.746138</td>\n",
       "      <td>0.746357</td>\n",
       "      <td>0.746008</td>\n",
       "      <td>0.746008</td>\n",
       "      <td>0.746008</td>\n",
       "      <td>0.746008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               50        100       150       200       250       300  \\\n",
       "Accuracy  0.844604  0.844727  0.844788  0.844788  0.844850  0.844911   \n",
       "AUC       0.739781  0.739233  0.746367  0.746277  0.746138  0.746357   \n",
       "\n",
       "               350       400       450       500  \n",
       "Accuracy  0.844788  0.844788  0.844788  0.844788  \n",
       "AUC       0.746008  0.746008  0.746008  0.746008  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#storing max accuracy and auc\n",
    "max_ab = [max(accuracy_results), max(auc_results)]\n",
    "print(\"Max Accuracy:\", max(accuracy_results), \"Max AUC:\", max(auc_results))\n",
    "\n",
    "#print results\n",
    "ab_results = pd.DataFrame(results, columns = n_estimators, index = ['Accuracy', 'AUC'])\n",
    "ab_results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Write your observations about the Classifier’s behavior with respect to the number of estimators. <br>\n",
    "The accuracy increases to 300 then drops after that. <br>\n",
    "2. Is there an optimal value of the estimator within the given range? <br>\n",
    "There seems to be a optimal value point of 300."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# finding optimal # of estimators for gradient boosting\n",
    "n_estimators = [50, 100, 150, 200, 250, 300, 350, 400, 450, 500]\n",
    "\n",
    "accuracy_results = []\n",
    "auc_results = []\n",
    "\n",
    "for trees in n_estimators:\n",
    "    gb = GradientBoostingClassifier(n_estimators=trees, random_state=101)\n",
    "    gb.fit(x_train, y_train)\n",
    "    y_pred = gb.predict(x_test)\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    auc = roc_auc_score(y_test, y_pred)\n",
    "\n",
    "    accuracy_results.append(accuracy)\n",
    "    auc_results.append(auc)\n",
    "\n",
    "results = ([accuracy_results, auc_results])\n",
    "\n",
    "\n",
    "# plot accuracy\n",
    "plt.figure(figsize=(8,4))\n",
    "pd.Series(accuracy_results, n_estimators).plot(color=\"rebeccapurple\", marker=\"o\")\n",
    "plt.title('Gradient Boost Accuracy')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot auc\n",
    "plt.figure(figsize=(8,4))\n",
    "pd.Series(auc_results, n_estimators).plot(color=\"rebeccapurple\", marker=\"o\")\n",
    "plt.title('Gradient Boost AUC')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max Accuracy: 0.8468767274737424 Max AUC: 0.7586890520252897\n"
     ]
    },
    {
     "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>50</th>\n",
       "      <th>100</th>\n",
       "      <th>150</th>\n",
       "      <th>200</th>\n",
       "      <th>250</th>\n",
       "      <th>300</th>\n",
       "      <th>350</th>\n",
       "      <th>400</th>\n",
       "      <th>450</th>\n",
       "      <th>500</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.843007</td>\n",
       "      <td>0.846754</td>\n",
       "      <td>0.845525</td>\n",
       "      <td>0.845894</td>\n",
       "      <td>0.846631</td>\n",
       "      <td>0.846877</td>\n",
       "      <td>0.845648</td>\n",
       "      <td>0.846263</td>\n",
       "      <td>0.846631</td>\n",
       "      <td>0.846078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUC</th>\n",
       "      <td>0.723021</td>\n",
       "      <td>0.750347</td>\n",
       "      <td>0.749633</td>\n",
       "      <td>0.754544</td>\n",
       "      <td>0.758349</td>\n",
       "      <td>0.758689</td>\n",
       "      <td>0.755820</td>\n",
       "      <td>0.756222</td>\n",
       "      <td>0.758169</td>\n",
       "      <td>0.757448</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               50        100       150       200       250       300  \\\n",
       "Accuracy  0.843007  0.846754  0.845525  0.845894  0.846631  0.846877   \n",
       "AUC       0.723021  0.750347  0.749633  0.754544  0.758349  0.758689   \n",
       "\n",
       "               350       400       450       500  \n",
       "Accuracy  0.845648  0.846263  0.846631  0.846078  \n",
       "AUC       0.755820  0.756222  0.758169  0.757448  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#storing max accuracy and auc\n",
    "max_gb = [max(accuracy_results), max(auc_results)]\n",
    "print(\"Max Accuracy:\", max(accuracy_results), \"Max AUC:\", max(auc_results))\n",
    "\n",
    "#print results\n",
    "gb_results = pd.DataFrame(results, columns = n_estimators, index = ['Accuracy', 'AUC'])\n",
    "gb_results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Write your observations about the Classifier’s behavior with respect to the number of estimators. <br>\n",
    "The accuracy increases to 100 initially but drops at 150. It then rises again to 300 and drops at 350 and then a gentle rise to 450, falling at 500. <br>\n",
    "2. Is there an optimal value of the estimator within the given range? <br>\n",
    "There seems to be an optimal point of 300."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# finding optimal # of estimators for XGB\n",
    "n_estimators = [50, 100, 150, 200, 250, 300, 350, 400, 450, 500]\n",
    "\n",
    "accuracy_results = []\n",
    "auc_results = []\n",
    "\n",
    "for trees in n_estimators:\n",
    "    xgb = XGBClassifier(n_estimators=trees, random_state=101)\n",
    "    xgb.fit(x_train, y_train)\n",
    "    y_pred = xgb.predict(x_test)\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    auc = roc_auc_score(y_test, y_pred)\n",
    "\n",
    "    accuracy_results.append(accuracy)\n",
    "    auc_results.append(auc)\n",
    "\n",
    "results = ([accuracy_results, auc_results])\n",
    "\n",
    "\n",
    "# plot accuracy\n",
    "plt.figure(figsize=(8,4))\n",
    "pd.Series(accuracy_results, n_estimators).plot(color=\"tomato\", marker=\"o\")\n",
    "plt.title('XGB Accuracy')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot auc\n",
    "plt.figure(figsize=(8,4))\n",
    "pd.Series(auc_results, n_estimators).plot(color=\"tomato\", marker=\"o\")\n",
    "plt.title('XGB AUC')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max Accuracy: 0.8446655610834716 Max AUC: 0.758229282126653\n"
     ]
    },
    {
     "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>50</th>\n",
       "      <th>100</th>\n",
       "      <th>150</th>\n",
       "      <th>200</th>\n",
       "      <th>250</th>\n",
       "      <th>300</th>\n",
       "      <th>350</th>\n",
       "      <th>400</th>\n",
       "      <th>450</th>\n",
       "      <th>500</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.844666</td>\n",
       "      <td>0.843314</td>\n",
       "      <td>0.844236</td>\n",
       "      <td>0.842700</td>\n",
       "      <td>0.842577</td>\n",
       "      <td>0.842393</td>\n",
       "      <td>0.841717</td>\n",
       "      <td>0.842270</td>\n",
       "      <td>0.841287</td>\n",
       "      <td>0.840612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUC</th>\n",
       "      <td>0.758229</td>\n",
       "      <td>0.756626</td>\n",
       "      <td>0.757589</td>\n",
       "      <td>0.756853</td>\n",
       "      <td>0.756683</td>\n",
       "      <td>0.757101</td>\n",
       "      <td>0.757018</td>\n",
       "      <td>0.754057</td>\n",
       "      <td>0.753234</td>\n",
       "      <td>0.752702</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               50        100       150       200       250       300  \\\n",
       "Accuracy  0.844666  0.843314  0.844236  0.842700  0.842577  0.842393   \n",
       "AUC       0.758229  0.756626  0.757589  0.756853  0.756683  0.757101   \n",
       "\n",
       "               350       400       450       500  \n",
       "Accuracy  0.841717  0.842270  0.841287  0.840612  \n",
       "AUC       0.757018  0.754057  0.753234  0.752702  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#storing max accuracy and auc\n",
    "max_xgb = [max(accuracy_results), max(auc_results)]\n",
    "print(\"Max Accuracy:\", max(accuracy_results), \"Max AUC:\", max(auc_results))\n",
    "\n",
    "#print results\n",
    "xgb_results = pd.DataFrame(results, columns = n_estimators, index = ['Accuracy', 'AUC'])\n",
    "xgb_results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Write your observations about the Classifier’s behavior with respect to the number of estimators. <br>\n",
    "The accuracy starts off highest at 50 and declines all the way down. <br>\n",
    "2. Is there an optimal value of the estimator within the given range? <br>\n",
    "There seems to be an optimal point at 50."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Compare Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>Accuracy</th>\n",
       "      <th>AUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.838401</td>\n",
       "      <td>0.748279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AdaBoost</th>\n",
       "      <td>0.844911</td>\n",
       "      <td>0.746367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gradient Boost</th>\n",
       "      <td>0.846877</td>\n",
       "      <td>0.758689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGB</th>\n",
       "      <td>0.844666</td>\n",
       "      <td>0.758229</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Accuracy       AUC\n",
       "Random Forest   0.838401  0.748279\n",
       "AdaBoost        0.844911  0.746367\n",
       "Gradient Boost  0.846877  0.758689\n",
       "XGB             0.844666  0.758229"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_scores = pd.DataFrame([max_rf, max_ab, max_gb, max_xgb], columns = ['Accuracy', 'AUC'], index = ['Random Forest', 'AdaBoost', 'Gradient Boost', 'XGB'])\n",
    "max_scores"
   ]
  }
 ],
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