DecissionTree_Iteration_Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn import svm\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "import matplotlib.pyplot as plt\n",
    "# from sklearn.externals.six import StringIO  \n",
    "from IPython.display import Image  \n",
    "from sklearn.tree import export_graphviz\n",
    "import seaborn as sns\n",
    "import pydotplus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pydotplus in c:\\users\\user\\anaconda3\\lib\\site-packages (2.0.2)\n",
      "Requirement already satisfied: pyparsing>=2.0.1 in c:\\users\\user\\anaconda3\\lib\\site-packages (from pydotplus) (3.0.4)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install pydotplus"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('bank.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>deposit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>2343</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>1042</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>45</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>1467</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>1270</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>1389</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>2476</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>579</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>184</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>673</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11157</th>\n",
       "      <td>33</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>single</td>\n",
       "      <td>primary</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>20</td>\n",
       "      <td>apr</td>\n",
       "      <td>257</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11158</th>\n",
       "      <td>39</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>733</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>16</td>\n",
       "      <td>jun</td>\n",
       "      <td>83</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11159</th>\n",
       "      <td>32</td>\n",
       "      <td>technician</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>29</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>19</td>\n",
       "      <td>aug</td>\n",
       "      <td>156</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11160</th>\n",
       "      <td>43</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>cellular</td>\n",
       "      <td>8</td>\n",
       "      <td>may</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>172</td>\n",
       "      <td>5</td>\n",
       "      <td>failure</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11161</th>\n",
       "      <td>34</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>9</td>\n",
       "      <td>jul</td>\n",
       "      <td>628</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11162 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age          job  marital  education default  balance housing loan  \\\n",
       "0       59       admin.  married  secondary      no     2343     yes   no   \n",
       "1       56       admin.  married  secondary      no       45      no   no   \n",
       "2       41   technician  married  secondary      no     1270     yes   no   \n",
       "3       55     services  married  secondary      no     2476     yes   no   \n",
       "4       54       admin.  married   tertiary      no      184      no   no   \n",
       "...    ...          ...      ...        ...     ...      ...     ...  ...   \n",
       "11157   33  blue-collar   single    primary      no        1     yes   no   \n",
       "11158   39     services  married  secondary      no      733      no   no   \n",
       "11159   32   technician   single  secondary      no       29      no   no   \n",
       "11160   43   technician  married  secondary      no        0      no  yes   \n",
       "11161   34   technician  married  secondary      no        0      no   no   \n",
       "\n",
       "        contact  day month  duration  campaign  pdays  previous poutcome  \\\n",
       "0       unknown    5   may      1042         1     -1         0  unknown   \n",
       "1       unknown    5   may      1467         1     -1         0  unknown   \n",
       "2       unknown    5   may      1389         1     -1         0  unknown   \n",
       "3       unknown    5   may       579         1     -1         0  unknown   \n",
       "4       unknown    5   may       673         2     -1         0  unknown   \n",
       "...         ...  ...   ...       ...       ...    ...       ...      ...   \n",
       "11157  cellular   20   apr       257         1     -1         0  unknown   \n",
       "11158   unknown   16   jun        83         4     -1         0  unknown   \n",
       "11159  cellular   19   aug       156         2     -1         0  unknown   \n",
       "11160  cellular    8   may         9         2    172         5  failure   \n",
       "11161  cellular    9   jul       628         1     -1         0  unknown   \n",
       "\n",
       "      deposit  \n",
       "0         yes  \n",
       "1         yes  \n",
       "2         yes  \n",
       "3         yes  \n",
       "4         yes  \n",
       "...       ...  \n",
       "11157      no  \n",
       "11158      no  \n",
       "11159      no  \n",
       "11160      no  \n",
       "11161      no  \n",
       "\n",
       "[11162 rows x 17 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking Null Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrmat = df.corr()\n",
    "k = 10 #number of variables for heatmap\n",
    "cols = corrmat.nlargest(k, 'age')['age'].index\n",
    "cm = np.corrcoef(df[cols].values.T)\n",
    "sns.set(font_scale=1.25)\n",
    "hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PBmAY4Bw2APDCLbfcqr17f6cZM27RhQsXNGPGLdq793e65ZZbjR4NwDDAIVEA8ILdnqgJEybqj398Wy5Xm/z9AzRnzu367LOTKix0Gj0egEGOQ6IA0Ac+/fS4/vjHIlmtVs85bO3t7Ro/PsLo0QAMAxwSBQAvxMVNktNZ3O0xp7NYcXGTDJoIwHBCsAGAFxyO1XI40lRUVKj29nYVFRXK4UiTw8F12AD0P85hAwAvFRTkKycn2/MtUYdjtZKTFxk9FoAhgOuwEWwA+tiVc9gAoK/0FGwcEgUAADA5gg0AAMDkCDYAAACTI9gAAABMjmADAAAwOYINAADA5Ag2AAAAkyPYAAAATI5gAwAAMDmCDQAAwOQINgAAAJMj2AAAAEyOYAMAADA5gg0AvFRQkC+7PVG+vr6y2xNVUJBv9EgAhgk/owcAgMGgoCBfmZnPKSdnmxYsuENvvvkHORxpkqTk5EUGTwdgqLO43W630UP0p7q6RnV1Dem3CGAA2O2JyszM0syZdkVEjFBtbYOKigq1fv0aFRY6jR4PwCDn42PRyJEhX7tOsAGAF0aPDtfp07WyWq2eYGtvb9f48RGqrq43ejwAg1xPwcY5bADghbi4SXI6i7s95nQWKy5ukkETARhOCDYA8ILDsVoOR5qKigrV3t6uoqJCORxpcjhWGz0agGGAQ6IA4KWCgnzl5GTr00+PKy5ukhyO1XzhAECf4Bw2gg1AH7tyDhsA9BXOYQMAABjkCDYAAACTI9gAAABMjmADAAAwOYINAADA5Ag2AAAAkyPYAAAATI5gAwAAMDmvg62zs1MpKSl68sknPY+99957mjdvnqZMmaKkpCSVlpZ61jo6OpSZmakZM2Zo2rRpWrVqlRobGz3rFRUVSklJ0dSpUzV79mzt2bOn2+/bu3ev5syZoxtvvFH33nuvysvLv8n7BAAAGLS8DrZt27Z1C7LKyko5HA6tXbtWpaWlWrx4sVJTU9Xc3CxJysvL05EjR3Tw4EG9++67qq+v1+bNmyVdjrnly5crPj5eTqdTWVlZ2rJli44ePSpJcjqd2rRpk7Zu3aqSkhIlJCRo5cqVGuI3ZQAAAPhKXgVbcXGx3nrrLd11112ex/bv36+EhATNmjVLVqtVS5YsUVRUlA4fPizp8h6yZcuWKTIyUuHh4Vq1apX279+vpqYmlZSU6Pz581qxYoX8/f0VHx+vpKQk7dq1S5KUn5+v+fPna8qUKfL399ejjz6quro6FRcX98NHAAAAYG49BltdXZ3S09O1ZcsWBQUFeR4vLy9XbGxst+dOnDhRx48fV0NDg6qrqxUTE9NtzeVyqbKyUidOnFB0dLSsVutV237Va/v6+io6OtqzDgAAMJxcM9i6urq0Zs0aLV26VN/97ne7rTU1Nclms3V7zGazqaWlRU1NTZKkwMBAz1pAQIAsFotn/ctrV57b2tra42sDAAAMN37XWty+fbv8/f31wAMPXLX25cC6orW1VZGRkZ4Y+/J6W1ub3G63QkJCFBQUdNW2LS0tCgkJ8bx2W1vbVa99Zb03rnXnewD4c0VEjDB6BADDyDWD7cCBA6qpqVF8fLyk/x9gx44d0w9+8IOrDlGWl5frlltuUVhYmCIjI1VRUeE5tFleXi6r1aro6GjV1dXp1KlT6uzslK+vryTp5MmTnkOosbGxqqio8LxuZ2enKisrrzoE6426ukZ1dfFlBQB9JyJihGprG4weA8AQ4uNjueZOpmseEn3rrbf0wQcfqLS0VKWlpUpKStKCBQt06NAh3X333SouLtY777yj9vZ27d69W1VVVbr99tslScnJycrLy1N1dbXq6+u1detWzZ07VzabTdOnT1doaKhyc3PlcrlUVlamAwcOKDk52bPtwYMHVVZWJpfLpdzcXIWGhnrCEQAAYDi55h62a4mJiVFubq6ys7O1Zs0aTZgwQdu3b1d4eLgkKTU1VS6XSwsXLlRbW5vsdrueeeYZSZLVatXOnTuVkZGhm2++WWFhYVqzZo1uvvlmSdKtt96qJ598UuvXr1dNTY2uv/56bd++vduXFAAAAIYLi3uIX9yMQ6IA+hqHRAH0tW90SBQAAADGI9gAwEsFBfmy2xPl6+sruz1RBQX5Ro8EYJj4s89hA4DhpKAgX+npT3guIN7U1KT09CckScnJi4wcDcAwwB42APDCs88+LT8/X7344i/V2tqqF1/8pfz8fPXss08bPRqAYYBgAwAvnD17VosX36f169fIZrNp/fo1Wrz4Pp09e9bo0QAMAwQbAHjpt799XZmZWWptbVVmZpZ++9vXjR4JwDBBsAGAF/z8/NTR0d7tsY6Odvn5cSowgP7HddgAwAtRUWH61rdGKigoSGfOnNa4cePV3NysCxfqdO7cF0aPB2CQ4zpsANAHJk2arAce+DsFBwfLYrEoODhYDzzwd5o0abLRowEYBgg2APCCw7Fa+/bldzuHbd++fDkcq40eDcAwwMkXAOCF5ORFOnLEqcWL/49crjb5+wcoJeVBrsEGYECwhw0AvFBQkK833ihQVFSUfHx8FBUVpTfeKOBuBwAGBMEGAF7gwrkAjESwAYAXzp49q5de2q6ZM+2yWq2aOdOul17azoVzAQwIgg0AAMDkCDYA8MLYsWOVlvYTFRUVqr29XUVFhUpL+4nGjh1r9GgAhgGCDQC8sGHDc2ppadY99/xY/v7+uueeH6ulpVkbNjxn9GgAhgGCDQC85O8foDFjxsjHx0djxoyRv3+A0SMBGCYINgDwQk5Otn7961dUVvaROjs7VVb2kX7961eUk5Nt9GgAhgGCDQC88Omnx1VVdVZ2e6J8fX1ltyeqquqsPv30uNGjARgGuPk7AHjhxhsnq7GxSWFhYZ6bv3/xxRcKCQnWf/7nJ0aPB2CQ4+bvANAHmptb1NjYoIcfXq7Gxsb/97NBzc0tRo8GYBgg2ADAC/X1F5WW5tDu3a9pxIgR2r37NaWlOVRff9Ho0QAMAwQbAHhp5ky7Cgud6uzsVGGhUzNn2o0eCcAwQbABgBfGjh2rlSu7Xzh35UounAtgYBBsAOCFDRueU0dHpx57bIVsNpsee2yFOjo6uXAugAHhZ/QAADAYJCcvkiTPddeCg4OVnv6M53EA6E9c1gMAeikiYoRqaxuMHgPAEMJlPQCgj9x22wxFRobKYrEoMjJUt902w+iRAAwTBBsAeOG222bo44//W3feOVe1tbW68865+vjj/ybaAAwIgg0AvHAl1l577XcaNWqUXnvtd55oA4D+RrABgJdeeOEX1/w3APQXgg0AvPT446nX/DcA9Bcu6wEAXvjOd76rf/mX3ysyMvSqxwGgv7GHDQC88MUXX/TqcQDoSwQbAHjh7NnPFR8/XTU1l+R2u1VTc0nx8dN19uznRo8GYBgg2ADASwsX/q3s9kT5+vrKbk/UwoV/a/RIAIYJ7nQAAF6IjAyVv7+/9uwp0IIFd+jNN/+gxYuT5XK5VFNzyejxAAxy3OkAAPqAn5+fXC6XMjOf1fnz55WZ+axcLpf8/PjuFoD+x18aAPBCV1eXwsPDVVpaorFjx0qSwsPDdekSe9cA9D/2sAGAF0aPHq2AgAAVFLwpl8ulgoI3FRAQoNGjRxs9GoBhgD1sAOCllpYWPfbYCi1ceEbf/vY4tbS0KCTk6885AYC+wh42APBCVVWVrFarJOnKd7WsVquqqqqMHAvAMEGwAYAXrFZ/zZ79QwUHB8tisSg4OFizZ/9QVqu/0aMBGAa4rAcAeCEyMlQ+Pj4aOXKUamtrFBERqbq68+rq6uKyHgC+sZ4u68E5bADgBT8/P/n4+OjixYuSpIsXL8rPz09dXV0GTwZgOCDYAMALHR0dkiQfH19Jly/z0dHRaeRIAIYRzmEDgF7o6urs9hMABgLBBgC94OPj0+0nAAwE/uIAQC9cOWeNc9cADCSCDQAAwOQINgDoBYvF0u0nAAwEgg0AeuHKpSuH+CUsAZgMwQYAvcAeNgBGINgAoBfYwwbACAQbAACAyRFsAAAAJkewAQAAmJxXwfbhhx/q/vvvV3x8vL7//e/rZz/7mVpaWiRJ7733nubNm6cpU6YoKSlJpaWlnu06OjqUmZmpGTNmaNq0aVq1apUaGxs96xUVFUpJSdHUqVM1e/Zs7dmzp9vv3bt3r+bMmaMbb7xR9957r8rLy/viPQPAn40vHQAwQo/B1tDQoGXLlumHP/yh/uM//kP5+fk6evSosrOzVVlZKYfDobVr16q0tFSLFy9WamqqmpubJUl5eXk6cuSIDh48qHfffVf19fXavHmzpMsxt3z5csXHx8vpdCorK0tbtmzR0aNHJUlOp1ObNm3S1q1bVVJSooSEBK1cuZITfQEYymLx6fYTAAZCj39xPv/8c8XHx2vp0qXy8/PT6NGjlZSUpCNHjmj//v1KSEjQrFmzZLVatWTJEkVFRenw4cOSLu8hW7ZsmSIjIxUeHq5Vq1Zp//79ampqUklJic6fP68VK1bI399f8fHxSkpK0q5duyRJ+fn5mj9/vqZMmSJ/f389+uijqqurU3Fxcf9+IgBwDVd2rLGDDcBA6jHYJk+erF/84heef7vdbr399tu64YYbVF5ertjY2G7Pnzhxoo4fP66GhgZVV1crJiam25rL5VJlZaVOnDih6OhoWa3Wq7aVdNVr+/r6Kjo62rMOAEbo7Ozs9hMABoJfb57c0dGhf/zHf9Tp06eVnZ2tJ554QjabrdtzbDabWlpa1NTUJEkKDAz0rAUEBMhisXjWv7x25bmtra2SpKampq997d4YOTKkV88HgN6KiBhh9AgAhjivg62urk4//elPdeHCBe3atUtRUVHdAuuK1tZWRUZGemLsy+ttbW1yu90KCQlRUFDQVdu2tLQoJORyYAUGBqqtre2q176y7v3cjerq4rw3AP2ntrbB6BEADHI+PpZr7mTy6qzZ48ePKzk5WWFhYdqzZ4++/e1vS5Li4uL02WefdXtueXm5YmJiFBYWpsjISFVUVHRbs1qtio6OVmxsrE6dOtXtsMLJkyc9h1BjY2O7bdvZ2anKysqrDsECwEDiW6IAjNBjsJ0/f14PPfSQ7rrrLuXm5io4ONizdvfdd6u4uFjvvPOO2tvbtXv3blVVVen222+XJCUnJysvL0/V1dWqr6/X1q1bNXfuXNlsNk2fPl2hoaHKzc2Vy+VSWVmZDhw4oOTkZM+2Bw8eVFlZmVwul3JzcxUaGqr4+Ph++igAoGfcmgqAESzuHv7qbNu2TS+99JICAwO7/R/l2LFjdfjwYb3//vvKzs7WmTNnNGHCBKWnp+umm26SJLlcLr3wwgs6dOiQ2traZLfblZGR4TmsWVFRoYyMDH300UcKCwvT8uXLdc8993h+x759+7Rjxw7V1NTo+uuvV0ZGhiZOnNirN8ghUQB9ITIy9GvXamouDeAkAIaing6J9hhsgx3BBqAvEGwA+lOfnMMGAAAA4xBsAAAAJkewAQAAmBzBBgAAYHIEGwAAgMkRbADQC76+vt1+AsBAINgAoBe4+TsAIxBsAAAAJkewAQAAmBzBBgAAYHIEGwAAgMkRbAAAACZHsAEAAJgcwQYAAGByBBsAAIDJEWwAAAAmR7ABAACYHMEGAABgcgQbAACAyRFsAAAAJkewAQAAmBzBBgAAYHIEGwAAgMkRbAAAACZHsAEAAJgcwQYAAGByBBsAAIDJEWwAAAAmR7ABAACYHMEGAABgcgQbAACAyRFsAAAAJkewAQAAmBzBBgAAYHIEGwAAgMkRbAAAACZHsAEAAJgcwQYAAGByBBsAAIDJEWwAAAAmR7ABAACYHMEGAABgcgQbAACAyRFsAAAAJkewAQAAmBzBBgAAYHIEGwAAgMkRbAAAACbnZ/QAANCfnt7p1Ofnm/r1dzy06d1v/BrfHhWs5x5O7INpAAxFFrfb7TZ6iP5UV9eorq4h/RYBDIDIyNCvXaupuTSAkwAYinx8LBo5MuTr1wdwFgAAAPwZCDYA8MLX7UVj7xqAgUCwAYCXamouqabmkhb89A3PfwPAQCDYAAAATI5gAwAAMDmCDQAAwOS4rAcA01iZU6im1g6jxxgygm1+eslhN3oMAF7o6bIepr5w7vnz5/XUU0/pyJEjstlsWrJkidLS0oweC0A/aWrt0MtP/sDoMXoUETFCtbUNRo/Ro764oC8AczB1sD3++OMaN26c/v3f/12nT5/WP/zDPyg6OloLFiwwejQAAIABY9pz2E6dOqWSkhKtWrVKNptNsbGxevDBB7Vr1y6jRwMAABhQpt3DduLECYWHh2vUqFGex/7qr/5Kx48fN3AqAP0p4IYipb77ltFjDBkBN4RIMv8hZgA9M22wNTU1KTAwsNtjgYGBam1t7dXrXOsEPgDm0vbRTKNHGFJCAq2KiBhh9BgA+oBpgy0oKOiqOGtpaVFISO8CjG+JAoPHYPjCgXT5ZP7BMutg+HIEgEF88/fY2FhdvHhRFy5c8Dx28uRJxcTEGDgVAADAwDNtsEVHR2vq1KnavHmzmpubVV5ert/85jf68Y9/bPRoAAAAA8q0wSZJubm5amxs1OzZs7V06VItWrRIixYtMnosAACAAcWdDgDASwUF+crJydYnx49r8qRJcjhWKzmZ/4kE8M0N6jsdAIBZFBTkKzPzOeXkbNPO91v18G02ORyX77xCtAHob+xhAzCkPb3Tqc/PN33j13n/1UcVNTFR50461XDhjEZ8a5zn37c9mPuNX//bo4L13MOJ3/h1AAxOPe1hI9gAwAtRUWH61rdGKigoSGfOnNa4cePV3NysCxfqdO7cF0aPB2CQG7SX9QAAM/H19VVnZ6defPGXamtr04sv/lKdnZ3y9fU1ejQAwwDBBgBe6OjokL+/tdtj/v5WdXR0GDQRgOGEYAMALy1Zcr/Wr18jm82m9evXaMmS+40eCcAwQbABgBfGjh2rPXt2KTMzS62trcrMzNKePbs0duxYo0cDMAwQbADghQ0bnlNHR6cee2yFbDabHntshTo6OrVhw3NGjwZgGOA6bADghSvXWsvJyZYkBQcHKz39Ga7BBmBAcFkPAOiliIgRqq1tMHoMAEMIl/UAAAAY5Ag2AAAAkyPYAAAATI5gAwAAMDmCDQC8VFCQL7s9Ub6+vrLbE1VQkG/0SACGCS7rAQBeKCjIV2bmc8rJ2aYFC+7Qm2/+QQ5HmiRxaQ8A/Y7LegCAF+z2RGVmZmnmTLvnsh5FRYVav36NCgudRo8HYJDr6bIeBBsAeGH06HCdPl0rq9XqCbb29naNHx+h6up6o8cDMMhxHTYA6ANxcZPkdBZ3e8zpLFZc3CSDJgIwnBBsAOAFh2O1HI40FRUVqr29XUVFhXI40uRwrDZ6NADDAIdEAcBLBQX5ysnJ1qefHldc3CQ5HKv5wgGAPsE5bAQbgD7GvUQB9DXOYQMAABjkCDYAAACTI9gAAABMjmADAAAwOYINAADA5Ag2AAAAkyPYAAAATI5gAwAAMDmCDQAAwOT8jB6gv/n4WIweAcAQxN8WAH2pp78pQ/7WVAAAAIMdh0QBAABMjmADAAAwOYINAADA5Ag2AAAAkyPYAAAATI5gAwAAMDmCDQAAwOQINgAAAJMj2AAAAEyOYAMw5Jw5c0aTJk3SmTNner1tSkqKXnrppX6YCgD+fAQbAACAyRFsAIasN954Q3PmzNGtt96qDRs2qLm5We3t7dq8ebPmzp2radOm6dZbb9Xzzz//lds3Njbqqaee0h133KGpU6dq1qxZ+qd/+ifP+g9+8AP96le/UlJSkqZOnark5GQdOXLEs37s2DGlpKRo6tSpstvtysvL86x9+OGHuv/++5WQkKA777xTe/bs6b8PAsCgR7ABGLI++OAD7d27V2+88YY++eQTbdq0Sa+++qqKior02muv6YMPPtC2bdv0m9/8RsXFxVdtv2XLFlVVVWnfvn364IMPtG7dOmVlZel//ud/PM8pKChQbm6uiouLNXnyZGVkZEiSLl68qIceekiJiYlyOp169dVX9frrr+vw4cM6d+6cli5dqrlz5+pPf/qTXnzxRf3qV7/S4cOHB+yzATC4+Bk9AAD0lyeffFLXXXedJGnlypVKTU3V+++/r7/5m7/RqFGjVFdXp9bWVgUHB+vcuXNXbZ+WliZfX1+FhISopqZGNptNbrdb586d01/8xV9IkhYuXKi//Mu/lCTNnz9f+/fvlyT967/+qwICApSamiqLxaIJEybo1VdfVWhoqA4cOKDY2Fjdd999kqTJkycrJSVFu3bt0vz58wfiowEwyBBsAIas8ePHe/577NixamtrU2trqzZu3KgjR44oMjJS3/nOd+R2u9XV1XXV9nV1ddq0aZOOHTumMWPG6IYbbpCkbs8dNWqU57/9/Pw8a7W1tRozZowsFotnPSYmRpL0+eef69ixY4qPj/esdXV1KTw8vG/eOIAhh2ADMGSdO3dO0dHRki5/czQoKEhPP/20wsLCVFhYqICAAHV1dSkhIeErt3c4HJo9e7Z27NghPz8/Xbx4Ufn5+V797tGjR6u6ulput9sTbb///e9lsVgUFRWlGTNmaOfOnZ7nnz9/Xi6X65u9YQBDFuewARiysrKydOnSJVVXV+vFF1/U4sWL1dDQIJvNJh8fHzU2Nmrz5s1qbGxUR0fHVdtfunRJgYGB8vX11YULF/Szn/1Mkr7yuf/bbbfdJpfLpZ07d6q9vV0nT57Uxo0b1dXVpQULFqisrEyHDx9WZ2enqqqq9Pd///fatm1bn38GAIYGgg3AkPW9731Pd955pxYuXKjExEQ9/vjjevrpp/Xxxx9r+vTpuuuuu9TY2KiZM2fqxIkTV23/85//XIcPH9a0adOUnJys0aNHa/LkyV/53P8tLCxML7/8sgoLC3XLLbfo4Ycf1sMPP6x58+Zp/Pjx2rFjh15//XXNmDFDCxcu1LRp07Rhw4b++BgADAEWt9vtNnoIAAAAfD32sAEAAJgcwQYAAGByBBsAAIDJEWwAAAAmR7ABAACYHMEGAABgcgQbAACAyRFsAAAAJkewAQAAmNz/BVbi2+eDRDDwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "df.balance.plot(kind = 'box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    11162.000000\n",
       "mean      1528.538524\n",
       "std       3225.413326\n",
       "min      -6847.000000\n",
       "25%        122.000000\n",
       "50%        550.000000\n",
       "75%       1708.000000\n",
       "max      81204.000000\n",
       "Name: balance, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['balance'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df.drop(df[df['balance']>1708].index,axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(df[df['balance']<122].index,axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "df.age.plot(kind='box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    5589.000000\n",
       "mean       40.385758\n",
       "std        11.693628\n",
       "min        18.000000\n",
       "25%        32.000000\n",
       "50%        38.000000\n",
       "75%        48.000000\n",
       "max        93.000000\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(df[df['age']>48].index,axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(df[df['age']<32].index,axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "df.day.plot(kind='box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2896.000000\n",
       "mean       15.275552\n",
       "std         8.346845\n",
       "min         1.000000\n",
       "25%         8.000000\n",
       "50%        15.000000\n",
       "75%        21.000000\n",
       "max        31.000000\n",
       "Name: day, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['day'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(df[df['day']>19].index,axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(df[df['day']<12].index,axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "df.duration.plot(kind='box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     937.000000\n",
       "mean      410.012807\n",
       "std       396.044313\n",
       "min         2.000000\n",
       "25%       137.000000\n",
       "50%       262.000000\n",
       "75%       567.000000\n",
       "max      3076.000000\n",
       "Name: duration, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['duration'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(df[df['duration']>290].index,axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(df[df['duration']<106].index,axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "df.campaign.plot(kind='box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    334.000000\n",
       "mean       2.326347\n",
       "std        1.881974\n",
       "min        1.000000\n",
       "25%        1.000000\n",
       "50%        2.000000\n",
       "75%        3.000000\n",
       "max       15.000000\n",
       "Name: campaign, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['campaign'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(df[df['campaign']>3].index,axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "df.pdays.plot(kind='box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    273.000000\n",
       "mean      61.135531\n",
       "std      122.505582\n",
       "min       -1.000000\n",
       "25%       -1.000000\n",
       "50%       -1.000000\n",
       "75%       92.000000\n",
       "max      826.000000\n",
       "Name: pdays, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"pdays\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(df[df['pdays']>92].index,axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "df.previous.plot(kind='box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    208.000000\n",
       "mean       0.201923\n",
       "std        0.899595\n",
       "min        0.000000\n",
       "25%        0.000000\n",
       "50%        0.000000\n",
       "75%        0.000000\n",
       "max        7.000000\n",
       "Name: previous, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['previous'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(df[df['previous']>0].index,axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>deposit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>939</th>\n",
       "      <td>35</td>\n",
       "      <td>unemployed</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>127</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>14</td>\n",
       "      <td>nov</td>\n",
       "      <td>162</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1250</th>\n",
       "      <td>44</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>348</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>feb</td>\n",
       "      <td>170</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1252</th>\n",
       "      <td>37</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>1543</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>feb</td>\n",
       "      <td>138</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1255</th>\n",
       "      <td>32</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>312</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>17</td>\n",
       "      <td>feb</td>\n",
       "      <td>232</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1260</th>\n",
       "      <td>47</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>335</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>17</td>\n",
       "      <td>feb</td>\n",
       "      <td>226</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      age         job  marital  education default  balance housing loan  \\\n",
       "939    35  unemployed   single  secondary      no      127     yes   no   \n",
       "1250   44      admin.  married   tertiary      no      348     yes  yes   \n",
       "1252   37    services  married  secondary      no     1543     yes   no   \n",
       "1255   32  management   single   tertiary      no      312      no   no   \n",
       "1260   47  technician  married  secondary      no      335      no   no   \n",
       "\n",
       "        contact  day month  duration  campaign  pdays  previous poutcome  \\\n",
       "939   telephone   14   nov       162         1     -1         0  unknown   \n",
       "1250   cellular   16   feb       170         2     -1         0  unknown   \n",
       "1252   cellular   16   feb       138         2     -1         0  unknown   \n",
       "1255   cellular   17   feb       232         2     -1         0  unknown   \n",
       "1260   cellular   17   feb       226         1     -1         0  unknown   \n",
       "\n",
       "     deposit  \n",
       "939      yes  \n",
       "1250     yes  \n",
       "1252     yes  \n",
       "1255     yes  \n",
       "1260     yes  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Pre-Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocessor(df):\n",
    "    res_df = df.copy()\n",
    "    le = preprocessing.LabelEncoder()\n",
    "    \n",
    "    res_df['job'] = le.fit_transform(res_df['job'])\n",
    "    res_df['marital'] = le.fit_transform(res_df['marital'])\n",
    "    res_df['education'] = le.fit_transform(res_df['education'])\n",
    "    res_df['default'] = le.fit_transform(res_df['default'])\n",
    "    res_df['housing'] = le.fit_transform(res_df['housing'])\n",
    "    res_df['loan'] = le.fit_transform(res_df['loan'])\n",
    "    res_df['contact'] = le.fit_transform(res_df['contact'])\n",
    "    res_df['month'] = le.fit_transform(res_df['month'])\n",
    "    res_df['poutcome'] = le.fit_transform(res_df['poutcome'])\n",
    "    res_df['deposit'] = le.fit_transform(res_df['deposit'])\n",
    "    return res_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>deposit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>939</th>\n",
       "      <td>35</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>127</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>162</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1250</th>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>348</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>170</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1252</th>\n",
       "      <td>37</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1543</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>138</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1255</th>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>312</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>232</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1260</th>\n",
       "      <td>47</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>335</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>226</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1261</th>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>755</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>120</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1355</th>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>220</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1356</th>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>195</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1364</th>\n",
       "      <td>33</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1107</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>201</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1482</th>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>855</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>110</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1485</th>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>133</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>171</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1505</th>\n",
       "      <td>44</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>294</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>165</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  job  marital  education  default  balance  housing  loan  contact  \\\n",
       "939    35    8        2          1        0      127        1     0        1   \n",
       "1250   44    0        1          2        0      348        1     1        0   \n",
       "1252   37    6        1          1        0     1543        1     0        0   \n",
       "1255   32    4        2          2        0      312        0     0        0   \n",
       "1260   47    7        1          1        0      335        0     0        0   \n",
       "1261   38    0        1          1        0      755        1     0        0   \n",
       "1355   43    0        1          1        0      132        0     0        0   \n",
       "1356   37    0        2          2        0     1000        1     0        0   \n",
       "1364   33    3        2          2        0     1107        1     0        0   \n",
       "1482   33    0        1          1        0      855        0     0        0   \n",
       "1485   32    6        2          1        0      133        0     0        0   \n",
       "1505   44    4        1          0        0      294        0     0        0   \n",
       "\n",
       "      day  month  duration  campaign  pdays  previous  poutcome  deposit  \n",
       "939    14      8       162         1     -1         0         0        1  \n",
       "1250   16      3       170         2     -1         0         0        1  \n",
       "1252   16      3       138         2     -1         0         0        1  \n",
       "1255   17      3       232         2     -1         0         0        1  \n",
       "1260   17      3       226         1     -1         0         0        1  \n",
       "1261   17      3       120         1     -1         0         0        1  \n",
       "1355   17      6       220         1     -1         0         0        1  \n",
       "1356   17      6       195         1     -1         0         0        1  \n",
       "1364   18      6       201         1     -1         0         0        1  \n",
       "1482   15      0       110         1     -1         0         0        1  \n",
       "1485   15      0       171         1     -1         0         0        1  \n",
       "1505   16      0       165         3     -1         0         0        1  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded_df = preprocessor(df)\n",
    "encoded_df.head(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = encoded_df.drop('deposit',axis=1).values\n",
    "y = encoded_df['deposit'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split Data into Train and Test data sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train ,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build Decision Tree Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree with Cart(Gini)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Decision Tree with depth 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score 0.8387096774193549\n",
      "Testing Score 0.6666666666666666\n"
     ]
    }
   ],
   "source": [
    "model_dt_2 = DecisionTreeClassifier(random_state=1,max_depth=2)\n",
    "\n",
    "model_dt_2.fit(x_train,y_train)\n",
    "\n",
    "model_dt_2_score_train = model_dt_2.score(x_train,y_train)\n",
    "\n",
    "model_dt_2_score_test = model_dt_2.score(x_test,y_test)\n",
    "\n",
    "print('Training Score',model_dt_2_score_train)\n",
    "\n",
    "print('Testing Score',model_dt_2_score_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Decision Tree with depth 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score 0.8838709677419355\n",
      "Testing Score 0.8205128205128205\n"
     ]
    }
   ],
   "source": [
    "model_dt_4 = DecisionTreeClassifier(random_state=1,max_depth=4)\n",
    "\n",
    "model_dt_4.fit(x_train,y_train)\n",
    "\n",
    "model_dt_4_score_train = model_dt_4.score(x_train,y_train)\n",
    "\n",
    "model_dt_4_score_test = model_dt_4.score(x_test,y_test)\n",
    "\n",
    "print('Training Score',model_dt_4_score_train)\n",
    "\n",
    "print('Testing Score',model_dt_4_score_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Decision Tree with depth 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score 0.9741935483870968\n",
      "Testing Score 0.8205128205128205\n"
     ]
    }
   ],
   "source": [
    "model_dt_6 = DecisionTreeClassifier(random_state=1,max_depth=6)\n",
    "\n",
    "model_dt_6.fit(x_train,y_train)\n",
    "\n",
    "model_dt_6_score_train = model_dt_6.score(x_train,y_train)\n",
    "\n",
    "model_dt_6_score_test = model_dt_6.score(x_test,y_test)\n",
    "\n",
    "print('Training Score',model_dt_6_score_train)\n",
    "\n",
    "print('Testing Score',model_dt_6_score_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Decision Tree with depth 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score 0.9870967741935484\n",
      "Testing Score 0.8205128205128205\n"
     ]
    }
   ],
   "source": [
    "model_dt_8 = DecisionTreeClassifier(random_state=1,max_depth=8)\n",
    "\n",
    "model_dt_8.fit(x_train,y_train)\n",
    "\n",
    "model_dt_8_score_train = model_dt_8.score(x_train,y_train)\n",
    "\n",
    "model_dt_8_score_test = model_dt_8.score(x_test,y_test)\n",
    "\n",
    "print('Training Score',model_dt_8_score_train)\n",
    "\n",
    "print('Testing Score',model_dt_8_score_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score 1.0\n",
      "Testing Score 0.7948717948717948\n"
     ]
    }
   ],
   "source": [
    "model_dt_10 = DecisionTreeClassifier(random_state=1,max_depth=10)\n",
    "\n",
    "model_dt_10.fit(x_train,y_train)\n",
    "\n",
    "model_dt_10_score_train = model_dt_10.score(x_train,y_train)\n",
    "\n",
    "model_dt_10_score_test = model_dt_10.score(x_test,y_test)\n",
    "\n",
    "print('Training Score',model_dt_10_score_train)\n",
    "\n",
    "print('Testing Score',model_dt_10_score_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score 1.0\n",
      "Testing Score 0.7948717948717948\n"
     ]
    }
   ],
   "source": [
    "model_dt_12 = DecisionTreeClassifier(random_state=1,max_depth=12)\n",
    "\n",
    "model_dt_12.fit(x_train,y_train)\n",
    "\n",
    "model_dt_12_score_train = model_dt_12.score(x_train,y_train)\n",
    "\n",
    "model_dt_12_score_test = model_dt_12.score(x_test,y_test)\n",
    "\n",
    "print('Training Score',model_dt_12_score_train)\n",
    "\n",
    "print('Testing Score',model_dt_12_score_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree with c4.5(entropy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8387096774193549\n",
      "0.6666666666666666\n"
     ]
    }
   ],
   "source": [
    "model_dt = DecisionTreeClassifier(max_depth=2,criterion='entropy')\n",
    "\n",
    "model_dt.fit(x_train,y_train)\n",
    "\n",
    "y_pred_dt = model_dt.predict_proba(x_test)[:,1]\n",
    "# y_pred_dt\n",
    "\n",
    "model_dt_score_train = model_dt.score(x_train,y_train)\n",
    "print(model_dt_score_train)\n",
    "\n",
    "model_dt_score_test = model_dt.score(x_test,y_test)\n",
    "print(model_dt_score_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Graphical Represetation of Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: graphviz in c:\\users\\user\\anaconda3\\lib\\site-packages (0.20)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install graphviz\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from six import StringIO\n",
    "from graphviz import Digraph\n",
    "import pydotplus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: graphviz in c:\\users\\user\\anaconda3\\lib\\site-packages (0.20)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install graphviz\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "ename": "InvocationException",
     "evalue": "GraphViz's executables not found",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mInvocationException\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_12620/3796926155.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      5\u001b[0m                special_characters=True)\n\u001b[0;32m      6\u001b[0m \u001b[0mgraph\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpydotplus\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgraph_from_dot_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdot_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mImage\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgraph\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcreate_png\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pydotplus\\graphviz.py\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(f, prog)\u001b[0m\n\u001b[0;32m   1795\u001b[0m             self.__setattr__(\n\u001b[0;32m   1796\u001b[0m                 \u001b[1;34m'create_'\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mfrmt\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1797\u001b[1;33m                 \u001b[1;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfrmt\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprog\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprog\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprog\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mprog\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1798\u001b[0m             )\n\u001b[0;32m   1799\u001b[0m             \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__dict__\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'create_'\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mfrmt\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pydotplus\\graphviz.py\u001b[0m in \u001b[0;36mcreate\u001b[1;34m(self, prog, format)\u001b[0m\n\u001b[0;32m   1957\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprogs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfind_graphviz\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1958\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprogs\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1959\u001b[1;33m                 raise InvocationException(\n\u001b[0m\u001b[0;32m   1960\u001b[0m                     'GraphViz\\'s executables not found')\n\u001b[0;32m   1961\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mInvocationException\u001b[0m: GraphViz's executables not found"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "dot_data = StringIO()\n",
    "export_graphviz(model_dt_8,out_file=dot_data,\n",
    "               filled=True,rounded=True,\n",
    "               special_characters=True)\n",
    "graph = pydotplus.graph_from_dot_data(dot_data.getvalue())\n",
    "Image(graph.create_png())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance Matrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr_dt,tpr_dt,_=roc_curve(y_test,y_pred_dt)\n",
    "roc_auc_dt = auc(fpr_dt,tpr_dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8205128205128205\n"
     ]
    }
   ],
   "source": [
    "predictions = model_dt_6.predict(x_test)\n",
    "# print(predictions)\n",
    "\n",
    "print(model_dt_6.score(x_test,y_test))\n",
    "y_actual_result = y_test[0]\n",
    "# print(y_actual_result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Insert New Column with Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0\n",
      " 0 0]\n"
     ]
    }
   ],
   "source": [
    "print(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_preditor_without_target = encoded_df.drop('deposit',axis=1).values\n",
    "df_preditor_only_target = encoded_df['deposit'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "print(df_preditor_only_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions1 = model_dt_6.predict(df_preditor_without_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1\n",
      " 1 1 1 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0\n",
      " 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "print(predictions1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoded_df['test_data'] = np.where(df_preditor_only_target,predictions1,df_preditor_only_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['test_data'] = np.where(df_preditor_only_target,predictions1,df_preditor_only_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>deposit</th>\n",
       "      <th>test_data</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>939</th>\n",
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       "      <td>127</td>\n",
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       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1250</th>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>348</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>170</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1252</th>\n",
       "      <td>37</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1543</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>138</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  job  marital  education  default  balance  housing  loan  contact  \\\n",
       "939    35    8        2          1        0      127        1     0        1   \n",
       "1250   44    0        1          2        0      348        1     1        0   \n",
       "1252   37    6        1          1        0     1543        1     0        0   \n",
       "\n",
       "      day  month  duration  campaign  pdays  previous  poutcome  deposit  \\\n",
       "939    14      8       162         1     -1         0         0        1   \n",
       "1250   16      3       170         2     -1         0         0        1   \n",
       "1252   16      3       138         2     -1         0         0        1   \n",
       "\n",
       "      test_data  \n",
       "939           0  \n",
       "1250          0  \n",
       "1252          1  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>deposit</th>\n",
       "      <th>test_data</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>939</th>\n",
       "      <td>35</td>\n",
       "      <td>unemployed</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>127</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>14</td>\n",
       "      <td>nov</td>\n",
       "      <td>162</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1250</th>\n",
       "      <td>44</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>348</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>feb</td>\n",
       "      <td>170</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1252</th>\n",
       "      <td>37</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>1543</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>feb</td>\n",
       "      <td>138</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      age         job  marital  education default  balance housing loan  \\\n",
       "939    35  unemployed   single  secondary      no      127     yes   no   \n",
       "1250   44      admin.  married   tertiary      no      348     yes  yes   \n",
       "1252   37    services  married  secondary      no     1543     yes   no   \n",
       "\n",
       "        contact  day month  duration  campaign  pdays  previous poutcome  \\\n",
       "939   telephone   14   nov       162         1     -1         0  unknown   \n",
       "1250   cellular   16   feb       170         2     -1         0  unknown   \n",
       "1252   cellular   16   feb       138         2     -1         0  unknown   \n",
       "\n",
       "     deposit  test_data  \n",
       "939      yes          0  \n",
       "1250     yes          0  \n",
       "1252     yes          1  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['test_data'].replace(to_replace=1,value='True',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['test_data'].replace(to_replace=0,value='False',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('Bank_with_Prediction_3_Point_Summary.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Area Under the Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x28fa9f31d90>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "lw=2\n",
    "plt.plot(fpr_dt,tpr_dt,color='orange',lw=lw,label='Decision Tree(AUC = %0.2f)'%roc_auc_dt)\n",
    "plt.plot([0,1],[0,1],color='blue',lw=lw,linestyle='--')\n",
    "\n",
    "plt.xlim([0.0,1.0])\n",
    "plt.ylim([0.0,1.05])\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "plt.title(\"area Under the Curve\")\n",
    "# plt.legend(loc=\"upper left\")\n",
    "plt.legend(loc=\"lower right\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[27  3]\n",
      " [ 4  5]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8205128205128205"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_conf_mat(y_test, predictions):\n",
    "    \"\"\"Function returns confusion matrix comparing two arrays\"\"\"\n",
    "    if (len(y_test.shape) != len(predictions.shape) == 1):\n",
    "        return print('Arrays entered are not 1-D.\\nPlease enter the correctly sized sets.')\n",
    "    elif (y_test.shape != predictions.shape):\n",
    "        return print('Number of values inside the Arrays are not equal to each other.\\nPlease make sure the array has the same number of instances.')\n",
    "    else:\n",
    "        # Set Metrics\n",
    "        test_crosstb_comp = pd.crosstab(index = y_test,\n",
    "                                        columns = predictions)\n",
    "        # Changed for Future deprecation of as_matrix\n",
    "        test_crosstb = test_crosstb_comp.values\n",
    "        return test_crosstb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "conf_mat = create_conf_mat(y_test, predictions)\n",
    "sns.heatmap(conf_mat, annot=True, fmt='d', cbar=False)\n",
    "plt.xlabel('Predicted Values')\n",
    "plt.ylabel('Actual Values')\n",
    "plt.title('Actual vs. Predicted Confusion Matrix')\n",
    "plt.show()"
   ]
  }
 ],
 "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": 2
}