CE802_P2_Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2 a) Investigate the performance of a number of machine learning procedures on this dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Import Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix, classification_report, f1_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Load DataSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>F1</th>\n",
       "      <th>F2</th>\n",
       "      <th>F3</th>\n",
       "      <th>F4</th>\n",
       "      <th>F5</th>\n",
       "      <th>F6</th>\n",
       "      <th>F7</th>\n",
       "      <th>F8</th>\n",
       "      <th>F9</th>\n",
       "      <th>F10</th>\n",
       "      <th>F11</th>\n",
       "      <th>F12</th>\n",
       "      <th>F13</th>\n",
       "      <th>F14</th>\n",
       "      <th>F15</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>186.88</td>\n",
       "      <td>-21.06</td>\n",
       "      <td>-17.68</td>\n",
       "      <td>14.20</td>\n",
       "      <td>563.22</td>\n",
       "      <td>-3.29</td>\n",
       "      <td>-5.32</td>\n",
       "      <td>-18.90</td>\n",
       "      <td>9.20</td>\n",
       "      <td>-9.44</td>\n",
       "      <td>10.89</td>\n",
       "      <td>-8.54</td>\n",
       "      <td>510</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30</td>\n",
       "      <td>196.88</td>\n",
       "      <td>-22.89</td>\n",
       "      <td>-19.38</td>\n",
       "      <td>19.70</td>\n",
       "      <td>353.22</td>\n",
       "      <td>-5.71</td>\n",
       "      <td>-1.48</td>\n",
       "      <td>-18.33</td>\n",
       "      <td>7.15</td>\n",
       "      <td>-8.58</td>\n",
       "      <td>15.89</td>\n",
       "      <td>-8.44</td>\n",
       "      <td>760</td>\n",
       "      <td>7.22</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>96.88</td>\n",
       "      <td>-19.65</td>\n",
       "      <td>-16.46</td>\n",
       "      <td>17.30</td>\n",
       "      <td>398.22</td>\n",
       "      <td>-5.19</td>\n",
       "      <td>-1.96</td>\n",
       "      <td>-14.64</td>\n",
       "      <td>7.10</td>\n",
       "      <td>-9.18</td>\n",
       "      <td>20.89</td>\n",
       "      <td>-8.79</td>\n",
       "      <td>610</td>\n",
       "      <td>7.69</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>216.88</td>\n",
       "      <td>-33.24</td>\n",
       "      <td>-27.80</td>\n",
       "      <td>0.84</td>\n",
       "      <td>344.22</td>\n",
       "      <td>-0.86</td>\n",
       "      <td>-6.89</td>\n",
       "      <td>-14.73</td>\n",
       "      <td>0.09</td>\n",
       "      <td>-17.98</td>\n",
       "      <td>7.89</td>\n",
       "      <td>-3.78</td>\n",
       "      <td>32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>198.88</td>\n",
       "      <td>-32.52</td>\n",
       "      <td>-26.26</td>\n",
       "      <td>1.52</td>\n",
       "      <td>278.22</td>\n",
       "      <td>0.08</td>\n",
       "      <td>-7.73</td>\n",
       "      <td>-13.62</td>\n",
       "      <td>1.91</td>\n",
       "      <td>-23.94</td>\n",
       "      <td>7.89</td>\n",
       "      <td>-3.25</td>\n",
       "      <td>36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   F1      F2     F3     F4     F5      F6    F7    F8     F9   F10    F11  \\\n",
       "0  30  186.88 -21.06 -17.68  14.20  563.22 -3.29 -5.32 -18.90  9.20  -9.44   \n",
       "1  30  196.88 -22.89 -19.38  19.70  353.22 -5.71 -1.48 -18.33  7.15  -8.58   \n",
       "2  30   96.88 -19.65 -16.46  17.30  398.22 -5.19 -1.96 -14.64  7.10  -9.18   \n",
       "3   3  216.88 -33.24 -27.80   0.84  344.22 -0.86 -6.89 -14.73  0.09 -17.98   \n",
       "4   3  198.88 -32.52 -26.26   1.52  278.22  0.08 -7.73 -13.62  1.91 -23.94   \n",
       "\n",
       "     F12   F13  F14   F15  Class  \n",
       "0  10.89 -8.54  510   NaN  False  \n",
       "1  15.89 -8.44  760  7.22  False  \n",
       "2  20.89 -8.79  610  7.69  False  \n",
       "3   7.89 -3.78   32   NaN  False  \n",
       "4   7.89 -3.25   36   NaN   True  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# making data frame from csv file\n",
    "dataset = pd.read_csv(\"D:\\\\Machine Learning\\CE802_P2_Data\\CE802_P2_Data.csv\")\n",
    "dataset.shape\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1500 entries, 0 to 1499\n",
      "Data columns (total 16 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   F1      1500 non-null   int64  \n",
      " 1   F2      1500 non-null   float64\n",
      " 2   F3      1500 non-null   float64\n",
      " 3   F4      1500 non-null   float64\n",
      " 4   F5      1500 non-null   float64\n",
      " 5   F6      1500 non-null   float64\n",
      " 6   F7      1500 non-null   float64\n",
      " 7   F8      1500 non-null   float64\n",
      " 8   F9      1500 non-null   float64\n",
      " 9   F10     1500 non-null   float64\n",
      " 10  F11     1500 non-null   float64\n",
      " 11  F12     1500 non-null   float64\n",
      " 12  F13     1500 non-null   float64\n",
      " 13  F14     1500 non-null   int64  \n",
      " 14  F15     750 non-null    float64\n",
      " 15  Class   1500 non-null   bool   \n",
      "dtypes: bool(1), float64(13), int64(2)\n",
      "memory usage: 177.4 KB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Find Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>F1</th>\n",
       "      <th>F2</th>\n",
       "      <th>F3</th>\n",
       "      <th>F4</th>\n",
       "      <th>F5</th>\n",
       "      <th>F6</th>\n",
       "      <th>F7</th>\n",
       "      <th>F8</th>\n",
       "      <th>F9</th>\n",
       "      <th>F10</th>\n",
       "      <th>F11</th>\n",
       "      <th>F12</th>\n",
       "      <th>F13</th>\n",
       "      <th>F14</th>\n",
       "      <th>F15</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>186.88</td>\n",
       "      <td>-21.06</td>\n",
       "      <td>-17.68</td>\n",
       "      <td>14.20</td>\n",
       "      <td>563.22</td>\n",
       "      <td>-3.29</td>\n",
       "      <td>-5.32</td>\n",
       "      <td>-18.90</td>\n",
       "      <td>9.20</td>\n",
       "      <td>-9.44</td>\n",
       "      <td>10.89</td>\n",
       "      <td>-8.54</td>\n",
       "      <td>510</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>216.88</td>\n",
       "      <td>-33.24</td>\n",
       "      <td>-27.80</td>\n",
       "      <td>0.84</td>\n",
       "      <td>344.22</td>\n",
       "      <td>-0.86</td>\n",
       "      <td>-6.89</td>\n",
       "      <td>-14.73</td>\n",
       "      <td>0.09</td>\n",
       "      <td>-17.98</td>\n",
       "      <td>7.89</td>\n",
       "      <td>-3.78</td>\n",
       "      <td>32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>198.88</td>\n",
       "      <td>-32.52</td>\n",
       "      <td>-26.26</td>\n",
       "      <td>1.52</td>\n",
       "      <td>278.22</td>\n",
       "      <td>0.08</td>\n",
       "      <td>-7.73</td>\n",
       "      <td>-13.62</td>\n",
       "      <td>1.91</td>\n",
       "      <td>-23.94</td>\n",
       "      <td>7.89</td>\n",
       "      <td>-3.25</td>\n",
       "      <td>36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>30</td>\n",
       "      <td>386.88</td>\n",
       "      <td>-20.94</td>\n",
       "      <td>-20.34</td>\n",
       "      <td>10.70</td>\n",
       "      <td>533.22</td>\n",
       "      <td>-4.12</td>\n",
       "      <td>-4.22</td>\n",
       "      <td>-8.97</td>\n",
       "      <td>14.50</td>\n",
       "      <td>-11.00</td>\n",
       "      <td>20.89</td>\n",
       "      <td>-9.09</td>\n",
       "      <td>330</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>182.88</td>\n",
       "      <td>-32.76</td>\n",
       "      <td>-25.48</td>\n",
       "      <td>1.82</td>\n",
       "      <td>323.22</td>\n",
       "      <td>2.55</td>\n",
       "      <td>-7.31</td>\n",
       "      <td>-17.85</td>\n",
       "      <td>0.99</td>\n",
       "      <td>-20.30</td>\n",
       "      <td>5.89</td>\n",
       "      <td>-3.74</td>\n",
       "      <td>66</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1487</th>\n",
       "      <td>30</td>\n",
       "      <td>256.88</td>\n",
       "      <td>-18.39</td>\n",
       "      <td>-15.72</td>\n",
       "      <td>18.70</td>\n",
       "      <td>503.22</td>\n",
       "      <td>-5.13</td>\n",
       "      <td>-1.94</td>\n",
       "      <td>-9.75</td>\n",
       "      <td>13.40</td>\n",
       "      <td>-9.50</td>\n",
       "      <td>20.89</td>\n",
       "      <td>-8.59</td>\n",
       "      <td>630</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1493</th>\n",
       "      <td>3</td>\n",
       "      <td>164.88</td>\n",
       "      <td>-36.24</td>\n",
       "      <td>-29.68</td>\n",
       "      <td>1.90</td>\n",
       "      <td>302.22</td>\n",
       "      <td>1.26</td>\n",
       "      <td>-9.04</td>\n",
       "      <td>-13.44</td>\n",
       "      <td>1.23</td>\n",
       "      <td>-19.72</td>\n",
       "      <td>7.89</td>\n",
       "      <td>-2.94</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1494</th>\n",
       "      <td>30</td>\n",
       "      <td>156.88</td>\n",
       "      <td>-20.82</td>\n",
       "      <td>-16.82</td>\n",
       "      <td>16.70</td>\n",
       "      <td>398.22</td>\n",
       "      <td>-5.46</td>\n",
       "      <td>-3.46</td>\n",
       "      <td>-12.60</td>\n",
       "      <td>7.40</td>\n",
       "      <td>-7.32</td>\n",
       "      <td>20.89</td>\n",
       "      <td>-11.14</td>\n",
       "      <td>490</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1497</th>\n",
       "      <td>30</td>\n",
       "      <td>106.88</td>\n",
       "      <td>-17.91</td>\n",
       "      <td>-17.48</td>\n",
       "      <td>13.50</td>\n",
       "      <td>623.22</td>\n",
       "      <td>-5.96</td>\n",
       "      <td>-2.59</td>\n",
       "      <td>-13.89</td>\n",
       "      <td>9.40</td>\n",
       "      <td>-7.06</td>\n",
       "      <td>20.89</td>\n",
       "      <td>-11.79</td>\n",
       "      <td>370</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1498</th>\n",
       "      <td>30</td>\n",
       "      <td>336.88</td>\n",
       "      <td>-23.73</td>\n",
       "      <td>-18.68</td>\n",
       "      <td>17.60</td>\n",
       "      <td>503.22</td>\n",
       "      <td>-5.01</td>\n",
       "      <td>-3.72</td>\n",
       "      <td>-7.98</td>\n",
       "      <td>6.50</td>\n",
       "      <td>-8.60</td>\n",
       "      <td>30.89</td>\n",
       "      <td>-8.04</td>\n",
       "      <td>250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>750 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      F1      F2     F3     F4     F5      F6    F7    F8     F9    F10  \\\n",
       "0     30  186.88 -21.06 -17.68  14.20  563.22 -3.29 -5.32 -18.90   9.20   \n",
       "3      3  216.88 -33.24 -27.80   0.84  344.22 -0.86 -6.89 -14.73   0.09   \n",
       "4      3  198.88 -32.52 -26.26   1.52  278.22  0.08 -7.73 -13.62   1.91   \n",
       "5     30  386.88 -20.94 -20.34  10.70  533.22 -4.12 -4.22  -8.97  14.50   \n",
       "8      3  182.88 -32.76 -25.48   1.82  323.22  2.55 -7.31 -17.85   0.99   \n",
       "...   ..     ...    ...    ...    ...     ...   ...   ...    ...    ...   \n",
       "1487  30  256.88 -18.39 -15.72  18.70  503.22 -5.13 -1.94  -9.75  13.40   \n",
       "1493   3  164.88 -36.24 -29.68   1.90  302.22  1.26 -9.04 -13.44   1.23   \n",
       "1494  30  156.88 -20.82 -16.82  16.70  398.22 -5.46 -3.46 -12.60   7.40   \n",
       "1497  30  106.88 -17.91 -17.48  13.50  623.22 -5.96 -2.59 -13.89   9.40   \n",
       "1498  30  336.88 -23.73 -18.68  17.60  503.22 -5.01 -3.72  -7.98   6.50   \n",
       "\n",
       "        F11    F12    F13  F14  F15  Class  \n",
       "0     -9.44  10.89  -8.54  510  NaN  False  \n",
       "3    -17.98   7.89  -3.78   32  NaN  False  \n",
       "4    -23.94   7.89  -3.25   36  NaN   True  \n",
       "5    -11.00  20.89  -9.09  330  NaN  False  \n",
       "8    -20.30   5.89  -3.74   66  NaN   True  \n",
       "...     ...    ...    ...  ...  ...    ...  \n",
       "1487  -9.50  20.89  -8.59  630  NaN   True  \n",
       "1493 -19.72   7.89  -2.94    2  NaN  False  \n",
       "1494  -7.32  20.89 -11.14  490  NaN   True  \n",
       "1497  -7.06  20.89 -11.79  370  NaN   True  \n",
       "1498  -8.60  30.89  -8.04  250  NaN  False  \n",
       "\n",
       "[750 rows x 16 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_data = pd.isnull(dataset[\"F15\"])\n",
    " \n",
    "# filtering data\n",
    "# displayind data only with F15 = NaN\n",
    "dataset[missing_data]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Missing Values Imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      F1      F2     F3     F4     F5      F6    F7    F8     F9   F10    F11  \\\n",
      "0     30  186.88 -21.06 -17.68  14.20  563.22 -3.29 -5.32 -18.90  9.20  -9.44   \n",
      "1     30  196.88 -22.89 -19.38  19.70  353.22 -5.71 -1.48 -18.33  7.15  -8.58   \n",
      "2     30   96.88 -19.65 -16.46  17.30  398.22 -5.19 -1.96 -14.64  7.10  -9.18   \n",
      "3      3  216.88 -33.24 -27.80   0.84  344.22 -0.86 -6.89 -14.73  0.09 -17.98   \n",
      "4      3  198.88 -32.52 -26.26   1.52  278.22  0.08 -7.73 -13.62  1.91 -23.94   \n",
      "...   ..     ...    ...    ...    ...     ...   ...   ...    ...   ...    ...   \n",
      "1495  30  266.88 -18.69 -17.30  14.70  383.22 -4.94 -3.05  -9.78  5.95 -14.20   \n",
      "1496  30  206.88 -22.35 -18.00  12.30  428.22 -3.27 -3.48  -8.46  9.20 -11.56   \n",
      "1497  30  106.88 -17.91 -17.48  13.50  623.22 -5.96 -2.59 -13.89  9.40  -7.06   \n",
      "1498  30  336.88 -23.73 -18.68  17.60  503.22 -5.01 -3.72  -7.98  6.50  -8.60   \n",
      "1499   3  218.88 -34.08 -26.14   1.18  335.22  0.77 -7.68 -15.21  1.43 -25.30   \n",
      "\n",
      "        F12    F13  F14       F15  Class  \n",
      "0     10.89  -8.54  510  7.903013  False  \n",
      "1     15.89  -8.44  760  7.220000  False  \n",
      "2     20.89  -8.79  610  7.690000  False  \n",
      "3      7.89  -3.78   32  7.903013  False  \n",
      "4      7.89  -3.25   36  7.903013   True  \n",
      "...     ...    ...  ...       ...    ...  \n",
      "1495  20.89  -9.29  110  9.380000   True  \n",
      "1496  30.89  -8.44  260  6.520000  False  \n",
      "1497  20.89 -11.79  370  7.903013   True  \n",
      "1498  30.89  -8.04  250  7.903013  False  \n",
      "1499   9.89  -2.86   16  7.960000   True  \n",
      "\n",
      "[1500 rows x 16 columns]\n"
     ]
    }
   ],
   "source": [
    "# filling data\n",
    "dataset['F15'] = dataset['F15'].fillna(dataset['F15'].mean()) \n",
    "print(dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Create X and Y Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs=dataset.drop('Class',axis=1)\n",
    "target=dataset['Class']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Data Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(inputs, target, test_size=0.20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scalar=StandardScaler()\n",
    "scalar.fit(X_train)\n",
    "X_train = scalar.transform(X_train)\n",
    "X_test = scalar.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Instantiate Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tree.DecisionTreeClassifier(criterion='gini',max_depth=6, min_samples_split=15, class_weight='balanced') # Using the best parameters obtained by Randomsearch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Fit the Decision Tree to the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight='balanced', criterion='gini', max_depth=6,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=15,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Do prediction using our test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Calculate the performance of Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on test set: 75.667%\n",
      "Precision on test set: 0.775\n",
      "Recall on test set: 0.669\n",
      "F1-Score on test set: 0.718\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Accuracy on test set: %0.3f%%\"%(accuracy_score(y_test, y_pred)*100))\n",
    "print(\"Precision on test set: %0.3f\"%(precision_score(y_test, y_pred)))\n",
    "print(\"Recall on test set: %0.3f\"%(recall_score(y_test, y_pred)))\n",
    "print(\"F1-Score on test set: %0.3f\"%(f1_score(y_test, y_pred)))\n",
    "\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "class_label = [\"negative\", \"positive\"]\n",
    "df_cm = pd.DataFrame(cm, index = class_label, columns = class_label)\n",
    "sns.heatmap(df_cm, annot = True, fmt = \"d\")\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.xlabel(\"Predicted Label\")\n",
    "plt.ylabel(\"True Label\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support Vector Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Instantiate Support Vector Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "svmclassifier = SVC()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Fit the Support Vector to the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svmclassifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Do prediction using our test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_svm = svmclassifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Calculate the performance of Support Vector Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on test set: 80.000%\n",
      "Precision on test set: 0.788\n",
      "Recall on test set: 0.777\n",
      "F1-Score on test set: 0.783\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Accuracy on test set: %0.3f%%\"%(accuracy_score(y_test, y_pred_svm)*100))\n",
    "print(\"Precision on test set: %0.3f\"%(precision_score(y_test, y_pred_svm)))\n",
    "print(\"Recall on test set: %0.3f\"%(recall_score(y_test, y_pred_svm)))\n",
    "print(\"F1-Score on test set: %0.3f\"%(f1_score(y_test, y_pred_svm)))\n",
    "\n",
    "cm = confusion_matrix(y_test, y_pred_svm)\n",
    "class_label = [\"negative\", \"positive\"]\n",
    "df_cm = pd.DataFrame(cm, index = class_label, columns = class_label)\n",
    "sns.heatmap(df_cm, annot = True, fmt = \"d\")\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.xlabel(\"Predicted Label\")\n",
    "plt.ylabel(\"True Label\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Instantiate Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfclassifier = RandomForestClassifier(n_estimators=40, min_samples_split= 10, max_features= 'auto', max_depth= 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Fit the Random Forest to the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=5, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=10,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=40, n_jobs=None,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfclassifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Do prediction using our test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_rf = rfclassifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Calculate the performance of Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on test set: 83.000%\n",
      "Precision on test set: 0.819\n",
      "Recall on test set: 0.813\n",
      "F1-Score on test set: 0.816\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAEWCAYAAABG030jAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAAjh0lEQVR4nO3deZyd4/3/8dc7C7KQCIkgja3Bt5S0YotSqpS2SluKiq36S9Xy7beK0iot1a+ltFpVYqnY910taVC1RiSRBLFUikQiRINENJmZz++P+xqO+U5mzpyZe865J+9nHvdj7u1c13VmJp9zzee+7utWRGBmZsXRrdoNMDOztnHgNjMrGAduM7OCceA2MysYB24zs4Jx4DYzKxgHbms3Sb0k3SnpXUk3tqOcAyTd35FtqwZJ90g6uNrtsK7LgXs5Ium7kiZKWihpTgowX+iAovcG1gBWi4h9Ki0kIq6OiF07oD2fIGlHSSHplib7N0/7HyqznF9Kuqq18yJi94gYW2FzzVrlwL2ckHQM8HvgN2RBdihwAbBnBxS/DvBiRNR1QFl5eQsYKWm1kn0HAy92VAXK+P+U5c6/ZMsBSf2AU4EjI+KWiFgUEUsj4s6IOC6ds6Kk30t6Iy2/l7RiOrajpFmSfiJpXuqtH5qO/Qo4Gdg39eQPa9ozlbRu6tn2SNuHSHpF0vuSZko6oGT/IyWvGynpqZSCeUrSyJJjD0k6TdKjqZz7Ja3ewrdhCXAbsF96fXfgO8DVTb5X50l6XdJ7kp6WtH3avxvws5L3+UxJO06X9CjwAbB+2vf9dPzPkm4qKf9MSeMlqdyfn1lTDtzLh22BlYBbWzjn58A2wHBgc2Ar4KSS44OBfsDawGHAnyStGhGnkPXir4+IvhFxaUsNkdQH+AOwe0SsDIwEpjRz3gDg7nTuasC5wN1NeszfBQ4FBgErAMe2VDdwBXBQWv8K8CzwRpNzniL7HgwArgFulLRSRNzb5H1uXvKaA4HRwMrAq03K+wmwWfpQ2p7se3dweK4JawcH7uXDasDbraQyDgBOjYh5EfEW8CuygNRoaTq+NCL+CiwENqqwPQ3AppJ6RcSciHi2mXO+BrwUEVdGRF1EXAvMAPYoOecvEfFiRCwGbiALuMsUEY8BAyRtRBbAr2jmnKsiYn6q8xxgRVp/n5dHxLPpNUublPcBMIrsg+cq4OiImNVKeWYtcuBePswHVm9MVSzDWnyyt/hq2vdRGU0C/wdA37Y2JCIWAfsChwNzJN0taeMy2tPYprVLtudW0J4rgaOAnWjmL5CUDno+pWcWkP2V0VIKBuD1lg5GxATgFUBkHzBm7eLAvXx4HPgQ2KuFc94gu8jYaCj/N41QrkVA75LtwaUHI+K+iNgFWJOsF31xGe1pbNPsCtvU6ErgCOCvqTf8kZTK+ClZ7nvViOgPvEsWcAGWld5oMe0h6UiynvsbwPEVt9wsceBeDkTEu2QXEP8kaS9JvSX1lLS7pLPSadcCJ0kamC7ynUz2p30lpgA7SBqaLoye2HhA0hqSvpFy3f8hS7nUN1PGX4EN0xDGHpL2BT4D3FVhmwCIiJnAF8ly+k2tDNSRjUDpIelkYJWS428C67Zl5IikDYFfk6VLDgSOlzS8stabZRy4lxMRcS5wDNkFx7fI/rw/imykBWTBZSIwFZgGTEr7KqlrHHB9KutpPhlsu5FdsHsDeIcsiB7RTBnzga+nc+eT9VS/HhFvV9KmJmU/EhHN/TVxH3AP2RDBV8n+SilNgzTeXDRf0qTW6kmpqauAMyPimYh4iWxkypWNI3bMKiFf3DYzKxb3uM3MCsaB28ysYBy4zcwKxoHbzKxgWroho6qWvv2Kr5ra/9Frre2r3QSrQXVLZrd77pe2xJyeq69f1blm3OM2MyuYmu1xm5l1qobm7gOrTQ7cZmYA9bU8nfwnOXCbmQERDdVuQtkcuM3MABocuM3MisU9bjOzgvHFSTOzgnGP28ysWMKjSszMCsYXJ83MCsapEjOzgvHFSTOzgnGP28ysYHxx0sysYHxx0sysWCKc4zYzKxbnuM3MCsapEjOzgnGP28ysYOqXVrsFZXPgNjMDp0rMzArHqRIzs4Jxj9vMrGAcuM3MiiV8cdLMrGCc4zYzKxinSszMCqZAPe5u1W6AmVlNaGgof2mFpMskzZM0vWTf2ZJmSJoq6VZJ/UuOnSjpZUkvSPpKa+U7cJuZQdbjLndp3eXAbk32jQM2jYjNgBeBEwEkfQbYD9gkveYCSd1bKtyB28wMoK6u/KUVEfEw8E6TffdHROOLnwCGpPU9gesi4j8RMRN4GdiqpfIduM3MoE09bkmjJU0sWUa3sbbvAfek9bWB10uOzUr7lskXJ83MoE2jSiJiDDCmkmok/RyoA65u3NVcFS2V4cBtZgadMqpE0sHA14GdI6IxOM8CPlVy2hDgjZbKcarEzAw6dFRJcyTtBvwU+EZEfFBy6A5gP0krSloPGAZMaKks97jNzKBDe9ySrgV2BFaXNAs4hWwUyYrAOEkAT0TE4RHxrKQbgOfIUihHRisPwHTgNjODskaLlCsi9m9m96UtnH86cHq55Ttwm5kBRIvXA2uKA7eZGXiuEjOzwnHgNjMrmAJNMuXAbWYGUN/iQI6a4sBtZgZOlZiZFY4Dt5lZwTjHbWZWLNHgcdxmZsXiVImZWcF4VImZWcG4x21mVjAO3NYWJ/3mXB5+dAIDVu3PbVddCMAfx1zBA488Tjd1Y8Cq/Tj95z9h0MDVAHjh5ZmcetYfWLjoA7p168Z1l5zHiiuuUM23YDkaMmQtLr/sPNYYPJCGhgYuueRq/nj+pZz8i2M47Hvf5a23s0cb/uIXZ3DPvQ9UubUFVqBJphQ12tilb79Smw3LwcQp0+jdqxc/O+23HwXuhYsW0bdPHwCuuvF2/jnzNU45/mjq6urZ53tH8b+/OI6Nh63PgnffY+W+fejevcWHQncZvdbavtpN6HSDBw9izcGDmDxlOn379mHCk/fy7b2/xz5778HChYs493cXVbuJVVe3ZHZzj/9qkw/O/X9lx5zex1zc7vraI/cet6RewNCIeCHvuopqxPDPMnvOm5/Y1xi0ARYv/hClX5PHJjzNhhusx8bD1gegf79VOq2dVh1z585j7tx5ACxcuIgZM15i7bUGV7lVXVCBhgPm+ugySXsAU4B70/ZwSXfkWWdXct5Fl7PzNw/k7vsf5KjvHwjAq6/PRhKjf/xz9jn0KC67+sYqt9I60zrrDGH45pvy5ITJABzxw0OZ9PQ4Lh5zDv3796ty6wquvr78pcryfubkL4GtgAUAETEFWHdZJ5c+8v6SK67NuWm170c/OITxt17J13bdiWtuvhOAuvp6Jk99ljNPOZ4r/vxbxv/9MZ6YOLnKLbXO0KdPb264/mKOOfYU3n9/IRdedAUbbjySLUbsyty58zj7rJOr3cRCi4aGspdqyztw10XEu+WeHBFjImJERIz4/kHNPfln+fS1XXfkbw89CsAag1ZnxPDPsmr/fvRaaSW233ZLnnvhn1VuoeWtR48e3Hj9xVx77a3cdts9AMyb9zYNDQ1EBJdcejVbbjm8uo0suoYof6myvAP3dEnfBbpLGibpj8BjOdfZJbz6+uyP1h/8xxOst84QALbbagte/OdMFn/4IXV19UycMo0N1htarWZaJ7l4zDk8P+Nlfn/emI/2DR486KP1vfbcnWef9WWkdomG8pcqy/vi5NHAz4H/ANcA9wG/zrnOwjnulDN4avJUFix4j533GsURhx3IPx5/in+9Ngt1E2sNHsTJxx0NQL9VVuag/b7Ffof9CElsv+2WfHHkVlV+B5an7UZuyYGj9mbqtOeY+NT9QDb0b99992LzzT9DRPDqq7P44RE/rXJLC64GetLlynU4oKTPRURFCdjlaTiglW95HA5oreuI4YCLTt6v7JjT59TrqjocMO9UybmSZkg6TdImOddlZla5AqVKcg3cEbETsCPwFjBG0jRJJ+VZp5lZRXxx8mMRMTci/gAcTjam22OWzKzmFGk4YK4XJyX9F7AvsDcwH7gO+EmedZqZVaQGetLlyntUyV+Aa4FdI+KNnOsyM6ucA3cmIrbJs3wzsw5TA7eylyuXwC3phoj4jqRpQOnHmICIiM3yqNfMrFJ+5iT8KH39ek7lm5l1rAIF7lxGlUTEnLR6RES8WroAR+RRp5lZuzQ0lL+0QtJlkuZJml6yb4CkcZJeSl9XLTl2oqSXJb0g6SutlZ/3cMBdmtm3e851mpm1XceO474c2K3JvhOA8RExDBiftpH0GWA/YJP0mgsktfhklFwCt6Qfpvz2RpKmliwzgal51Glm1i4dGLgj4mHgnSa79wTGpvWxwF4l+6+LiP9ExEzgZbLpsJcprxz3NcA9wP+SPlWS9yOi6ZsxM6u6qC//xhpJo4HRJbvGRMSYZZ2frNGYRo6IOZIap3dcG3ii5LxZad8y5RK40xzc7wL7A6QGrgT0ldQ3Il7Lo14zs4q14eJkCtKtBepyNTdhVYuNyf3RZZJeAmYCfwf+RdYTNzOrKdEQZS8VelPSmgDp67y0fxbwqZLzhgAt3rCY98XJXwPbAC9GxHrAzsCjOddpZtZ2+U8ydQdwcFo/GLi9ZP9+klaUtB4wDJjQUkF5B+6lETEf6CapW0Q8CAzPuU4zs7ZraMPSCknXAo+TDdCYJekw4Axgl5SF2CVtExHPAjcAz5E9WP3IiGjxNs685ypZIKkv8DBwtaR5QF3OdZqZtVnUddysfxGxrIfm7ryM808HTi+3/Lx73HsCi4Efk32S/BPYI+c6zczargN73HnLe5KpRSWbY5d5oplZlXmukkTS+/zfYS3vAhOBn0TEK3nWb2ZWthroSZcr7xz3uWTDWq4hG6u4HzAYeAG4jOyxZmZmVVekHnfeOe7dIuKiiHg/It5Lg9a/GhHXA6u29mIzs05ToBx33oG7QdJ3JHVLy3dKjhXn483MuryoK3+ptrwD9wHAgWR3CL2Z1kdJ6gUclXPdZmZli4byl2rLe1TJKyx7+N8jedZtZtYmNRCQy7XMwC3p8y29MCImtVa4pA2BP5PNirWppM2Ab0TEr9vcUjOzHNVCT7pcLfW4z2nhWABfKqP8i4HjgIsAImKqpGvI5jAxM6sZXSJwR8ROHVB+74iYIH1i1sIaSO2bmX1S1Dc3u2ptavXipKTekk6SNCZtD5NU7kOA35a0AWkEiaS9gTktv8TMrPN1tYuTfwGeBkam7VnAjcBdZbz2SLLJxjeWNJtsXu4DKminmVmuoqE4Pe5yAvcGEbGvpP0BImKxmuQ+WjCbLPA/CAwA3iObh/bUShprZpaXWuhJl6ucwL0kjbtuTHdsAPynzPJvBxYAk2jliQ5mZtUU0bV63KeQTcn6KUlXA9sBh5RZ/pCIaPqIejOzmtOletwRMU7SJLJHkAn4UUS8XWb5j0n6bERMa08jzczy1lCgUSXl3jn5ReALZOmSnsCtZb7uC8AhkmaSpVcERERs1taGmpnlqUtdnJR0AfBp4Nq06weSvhwRR5ZR/u7taZyZWWfpUoGbrLe9aUQ0XpwcC5SV+oiIV9vRNjOzThMFmq+0nMD9AjAUaAzCnwKm5tYiM7Mq6BI9bkl3kuW0+wHPS5qQtrcGHuuc5pmZdY6uMhzwt53WCjOzKqvvCqNKIuLvndkQM7NqKlKPu5xJpraR9JSkhZKWSKqX9F5nNM7MrLNEg8peqq2ci5Pnkz2d/UZgBHAQMCzPRpmZdbauNqqEiHhZUveIqAf+IskXJ82sS6mFnnS5ygncH0haAZgi6Syy+bT75NssM7POVd+Q97PTO045LT0wnXcUsIhsHPe38myUmVlniyh/qbZyJplqvPHmQ+BXAJKuB/bNsV1mZp2qoSuNKlmGbTu0FWZmVRahspfWSPqxpGclTZd0raSVJA2QNE7SS+nrqpW2tThJHTOzHHVUqkTS2sB/AyMiYlOgO9nIvBOA8RExDBiftivS0i3vn1/WIbKpXXPVZ+0d8q7CCui9879T7SZYF9XBqZIeQC9JS4HeZE8AOxHYMR0fCzwE/LTSwpflnBaOzaikMjOzWtWWUSWSRgOjS3aNiYgxABExW9JvgdeAxcD9EXG/pDUiYk46Z46kQZW2taVb3neqtFAzs6Jpy2CRFKTHNHcs5a73BNYje+bujZJGtbuBJcp9Ao6ZWZfWgamSLwMzI+ItAEm3ACOBNyWtmXrbawLzKq3AFyfNzOjQUSWvAdtI6i1JwM7A88AdwMHpnIOB2yttq3vcZmZARz3kPSKelHQTMAmoAyaTpVX6AjdIOowsuO9TaR3lPHNSwAHA+hFxqqShwOCImFBppWZmtSbouFElEXEKcEqT3f8h6323WzmpkgvIbrjZP22/D/ypIyo3M6sVdaGyl2orJ1WydUR8XtJkgIj4d5p0ysysy+jIHnfeygncSyV1J42WkTSQjksHmZnVhCIFtXJSJX8AbgUGSTodeAT4Ta6tMjPrZIHKXqqtnNkBr5b0NFlSXcBeEfF87i0zM+tERepxlzOqZCjwAXBn6b6IeC3PhpmZdab6GuhJl6ucHPfdZPltASuR3cb5ArBJju0yM+tUBXpyWVmpks+WbqdZA3+QW4vMzKqgoYv1uD8hIiZJ2jKPxpiZVUsNPJGsbOXkuI8p2ewGfB54K7cWmZlVQZe6OAmsXLJeR5bzvjmf5piZVUeDukiqJN140zcijuuk9piZVUV9tRvQBi09uqxHRNS18AgzM7Muo6uMKplAls+eIukO4EZgUePBiLgl57aZmXWarjaqZAAwH/gSH4/nDsCB28y6jK4yqmRQGlEynY8DdqMivUczs1Z1lVRJd7InNjT3dhy4zaxL6SrDAedExKmd1hIzsyqq7yI97gK9DTOz9ukqPe4OeTaamVkRdInAHRHvdGZDzMyqqQYeJVm2Nk8yZWbWFXWJHreZ2fKkS9zybma2POkq47jNzJYbTpWYmRWMA7eZWcEU6XZwB24zM5zjNjMrHI8qMTMrmIYCJUu6VbsBZma1oKENS2sk9Zd0k6QZkp6XtK2kAZLGSXopfV210rY6cJuZkV2cLHcpw3nAvRGxMbA58DxwAjA+IoYB49N2RRy4zczouB63pFWAHYBLASJiSUQsAPYExqbTxgJ7VdpWB24zM6BOUfYiabSkiSXL6JKi1gfeAv4iabKkSyT1AdaIiDkA6eugStvqi5NmZrRtHHdEjAHGLONwD7IHrR8dEU9KOo92pEWa4x63mRkdenFyFjArIp5M2zeRBfI3Ja0JkL7Oq7StDtxmZmTDActdWhIRc4HXJW2Udu0MPAfcARyc9h0M3F5pW50qMTOjw295Pxq4WtIKwCvAoWQd5RskHQa8BuxTaeEO3GZmdOwkUxExBRjRzKEOeSSkA7eZGVBfoDsnHbjNzPC0rmZmhRPucZuZFYt73FaxIUPW5LJLz2Pw4IE0NDRwyaXXcP75lwJwxBGHcsQPD6Guro577nmAE392epVba3n65b1TefiVeQzovQI3HbIDAONemMOFj7/EzPkLufKAkWwyuD8A0+cs4LRx04BsdMTh2w7jS8MGV6nlxVSk2QEduGtMXV09x//0VKZMmU7fvn148ol7GP+3hxm0xkD22GNXPr/FLixZsoSBA1erdlMtZ3tsOoR9P7cOv7jnmY/2bbD6ypzzjc/z63HTP3HuBquvzNWjtqNHt268tfBD9r3iEXbYYBA9uvlWjXIVJ2w7cNecuXPnMXdudkPVwoWLmDHjJdZaezCHfe+7nH32n1iyZAkAb701v5rNtE6wxZABvPHuB5/Yt/5qfZs9t1fP7h+tL6lvQAV6mkutqCtQ6M7141iZUZJOTttDJW2VZ51dyTrrDGHzzTdlwoTJDBu2Pl/Ybmse+ced/G3cTWyxxebVbp7VmGlzFvDtyx9mn7H/4Odf3tS97TaKNvyrtrx73BeQ5fy/BJwKvA/cDGzZ3Mlphq3RAN2796db9z45N6929enTm+uvG8Oxx/6S999fSI8e3em/aj++sP0ejBgxnGuu+TMbbTSy2s20GvLZNftz8yE78Mr8hZx87zNst95AVuzRvfUXGlCsi5N5fyRvHRFHAh8CRMS/gRWWdXJEjImIERExYnkO2j169OD668dw7XW3ctvt9wAwa/ZcbrstW584cQoNDQ2svvqAajbTatT6q/WlV8/uvPz2+9VuSqEUqcedd+BeKqk7Ke8vaSDF+mCrijEX/ZYZM17mvPMu/mjfHXfcy047bgfAsGHrsULPFXj77Xeq1USrMbPf/YC6huy/1hvvLeZf7yxirVV6V7lVxdKRjy7LW96pkj8AtwKDJJ0O7A2clHOdhTZy5JaMGrU306Y9z1MT7gPgFyefyeWXX8/FY85h8qS/sWTJUg77/v9Ut6GWuxPumszTs95hweIlfOWiBzh85DD6rdSTMx94jn8vXsJ/3zqRjQauwgV7b8Xk2f/mLxP+SY9uopvEz3behFV7L/OPW2tGfVS/J10uRc6NlbQx2cQqInve2vPlvG6FFYcU57tonWbBHyueUM26sN6jf9fucTTfXeebZceca169tarjdnLtcacnP1wfEX/Ksx4zs/aqhdx1ufLOcU8CTpL0sqSzJTU3zaGZWdUVKceda+COiLER8VVgK+BF4ExJL+VZp5lZJTrqCTidobPunPw0sDGwLtkjfMzMakqRUiV557jPBL4F/BO4ATgtIhbkWaeZWSWKNKok7x73TGDbiHg753rMzNqlFlIg5colcEvaOCJmABOAoZKGlh6PiEl51GtmVqlauOhYrrx63MeQzTlyTjPHgmzuEjOzmrHc57gjYnRa3T0iPiw9JmmlPOo0M2uPIqVK8h7H/ViZ+8zMqioiyl6qLa8c92BgbaCXpM+R3e4OsArgmW/MrObUF6jHnVeO+yvAIcAQ4NyS/e8DP8upTjOzihUpVZJXjnssMFbStyPi5jzqMDPrSLWQAilXXqmSURFxFbCupGOaHo+Ic5t5mZlZ1Sz3PW6g8fE1zT/Z1Mysxng4YMRF6euv8ijfzKyjFemW97yf8n6WpFUk9ZQ0XtLbkkblWaeZWSWKNDtg3uO4d42I94CvA7OADYHjcq7TzKzNOjpwS+ouabKku9L2AEnjJL2Uvq5aaVvzDtw909evAtdGhJ9ua2Y1KYcbcH4ElD6q8QSyxzcOA8an7YrkHbjvlDQDGAGMT095/7CV15iZdbqO7HFLGgJ8DbikZPeewNi0PhbYq9K25v0EnBOAbYEREbEUWETWeDOzmhJt+CdptKSJJcvoJsX9HjieT046uEZEzAFIXwdV2ta8H6TQEzgQ2EESwN+BC/Os08ysEvVR/sSuETEGGNPcMUlfB+ZFxNOSduyQxjWR94MU/kyW574gbR+Y9n0/53rNzNqkA++c3A74hqSvAisBq0i6CnhT0poRMUfSmsC8SivIO3BvGRGbl2w/IOmZnOs0M2uzjhrmFxEnAicCpB73sRExStLZwMHAGenr7ZXWkffFyXpJGzRuSFofqM+5TjOzNmtLjrtCZwC7SHoJ2CVtVyTvHvdxwIOSXknb6wKH5lynmVmbNeRw52REPAQ8lNbnAzt3RLl597gfBS4iu7LakNYfz7lOM7M264Qed4fJu8d9BfAecFra3h+4Etgn53rNzNqkLaNKqi3vwL1Rk4uTD/ripJnVojxSJXnJO1UyWdI2jRuStiZLn5iZ1RSnSj62NXCQpNfS9lDgeUnTgIiIzXKu38ysLEXqcecduHfLuXwzsw5RCz3pcuUauCPi1TzLNzPrKPVRnFtM8u5xm5kVwnL/sGAzs6KphSfblMuB28wM97jNzArHo0rMzArGo0rMzArGt7ybmRWMc9xmZgXjHLeZWcG4x21mVjAex21mVjDucZuZFYxHlZiZFYwvTpqZFYxTJWZmBeM7J83MCsY9bjOzgilSjltF+pRZXkkaHRFjqt0Oqy3+vVh+5f2Ud+sYo6vdAKtJ/r1YTjlwm5kVjAO3mVnBOHAXg/OY1hz/XiynfHHSzKxg3OM2MysYB24zs4Jx4C4YSf0lHVGyvZakm6rZJutckg6XdFBaP0TSWiXHLpH0meq1zjqDc9wFI2ld4K6I2LTabbHqk/QQcGxETKx2W6zzuMfdwSStK+l5SRdLelbS/ZJ6SdpA0r2Snpb0D0kbp/M3kPSEpKcknSppYdrfV9J4SZMkTZO0Z6riDGADSVMknZ3qm55e86SkTUra8pCkLST1kXRZqmNySVnWydLPa4aksZKmSrpJUm9JO6efzbT0s1oxnX+GpOfSub9N+34p6VhJewMjgKvT70Ov9DMfIemHks4qqfcQSX9M66MkTUivuUhS92p8L6wdIsJLBy7AukAdMDxt3wCMAsYDw9K+rYEH0vpdwP5p/XBgYVrvAayS1lcHXgaUyp/epL7paf3HwK/S+prAi2n9N8CotN4feBHoU+3v1fK4pJ9XANul7cuAk4DXgQ3TviuA/wEGAC/w8V/G/dPXX5L1sgEeAkaUlP8QWTAfCLxcsv8e4AvAfwF3Aj3T/guAg6r9ffHStsU97nzMjIgpaf1psv+sI4EbJU0BLiILrADbAjem9WtKyhDwG0lTgb8BawNrtFLvDcA+af07JeXuCpyQ6n4IWAkY2ra3ZB3o9Yh4NK1fBexM9jvzYto3FtgBeA/4ELhE0reAD8qtICLeAl6RtI2k1YCNgEdTXVsAT6Xfh52B9dv/lqwzeXbAfPynZL2eLOAuiIjhbSjjALJe0xYRsVTSv8gC7jJFxGxJ8yVtBuwL/CAdEvDtiHihDfVbfsq6sBQRdZK2Iguu+wFHAV9qQz3Xk32AzwBujYiQJGBsRJzYxjZbDXGPu3O8B8yUtA+AMpunY08A307r+5W8ph8wLwXtnYB10v73gZVbqOs64HigX0RMS/vuA45O/2mR9Ln2viFrl6GStk3r+5P9RbWupE+nfQcCf5fUl+zn+Fey1MnwZspq6ffhFmCvVMf1ad94YG9JgwAkDZC0TvMvt1rlwN15DgAOk/QM8CzQeIHwf4BjJE0gS5+8m/ZfDYyQNDG9dgZARMwHHpU0XdLZzdRzE9kHwA0l+04DegJT04XM0zryjVmbPQ8cnNJgA4DfAYeSpdKmAQ3AhWQB+a503t/JrmE0dTlwYePFydIDEfFv4DlgnYiYkPY9R5ZTvz+VO46P03ZWEB4OWGWSegOL05+x+5FdqPSojy7KwzmtIzjHXX1bAOenNMYC4HvVbY6Z1Tr3uM3MCsY5bjOzgnHgNjMrGAduM7OCceC2ZZJUn4aZTZd0YxoBU2lZl6e5NVqdwU7SjpJGVlDHvyStXu7+ZZRxiKTzO6Jes7w4cFtLFkfE8DR0bQnZXCofqXRyooj4fhpPvCw7kk0RYGbNcOC2cv0D+HTqDT8o6RpgmqTuaZbCp9IMdj+Aj+4OPT/NbHc3MKixoMYZ7NL6bspmQHxG2WyI65J9QPw49fa3lzRQ0s2pjqckbZdeu5qy2RcnS7qI7Nb+skjaStJj6bWPSdqo5PCnlM3k+IKkU0pe41n1rCZ4HLe1SlIPYHfg3rRrK2DTiJgpaTTwbkRsqWwq0kcl3Q98jmxio8+SzdXyHNlMeKXlDgQuBnZIZQ2IiHckXUg2S2LjNKbXAL+LiEckDSW7hf+/gFOARyLiVElfA0a34W3NSPXWSfoy2QyKjVMPbAVsSjap01Ppg2cR2fwv26VpCC4gu6P1ijbUadYhHLitJb3SDHKQ9bgvJUthTIiImWn/rsBmjflrsjlWhpHNbndtRNQDb0h6oJnytwEebiwrIt5ZRju+DHwmTbUCsIqklVMd30qvvVvSv9vw3voBYyUNI5v0qWfJsXFpagEk3UI2HWodH8+qB9ALmNeG+sw6jAO3tWRx0xkNU9BaVLoLODoi7mty3ldpfRY8lXEOZCm9bSNicTNtqfQOstOAByPimyk981DJsaZlRmqrZ9WzmuAct7XXfcAPJfUEkLShpD7Aw8B+KQe+JrBTM699HPiipPXSawek/U1nvLufbEpT0nnD0+rDZOkKJO0OrNqGdvcDZqf1Q5oc2yXNmteLbHa9R/GselZDHLitvS4hy19PSjMPXkT2l9ytwEvANODPZLPbfUKa7H80cEuaNbFx6tE7gW82XpwE/ptspsSpkp7j49EtvwJ2kDSJLGXzWgvtnCppVlrOBc4C/lfSo0DTi4yPAFcCU4CbI2KiZ9WzWuK5SszMCsY9bjOzgnHgNjMrGAduM7OCceA2MysYB24zs4Jx4DYzKxgHbjOzgvn/cz5YD2OKt3YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Accuracy on test set: %0.3f%%\"%(accuracy_score(y_test, y_pred_rf)*100))\n",
    "print(\"Precision on test set: %0.3f\"%(precision_score(y_test, y_pred_rf)))\n",
    "print(\"Recall on test set: %0.3f\"%(recall_score(y_test, y_pred_rf)))\n",
    "print(\"F1-Score on test set: %0.3f\"%(f1_score(y_test, y_pred_rf)))\n",
    "\n",
    "cm = confusion_matrix(y_test, y_pred_rf)\n",
    "class_label = [\"negative\", \"positive\"]\n",
    "df_cm = pd.DataFrame(cm, index = class_label, columns = class_label)\n",
    "sns.heatmap(df_cm, annot = True, fmt = \"d\")\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.xlabel(\"Predicted Label\")\n",
    "plt.ylabel(\"True Label\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----+---------------------------+------------+-------------+----------+------------+\n",
      "|    | Algorithm                 | Accuracy   |   Precision |   Recall |   F1-Score |\n",
      "|----+---------------------------+------------+-------------+----------+------------|\n",
      "|  0 | Decision Tree Classifier  | 75.667%    |       0.775 |    0.669 |      0.718 |\n",
      "|  1 | Support Vector Classifier | 80%        |       0.788 |    0.777 |      0.783 |\n",
      "|  2 | Random Forest Classifier  | 83%        |       0.819 |    0.813 |      0.816 |\n",
      "+----+---------------------------+------------+-------------+----------+------------+\n"
     ]
    }
   ],
   "source": [
    "from tabulate import tabulate \n",
    "   \n",
    "values1 = {'Algorithm':['Decision Tree Classifier', 'Support Vector Classifier', 'Random Forest Classifier'], \n",
    "        'Accuracy':['75.667%','80%' ,'83%'], \n",
    "        'Precision':['0.775', '0.788', '0.819'],\n",
    "        'Recall':['0.669','0.777','0.813'],\n",
    "        'F1-Score':['0.718','0.783','0.816']\n",
    "       } \n",
    "\n",
    "df2 = pd.DataFrame(values1) \n",
    "  \n",
    "# displaying the DataFrame \n",
    "print(tabulate(df2, headers = 'keys', tablefmt = 'psql')) "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}