Heart_disease_predictions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction :\n",
    "**Dataset:** The Dataset contains the details of patients .\n",
    "The original database contains 76 attributes, but all published experiments refer to using a subset of 14 of them. The \"target\" field refers to the presence of heart disease in the patient or not(1 or 0). \n",
    "\n",
    "**Problem statement:** The goal is to make a ML model to predict whether the paitent has heart disease or not.\n",
    "\n",
    "**Attributes:** \n",
    "\n",
    "- Age\n",
    "- sex\t\n",
    "- cp\t\n",
    "- trestbps\t\n",
    "- chol\t\n",
    "- fbs\t\n",
    "- restecg\t\n",
    "- thalach\t\n",
    "- exang\t\n",
    "- oldpeak\t\n",
    "- slope\t\n",
    "- ca\t\n",
    "- thal\t\n",
    "- target\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing necessary libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rcParams\n",
    "from matplotlib.cm import rainbow\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Other libraries\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Machine Learning\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load the dataset using pandas\n",
    "df = pd.read_csv(\"datasets_heart.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Look at first five rows\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303, 14)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Number of rows and columns in dataset\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Description : The dataset contains 303 rows and 14 columns**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 303 entries, 0 to 302\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       303 non-null    int64  \n",
      " 1   sex       303 non-null    int64  \n",
      " 2   cp        303 non-null    int64  \n",
      " 3   trestbps  303 non-null    int64  \n",
      " 4   chol      303 non-null    int64  \n",
      " 5   fbs       303 non-null    int64  \n",
      " 6   restecg   303 non-null    int64  \n",
      " 7   thalach   303 non-null    int64  \n",
      " 8   exang     303 non-null    int64  \n",
      " 9   oldpeak   303 non-null    float64\n",
      " 10  slope     303 non-null    int64  \n",
      " 11  ca        303 non-null    int64  \n",
      " 12  thal      303 non-null    int64  \n",
      " 13  target    303 non-null    int64  \n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 33.3 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Description : As we can see all the attributes are of numeric type**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.366337</td>\n",
       "      <td>0.683168</td>\n",
       "      <td>0.966997</td>\n",
       "      <td>131.623762</td>\n",
       "      <td>246.264026</td>\n",
       "      <td>0.148515</td>\n",
       "      <td>0.528053</td>\n",
       "      <td>149.646865</td>\n",
       "      <td>0.326733</td>\n",
       "      <td>1.039604</td>\n",
       "      <td>1.399340</td>\n",
       "      <td>0.729373</td>\n",
       "      <td>2.313531</td>\n",
       "      <td>0.544554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.082101</td>\n",
       "      <td>0.466011</td>\n",
       "      <td>1.032052</td>\n",
       "      <td>17.538143</td>\n",
       "      <td>51.830751</td>\n",
       "      <td>0.356198</td>\n",
       "      <td>0.525860</td>\n",
       "      <td>22.905161</td>\n",
       "      <td>0.469794</td>\n",
       "      <td>1.161075</td>\n",
       "      <td>0.616226</td>\n",
       "      <td>1.022606</td>\n",
       "      <td>0.612277</td>\n",
       "      <td>0.498835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>47.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>133.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>274.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age         sex          cp    trestbps        chol         fbs  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean    54.366337    0.683168    0.966997  131.623762  246.264026    0.148515   \n",
       "std      9.082101    0.466011    1.032052   17.538143   51.830751    0.356198   \n",
       "min     29.000000    0.000000    0.000000   94.000000  126.000000    0.000000   \n",
       "25%     47.500000    0.000000    0.000000  120.000000  211.000000    0.000000   \n",
       "50%     55.000000    1.000000    1.000000  130.000000  240.000000    0.000000   \n",
       "75%     61.000000    1.000000    2.000000  140.000000  274.500000    0.000000   \n",
       "max     77.000000    1.000000    3.000000  200.000000  564.000000    1.000000   \n",
       "\n",
       "          restecg     thalach       exang     oldpeak       slope          ca  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean     0.528053  149.646865    0.326733    1.039604    1.399340    0.729373   \n",
       "std      0.525860   22.905161    0.469794    1.161075    0.616226    1.022606   \n",
       "min      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n",
       "50%      1.000000  153.000000    0.000000    0.800000    1.000000    0.000000   \n",
       "75%      1.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
       "max      2.000000  202.000000    1.000000    6.200000    2.000000    4.000000   \n",
       "\n",
       "             thal      target  \n",
       "count  303.000000  303.000000  \n",
       "mean     2.313531    0.544554  \n",
       "std      0.612277    0.498835  \n",
       "min      0.000000    0.000000  \n",
       "25%      2.000000    0.000000  \n",
       "50%      2.000000    1.000000  \n",
       "75%      3.000000    1.000000  \n",
       "max      3.000000    1.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#five points summary of each attribute\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Checking the presence of missing values:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age         0\n",
       "sex         0\n",
       "cp          0\n",
       "trestbps    0\n",
       "chol        0\n",
       "fbs         0\n",
       "restecg     0\n",
       "thalach     0\n",
       "exang       0\n",
       "oldpeak     0\n",
       "slope       0\n",
       "ca          0\n",
       "thal        0\n",
       "target      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Description : There is no missing value in the dataset.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x18706918c88>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Correlation matrix\n",
    "rcParams['figure.figsize'] = 20, 14\n",
    "plt.matshow(df.corr())\n",
    "plt.yticks(np.arange(df.shape[1]), df.columns)\n",
    "plt.xticks(np.arange(df.shape[1]), df.columns)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x00000187070B5588>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000187070E17F0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000187077C4A58>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000187077F5CC0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000018707827F28>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000187078621D0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000018707897438>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000187078C9668>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x00000187078C96D8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000018707933B70>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000018707968DD8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000187079AA048>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x00000187079DB2E8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000018707A0F550>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000018707A427B8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000018707A76A20>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1008 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Count of each Target Class')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfQAAAGDCAYAAADd8eLzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAActElEQVR4nO3dfbylZV3v8c9XRlACAZ0Njjw4qKMJnCDbkA9pGppkHodOSWNkk3EiDS0fsiBN0Jd0KHuZHYs6kxCjITQqJT6L+ICVgMOTPEmgCAwMzFZEwBId/J0/1j263G1m1gx7rbXXNZ/367Vfa93Xdd3r/q3FHr77uta97pWqQpIkTbaHjLsASZL04BnokiQ1wECXJKkBBrokSQ0w0CVJaoCBLklSAwx0aQIl+aUktyS5N8lPjuiYZyR56yiOtVAkOSnJP467DmkQBrq2a0l+LcnaLhjXJ/lYkp8ZwXEryRMexEP8BfDKqtqlqi6br7rmQ5Jndq/nvUm+3T3Xe/t+9htRHQOF8bh+B6T5ZqBru5XktcA7gD8F9gL2A04Flo+zrgE9Frh63EXMpao+3/2hsQtwYNe8+6a2qrp5kMdJsmh4Vf7gGJP8OyD9CANd26UkuwFvAY6rqnOq6ttV9b2q+lBVvb4bs1OSdyS5rft5R5Kdur7fTPKvsx7zB7Pubnn6b5J8JMk9SS5K8viu74Julyu6WeGvzlHfQ5K8MclNSTYkeXeS3bqa7gV26Pb/ygM8vx9Pcl6SO5Ncl+Sovr5fTHJZkru7ZfuTZu37M0n+PcldXf9v9nXvMddz2orX/WVJru32/2qS3+nre3aSdUn+KMntwD8keXiS1Um+2e33h0nW9e3zmCQfSDKT5MYkv9e1HwH8MfCr3Wt8xRy1bPF3YI593pfk9iTfSnJBkgP7+l6Q5Jruud2a5A+69sVJPty9nncm+XwS/9+reecvlbZXTwMeBvzzZsa8AXgqcAhwMHAY8MatOMZLgDcDewA3ACcDVNWzuv6DuxnrP82x7292P88BHgfsAvx1Vd3XzXw37f/fAjXJjwHnAe8F9uzqOLUvfL4N/AawO/CLwCuSHNntux/wMeCdwFT33C/f0nPaChuAFwKPAF4G/GWSp/T1Pxp4JL0ViGOBE4Gl3WvwPODX+57nQ4APAVcAewOHA69O8vyq+ji9Wfc/da/xwXPUMsjvwGwfA5bRe10vBc7s6zsN+J2q2hU4CPh01/46YB2913Mven9oeM1tzTsDXdurRwFfr6qNmxlzNPCWqtpQVTP0guylW3GMc6rq4u4YZ9ILx0EdDby9qr5aVfcCJwArBlyGfiHwtar6h6raWFWXAh8AfgWgqj5bVVdW1fer6kvAWcDP9h33U1V1Vjdb/UZV9Qf6g3lOVNVHquor1fM54JPAM/uGfB84sfvD5b+Ao4A/rapvVtU64P/2jT0UmKqqt1TVd6vqq8DfAysGLGeQ34HZ9Z9eVfdU1X3AScDB3Uwf4HvAAUke0dV7aV/7EuCx3Wv6+fJLNDQEBrq2V98AFm8hIB8D3NS3fVPXNqjb++7/J71Z9qDmOvYiejO8LXks8NPdEu9dSe6iF9SPBkjy00k+0y1Tfwt4ObC423dfYM5l/M6DeU4k+YUkF3ZLz3cBL+g7NsBMVX2nb/sxwC192/33Hws8Ztbz/GMGe41gsN+B/tp3SHJKkq8kuRv4Wte1qf5f7p7PTUk+l+RpXfvb6K1mfLJ7m+H4AeuTtoqBru3VF4DvAEduZsxt9EJjk/26NugtW++8qSPJo+e5vrmOvRG4Y4B9bwE+V1W79/3sUlWv6PrfC5wL7FtVuwF/B6Rv3616X3xQ3fkHH6B3hv5eVbU78NG+Y8N/X4peD+zTt71v3/1bgBtnPc9dq+oFD/BYsw3yO9Dv1+idLPdcYDd6bwWwqf6q+mJVLae3HP8vwJqu/Z6qel1VPQ74n8Brkxw+4DGlgRno2i5V1beANwF/k+TIJDsneWg3g/zzbthZwBuTTCVZ3I3f9DGoK4ADkxyS5GH0ll+3xh303hd+IGcBr0myf5Jd+OH7wYMsD38YeGKSl3bP6aFJDk3y5K5/V+DOqvpOksPoBdUmZwLPTXJUkkVJHpVkq5bVN2NHYCdgBtiY5BeAn9/CPmuAE5LskWRv4JV9fRcDd3cn0T28m0EflOTQrv8OYOkDnYA24O9Av12B++jN7Hem998EgCQ7Jjk6yW5V9T3gbuD+ru+FSZ6QJH3t92/heUtbzUDXdquq3g68lt6JbjP0ZnyvpDe7AngrsBb4EnAlvZOg3trt+x/0zpD+FHA98CNnvA/gJGB1t1R81Bz9pwPvAS4AbqQ3k3zVgM/rHnpBuYLeTP924M/ohSnA7wJvSXIPvUBb07fvzfSWjV8H3EnvhLi5Tijbal1dv9cd75v0/pA4dwu7vYXeCWU30nut308vVKmq++nNeA/p+r8OvIve7Bngfd3tN5JcyhwG+B3o9256b33cClwDXDir/6XA17rl+JfzwxP4lnW130tvVeDUqvrsFp63tNXiuRmSJkWSVwArqupntzhY2s44Q5e0YCVZkuQZ6X0u/0n0Vg625mNm0nZj6FdikqQHYUfg/wH7A3cBZ9O7kpukWVxylySpAS65S5LUAANdkqQGTPR76IsXL66lS5eOuwxJkkbmkksu+XpVTc1un+hAX7p0KWvXrh13GZIkjUySm+Zqd8ldkqQGGOiSJDXAQJckqQEGuiRJDTDQJUlqgIEuSVIDDHRJkhpgoEuS1AADXZKkBhjokiQ1wECXJKkBBrokSQ0w0CVJasBEf9uapAYl465Amj9VIzuUM3RJkhpgoEuS1AADXZKkBhjokiQ1wECXJKkBQwv0JKcn2ZDkqlntr0pyXZKrk/x5X/sJSW7o+p4/rLokSWrRMD+2dgbw18C7NzUkeQ6wHPiJqrovyZ5d+wHACuBA4DHAp5I8saruH2J9kiQ1Y2gz9Kq6ALhzVvMrgFOq6r5uzIaufTlwdlXdV1U3AjcAhw2rNkmSWjPq99CfCDwzyUVJPpfk0K59b+CWvnHrujZJkjSAUV8pbhGwB/BU4FBgTZLHAXNdGmrOy+skORY4FmC//fYbUpmSJE2WUc/Q1wHnVM/FwPeBxV37vn3j9gFum+sBqmpVVU1X1fTU1NTQC5YkaRKMOtD/Bfg5gCRPBHYEvg6cC6xIslOS/YFlwMUjrk2SpIk1tCX3JGcBzwYWJ1kHnAicDpzefZTtu8DKqirg6iRrgGuAjcBxnuEuSdLgUiP8Jpj5Nj09XWvXrh13GZLmk9+2ppYMIWOTXFJV07PbvVKcJEkNMNAlSWqAgS5JUgMMdEmSGmCgS5LUAANdkqQGGOiSJDXAQJckqQEGuiRJDTDQJUlqgIEuSVIDDHRJkhpgoEuS1AADXZKkBhjokiQ1wECXJKkBBrokSQ0w0CVJaoCBLklSAwx0SZIaYKBLktQAA12SpAYY6JIkNcBAlySpAQa6JEkNMNAlSWqAgS5JUgMMdEmSGmCgS5LUgKEFepLTk2xIctUcfX+QpJIs7ms7IckNSa5L8vxh1SVJUouGOUM/AzhidmOSfYHnATf3tR0ArAAO7PY5NckOQ6xNkqSmDC3Qq+oC4M45uv4S+EOg+tqWA2dX1X1VdSNwA3DYsGqTJKk1I30PPcmLgFur6opZXXsDt/Rtr+va5nqMY5OsTbJ2ZmZmSJVKkjRZRhboSXYG3gC8aa7uOdpqjjaqalVVTVfV9NTU1HyWKEnSxFo0wmM9HtgfuCIJwD7ApUkOozcj37dv7D7AbSOsTZKkiTayGXpVXVlVe1bV0qpaSi/En1JVtwPnAiuS7JRkf2AZcPGoapMkadINbYae5Czg2cDiJOuAE6vqtLnGVtXVSdYA1wAbgeOq6v5h1bY5efNcq//SZKoT53znSlKDhhboVfWSLfQvnbV9MnDysOqRJKllXilOkqQGGOiSJDXAQJckqQEGuiRJDTDQJUlqgIEuSVIDDHRJkhpgoEuS1AADXZKkBhjokiQ1wECXJKkBBrokSQ0w0CVJaoCBLklSAwx0SZIaYKBLktQAA12SpAYY6JIkNcBAlySpAQa6JEkNMNAlSWqAgS5JUgMMdEmSGmCgS5LUAANdkqQGGOiSJDXAQJckqQEGuiRJDRhaoCc5PcmGJFf1tb0tyZeTfCnJPyfZva/vhCQ3JLkuyfOHVZckSS0a5gz9DOCIWW3nAQdV1U8A/wGcAJDkAGAFcGC3z6lJdhhibZIkNWVogV5VFwB3zmr7ZFVt7DYvBPbp7i8Hzq6q+6rqRuAG4LBh1SZJUmvG+R76bwEf6+7vDdzS17eua5MkSQMYS6AneQOwEThzU9Mcw+oB9j02ydoka2dmZoZVoiRJE2XkgZ5kJfBC4Oiq2hTa64B9+4btA9w21/5VtaqqpqtqempqarjFSpI0IUYa6EmOAP4IeFFV/Wdf17nAiiQ7JdkfWAZcPMraJEmaZIuG9cBJzgKeDSxOsg44kd5Z7TsB5yUBuLCqXl5VVydZA1xDbyn+uKq6f1i1SZLUmqEFelW9ZI7m0zYz/mTg5GHVI0lSy7xSnCRJDTDQJUlqgIEuSVIDDHRJkhpgoEuS1AADXZKkBhjokiQ1wECXJKkBBrokSQ0w0CVJaoCBLklSAwx0SZIaYKBLktQAA12SpAYY6JIkNcBAlySpAQa6JEkNMNAlSWqAgS5JUgMMdEmSGmCgS5LUAANdkqQGGOiSJDXAQJckqQEGuiRJDTDQJUlqgIEuSVIDDHRJkhpgoEuS1IChBXqS05NsSHJVX9sjk5yX5Prudo++vhOS3JDkuiTPH1ZdkiS1aJgz9DOAI2a1HQ+cX1XLgPO7bZIcAKwADuz2OTXJDkOsTZKkpgwt0KvqAuDOWc3LgdXd/dXAkX3tZ1fVfVV1I3ADcNiwapMkqTWjfg99r6paD9Dd7tm17w3c0jduXdcmSZIGsFBOisscbTXnwOTYJGuTrJ2ZmRlyWZIkTYZRB/odSZYAdLcbuvZ1wL594/YBbpvrAapqVVVNV9X01NTUUIuVJGlSjDrQzwVWdvdXAh/sa1+RZKck+wPLgItHXJskSRNr0bAeOMlZwLOBxUnWAScCpwBrkhwD3Ay8GKCqrk6yBrgG2AgcV1X3D6s2SZJaM7RAr6qXPEDX4Q8w/mTg5GHVI0lSyxbKSXGSJOlBMNAlSWqAgS5JUgMMdEmSGmCgS5LUAANdkqQGGOiSJDVgoEBP8oxB2iRJ0ngMOkN/54BtkiRpDDZ7pbgkTwOeDkwleW1f1yOAHYZZmCRJGtyWLv26I7BLN27Xvva7gV8ZVlGSJGnrbDbQq+pzwOeSnFFVN42oJkmStJUG/XKWnZKsApb271NVPzeMoiRJ0tYZNNDfB/wd8C7ArzWVJGmBGTTQN1bV3w61EkmStM0G/djah5L8bpIlSR656WeolUmSpIENOkNf2d2+vq+tgMfNbzmSJGlbDBToVbX/sAuRJEnbbqBAT/Ibc7VX1bvntxxJkrQtBl1yP7Tv/sOAw4FLAQNdkqQFYNAl91f1byfZDXjPUCqSJElbbVu/PvU/gWXzWYgkSdp2g76H/iF6Z7VD70tZngysGVZRkiRp6wz6Hvpf9N3fCNxUVeuGUI8kSdoGAy25d1/S8mV637i2B/DdYRYlSZK2zkCBnuQo4GLgxcBRwEVJ/PpUSZIWiEGX3N8AHFpVGwCSTAGfAt4/rMIkSdLgBj3L/SGbwrzzja3YV5IkDdmgM/SPJ/kEcFa3/avAR4dTkiRJ2lqbDfQkTwD2qqrXJ/lfwM8AAb4AnLmtB03yGuB/0/so3JXAy4CdgX8ClgJfA46qqm9u6zEkSdqebGnZ/B3APQBVdU5VvbaqXkNvdv6ObTlgkr2B3wOmq+ogep9rXwEcD5xfVcuA87ttSZI0gC0F+tKq+tLsxqpaS28mva0WAQ9PsojezPw2YDmwuutfDRz5IB5fkqTtypYC/WGb6Xv4thywqm6ld6Gam4H1wLeq6pP0lvbXd2PWA3vOtX+SY5OsTbJ2ZmZmW0qQJKk5Wwr0Lyb57dmNSY4BLtmWAybZg95sfH/gMcCPJfn1QfevqlVVNV1V01NTU9tSgiRJzdnSWe6vBv45ydH8MMCngR2BX9rGYz4XuLGqZgCSnAM8HbgjyZKqWp9kCbBhcw8iSZJ+aLOBXlV3AE9P8hzgoK75I1X16QdxzJuBpybZGfgvet+tvhb4NrASOKW7/eCDOIYkSduVQb8P/TPAZ+bjgFV1UZL3A5fS+6KXy4BVwC7Amm45/2Z6l5mVJEkDGPTCMvOqqk4ETpzVfB+92bokSdpKXr5VkqQGGOiSJDXAQJckqQEGuiRJDTDQJUlqgIEuSVIDDHRJkhpgoEuS1AADXZKkBhjokiQ1wECXJKkBBrokSQ0w0CVJaoCBLklSAwx0SZIaYKBLktQAA12SpAYY6JIkNcBAlySpAQa6JEkNMNAlSWqAgS5JUgMMdEmSGmCgS5LUAANdkqQGGOiSJDXAQJckqQEGuiRJDRhLoCfZPcn7k3w5ybVJnpbkkUnOS3J9d7vHOGqTJGkSjWuG/lfAx6vqx4GDgWuB44Hzq2oZcH63LUmSBjDyQE/yCOBZwGkAVfXdqroLWA6s7oatBo4cdW2SJE2qcczQHwfMAP+Q5LIk70ryY8BeVbUeoLvdcwy1SZI0kcYR6IuApwB/W1U/CXybrVheT3JskrVJ1s7MzAyrRkmSJso4An0dsK6qLuq2308v4O9IsgSgu90w185VtaqqpqtqempqaiQFS5K00I080KvqduCWJE/qmg4HrgHOBVZ2bSuBD466NkmSJtWiMR33VcCZSXYEvgq8jN4fF2uSHAPcDLx4TLVJkjRxxhLoVXU5MD1H1+GjrkWSpBZ4pThJkhpgoEuS1AADXZKkBhjokiQ1wECXJKkBBrokSQ0w0CVJaoCBLklSAwx0SZIaYKBLktQAA12SpAYY6JIkNcBAlySpAQa6JEkNMNAlSWqAgS5JUgMMdEmSGmCgS5LUAANdkqQGGOiSJDXAQJckqQEGuiRJDTDQJUlqgIEuSVIDDHRJkhpgoEuS1AADXZKkBhjokiQ1wECXJKkBYwv0JDskuSzJh7vtRyY5L8n13e0e46pNkqRJM84Z+u8D1/ZtHw+cX1XLgPO7bUmSNICxBHqSfYBfBN7V17wcWN3dXw0cOeq6JEmaVOOaob8D+EPg+31te1XVeoDuds+5dkxybJK1SdbOzMwMv1JJkibAyAM9yQuBDVV1ybbsX1Wrqmq6qqanpqbmuTpJkibTojEc8xnAi5K8AHgY8Igk/wjckWRJVa1PsgTYMIbaJEmaSCOfoVfVCVW1T1UtBVYAn66qXwfOBVZ2w1YCHxx1bZIkTaqF9Dn0U4DnJbkeeF63LUmSBjCOJfcfqKrPAp/t7n8DOHyc9UiSNKkW0gxdkiRtIwNdkqQGGOiSJDXAQJckqQEGuiRJDTDQJUlqgIEuSVIDDHRJkhpgoEuS1AADXZKkBhjokiQ1wECXJKkBBrokSQ0w0CVJaoCBLklSAwx0SZIaYKBLktQAA12SpAYY6JIkNcBAlySpAQa6JEkNMNAlSWqAgS5JUgMMdEmSGmCgS5LUAANdkqQGGOiSJDXAQJckqQEGuiRJDRh5oCfZN8lnklyb5Ookv9+1PzLJeUmu7273GHVtkiRNqnHM0DcCr6uqJwNPBY5LcgBwPHB+VS0Dzu+2JUnSAEYe6FW1vqou7e7fA1wL7A0sB1Z3w1YDR466NkmSJtVY30NPshT4SeAiYK+qWg+90Af2fIB9jk2yNsnamZmZUZUqSdKCNrZAT7IL8AHg1VV196D7VdWqqpququmpqanhFShJ0gQZS6AneSi9MD+zqs7pmu9IsqTrXwJsGEdtkiRNonGc5R7gNODaqnp7X9e5wMru/krgg6OuTZKkSbVoDMd8BvBS4Mokl3dtfwycAqxJcgxwM/DiMdQmSdJEGnmgV9W/AnmA7sNHWYskSa3wSnGSJDXAQJckqQEGuiRJDTDQJUlqgIEuSVIDDHRJkhpgoEuS1AADXZKkBhjokiQ1wECXJKkBBrokSQ0w0CVJaoCBLklSAwx0SZIaYKBLktQAA12SpAYY6JIkNcBAlySpAQa6JEkNMNAlSWqAgS5JUgMMdEmSGmCgS5LUAANdkqQGGOiSJDXAQJckqQEGuiRJDTDQJUlqwIIL9CRHJLkuyQ1Jjh93PZIkTYIFFehJdgD+BvgF4ADgJUkOGG9VkiQtfAsq0IHDgBuq6qtV9V3gbGD5mGuSJGnBW2iBvjdwS9/2uq5NkiRtxqJxFzBL5mirHxmQHAsc223em+S6oVelYVgMfH3cRbQuJ831T0oC/Dc4GhnKv8HHztW40AJ9HbBv3/Y+wG39A6pqFbBqlEVp/iVZW1XT465D2l75b7A9C23J/YvAsiT7J9kRWAGcO+aaJEla8BbUDL2qNiZ5JfAJYAfg9Kq6esxlSZK04C2oQAeoqo8CHx13HRo63zaRxst/g41JVW15lCRJWtAW2nvokiRpGxjoGikv7SuNV5LTk2xIctW4a9H8MtA1Ml7aV1oQzgCOGHcRmn8GukbJS/tKY1ZVFwB3jrsOzT8DXaPkpX0laUgMdI3SFi/tK0naNga6RmmLl/aVJG0bA12j5KV9JWlIDHSNTFVtBDZd2vdaYI2X9pVGK8lZwBeAJyVZl+SYcdek+eGV4iRJaoAzdEmSGmCgS5LUAANdkqQGGOiSJDXAQJckqQEGujQhkjwqyeXdz+1Jbu3b3nGej7V7kt/dTP+jk5yd5CtJrkny0SRPTLLUb/GSxsOPrUkTKMlJwL1V9RcDjF3UXQNgax5/KfDhqjpojr4A/w6srqq/69oOAXald63+OfeTNFzO0KUJluS3k3wxyRVJPpBk5679jCRvT/IZ4M+SPD7Jhd3YtyS5t+8xXt+1fynJm7vmU4DHd7P/t8067HOA720Kc4CquryqPj+rtqVJPp/k0u7n6V37kiQXdI99VZJnJtmhq/mqJFcmeU039vFJPp7kku6xfrxrf3E39ookF8zzyypNpEXjLkDSg3JOVf09QJK3AscA7+z6ngg8t6ruT/Jh4K+q6qwkL9+0c5KfB5bR+2rbAOcmeRZwPHBQVR0yxzEPAi4ZoLYNwPOq6jtJlgFnAdPArwGfqKqTk+wA7AwcAuy9aWafZPfuMVYBL6+q65P8NHAq8HPAm4DnV9WtfWOl7ZqBLk22g7og3x3Yhd5ldTd5X1Xd391/GnBkd/+9wKal+p/vfi7rtnehF/A3z0NtDwX+uluOv5/eHxjQu6b/6UkeCvxLVV2e5KvA45K8E/gI8MkkuwBPB97XW+UHYKfu9t+AM5KsAc6Zh1qlieeSuzTZzgBeWVX/A3gz8LC+vm8PsH+A/1NVh3Q/T6iq07awz9XATw3w2K8B7gAOpjcz3xGgqi4AngXcCrwnyW9U1Te7cZ8FjgPeRe//T3f11XZIVT25e4yXA2+k9+19lyd51AD1SE0z0KXJtiuwvpvtHr2ZcRcCv9zdX9HX/gngt7rZMEn2TrIncE/32HP5NLBTkt/e1JDk0CQ/O2vcbsD6qvo+8FJgh27sY4EN3VsFpwFPSbIYeEhVfQD4E+ApVXU3cGOSF3f7JcnB3f3HV9VFVfUm4Ov86NfyStslA12abH8CXAScB3x5M+NeDbw2ycXAEuBbAFX1SXpL8F9IciXwfmDXqvoG8G/diWc/clJc9T4a80vA87qPrV0NnMR//277U4GVSS6kt9y+acXg2fRm1ZfR+yPjr4C9gc8muZzeqsMJ3dijgWOSXEFvZWB51/627uS5q4ALgCu29EJJrfNja9J2oDv7/b+qqpKsAF5SVcu3tJ+kyeFJcdL24afonaAW4C7gt8Zcj6R55gxdkqQG+B66JEkNMNAlSWqAgS5JUgMMdEmSGmCgS5LUAANdkqQG/H/Jnpd0hQpMywAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams['figure.figsize'] = 8,6\n",
    "plt.bar(df['target'].unique(), df['target'].value_counts(), color = ['red', 'green'])\n",
    "plt.xticks([0, 1])\n",
    "plt.xlabel('Target Classes')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Count of each Target Class')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    165\n",
       "0    138\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.target.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing:\n",
    "**To work with categorical variables, we should break each categorical column into dummy columns with 1s and 0s.\n",
    "Let’s say we have a column Gender, with values 1 for Male and 0 for Female. It needs to be converted into two columns with the value 1 where the column would be true and 0 where it will be false.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.get_dummies(df, columns = ['sex', 'cp', 'fbs', 'restecg', 'exang', 'slope', 'ca', 'thal'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Next, we need to scale the dataset for which we will use the StandardScaler. The fit_transform() method of the scaler scales the data and we update the columns.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()\n",
    "columns_to_scale = ['age', 'trestbps', 'chol', 'thalach', 'oldpeak']\n",
    "dataset[columns_to_scale] = sc.fit_transform(df[columns_to_scale])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Splitting the dataset using train_test_split\n",
    "y = df['target']\n",
    "X = df.drop(['target'], axis = 1)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.33, random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**K Neighbors Classifier:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_scores = []\n",
    "for k in range(1,21):\n",
    "    knn_classifier = KNeighborsClassifier(n_neighbors = k)\n",
    "    knn_classifier.fit(X_train, y_train)\n",
    "    knn_scores.append(knn_classifier.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'K Neighbors Classifier scores for different K values')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([k for k in range(1, 21)], knn_scores, color = 'red')\n",
    "for i in range(1,21):\n",
    "    plt.text(i, knn_scores[i-1], (i, knn_scores[i-1]))\n",
    "plt.xticks([i for i in range(1, 21)])\n",
    "plt.xlabel('Number of Neighbors (K)')\n",
    "plt.ylabel('Scores')\n",
    "plt.title('K Neighbors Classifier scores for different K values')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**As you can see, we achieved the maximum score of 74% when the number of neighbors was chosen to be 8.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Support Vector Classifier:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "svc_scores = []\n",
    "kernels = ['linear', 'poly', 'rbf', 'sigmoid']\n",
    "for i in range(len(kernels)):\n",
    "    svc_classifier = SVC(kernel = kernels[i])\n",
    "    svc_classifier.fit(X_train, y_train)\n",
    "    svc_scores.append(svc_classifier.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Support Vector Classifier scores for different kernels')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Applying the rainbow method to select different colors for each bar and plot a bar graph of the scores achieved by each.\n",
    "colors = rainbow(np.linspace(0, 1, len(kernels)))\n",
    "plt.bar(kernels, svc_scores, color = colors)\n",
    "for i in range(len(kernels)):\n",
    "    plt.text(i, svc_scores[i], svc_scores[i])\n",
    "plt.xlabel('Kernels')\n",
    "plt.ylabel('Scores')\n",
    "plt.title('Support Vector Classifier scores for different kernels')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**As can be seen from the plot above, the linear kernel performed the best for this dataset and achieved a score of 81%**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Decision Tree Classifier:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "dt_scores = []\n",
    "for i in range(1, len(X.columns) + 1):\n",
    "    dt_classifier = DecisionTreeClassifier(max_features = i, random_state = 0)\n",
    "    dt_classifier.fit(X_train, y_train)\n",
    "    dt_scores.append(dt_classifier.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Decision Tree Classifier scores for different number of maximum features')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#we can now plot a line graph and see the effect of the number of features on the model scores.\n",
    "plt.plot([i for i in range(1, len(X.columns) + 1)], dt_scores, color = 'green')\n",
    "for i in range(1, len(X.columns) + 1):\n",
    "    plt.text(i, dt_scores[i-1], (i, dt_scores[i-1]))\n",
    "plt.xticks([i for i in range(1, len(X.columns) + 1)])\n",
    "plt.xlabel('Max features')\n",
    "plt.ylabel('Scores')\n",
    "plt.title('Decision Tree Classifier scores for different number of maximum features')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**From the line graph above, we can clearly see that the maximum score is 83% and is achieved for maximum features being selected to be 10.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Random Forest Classifier:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_scores = []\n",
    "estimators = [10, 100, 200, 500, 1000]\n",
    "for i in estimators:\n",
    "    rf_classifier = RandomForestClassifier(n_estimators = i, random_state = 0)\n",
    "    rf_classifier.fit(X_train, y_train)\n",
    "    rf_scores.append(rf_classifier.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Random Forest Classifier scores for different number of estimators')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fzW677cb3vvc9lixZws4778wxxxzDmDFjALjiiivYfPPNh2oWpBHFI3FJXbv22muZMGEC22+/PWPGjOGoo47iu9/97ir9RASPPPIImcmjjz7KZpttxujRHi9ITTDEJXXt7rvvZuutt366edy4cdx9992r9DNt2jRuueUWttxyS/bYYw/OPPNM1luv2tVEBAcccAB77rknM2fOXKu1D2drcokCqjMkL33pSznkkEPWVskaJgxxSV3LzGe0i4hVmi+55BImTZrE4sWLmTdvHtOmTePhhx8G4Oqrr+aXv/wlF110EWeffTZXXXXVWql7OFt5ieKiiy7i5ptv5vzzz+fmm29epZ+Vlyiuv/56rrzySt773veydOnSp7ufeeaZ7Lrrrmu7dA0Dhrikro0bN46FCxc+3bxo0SK23HLLVfr56le/yl/91V8REUyYMIHx48dz6623Ajzd7xZbbMERRxzBtddeu/aKH6bW9BLFokWL+MEPfsCJJ544FOVriBnikro2ZcoUbrvtNu68806WLl3KrFmzOOyww1bpZ5tttuGyyy4D4Pe//z3z589n++2357HHHuORRx4B4LHHHuPSSy9l9913X+vzMNys6SWKU089lU9/+tNPN2vd4t0mGtEuvvhi3vOe97B8+XJOPPFEpk+fvkr3hx56iGOPPZbf/e53LFu2jPe9732ccMIJPPHEE7zmNa/hySefZNmyZbzpTW/i9NNPH6K5gI/ygyGb9ipGwyvPOo4pB+7DiuUreOnb9+fCiXcxfcYXAZhy8lQ2/PArOO/4L/ClPc6FTPY542i+uPnPuf+Oe5l1xMcBWLFsBXu89bVce9Byrh3gvH2EvxzsuRpSA7lEcfnll3P77bez//778+pXv5qrrrqKLbbYgj333JMrr7xyLVU8/I2U7b4bhrhGrDX5d6gNNtiAyy+/nI022oinnnqKffbZh4MPPpi99957COdoeNhp6hR2mjpllXZTTp769PuNt3w+x136sWcMt9n2L+Tvrj+r8frapttLFNOnT3/GJYqrr76a2bNnM2fOHJ544gkefvhhjj32WM4777y1PRvDxrq23Xv+RSPWmlxrjAg22mgjAJ566imeeuqpZxwdSYNhTS5RfPKTn2TRokXcddddzJo1i/3222+dDnBY97Z7Q1wj1ppea1y+fDmTJk1iiy22YP/99+flL3/5Wq1f64bRo0dz1llnceCBB7Lrrrvylre8hYkTJzJjxgxmzJgBwIc//GGuueYa9thjD173utdxxhln+MCcXqxr272n0zVircm1xo033phRo0Yxb948HnzwQY444ghuvPFGb8QaKb5/wlBXsIqpwNTPv6o0LYDvn8DJ40rj93/OlsClp2wFbFVaXgbfv2yVcewL7Hvy2NWbt0O+2n8/LbGubfceiWvEWtN/h1pp0003Zd999+Xiiy9eK3VLWn3r2nZviGvEWpNrjUuWLOHBBx8E4PHHH+dHP/oRu+yyy1qfB0kDs65t955O16CLOfcNdQl/9rZPsP0+r4cVK2D/o9n9ty+AL3226jb1eHjlu+Bf383p5+4GJBz9T4y9FrjzZvj8tGq4XAH7HM7/rbc3rMa85VSvXWodMExuABsNnAUcuP32LAfeDkzcfXdmlO4nAx8Gjgf2OP10EjgD2HzsWG4A3gYsB1YAbwEO+b//G3gRPZzSb4ohrpFtyv7Vq27q8X9+//wXwsf/+5nDjZ8I/35Fo6VJasbU8qo7ufZ+S+DSHoZ7MfCrpopqiKfTJUlqKUNckqSWMsSHmTX9SUJJ0rrDEB9GBuMnCSVJ6w5DfBhZ058klCStWwzxYWRNHxcoSVq3NLr3j4iDImJ+RCyIiOk9dN8kIr4XEddHxE0RMbyehbiWDeRxgYsXL2bevHlMmzaNhx9+eG2VKEkaRhoL8YgYBZwNHAzsBhwdEbt19PYu4ObMfAnVo38/FxFjmqppuBusxwVKktYNTR6J7wUsyMw7MnMpMAs4vKOfBJ4b1eHmRsD9wLIGaxrW1uRxgZKkdU+Td0RtBSysNS8COn/T7SxgNrAYeC5wZGau6BxRRJwEnARViA2m04fHkwKL0byCs5i8/YEky3kpb+d/dp/I+8sDA6dwMs/hw3yD4/nS6XuQJPtwBmePHbzHev7z2ntaoCRpDTUZ4j3FY2dEHAjMA/YDdgB+GBE/ycxVLvJm5kxgJsDkyZNHdMzsxFR26nhg4JTaAwM3ZkuO6/GBgZKkdU2Tp9MXAVvXmsdRHXHXnQB8OysLgDuB4f2TMZIkDRNNhvhcYMeIGF9uVjuK6tR53e+A1wFExAuAnYE7GqxJkqQRo7HT6Zm5LCKmAZcAo4BzMvOmiDi5dJ8BfAw4NyJ+TXX6/f2ZOYx+x1KSpOGr0Ud9ZeYcYE5Huxm194uBA5qsQZKkkcpHfUmS1FKGuCRJLWWIS5LUUoa4JEktZYhLktRShrgkSS1liEuS1FKGuCRJLWWIS5LUUoa4JEktZYhLktRShrgkSS1liEuS1FKGuCRJLWWIS5LUUoa4JEktZYhLktRShrgkSS1liEuS1FKGuCRJLWWIS5LUUoa4JEktZYhLktRShrgkSS1liEuS1FKGuCRJLWWIS5LUUoa4JEkt1WiIR8RBETE/IhZExPQeuv9jRMwrrxsjYnlEbNZkTZIkjRSNhXhEjALOBg4GdgOOjojd6v1k5mcyc1JmTgI+APw4M+9vqiZJkkaSJo/E9wIWZOYdmbkUmAUc3kf/RwPnN1iPJEkjSpMhvhWwsNa8qLR7hoh4DnAQcGEv3U+KiOsi4rolS5YMeqGSJLVRkyEePbTLXvo9FLi6t1PpmTkzMydn5uSxY8cOWoGSJLVZkyG+CNi61jwOWNxLv0fhqXRJkgakyRCfC+wYEeMjYgxVUM/u7CkiNgFeC3y3wVokSRpxRjc14sxcFhHTgEuAUcA5mXlTRJxcus8ovR4BXJqZjzVViyRJI1FjIQ6QmXOAOR3tZnQ0nwuc22QdkiSNRD6xTZKkljLEJUlqKUNckqSWMsQlSWopQ1ySpJYyxCVJailDXJKkljLEJUlqKUNckqSWMsQlSWopQ1ySpJYyxCVJailDXJKkljLEJUlqKUNckqSWMsQlSWopQ1ySpJYyxCVJailDXJKkljLEJUlqKUNckqSWMsQlSWopQ1ySpJYyxCVJailDXJKkljLEJUlqKUNckqSWajTEI+KgiJgfEQsiYnov/ewbEfMi4qaI+HGT9UiSNJKMbmrEETEKOBvYH1gEzI2I2Zl5c62fTYEvAgdl5u8iYoum6pEkaaRp8kh8L2BBZt6RmUuBWcDhHf28Ffh2Zv4OIDP/0GA9kiSNKE2G+FbAwlrzotKubifgeRFxZUT8IiKOa7AeSZJGlMZOpwPRQ7vsYfp7Aq8Dng38NCJ+lpm/WWVEEScBJwFss802DZQqSVL7NHkkvgjYutY8DljcQz8XZ+ZjmXkfcBXwks4RZebMzJycmZPHjh3bWMGSJLVJkyE+F9gxIsZHxBjgKGB2Rz/fBV4dEaMj4jnAy4FbGqxJkqQRo7HT6Zm5LCKmAZcAo4BzMvOmiDi5dJ+RmbdExMXADcAK4MuZeWNTNUmSNJI0eU2czJwDzOloN6Oj+TPAZ5qsQ5KkkcgntkmS1FKGuCRJLWWIS5LUUoa4JEktZYhLktRShrgkSS1liEuS1FKGuCRJLWWIS5LUUoa4JEktZYhLktRShrgkSS1liEuS1FKGuCRJLWWIS5LUUoa4JEktZYhLktRShrgkSS1liEuS1FKGuCRJLWWIS5LUUoa4JEktZYhLktRShrgkSS1liEuS1FKGuCRJLWWIS5LUUoa4JEkt1WiIR8RBETE/IhZExPQeuu8bEQ9FxLzy+kiT9UiSNJKMbmrEETEKOBvYH1gEzI2I2Zl5c0evP8nMQ5qqQ5KkkarJI/G9gAWZeUdmLgVmAYc3OD1JktYpTYb4VsDCWvOi0q7TKyLi+oi4KCIm9jSiiDgpIq6LiOuWLFnSRK2SJLVOkyEePbTLjuZfAttm5kuAfwe+09OIMnNmZk7OzMljx44d5DIlSWqnJkN8EbB1rXkcsLjeQ2Y+nJmPlvdzgPUjYvMGa5IkacRoMsTnAjtGxPiIGAMcBcyu9xARL4yIKO/3KvX8scGaJEkaMRq7Oz0zl0XENOASYBRwTmbeFBEnl+4zgDcB74yIZcDjwFGZ2XnKXZIk9aCxEIenT5HP6Wg3o/b+LOCsJmuQJGmk6up0ekTsEBEblPf7RsQpEbFps6VJkqS+dHtN/EJgeURMAL4CjAe+1VhVkiSpX92G+IrMXAYcAXwhM/8eeFFzZUmSpP50G+JPRcTRwNuA75d26zdTkiRJ6ka3IX4C8ArgE5l5Z0SMB85rrixJktSfru5Oz8ybI+L9wDal+U7gU00WJkmS+tbt3emHAvOAi0vzpIiY3fdQkiSpSd2eTj+N6lfJHgTIzHlUd6hLkqQh0m2IL8vMhzra+WQ1SZKGULdPbLsxIt4KjIqIHYFTgGuaK0uSJPWn2yPxdwMTgSepHvLyEHBqU0VJkqT+9XskHhGjgNmZ+XrgQ82XJEmSutHvkXhmLgf+FBGbrIV6JElSl7q9Jv4E8OuI+CHw2MqWmXlKI1VJkqR+dRviPygvSZI0THT7xLavRcQYYKfSan5mPtVcWZIkqT9dhXhE7At8DbgLCGDriHhbZl7VXGmSJKkv3Z5O/xxwQGbOB4iInYDzgT2bKkySJPWt2/8TX39lgANk5m/wp0glSRpS3R6JXxcRXwG+UZqPAX7RTEmSJKkb3Yb4O4F3UT1uNYCrgC82VZQkSepftyE+GjgzMz8PTz/FbYPGqpIkSf3q9pr4ZcCza83PBn40+OVIkqRudRviz8rMR1c2lPfPaaYkSZLUjW5D/LGIeNnKhoiYDDzeTEmSJKkb3V4TPxX474hYDCSwJXBkY1VJkqR+9XkkHhFTIuKFmTkX2AX4L2AZcDFw51qoT5Ik9aK/0+n/ASwt718BfBA4G3gAmNnfyCPioIiYHxELImJ6H/1NiYjlEfGmLuuWJGmd19/p9FGZeX95fyQwMzMvBC6MiHl9DVj+De1sYH9gETA3ImZn5s099HcGcMnqzIAkSeuq/o7ER0XEyqB/HXB5rVt/XwD2AhZk5h2ZuRSYBRzeQ3/vBi4E/tBFvZIkqegviM8HfhwR91Hdjf4TgIiYADzUz7BbAQtrzYuAl9d7iIitgCOA/YAp3ZctSZL6DPHM/EREXAa8CLg0M7N0Wo/qCLov0dMoO5q/ALw/M5dH9NR7GVHEScBJANtss00/k5Ukad3Q77+YZebPemj3my7GvQjYutY8Dljc0c9kYFYJ8M2BqRGxLDO/0zG9mZQb6SZPntz5RUCSpHVSt/8nvjrmAjtGxHjgbuAo4K31HjJz/Mr3EXEu8P3OAJckST1rLMQzc1lETKO663wUcE5m3hQRJ5fuM5qatiRJ64Imj8TJzDnAnI52PYZ3Zh7fZC2SJI003T47XZIkDTOGuCRJLWWIS5LUUoa4JEktZYhLktRShrgkSS1liEuS1FKGuCRJLWWIS5LUUoa4JEktZYhLktRShrgkSS1liEuS1FKGuCRJLWWIS5LUUoa4JEktZYhLktRShrgkSS1liEuS1FKGuCRJLWWIS5LUUoa4JEktZYhLktRShrgkSS1liEuS1FKGuCRJLWWIS5LUUoa4JEkt1WiIR8RBETE/IhZExPQeuh8eETdExLyIuC4i9mmyHkmSRpLRTY04IkYBZwP7A4uAuRExOzNvrvV2GTA7MzMiXgxcAOzSVE2SJI0kTR6J7wUsyMw7MnMpMAs4vN5DZj6amVkaNwQSSZLUlSZDfCtgYa15UWm3iog4IiJuBX4AvL2nEUXESeV0+3VLlixppFhJktqmyRCPHto940g7M/83M3cB3gB8rKcRZebMzJycmZPHjh07yGVKktROTYb4ImDrWvM4YHFvPWfmVcAOEbF5gzVJkjRiNBnic4EdI2J8RIwBjgJm13uIiAkREeX9y4AxwB8brEmSpBGjsbvTM3NZREwDLgFGAedk5k0RcXLpPgN4I3BcRDwFPA4cWbvRTZIk9aGxEAfIzDnAnI52M2rvzwDOaDeWANMAAAxxSURBVLIGSZJGKp/YJklSSxnikiS1lCEuSVJLGeKSJLWUIS5JUksZ4pIktZQhLklSSxnikiS1lCEuSVJLGeKSJLWUIS5JUksZ4pIktZQhLklSSxnikiS1lCEuSVJLGeKSJLWUIS5JUksZ4pIktZQhLklSSxnikiS1lCEuSVJLGeKSJLWUIS5JUksZ4pIktZQhLklSSxnikiS1lCEuSVJLNRriEXFQRMyPiAURMb2H7sdExA3ldU1EvKTJeiRJGkkaC/GIGAWcDRwM7AYcHRG7dfR2J/DazHwx8DFgZlP1SJI00jR5JL4XsCAz78jMpcAs4PB6D5l5TWY+UBp/BoxrsB5JkkaUJkN8K2BhrXlRadebdwAXNViPJEkjyugGxx09tMsee4z4C6oQ36eX7icBJwFss802g1WfJEmt1uSR+CJg61rzOGBxZ08R8WLgy8DhmfnHnkaUmTMzc3JmTh47dmwjxUqS1DZNhvhcYMeIGB8RY4CjgNn1HiJiG+DbwF9n5m8arEWSpBGnsdPpmbksIqYBlwCjgHMy86aIOLl0nwF8BHg+8MWIAFiWmZObqkmSpJGkyWviZOYcYE5Huxm19ycCJzZZgyRJI5VPbJMkqaUMcUmSWsoQlySppQxxSZJayhCXJKmlDHFJklrKEJckqaUMcUmSWsoQlySppQxxSZJayhCXJKmlDHFJklrKEJckqaUMcUmSWsoQlySppQxxSZJayhCXJKmlDHFJklrKEJckqaUMcUmSWsoQlySppQxxSZJayhCXJKmlDHFJklrKEJckqaUMcUmSWsoQlySppQxxSZJaqtEQj4iDImJ+RCyIiOk9dN8lIn4aEU9GxPuarEWSpJFmdFMjjohRwNnA/sAiYG5EzM7Mm2u93Q+cAryhqTokSRqpmjwS3wtYkJl3ZOZSYBZweL2HzPxDZs4FnmqwDkmSRqQmQ3wrYGGteVFpN2ARcVJEXBcR1y1ZsmRQipMkqe2aDPHooV2uzogyc2ZmTs7MyWPHjl3DsiRJGhmaDPFFwNa15nHA4ganJ0nSOqXJEJ8L7BgR4yNiDHAUMLvB6UmStE5p7O70zFwWEdOAS4BRwDmZeVNEnFy6z4iIFwLXARsDKyLiVGC3zHy4qbokSRopGgtxgMycA8zpaDej9v5eqtPskiRpgHximyRJLWWIS5LUUoa4JEktZYhLktRShrgkSS1liEuS1FKGuCRJLWWIS5LUUoa4JEktZYhLktRShrgkSS1liEuS1FKGuCRJLWWIS5LUUoa4JEktZYhLktRShrgkSS1liEuS1FKGuCRJLWWIS5LUUoa4JEktZYhLktRShrgkSS1liEuS1FKGuCRJLWWIS5LUUoa4JEkt1WiIR8RBETE/IhZExPQeukdE/FvpfkNEvKzJeiRJGkkaC/GIGAWcDRwM7AYcHRG7dfR2MLBjeZ0EfKmpeiRJGmmaPBLfC1iQmXdk5lJgFnB4Rz+HA1/Pys+ATSPiRQ3WJEnSiNFkiG8FLKw1LyrtBtqPJEnqwegGxx09tMvV6IeIOInqdDvAoxExfw1rG442B+4b6iJO6+kTaa9hsUxH0CIdFsvzn4e6gME1LJYpnDvUBQyW4bE8o5GtftueWjYZ4ouArWvN44DFq9EPmTkTmDnYBQ4nEXFdZk4e6jpGEpfp4HJ5Dj6X6eBaF5dnk6fT5wI7RsT4iBgDHAXM7uhnNnBcuUt9b+ChzLynwZokSRoxGjsSz8xlETENuAQYBZyTmTdFxMml+wxgDjAVWAD8CTihqXokSRppmjydTmbOoQrqersZtfcJvKvJGlpkRF8uGCIu08Hl8hx8LtPBtc4tz6hyVJIktY2PXZUkqaUM8SEQEedExB8i4sZau80i4ocRcVv5+7yhrHG4G+gyjIgPlMf7zo+IA4em6uEtIraOiCsi4paIuCki3lPau1xXU0TcFRG/joh5EXFdaefyHIDB2tYjYs/yWSwoj/seEf/9aYgPjXOBgzraTQcuy8wdgctKs3p3Ll0uw/K436OAiWWYL5bHAmtVy4D3ZuauwN7Au8qyc7mumb/IzEm1f31yeQ7MuQzOtv4lqueNrHzUd+c4W8kQHwKZeRVwf0frw4GvlfdfA96wVotqmQEuw8OBWZn5ZGbeSfXfEHutlUJbJDPvycxflvePALdQPUHR5Tq4XJ4DMBjbenmc98aZ+dNyQ/XXGSH7WEN8+HjByv+RL3+3GOJ62qi3ZejjfQcoIrYDXgr8HJfrmkjg0oj4RXnyJLg8B8NAl+FW5X1n+9Zr9F/MpGGiq8f7qhIRGwEXAqdm5sN9XDp0ufbvVZm5OCK2AH4YEbf20a/Lc831tgxH7LL1SHz4+P3KX3Arf/8wxPW0UW/LsKvH+woiYn2qAP9mZn67tHa5rqbMXFz+/gH4X6rT4y7PNTfQZbiovO9s33qG+PAxG3hbef824LtDWEtb9bYMZwNHRcQGETGe6qaWa4egvmGt3K37FeCWzPx8rZPLdTVExIYR8dyV74EDgBtxeQ6GAS3Dcsr9kYjYu6znxzFS9rGZ6Wstv4DzgXuAp6i+Ib4DeD7VXZa3lb+bDXWdw/k10GUIfAi4HZgPHDzU9Q/HF7AP1SnGG4B55TXV5bray3N74Pryugn4UGnv8hzYchyUbR2YTPUl6nbgLMrDztr+8oltkiS1lKfTJUlqKUNckqSWMsQlSWopQ1ySpJYyxCVJailDXGpARGREfK7W/L6IOG2Qxn1uRLxpMMbVz3TeXH7R7Io1HM+pEfGcWvOciNh0EOqbFBFT13Q8UpsZ4lIzngT+KiI2H+pC6gb4q1jvAP4uM/9iDSd7KvB0iGfm1Mx8cA3HCTCJ6v/YuxYRPmpaI4ohLjVjGTAT+PvODp1H0hHxaPm7b0T8OCIuiIjfRMSnIuKYiLi2/A7yDrXRvD4iflL6O6QMPyoiPhMRcyPihoj429p4r4iIbwG/7qGeo8v4b4yIM0q7j1A9/GVGRHymh2H+sTad00u7DSPiBxFxfRnXkRFxCrAlcMXKI/qofmN784jYLiJujYgvl/6/GRGvj4iry+9E71X63ysiromIX5W/O0fEGOCjwJFR/Vb3kVH9xvR3Sk0/i4gXl+FPi4iZEXEp8PWImFiW6bzS744D/GylYcNvpVJzzgZuiIhPD2CYlwC7Uv304h3AlzNzr4h4D/BuqqNagO2A1wI7UAXkBKpHST6UmVMiYgPg6hJcUD2ze/esfp7xaRGxJXAGsCfwANUvbr0hMz8aEfsB78vM6zqGOYDqcZZ7Uf2wxOyIeA0wFlicmX9Z+tskMx+KiH+g+k3t+3qY3wnAm6l+53ku8FaqLw+HAR+k+rnIW4HXZOayiHg98C+Z+cbyRWNyZk4r0/t34FeZ+YZS+9epjtYp87dPZj5e+jszM79Zvgz4m91qLUNcakhWvwD2deAU4PEuB5ub5ScWI+J2YGUI/xqon9a+IDNXALdFxB3ALlTP5n5x7Sh/E6qwXUr1/OhVAryYAlyZmUvKNL8JvAb4Th81HlBevyrNG5Xp/AT4bDma/35m/qSL+b0zM39dpn0TcFlmZkT8muqLysr5+Fo5Yk5g/V7GtQ/wRoDMvDwinh8Rm5RuszNz5WfwU+BDETEO+HZm3tZFndKw5Ol0qVlfoLq2vGGt3TLKtld+jGFMrduTtfcras0rWPVLd+fzklf+3OK7M3NSeY3PzJVfAh7rpb5ef2e0DwF8sjadCZn5lcz8DdUR76+BT5Yj5f50M78fA67IzN2BQ4Fn9VFXp5XL6en5z8xvUR3pPw5cUo7apVYyxKUGZeb9wAVUQb7SXVRhB3A4vR9Z9uXNEbFeuU6+PdWPPVwCvDOqnxMlInaK6tez+vJz4LXlGvUo4Gjgx/0Mcwnw9qh+d5yI2Coitiin5v+UmecBnwVeVvp/BHjuaszjSpsAd5f3x9fad473KuCYUtO+wH2Z+XDnyCJie+COzPw3ql+9evEa1CYNKU+nS837HDCt1vyfwHcj4lqqX2Dq7Si5L/OpwvYFwMmZ+UREfJnqFPQvyxH+Eqpryr3KzHsi4gPAFVRHsnMys8+faMzMSyNiV+Cn1WR4FDiW6vr2ZyJiBdUvTr2zDDITuCgi7lnNO90/TXU6/R+Ay2vtrwCmR8Q84JPAacBXI+IG4E/8+acqOx0JHBsRTwH3Ut0gJ7WSv2ImSVJLeTpdkqSWMsQlSWopQ1ySpJYyxCVJailDXJKkljLEJUlqKUNckqSWMsQlSWqp/w8B9USeVVb7ygAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot these scores across a bar graph to see which gave the best results.\n",
    "colors = rainbow(np.linspace(0, 1, len(estimators)))\n",
    "plt.bar([i for i in range(len(estimators))], rf_scores, color = colors, width = 0.8)\n",
    "for i in range(len(estimators)):\n",
    "    plt.text(i, rf_scores[i], rf_scores[i])\n",
    "plt.xticks(ticks = [i for i in range(len(estimators))], labels = [str(estimator) for estimator in estimators])\n",
    "plt.xlabel('Number of estimators')\n",
    "plt.ylabel('Scores')\n",
    "plt.title('Random Forest Classifier scores for different number of estimators')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Taking a look at the bar graph, we can see that the maximum score of 85% was achieved for both 200 trees.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusion**\n",
    "The project involved analysis of the heart disease patient dataset with proper data processing. Then, 4 models were trained and tested with maximum scores as follows:\n",
    "\n",
    "K Neighbors Classifier: 74%\n",
    "\n",
    "Support Vector Classifier: 81%\n",
    "\n",
    "Decision Tree Classifier: 83%\n",
    "\n",
    "Random Forest Classifier: 85%\n",
    "\n",
    "**RandomForest  Classifier scored the best score of 85% with 200 trees.**"
   ]
  },
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   "execution_count": null,
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   "outputs": [],
   "source": []
  }
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