Heart attack classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fd372134",
   "metadata": {},
   "source": [
    "### Import the necessary modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "55051000",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import relevant modules\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.feature_selection import SelectKBest,chi2\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from matplotlib import style\n",
    "style.use('ggplot')\n",
    "from matplotlib.pyplot import rcParams\n",
    "rcParams['figure.figsize']=10,5\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1a7b1e7",
   "metadata": {},
   "source": [
    "### Import the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "886c8466",
   "metadata": {},
   "outputs": [],
   "source": [
    "#read in the data\n",
    "df=pd.read_csv('/Users/brenden/Downloads/heart.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d606d803",
   "metadata": {},
   "source": [
    "### Dataset Overview\n",
    "\n",
    "age: age of the patient\n",
    "\n",
    "sex : sex of the patient\n",
    "\n",
    "cp : chest pain type\n",
    "0 = typical angina\n",
    "1 = atypical angina\n",
    "2 = non-anginal pain\n",
    "3 = asymptomatic\n",
    "\n",
    "trtbps : resting blood pressure in mm Hg\n",
    "\n",
    "chol : cholestoral in mg/dl\n",
    "\n",
    "exng : exercise induced angina\n",
    "1 = yes\n",
    "0 = no\n",
    "\n",
    "fbs : fasting blood sugar > 120 mg/dl\n",
    "1 = true\n",
    "0 = false\n",
    "\n",
    "restecg : resting electrocardiographic results\n",
    "0 = normal\n",
    "1 = having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV)\n",
    "2 = showing probable or definite left ventricular hypertrophy by Estes' criteria\n",
    "\n",
    "thalachh : maximum heart rate achieved\n",
    "\n",
    "slp : slope\n",
    "\n",
    "caa : number of major vessels\n",
    "\n",
    "thall : thalium stress test result\n",
    "\n",
    "target :\n",
    "0 = less chance of heart attack\n",
    "1 = more chance of heart attack"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81e9810a",
   "metadata": {},
   "source": [
    "### Check  if dataset has imported correctly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "97a9f526",
   "metadata": {},
   "outputs": [
    {
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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>trtbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalachh</th>\n",
       "      <th>exng</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slp</th>\n",
       "      <th>caa</th>\n",
       "      <th>thall</th>\n",
       "      <th>output</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",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>41</td>\n",
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       "      <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",
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       "    <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",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <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  trtbps  chol  fbs  restecg  thalachh  exng  oldpeak  slp  \\\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",
       "   caa  thall  output  \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": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "7174f203",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303, 14)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#shape of dataset\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "527a0e5a",
   "metadata": {},
   "source": [
    "#### Detailed informatiom about the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "aaba8a00",
   "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   trtbps    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   thalachh  303 non-null    int64  \n",
      " 8   exng      303 non-null    int64  \n",
      " 9   oldpeak   303 non-null    float64\n",
      " 10  slp       303 non-null    int64  \n",
      " 11  caa       303 non-null    int64  \n",
      " 12  thall     303 non-null    int64  \n",
      " 13  output    303 non-null    int64  \n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 33.3 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1242937e",
   "metadata": {},
   "source": [
    "### Check for missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "1f4c09c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age         0\n",
       "sex         0\n",
       "cp          0\n",
       "trtbps      0\n",
       "chol        0\n",
       "fbs         0\n",
       "restecg     0\n",
       "thalachh    0\n",
       "exng        0\n",
       "oldpeak     0\n",
       "slp         0\n",
       "caa         0\n",
       "thall       0\n",
       "output      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for null values\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ee7f6c2",
   "metadata": {},
   "source": [
    "### Check for duplicated values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "9bb7b622",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for duplicates\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78aaf297",
   "metadata": {},
   "source": [
    "### Removing the duplicated values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "8eb2bae0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove duplicates\n",
    "df.drop_duplicates(inplace= True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2625e643",
   "metadata": {},
   "source": [
    "#### Check if the duplicated values were removed.\n",
    "we can see the size of our dataset has changed ,indicating that the duplicated values has been removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "688bcdca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(302, 14)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32b3d400",
   "metadata": {},
   "source": [
    "### Perform descriptive statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "c438bb9a",
   "metadata": {},
   "outputs": [
    {
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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>trtbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
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       "      <th>exng</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slp</th>\n",
       "      <th>caa</th>\n",
       "      <th>thall</th>\n",
       "      <th>output</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>302.00000</td>\n",
       "      <td>302.000000</td>\n",
       "      <td>302.000000</td>\n",
       "      <td>302.000000</td>\n",
       "      <td>302.000000</td>\n",
       "      <td>302.000000</td>\n",
       "      <td>302.000000</td>\n",
       "      <td>302.000000</td>\n",
       "      <td>302.000000</td>\n",
       "      <td>302.000000</td>\n",
       "      <td>302.000000</td>\n",
       "      <td>302.000000</td>\n",
       "      <td>302.000000</td>\n",
       "      <td>302.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.42053</td>\n",
       "      <td>0.682119</td>\n",
       "      <td>0.963576</td>\n",
       "      <td>131.602649</td>\n",
       "      <td>246.500000</td>\n",
       "      <td>0.149007</td>\n",
       "      <td>0.526490</td>\n",
       "      <td>149.569536</td>\n",
       "      <td>0.327815</td>\n",
       "      <td>1.043046</td>\n",
       "      <td>1.397351</td>\n",
       "      <td>0.718543</td>\n",
       "      <td>2.314570</td>\n",
       "      <td>0.543046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.04797</td>\n",
       "      <td>0.466426</td>\n",
       "      <td>1.032044</td>\n",
       "      <td>17.563394</td>\n",
       "      <td>51.753489</td>\n",
       "      <td>0.356686</td>\n",
       "      <td>0.526027</td>\n",
       "      <td>22.903527</td>\n",
       "      <td>0.470196</td>\n",
       "      <td>1.161452</td>\n",
       "      <td>0.616274</td>\n",
       "      <td>1.006748</td>\n",
       "      <td>0.613026</td>\n",
       "      <td>0.498970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.00000</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>48.00000</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>133.250000</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>55.50000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>240.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>152.500000</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>61.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>274.750000</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>77.00000</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      trtbps        chol         fbs  \\\n",
       "count  302.00000  302.000000  302.000000  302.000000  302.000000  302.000000   \n",
       "mean    54.42053    0.682119    0.963576  131.602649  246.500000    0.149007   \n",
       "std      9.04797    0.466426    1.032044   17.563394   51.753489    0.356686   \n",
       "min     29.00000    0.000000    0.000000   94.000000  126.000000    0.000000   \n",
       "25%     48.00000    0.000000    0.000000  120.000000  211.000000    0.000000   \n",
       "50%     55.50000    1.000000    1.000000  130.000000  240.500000    0.000000   \n",
       "75%     61.00000    1.000000    2.000000  140.000000  274.750000    0.000000   \n",
       "max     77.00000    1.000000    3.000000  200.000000  564.000000    1.000000   \n",
       "\n",
       "          restecg    thalachh        exng     oldpeak         slp         caa  \\\n",
       "count  302.000000  302.000000  302.000000  302.000000  302.000000  302.000000   \n",
       "mean     0.526490  149.569536    0.327815    1.043046    1.397351    0.718543   \n",
       "std      0.526027   22.903527    0.470196    1.161452    0.616274    1.006748   \n",
       "min      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000  133.250000    0.000000    0.000000    1.000000    0.000000   \n",
       "50%      1.000000  152.500000    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",
       "            thall      output  \n",
       "count  302.000000  302.000000  \n",
       "mean     2.314570    0.543046  \n",
       "std      0.613026    0.498970  \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": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#descriptive statistics\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9cbff39",
   "metadata": {},
   "source": [
    "### Data preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "811433d6",
   "metadata": {},
   "source": [
    "#### Check for outliers in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "0145df65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df)\n",
    "plt.title('Detected outliers in the dataset before removal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "898438ee",
   "metadata": {},
   "source": [
    "#### Outlier removal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "34441c3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "q1 = df.quantile(0.25)\n",
    "q3 = df.quantile(0.75)\n",
    "iqr=q3-q1\n",
    "lwr_bound = q1-(1.5*iqr)\n",
    "upr_bound = q3+(1.5*iqr)\n",
    "\n",
    "df= df[((df >=lwr_bound) & (df <= upr_bound))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "253c2d10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df)\n",
    "plt.title('Dataset after outlier removal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "ba535479",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3ca1c4c",
   "metadata": {},
   "source": [
    "#### Data distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "25118000",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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XR44c0XPPPafy8nJlZmZq4MCBWrJkie666y4tXbpU3377rSQpPz9fWVlZysjIUFZWllJTUzV79mxVVlYG8yUBABBQQQ3qQ4cOqUuXLho9erRsNptatWqlYcOGafv27dqyZYtatWql4cOHKzQ0VImJiUpNTdWGDRskSTk5OUpJSVFCQoJsNpuuueYaRUVFadOmTcF8SQAABFRQp747duyoGTNmeI1t3rxZXbt2VUFBgeLi4ryWderUSe+9954kqbCwUGlpaV7LY2NjtXfvXr/rsNka3gbLshRiWQ3eTiBZP9djWRYfRvAB/fIP/fIdvfKPyf2yLEthYWFNus+gn6M+ye12a9WqVfr88881a9Ysvfnmm4qIiPBax263y+l0SpIqKipkt9tPu9wfMTEx9S/8ZyXlpapp4h+erwLxRqQ5oV/+oV++o1f+MbFfNptN0e3bN+0+m3Rvp3Hs2DE999xz2r17t2bNmqXOnTvLbrfr6NGjXutVVlaqRYsWkqSIiAhVVVXVWt6qVSu/93/48GG5XK76vwBJlsul6urqBm0j0CzLks1mk8vlktvtDnY5xqNf/qFfvqNX/jG5XyEul4qKigKyrfY+Bn7Qg/rHH39UZmam2rVrp8zMTEVFRUmS4uLi9MUXX3itW1hY6JkOj4uLU0FBgdfy/fv3q0+fPn7X4ApAyIa53aox7Rfq53/dBtZmIvrlH/rlO3rlH5P75Xa7m/ygLKjT/+Xl5Xr88ccVHx+vmTNnekJakvr376+SkhJlZ2fL5XIpLy9PGzdu9JyXTktL08aNG5WXlyeXy6Xs7GyVlJQoOTk5WC8HAICAC+oRdW5urg4dOqSPP/5YH3/8sdeyZcuW6ZFHHtGSJUu0evVqRUVF6bbbblNiYqIkqVevXpo0aZIWL16s4uJixcXFacaMGYqMjAzGSwEAoFFYbtNOAARBUVFRw6e+y0vlfOHpAFUUGCE/fzqxurrauOkjE9Ev/9Av39Er/5jcr4gp01Qd2Tog2+rYsaNP65n2yXcAAHAKghoAAIMR1AAAGIygBgDAYAQ1AAAGI6gBADAYQQ0AgMEIagAADEZQAwBgMIIaAACDEdQAABiMoAYAwGAENQAABiOoAQAwGEENAIDBCGoAAAxGUAMAYDCCGgAAgxHUAAAYjKAGAMBgBDUAAAYjqAEAMBhBDQCAwQhqAAAMRlADAGAwghoAAIMR1AAAGIygBgDAYAQ1AAAGI6gBADAYQQ0AgMEIagAADEZQAwBgMIIaAACDEdQAABiMoAYAwGAENQAABiOoAQAwGEENAIDBCGoAAAxGUAMAYDCCGgAAgxHUAAAYjKAGAMBgBDUAAAYjqAEAMBhBDQCAwQhqAAAMRlADAGAwghoAAIMR1AAAGIygBgDAYAQ1AAAGI6gBADAYQQ0AgMEIagAADEZQAwBgMIIaAACDEdQAABiMoAYAwGAENQAABiOoAQAwGEENAIDBCGoAAAxGUAMAYDCCGgAAgxHUAAAYjKAGAMBgBDUAAAYjqAEAMBhBDQCAwQhqAAAMRlADAGAwghoAAIMR1AAAGIygBgDAYAQ1AAAGI6gBADAYQQ0AgMEIagAADGYLdgEnlZWVaebMmZoyZYp69uwpSXrppZf0/vvvKzQ01LPexIkTNXToUElSbm6u1qxZo5KSEsXGxur222+Xw+EISv0AADQGI4J6x44devbZZ3XgwAGv8V27dmny5MkaPHhwrefk5+crKytL06dPV7du3bRhwwbNnj1bzz33nOx2exNVDgBA4wr61Hdubq4WLFigsWPHeo1XV1dr3759+t3vflfn83JycpSSkqKEhATZbDZdc801ioqK0qZNm5qibAAAmkTQj6iTkpJ0+eWXKzQ0VPPmzfOM7927V8ePH9eqVav09ddfq2XLlkpLS9N1112nkJAQFRYWKi0tzWtbsbGx2rt3r9812GwNb4NlWQqxrAZvJ5Csn+uxLCv478jOAPTLP/TLd/TKPyb3y7IshYWFNek+gx7U0dHRdY4fO3ZMF110kUaMGKF7771Xe/bs0V/+8heFhITouuuuU0VFRa0pbrvdLqfT6XcNMTEx9SndS0l5qWqa+Ifnq0C8EWlO6Jd/6Jfv6JV/TOyXzWZTdPv2TbvPJt2bHy6++GJdfPHFnsfdunXT1VdfrU2bNum6665TRESEqqqqvJ5TWVmpVq1a+b2vw4cPy+VyNahey+VSdXV1g7YRaJZlyWazyeVyye12B7sc49Ev/9Av39Er/5jcrxCXS0VFRQHZVnsfA9/YoP7kk09UWlqqYcOGecaqq6sVHh4uSYqLi1NBQYHXc/bv368+ffr4vS9XAEI2zO1WjWm/UD//6zawNhPRL//QL9/RK/+Y3C+3293kB2WmTf97Wbp0qb788ku53W598803evPNNz1fzUpLS9PGjRuVl5cnl8ul7OxslZSUKDk5OchVAwAQOMYeUScnJ2vixIlavHixfvrpJ0VHRys9PV0DBw6UJPXq1UuTJk3S4sWLVVxcrLi4OM2YMUORkZFBrhwAgMCx3KadAAiCoqKihk99l5fK+cLTAaooMEJ+/nRidXW1cdNHJqJf/qFfvqNX/jG5XxFTpqk6snVAttWxY0ef1jN66hsAgOaOoAYAwGAENQAABiOoAQAwGEENAIDBCGoAAAxGUAMAYDCCGgAAgxHUAAAYjKAGAMBgBDUAAAYjqAEAMBhBDQCAwQhqAAAMRlADAGAwghoAAIMR1AAAGIygBgDAYAQ1AAAGI6gBADAYQQ0AgMEIagAADEZQAwBgMIIaAACDEdQAABiMoAYAwGAENQAABiOoAQAwWL2C+quvvpLT6axz2dGjR/Wvf/2rQUUBAIAT6hXUs2bNUmFhYZ3Ldu/ereeee65BRQEAgBNsvq64aNEiFRcXex4vXrxYLVq0qLXeDz/8oOjo6IAUBwBAc+fzEfVll13m9djtdtfeWEiIunfvrrvvvrvhlQEAAN+PqPv166d+/fpJOjH1PWnSJHXq1KnRCgMAAH4E9akee+yxQNcBAADqUK+grqys1Nq1a7V161Y5nc5a0+CWZWnhwoUBKRAAgOasXkGdlZWl999/Xz179lSXLl0UEsLXsQEAaAz1CuotW7Zo7Nixuv766wNcDgAAOFW9DoVramrUrVu3QNcCAAB+oV5B3bt3b23bti3ApQAAgF+q19R3SkqKXnrpJZWWlqp79+6y2+211hk0aFCDiwMAoLmrV1A/88wzkqQPP/xQH374YZ3rENQAADRcvYJ60aJFga4DAADUoV5B3b59+0DXAQAA6lCvoH799dd/c53Ro0fXZ9MAAOAU9Qrqv/3tb6dd1qJFC8XExBDUAAAEQL2CetWqVbXGnE6nduzYocWLF+v2229vcGEAAKCe36OuS0REhJKSkjR69GgtX748UJsFAKBZC/hFutu2bavCwsJAbxYAgGapXlPfdXG73SouLtbf//53dejQIVCbBQCgWatXUI8ZM+ZXl0+dOrVexQAAAG/1CupRo0bJsiyvMcuy1KJFC/Xp00fnn39+QIoDAKC5q1dQp6enB7oOAABQh3qfo66urlZubq7y8/N19OhRtWrVSj169NCgQYMUHh4eyBoBAGi26hXUR48e1axZs7R37161a9dO0dHR+v777/Wvf/1Lb7/9th5//HG1bNky0LUCANDs1CuoX331VRUXF2vWrFlKSEjwjO/YsUNz5szRypUruegJAAABUK/vUX/66ae66aabvEJakhISEpSenq5PPvkkIMUBANDc1SuonU6nzj333DqXnXvuuTpy5EiDigIAACfUK6hjY2P12Wef1bns008/1XnnndegogAAwAn1Okd97bXXav78+XK5XEpNTVV0dLRKSkq0ceNG5eTk6M477wx0nQAANEv1CuqUlBT98MMPWrt2rXJycv5/YzabRo8eraFDhwasQAAAmrN6BbXT6dSoUaM0fPhw7dy5U+Xl5Tp06JCGDh2qyMjIQNcIAECz5dc56j179mjatGnKzs6WJJ1zzjlKSkpSUlKSVq5cqUcffZQ7ZwEAEEA+B/XBgwf1+OOPq7y8XJ06dfJaFhYWpltvvVUVFRV69NFHVVxcHPBCAQBojnwO6nXr1ikqKkpPP/20+vfv77XMbrdr+PDheuqppxQREaF169YFvFAAAJojn4M6Ly9P11133a+eg27durWuueYaffnllwEpDgCA5s7noD58+LBP34/u3LkzU98AAASIz0EdFRWlw4cP/+Z6ZWVlatWqVYOKAgAAJ/gc1BdddJFyc3N/c70PPvhAXbp0aUhNAADgZz4H9VVXXaW8vDy98sorqqqqqrXc5XJp2bJl2rZtm4YPHx7QIgEAaK58vuDJ7373O02cOFFLly7Vhx9+qMTERHXo0EE1NTU6dOiQ8vPzVVZWpjFjxigpKakRSwYAoPnw68pkw4cP1wUXXKD169frs88+U3V1tSQpIiJCvXv31rXXXqvu3bs3SqEAADRHfl9CNCEhwXMf6iNHjsiyLC4bCgBAI6nXtb5P4tPdAAA0rnrdjxoAADQNghoAAIMR1AAAGIygBgDAYAQ1AAAGI6gBADBYg76eFUhlZWWaOXOmpkyZop49e0qSdu7cqaysLBUUFCgqKkqjRo3SkCFDPM/Jzc3VmjVrVFJSotjYWN1+++1yOBzBegkAAAScEUfUO3bs0MyZM3XgwAHPWHl5uTIzMzVw4EAtWbJEd911l5YuXapvv/1WkpSfn6+srCxlZGQoKytLqampmj17tiorK4P1MgAACLigB3Vubq4WLFigsWPHeo1v2bJFrVq10vDhwxUaGqrExESlpqZqw4YNkqScnBylpKQoISFBNptN11xzjaKiorRp06ZgvAwAABpF0Ke+k5KSdPnllys0NFTz5s3zjBcUFCguLs5r3U6dOum9996TJBUWFiotLc1reWxsrPbu3et3DTZbw9tgWZZCLKvB2wkk6+d6LMsK/juyMwD98g/98h298o/J/bIsS2FhYU26z6AHdXR0dJ3jTqdTERERXmN2u11Op1OSVFFRIbvdftrl/oiJifH7Ob9UUl6qmib+4fkqEG9EmhP65R/65Tt65R8T+2Wz2RTdvn3T7rNJ9+YHu92uo0ePeo1VVlaqRYsWkk7cseuX98WurKys1/XHDx8+LJfLVf9iJVkul+duYqawLEs2m00ul0tutzvY5RiPfvmHfvmOXvnH5H6FuFwqKioKyLba+xj4xgZ1XFycvvjiC6+xwsJCz3R4XFycCgoKvJbv379fffr08XtfrgCEbJjbrRrTfqF+/tdtYG0mol/+oV++o1f+Mblfbre7yQ/KTJv+9+jfv79KSkqUnZ0tl8ulvLw8bdy40XNeOi0tTRs3blReXp5cLpeys7NVUlKi5OTkIFcOAEDgGHtE3apVKz3yyCNasmSJVq9eraioKN12221KTEyUJPXq1UuTJk3S4sWLVVxcrLi4OM2YMYN7YwMAziqW27QTAEFQVFTU8Knv8lI5X3g6QBUFRsjPn06srq42bvrIRPTLP/TLd/TKPyb3K2LKNFVHtg7Itjp27OjTesZOfQMAAIIaAACjEdQAABiMoAYAwGAENQAABiOoAQAwGEENAIDBCGoAAAxGUAMAYDCCGgAAgxHUAAAYjKAGAMBgBDUAAAYjqAEAMBhBDQCAwQhqAAAMRlADAGAwghoAAIMR1AAAGIygBgDAYAQ1AAAGI6gBADAYQQ0AgMEIagAADEZQAwBgMIIaAACDEdQAABiMoAYAwGAENQAABiOoAQAwGEENAIDBCGoAAAxGUAMAYDCCGgAAgxHUAAAYjKAGAMBgBDUAAAYjqAEAMBhBDQCAwQhqAAAMRlADAGAwghoAAIMR1AAAGIygBgDAYAQ1AAAGI6gBADAYQQ0AgMEIagAADEZQAwBgMIIaAACDEdQAABiMoAYAwGAENQAABiOoAQAwGEENAIDBCGoAAAxGUAMAYDCCGgAAgxHUAAAYjKAGAMBgBDUAAAYjqAEAMBhBDQCAwQhqAAAMRlADAGAwghoAAIMR1AAAGIygBgDAYAQ1AAAGI6gBADAYQQ0AgMEIagAADEZQAwBgMIIaAACDEdQAABiMoAYAwGAENQAABiOoAQAwGEENAIDBbMEu4Lds2rRJCxYsUFhYmGcsOTlZ99xzj3bu3KmsrCwVFBQoKipKo0aN0pAhQ4JYLQAAgWV8UH/77bcaOHCg7r77bq/x8vJyZWZmKj09XcOGDdP27dv1l7/8RZ07d1a3bt2CVC0AAIFl/NT3rl271LVr11rjW7ZsUatWrTR8+HCFhoYqMTFRqamp2rBhQxCqBACgcRh9RF1TU6Pdu3crIiJC69evV01NjS655BKNGzdOBQUFiouL81q/U6dOeu+99/zej83W8DZYlqUQy2rwdgLJ+rkey7LMf0dmAPrlH/rlO3rlH5P7ZVmW16nYpmB0UJeVlenCCy9U//79dd999+nIkSN69tlntXDhQsXExCgiIsJrfbvdLqfT6fd+YmJiGlxrSXmpapr4h+erQLwRaU7ol3/ol+/olX9M7JfNZlN0+/ZNu88m3ZufoqOjNWvWLM9ju92ucePGaebMmRo8eLAqKyu91q+srFSLFi383s/hw4flcrkaVKvlcqm6urpB2wg0y7Jks9nkcrnkdruDXY7x6Jd/6Jfv6JV/TO5XiMuloqKigGyrvY+Bb3RQ7927Vxs3btTNN9/smQpxuVyyLEvdunXTm2++6bV+YWFhrelwX7gCELJhbrdqTPuF+vlft4G1mYh++Yd++Y5e+cfkfrnd7iY/KDNt+t9LZGSkNmzYoPXr1+v48eM6dOiQli1bpsGDB+uyyy5TSUmJsrOz5XK5lJeXp40bNyotLS3YZQMAEDBGH1G3bdtW06dP16uvvqq1a9cqLCxMKSkpGj9+vMLDw/XII49oyZIlWr16taKionTbbbcpMTEx2GUDABAwltu0EwBBUFRU1PCp7/JSOV94OkAVBUbIz59OrK6uNm76yET0yz/0y3f0yj8m9ytiyjRVR7YOyLY6duzo03pGT30DANDcEdQAABiMoAYAwGAENQAABiOoAQAwGEENAIDBCGoAAAxGUAMAYDCCGgAAgxHUAAAYjKAGAMBgBDUAAAYjqAEAMBhBDQCAwQhqAAAMRlADAGAwghoAAIMR1AAAGIygBgDAYAQ1AAAGI6gBADAYQQ0AgMEIagAADEZQAwBgMIIaAACDEdQAABiMoAYAwGAENQAABiOoAQAwGEENAIDBCGoAAAxGUAMAYDCCGgAAgxHUAAAYjKAGAMBgBDUAAAYjqAEAMBhBDQCAwQhqAAAMRlADAGAwghoAAIMR1AAAGIygBgDAYAQ1AAAGI6gBADAYQQ0AgMEIagAADEZQAwBgMIIaAACDEdQAABiMoAYAwGAENQAABiOoAQAwGEENAIDBCGoAAAxGUAMAYDCCGgAAgxHUAAAYjKAGAMBgBDUAAAYjqAEAMBhBDQCAwQhqAAAMRlADAGAwghoAAIMR1AAAGIygBgDAYAQ1AAAGI6gBADAYQQ0AgMEIagAADEZQAwBgMIIaAACDEdQAABiMoAYAwGAENQAABiOoAQAwGEENAIDBCGoAAAxGUAMAYDBbsAtoqNLSUr344ov66quvFBoaqssvv1y33HKLQkNDg10aAAANdsYfUc+bN08RERF68cUX9eSTT+rLL79UdnZ2sMsCACAgzuig/vHHH5Wfn6/x48fLbrfr3HPP1ahRo7Rhw4ZglwYAQECc0VPfBQUFioyMVJs2bTxjnTp10qFDh3T06FGdc845Pm3HbrfLZmtgK1yVsnfq0rBtBJhlWQoNDZV1/LjcbnewyzEe/fIP/fIdvfKPyf2yRUTI1qJF0+6zSfcWYBUVFbLb7V5j4eHhkiSn0+lzUEdFRTW8mJgY6b4/N3w7AACc4oye+o6IiFBVVZXX2MnHLZr4HQ8AAI3hjA7quLg4HTlyRCUlJZ6xwsJCtW3bVi1btgxeYQAABMgZHdTnn3++EhIStGTJElVUVOjgwYNas2aN0tLSgl0aAAABYblNO1Pvp5KSEr388svKz8+XZVkaOHCgxo8fr5CQM/o9CAAAks6CoAYA4GzGYScAAAYjqAEAMBhBDQCAwQhqAAAMdkZfmQzenE6nXn75ZX322Wc6fvy4Lr30Ut1xxx2KiIj41ecdPnxY06ZN07hx4zR48OCmKdYA/vZr8+bNWrNmjQ4ePKjIyEgNHjxYo0aNOqu/YeDP3em2bt2qFStW6ODBg2rXrp3Gjx+vvn37BqHq4PCnV//85z+VnZ2tw4cPKyYmRiNGjNCVV14ZhKqDpz53Pty3b59mzJih6dOnq2fPnk1YbXCdvX9hmqGXX35ZxcXFWrBggRYsWKBDhw5p+fLlv/qcmpoaLViwQGVlZU1UpTn86dd3332nRYsW6aabblJWVpamT5+u3Nzcs/5Obb7ene6HH37QnDlzNGbMGC1ZskQ33nijnnnmGf30009BqDo4fO3VJ598otdee00ZGRlaunSpMjIytHLlSm3evDkIVQePv3c+rKys1Pz582tdjbI5IKjPEpWVlfroo4+Unp6uyMhItW7dWuPGjVNubq4qKytP+7zXX39dbdu2Vbt27Zqw2uDzt18HDx7UsGHD1LdvX4WEhKhTp05KTk7W9u3bg1B90/Dn7nQffPCBevTooeTkZIWGhiolJUUXXXSR3n333SBU3vT86dXhw4f1hz/8QQ6HQ5ZlyeFwqGfPnmf179Iv1efOh4sXL1ZycnITVmkOgvoMUlVVpR9//LHO/3744QcdP35cnTt39qzfqVMnVVVV6Ycffqhze3l5edq0aZPuuOOOpnoJTSqQ/brssss0ceJEr21v3bpVXbt2bZLXEgy/dXe6X657ai9Prrtnz56mKDXo/OnVlVdeqeuvv97zuLS0VNu3bz+rf5d+yZ9+SSfeCP7444+68cYbm7JMY3CO+gyyc+dOzZo1q85lY8aMkSSvu4md/H+n01lr/dLSUj3//PO6//77f/Mc9pkqkP06VUVFhebOnavw8HBdffXVAarWPP7cnc7pdNa57q/N5pxN6nsnv5KSEmVmZqpr165KTU1t9DpN4U+/9u/fr5UrV+o//uM/zurPg/wagvoM0rNnT61evbrOZbt379aqVatUVVXlCd6TfyR/GcRut1uLFi3SVVdddVa/iw9Uv071/fffa86cOWrdurUee+yxs/oubf7cnc5ut9cK5VN7e7arz538vvnmGz3zzDNKSEjQ3Xff/asfojrb+NqvqqoqzZs3TxMnTmx2p+dORVCfJTp27KjQ0FAVFBSoe/fukk7cScxms6ljx45e6xYXF+urr77Szp079frrr0s68Q538eLF2rx5sx5++OEmr7+p+dOvk7Zu3ar58+friiuu0Lhx4876P6yn3p0uOjpa0unvThcXF6fdu3d7jRUWFp7VbwRP5U+vJOm9995TVlaW0tPTde211zZxtcHna7927dql77//Xi+88IJeeOEFz/hTTz2lQYMGnbWn7X6JoD5L2O12paSk6NVXX9Wf/vQnSdKrr76qAQMGeKaUTmrXrp1WrFjhNZaRkaEbb7yx2Xw9y59+SSeOfv7617/qjjvu0JAhQ5q63KA49e50f/zjH3XkyJHT3p1u4MCB+sc//qFNmzapf//+2rJli/Lz83Xrrbc2feFB4E+vNm/erMWLF2vatGlKSkpq+mIN4Gu/evToUetvVXp6uh5++OFm9fUsbspxFqmoqNArr7yizz77TC6XS5deeqluv/12z/Tjfffdp9TUVN1www21ntvcglryr1+zZ8/W1q1ba4V4jx49NGPGjGCU3yR+7e50t9xyiyZPnqzLL79ckrRt2zatWLFCBw4cUPv27TVu3Dj16dMnyK+g6fjaqwceeEAFBQW1fpcuv/xyTZ48OUjVNz1/frdOlZ6erscee4ygBgAAZmieH6EDAOAMQVADAGAwghoAAIMR1AAAGIygBgDAYAQ1AAAGI6gBADAYVyYDUMvJ+3R/++23atGihcrKyprdRSYAU3BEDaCW7OxsffPNN8rIyNCIESOCXQ7QrBHUAGopLy9XTEyMUlJSFB8fH+xygGaNqW8AXjIyMlRUVCTpxHWVL7roIkkn7m60atUq7dq1S23atNGIESN01VVXeZ73xRdfaNWqVdq3b59sNpt69Oih8ePHn/ZuZAB8w7W+AXjZvXu3Vq5cqd27d+uBBx7Qjz/+qEWLFik0NFQjRoxQr1699Pnnn+vtt9/W5MmTNXToUB04cED333+/0tLS1L9/f5WXl2vlypU6fvy45s+fr5AQJu+A+uKIGoCXCy+8UFFRUQoLC5PD4VB1dbUkKS0tTbfccoskKSkpST/99JPWrFmjIUOG6Ntvv1VVVZVGjhypNm3aSDpxO9VPP/1UTqezznsyA/ANQQ3AJykpKV6P+/fvr08//VTff/+9unfvrrCwME2fPl0pKSm65JJLdNFFF6lbt25BqhY4exDUAHwSHR3t9TgqKkrSiQ+eJSQkaNasWfqf//kfvfvuu8rOztY555yj3//+9xozZgxT30ADENQAfHL06FGvxyUlJZKk1q1bS5K6deumBx54QC6XSzt27NA777yjdevWqUuXLrWOxgH4jre5AHyybds2r8ebNm1S27Ztdd555yk7O1sZGRmqrq6WzWZTYmKi/vjHP0qSDh06FIRqgbMHR9QAfPLWW2+pRYsWuuCCC7Rp0yZt27ZNU6dOlWVZSkxM1IoVK/TXv/5VV155pUJDQ/XOO+8oLCxM/fr1C3bpwBmNoAbgk8mTJ+uNN97QypUrde655+rf//3fNWDAAElSly5d9NBDD+n111/X/PnzVVNTo65du2rmzJl8jxpoIL5HDQCAwThHDQCAwQhqAAAMRlADAGAwghoAAIMR1AAAGIygBgDAYAQ1AAAGI6gBADAYQQ0AgMEIagAADEZQAwBgMIIaAACD/R9VNrpB1fWrSgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col in df.columns:\n",
    "    sns.displot(df[col])\n",
    "    plt.title(f'{col} Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff31a5a1",
   "metadata": {},
   "source": [
    "### Feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "7db7b36e",
   "metadata": {},
   "outputs": [],
   "source": [
    "features=df.iloc[:,0:13]\n",
    "\n",
    "target=df['output']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "3abceb2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best=SelectKBest(score_func=chi2)\n",
    "\n",
    "fit = best.fit(features, target)\n",
    "\n",
    "score=pd.Series(fit.scores_,features.columns)\n",
    "score.nlargest(10).plot(kind='barh')\n",
    "plt.title('Feature importance')\n",
    "plt.ylabel('features')\n",
    "plt.xlabel('scores')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "51c610ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=features[['thalachh','oldpeak','caa','cp','exng']]\n",
    "y=target"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90f8758a",
   "metadata": {},
   "source": [
    "### Standardize the  data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "ad57c79a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.60089528,  2.47200312, -0.68550748,  1.03837841, -0.67936622],\n",
       "       [ 0.93255277,  0.43938232, -0.68550748,  0.05601211, -0.67936622],\n",
       "       [ 1.19988977, -0.14136648, -0.68550748,  0.05601211, -0.67936622],\n",
       "       ...,\n",
       "       [-0.84969393,  0.24579938, -0.68550748,  2.0207447 , -0.67936622],\n",
       "       [-1.60714878,  0.24579938,  0.74839807, -0.92635418,  1.47196014],\n",
       "       [ 1.0216651 , -0.91569822,  0.74839807,  0.05601211, -0.67936622]])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std_scaler= StandardScaler()\n",
    "std_scaler.fit_transform(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcc4ca73",
   "metadata": {},
   "source": [
    "### Split the data into training & testing set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "35cc120a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(182, 5)\n",
      "(46, 5)\n",
      "(182,)\n",
      "(46,)\n"
     ]
    }
   ],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.20,random_state=100)\n",
    "\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "ea84c77e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a8bb4a2",
   "metadata": {},
   "source": [
    "## Hyperparameter tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3c63a23",
   "metadata": {},
   "source": [
    "### Create Logistic regression instance & train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "15b45d8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-11 {color: black;background-color: white;}#sk-container-id-11 pre{padding: 0;}#sk-container-id-11 div.sk-toggleable {background-color: white;}#sk-container-id-11 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-11 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-11 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-11 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-11 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-11 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-11 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-11 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-11 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-11 div.sk-item {position: relative;z-index: 1;}#sk-container-id-11 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-11 div.sk-item::before, #sk-container-id-11 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-11 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-11 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-11 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-11 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-11 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-11 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-11 div.sk-label-container {text-align: center;}#sk-container-id-11 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-11 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=LogisticRegression(),\n",
       "             param_grid={&#x27;multi_class&#x27;: [&#x27;auto&#x27;, &#x27;multinomial&#x27;],\n",
       "                         &#x27;penalty&#x27;: [&#x27;l2&#x27;, &#x27;l1&#x27;],\n",
       "                         &#x27;solver&#x27;: [&#x27;lbfgs&#x27;, &#x27;saga&#x27;, &#x27;newton-cg&#x27;]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-31\" type=\"checkbox\" ><label for=\"sk-estimator-id-31\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, estimator=LogisticRegression(),\n",
       "             param_grid={&#x27;multi_class&#x27;: [&#x27;auto&#x27;, &#x27;multinomial&#x27;],\n",
       "                         &#x27;penalty&#x27;: [&#x27;l2&#x27;, &#x27;l1&#x27;],\n",
       "                         &#x27;solver&#x27;: [&#x27;lbfgs&#x27;, &#x27;saga&#x27;, &#x27;newton-cg&#x27;]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-32\" type=\"checkbox\" ><label for=\"sk-estimator-id-32\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-33\" type=\"checkbox\" ><label for=\"sk-estimator-id-33\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=10, estimator=LogisticRegression(),\n",
       "             param_grid={'multi_class': ['auto', 'multinomial'],\n",
       "                         'penalty': ['l2', 'l1'],\n",
       "                         'solver': ['lbfgs', 'saga', 'newton-cg']})"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameters ={\"multi_class\":['auto','multinomial'],'penalty':['l2','l1'], 'solver':['lbfgs','saga','newton-cg']}# l1 lasso l2 ridge\n",
    "logreg =LogisticRegression()\n",
    "logreg = GridSearchCV(logreg, parameters, cv=10)\n",
    "logreg.fit(X_train,y_train)\n",
    "logreg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "c54cb116",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tuned hyperparameters :(best parameters)  {'multi_class': 'auto', 'penalty': 'l2', 'solver': 'lbfgs'}\n",
      "accuracy : 0.8011695906432749\n"
     ]
    }
   ],
   "source": [
    "print(\"tuned hyperparameters :(best parameters) \",logreg.best_params_)\n",
    "print(\"accuracy :\",logreg.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7da4462b",
   "metadata": {},
   "source": [
    "#### Calculate the accuracy of the model on the test dataset using score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "10e32b64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8260869565217391"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19a9f422",
   "metadata": {},
   "source": [
    "Lets look at the confusion matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "bbb7149c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred=logreg.predict(X_test)\n",
    "sns.heatmap(confusion_matrix(y_test, y_pred), annot = True)\n",
    "plt.title('Logistic Regresion confision matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f49d9bde",
   "metadata": {},
   "source": [
    "### Create KNN instance and train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "dba8d584",
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters =  {'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
    "              'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],\n",
    "              'p': [1,2]}\n",
    "\n",
    "knn = KNeighborsClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "2b17564a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-12 {color: black;background-color: white;}#sk-container-id-12 pre{padding: 0;}#sk-container-id-12 div.sk-toggleable {background-color: white;}#sk-container-id-12 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-12 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-12 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-12 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-12 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-12 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-12 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-12 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-12 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-12 div.sk-item {position: relative;z-index: 1;}#sk-container-id-12 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-12 div.sk-item::before, #sk-container-id-12 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-12 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-12 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-12 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-12 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-12 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-12 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-12 div.sk-label-container {text-align: center;}#sk-container-id-12 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-12 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n",
       "             param_grid={&#x27;algorithm&#x27;: [&#x27;auto&#x27;, &#x27;ball_tree&#x27;, &#x27;kd_tree&#x27;, &#x27;brute&#x27;],\n",
       "                         &#x27;n_neighbors&#x27;: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
       "                         &#x27;p&#x27;: [1, 2]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-34\" type=\"checkbox\" ><label for=\"sk-estimator-id-34\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n",
       "             param_grid={&#x27;algorithm&#x27;: [&#x27;auto&#x27;, &#x27;ball_tree&#x27;, &#x27;kd_tree&#x27;, &#x27;brute&#x27;],\n",
       "                         &#x27;n_neighbors&#x27;: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
       "                         &#x27;p&#x27;: [1, 2]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-35\" type=\"checkbox\" ><label for=\"sk-estimator-id-35\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-36\" type=\"checkbox\" ><label for=\"sk-estimator-id-36\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n",
       "             param_grid={'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],\n",
       "                         'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
       "                         'p': [1, 2]})"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = GridSearchCV(knn, parameters, cv=10)\n",
    "knn.fit(X_train,y_train)\n",
    "knn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "4ed52653",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tuned hyperparameters :(best parameters)  {'algorithm': 'brute', 'n_neighbors': 5, 'p': 1}\n",
      "accuracy : 0.7523391812865496\n"
     ]
    }
   ],
   "source": [
    "print(\"tuned hyperparameters :(best parameters) \",knn.best_params_)\n",
    "print(\"accuracy :\",knn.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "7d72e5fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.782608695652174"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9febb75a",
   "metadata": {},
   "source": [
    "Lets look at a KNN Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "e1c14139",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred=knn.predict(X_test)\n",
    "sns.heatmap(confusion_matrix(y_test, y_pred), annot = True)\n",
    "plt.title('KNeighbors confision matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae342a12",
   "metadata": {},
   "source": [
    "### Create intance for Decision tree classifier & train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "838338c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {'criterion': ['gini', 'entropy'],\n",
    "     'splitter': ['best', 'random'],\n",
    "     'max_depth': [2*n for n in range(1,10)],\n",
    "     'max_features': ['auto', 'sqrt'],\n",
    "     'min_samples_leaf': [1, 2, 4],\n",
    "     'min_samples_split': [2, 5, 10]}\n",
    "\n",
    "dt = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "42c0e62a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-13 {color: black;background-color: white;}#sk-container-id-13 pre{padding: 0;}#sk-container-id-13 div.sk-toggleable {background-color: white;}#sk-container-id-13 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-13 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-13 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-13 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-13 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-13 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-13 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-13 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-13 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-13 div.sk-item {position: relative;z-index: 1;}#sk-container-id-13 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-13 div.sk-item::before, #sk-container-id-13 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-13 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-13 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-13 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-13 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-13 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-13 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-13 div.sk-label-container {text-align: center;}#sk-container-id-13 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-13 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-13\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=DecisionTreeClassifier(),\n",
       "             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
       "                         &#x27;max_depth&#x27;: [2, 4, 6, 8, 10, 12, 14, 16, 18],\n",
       "                         &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;],\n",
       "                         &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n",
       "                         &#x27;min_samples_split&#x27;: [2, 5, 10],\n",
       "                         &#x27;splitter&#x27;: [&#x27;best&#x27;, &#x27;random&#x27;]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-37\" type=\"checkbox\" ><label for=\"sk-estimator-id-37\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, estimator=DecisionTreeClassifier(),\n",
       "             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
       "                         &#x27;max_depth&#x27;: [2, 4, 6, 8, 10, 12, 14, 16, 18],\n",
       "                         &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;],\n",
       "                         &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n",
       "                         &#x27;min_samples_split&#x27;: [2, 5, 10],\n",
       "                         &#x27;splitter&#x27;: [&#x27;best&#x27;, &#x27;random&#x27;]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-38\" type=\"checkbox\" ><label for=\"sk-estimator-id-38\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-39\" type=\"checkbox\" ><label for=\"sk-estimator-id-39\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=10, estimator=DecisionTreeClassifier(),\n",
       "             param_grid={'criterion': ['gini', 'entropy'],\n",
       "                         'max_depth': [2, 4, 6, 8, 10, 12, 14, 16, 18],\n",
       "                         'max_features': ['auto', 'sqrt'],\n",
       "                         'min_samples_leaf': [1, 2, 4],\n",
       "                         'min_samples_split': [2, 5, 10],\n",
       "                         'splitter': ['best', 'random']})"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt = GridSearchCV(dt, parameters, cv=10)\n",
    "dt.fit(X_train,y_train)\n",
    "dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "f0abd872",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tuned hyperparameters :(best parameters)  {'criterion': 'gini', 'max_depth': 4, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'splitter': 'best'}\n",
      "accuracy : 0.8122807017543859\n"
     ]
    }
   ],
   "source": [
    "print(\"tuned hyperparameters :(best parameters) \",dt.best_params_)\n",
    "print(\"accuracy :\",dt.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "785557a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8043478260869565"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4680d551",
   "metadata": {},
   "source": [
    "We can plot a confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "9cf1558f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred=dt.predict(X_test)\n",
    "sns.heatmap(confusion_matrix(y_test, y_pred), annot = True)\n",
    "plt.title('Decision tree confision matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "192fa149",
   "metadata": {},
   "source": [
    "### Create a Random Forest instance and train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "078c13f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters =  {'n_estimators': [10,30,50,60,80,100],\n",
    "              'max_depth': [50,70,100,None],\n",
    "              'max_features': ['auto']}\n",
    "\n",
    "rf = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "a7117f03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-20 {color: black;background-color: white;}#sk-container-id-20 pre{padding: 0;}#sk-container-id-20 div.sk-toggleable {background-color: white;}#sk-container-id-20 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-20 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-20 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-20 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-20 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-20 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-20 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-20 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-20 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-20 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-20 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-20 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-20 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-20 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-20 div.sk-item {position: relative;z-index: 1;}#sk-container-id-20 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-20 div.sk-item::before, #sk-container-id-20 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-20 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-20 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-20 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-20 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-20 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-20 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-20 div.sk-label-container {text-align: center;}#sk-container-id-20 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-20 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-20\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=RandomForestClassifier(),\n",
       "             param_grid={&#x27;max_depth&#x27;: [50, 70, 100, None],\n",
       "                         &#x27;max_features&#x27;: [&#x27;auto&#x27;],\n",
       "                         &#x27;n_estimators&#x27;: [10, 30, 50, 60, 80, 100]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-58\" type=\"checkbox\" ><label for=\"sk-estimator-id-58\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, estimator=RandomForestClassifier(),\n",
       "             param_grid={&#x27;max_depth&#x27;: [50, 70, 100, None],\n",
       "                         &#x27;max_features&#x27;: [&#x27;auto&#x27;],\n",
       "                         &#x27;n_estimators&#x27;: [10, 30, 50, 60, 80, 100]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-59\" type=\"checkbox\" ><label for=\"sk-estimator-id-59\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-60\" type=\"checkbox\" ><label for=\"sk-estimator-id-60\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=10, estimator=RandomForestClassifier(),\n",
       "             param_grid={'max_depth': [50, 70, 100, None],\n",
       "                         'max_features': ['auto'],\n",
       "                         'n_estimators': [10, 30, 50, 60, 80, 100]})"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = GridSearchCV(rf, parameters, cv=10)\n",
    "rf.fit(X_train,y_train)\n",
    "rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "aed8128a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tuned hyperparameters :(best parameters)  {'max_depth': 50, 'max_features': 'auto', 'n_estimators': 30}\n",
      "accuracy : 0.7742690058479532\n"
     ]
    }
   ],
   "source": [
    "print(\"tuned hyperparameters :(best parameters) \",rf.best_params_)\n",
    "print(\"accuracy :\",rf.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "bd75aa57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7608695652173914"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b1e90ba",
   "metadata": {},
   "source": [
    "### Best model performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "b96076ec",
   "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>index</th>\n",
       "      <th>Accuracy Train</th>\n",
       "      <th>Accuracy Test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LR</td>\n",
       "      <td>0.801170</td>\n",
       "      <td>0.826087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RF</td>\n",
       "      <td>0.752339</td>\n",
       "      <td>0.782609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DT</td>\n",
       "      <td>0.812281</td>\n",
       "      <td>0.804348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KNN</td>\n",
       "      <td>0.774269</td>\n",
       "      <td>0.760870</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  index  Accuracy Train  Accuracy Test\n",
       "0    LR        0.801170       0.826087\n",
       "1    RF        0.752339       0.782609\n",
       "2    DT        0.812281       0.804348\n",
       "3   KNN        0.774269       0.760870"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score = {'Accuracy Train': [logreg.best_score_, knn.best_score_ ,dt.best_score_, rf.best_score_], \n",
    "     'Accuracy Test': pd.Series([logreg.score(X_test,y_test), knn.score(X_test,y_test) ,dt.score(X_test,y_test), rf.score(X_test,y_test)], index=[\"LR\", \"RF\", \"DT\", \"KNN\"])}\n",
    "score = pd.DataFrame(data=score).reset_index()\n",
    "score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db58e0cc",
   "metadata": {},
   "source": [
    "### Plot model performance on the test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "cb4504ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=score, x=\"index\", y=\"Accuracy Test\")\n",
    "for i, v in enumerate(score[\"Accuracy Test\"]):\n",
    "    plt.text(i, v + 0.1, str(round(v, 2)), ha='center')\n",
    "plt.show()    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbedd35b",
   "metadata": {},
   "source": [
    "##### We can see that Logistic Regression & DecisionTreeClassifier are the best performing models "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24f9f499",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}