Supervised ML_v2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0fdee524",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9580966d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_iris = pd.read_csv(\"./iris_csv (1).csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8ff45632",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepallength</th>\n",
       "      <th>sepalwidth</th>\n",
       "      <th>petallength</th>\n",
       "      <th>petalwidth</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepallength  sepalwidth  petallength  petalwidth           class\n",
       "0            5.1         3.5          1.4         0.2     Iris-setosa\n",
       "1            4.9         3.0          1.4         0.2     Iris-setosa\n",
       "2            4.7         3.2          1.3         0.2     Iris-setosa\n",
       "3            4.6         3.1          1.5         0.2     Iris-setosa\n",
       "4            5.0         3.6          1.4         0.2     Iris-setosa\n",
       "..           ...         ...          ...         ...             ...\n",
       "145          6.7         3.0          5.2         2.3  Iris-virginica\n",
       "146          6.3         2.5          5.0         1.9  Iris-virginica\n",
       "147          6.5         3.0          5.2         2.0  Iris-virginica\n",
       "148          6.2         3.4          5.4         2.3  Iris-virginica\n",
       "149          5.9         3.0          5.1         1.8  Iris-virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "70e53ebe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop unnecessart rows\n",
    "df_iris = df_iris.drop(index = [2, 18])\n",
    "\n",
    "# Drop rows with na values\n",
    "df_iris = df_iris.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "50fadbc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(128, 5)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_iris.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6a7447ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class\n",
       "Iris-versicolor    50\n",
       "Iris-virginica     50\n",
       "Iris-setosa        28\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_iris[\"class\"].value_counts()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe401041",
   "metadata": {},
   "source": [
    "# Linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b9b669d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "573695c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      3.5\n",
       "1      3.0\n",
       "3      3.1\n",
       "4      3.6\n",
       "5      3.9\n",
       "      ... \n",
       "145    3.0\n",
       "146    2.5\n",
       "147    3.0\n",
       "148    3.4\n",
       "149    3.0\n",
       "Name: sepalwidth, Length: 128, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_iris.dropna()[\"sepalwidth\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "eb2d0409",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:             petalwidth   R-squared:                       0.041\n",
      "Model:                            OLS   Adj. R-squared:                  0.033\n",
      "Method:                 Least Squares   F-statistic:                     5.372\n",
      "Date:                Fri, 25 Aug 2023   Prob (F-statistic):             0.0221\n",
      "Time:                        20:30:54   Log-Likelihood:                -132.08\n",
      "No. Observations:                 128   AIC:                             268.2\n",
      "Df Residuals:                     126   BIC:                             273.9\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "sepalwidth    -0.3588      0.155     -2.318      0.022      -0.665      -0.052\n",
      "const          2.4370      0.465      5.239      0.000       1.516       3.358\n",
      "==============================================================================\n",
      "Omnibus:                       14.949   Durbin-Watson:                   0.422\n",
      "Prob(Omnibus):                  0.001   Jarque-Bera (JB):                4.846\n",
      "Skew:                          -0.083   Prob(JB):                       0.0887\n",
      "Kurtosis:                       2.061   Cond. No.                         25.6\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# spector_data.exog = sm.add_constant(spector_data.exog, prepend=False)\n",
    "\n",
    "mod = sm.OLS(endog = df_iris.dropna()[\"petalwidth\"], exog = sm.add_constant(df_iris.dropna()[\"sepalwidth\"], prepend=False))\n",
    "\n",
    "res = mod.fit()\n",
    "\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "eeb6bcaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>const</th>\n",
       "      <th>sepalwidth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>128 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     const  sepalwidth\n",
       "0      1.0         3.5\n",
       "1      1.0         3.0\n",
       "3      1.0         3.1\n",
       "4      1.0         3.6\n",
       "5      1.0         3.9\n",
       "..     ...         ...\n",
       "145    1.0         3.0\n",
       "146    1.0         2.5\n",
       "147    1.0         3.0\n",
       "148    1.0         3.4\n",
       "149    1.0         3.0\n",
       "\n",
       "[128 rows x 2 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm.add_constant(df_iris.dropna()[\"sepalwidth\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1b77b1ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8279999999999998"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2.55-(0.41*4.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "171787e1",
   "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>c</th>\n",
       "      <th>a</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   c    a\n",
       "0  1  4.2"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm.add_constant(pd.DataFrame({\"c\": [1], \"a\": [4.2]}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c49982aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>c</th>\n",
       "      <th>a</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   c    a\n",
       "0  1  4.2"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({\"c\": [1], \"a\": [4.2]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e5b1ff66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    9.876792\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.predict(pd.DataFrame({\"c\": [1], \"a\": [4.2]}))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4186bd85",
   "metadata": {},
   "source": [
    "# kNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1c5783f",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6aaf1881",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6764d5f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_knn = KNeighborsClassifier(n_neighbors = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a1bc87d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepallength</th>\n",
       "      <th>sepalwidth</th>\n",
       "      <th>petallength</th>\n",
       "      <th>petalwidth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>128 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepallength  sepalwidth  petallength  petalwidth\n",
       "0            5.1         3.5          1.4         0.2\n",
       "1            4.9         3.0          1.4         0.2\n",
       "3            4.6         3.1          1.5         0.2\n",
       "4            5.0         3.6          1.4         0.2\n",
       "5            5.4         3.9          1.7         0.4\n",
       "..           ...         ...          ...         ...\n",
       "145          6.7         3.0          5.2         2.3\n",
       "146          6.3         2.5          5.0         1.9\n",
       "147          6.5         3.0          5.2         2.0\n",
       "148          6.2         3.4          5.4         2.3\n",
       "149          5.9         3.0          5.1         1.8\n",
       "\n",
       "[128 rows x 4 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_iris.iloc[:, :-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9f6fb646",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting / training the kNN machine learning classifier\n",
    "\n",
    "model_knn.fit(X = df_iris.iloc[:, :-1], y = df_iris[\"class\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "40789381",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\python 3114\\Lib\\site-packages\\sklearn\\base.py:464: UserWarning: X does not have valid feature names, but KNeighborsClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['Iris-versicolor'], dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predicting the class label for a new datapoint\n",
    "\n",
    "model_knn.predict([[1.2, 1, 3, 4.9]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d687af63",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25ea915d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "534baa2b",
   "metadata": {},
   "source": [
    "# Train -test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0da16c69",
   "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>sepallength</th>\n",
       "      <th>sepalwidth</th>\n",
       "      <th>petallength</th>\n",
       "      <th>petalwidth</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>128 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepallength  sepalwidth  petallength  petalwidth           class\n",
       "0            5.1         3.5          1.4         0.2     Iris-setosa\n",
       "1            4.9         3.0          1.4         0.2     Iris-setosa\n",
       "3            4.6         3.1          1.5         0.2     Iris-setosa\n",
       "4            5.0         3.6          1.4         0.2     Iris-setosa\n",
       "5            5.4         3.9          1.7         0.4     Iris-setosa\n",
       "..           ...         ...          ...         ...             ...\n",
       "145          6.7         3.0          5.2         2.3  Iris-virginica\n",
       "146          6.3         2.5          5.0         1.9  Iris-virginica\n",
       "147          6.5         3.0          5.2         2.0  Iris-virginica\n",
       "148          6.2         3.4          5.4         2.3  Iris-virginica\n",
       "149          5.9         3.0          5.1         1.8  Iris-virginica\n",
       "\n",
       "[128 rows x 5 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_iris"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a900e03",
   "metadata": {},
   "source": [
    "# Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b3ddac78",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Target (dependent variable) : petalwidth\n",
    "# Independednt variables : sepallength, sepalwidth, petallength"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49035eba",
   "metadata": {},
   "source": [
    "### Linear train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3ad919d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = df_iris.iloc[:int(128*0.8), :]\n",
    "df_test = df_iris.iloc[int(128*0.8):, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "44b72a39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(102, 5)\n",
      "(26, 5)\n"
     ]
    }
   ],
   "source": [
    "print(df_train.shape)\n",
    "print(df_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be8866de",
   "metadata": {},
   "source": [
    "### Random train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8d7015bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 34, 110,  55,   7,  57, 112,  78,  65,  40,  87, 106,  95,  71,\n",
       "        58, 122,  52,  88, 121,  93,  25,  36, 105,   2,   6,  39,  90,\n",
       "        99, 114,  43,  83,  84,  92,  10,  32,  15,  33,  22,  79,   8,\n",
       "        81,  11,  61,  16,  80, 103,  62,  54,  66,  45,  41,  98, 127,\n",
       "        82,  38,  59,  75,  48,  49,  89,  96,  85, 100, 125,   9,  86,\n",
       "         3,  63,  47,  50,  13,  51,  53,  14,  28,  94,  19, 108,  69,\n",
       "        64, 104,  26,  12, 109, 118,   0,  29,  44,   4,  31,  68,  72,\n",
       "        42,  73, 117, 107, 119,  23, 101,   5,  60,  97,  67])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.choice(range(128), int(128*0.8), replace = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6445076f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>93</th>\n",
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       "      <td>Iris-versicolor</td>\n",
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       "      <th>68</th>\n",
       "      <td>6.2</td>\n",
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       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>6.4</td>\n",
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       "      <th>21</th>\n",
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       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>4.9</td>\n",
       "      <td>2.4</td>\n",
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       "      <td>6.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
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       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>7.4</td>\n",
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       "      <td>6.1</td>\n",
       "      <td>1.9</td>\n",
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       "      <td>Iris-setosa</td>\n",
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       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>6.0</td>\n",
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       "      <td>1.6</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepallength  sepalwidth  petallength  petalwidth            class\n",
       "93           5.0         2.3          3.3         1.0  Iris-versicolor\n",
       "68           6.2         2.2          4.5         1.5  Iris-versicolor\n",
       "115          6.4         3.2          5.3         2.3   Iris-virginica\n",
       "21           5.1         3.7          1.5         0.4      Iris-setosa\n",
       "57           4.9         2.4          3.3         1.0  Iris-versicolor\n",
       "..           ...         ...          ...         ...              ...\n",
       "118          7.7         2.6          6.9         2.3   Iris-virginica\n",
       "130          7.4         2.8          6.1         1.9   Iris-virginica\n",
       "49           5.0         3.3          1.4         0.2      Iris-setosa\n",
       "83           6.0         2.7          5.1         1.6  Iris-versicolor\n",
       "91           6.1         3.0          4.6         1.4  Iris-versicolor\n",
       "\n",
       "[102 rows x 5 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate random indices for the training data\n",
    "df_train = df_iris.iloc[np.random.choice(range(128), int(128*0.8), replace = False), :]\n",
    "df_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7cdbe5b6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{19,\n",
       " 25,\n",
       " 39,\n",
       " 42,\n",
       " 46,\n",
       " 50,\n",
       " 64,\n",
       " 82,\n",
       " 85,\n",
       " 86,\n",
       " 87,\n",
       " 88,\n",
       " 89,\n",
       " 92,\n",
       " 101,\n",
       " 103,\n",
       " 111,\n",
       " 112,\n",
       " 121,\n",
       " 127,\n",
       " 129,\n",
       " 138,\n",
       " 140,\n",
       " 147,\n",
       " 148,\n",
       " 149}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get those indices which has not been used in training\n",
    "set(df_iris.index) - set(df_train.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3db049f7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>sepallength</th>\n",
       "      <th>sepalwidth</th>\n",
       "      <th>petallength</th>\n",
       "      <th>petalwidth</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>4.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>7.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.2</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.3</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>5.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepallength  sepalwidth  petallength  petalwidth            class\n",
       "129          7.2         3.0          5.8         1.6   Iris-virginica\n",
       "138          6.0         3.0          4.8         1.8   Iris-virginica\n",
       "140          6.7         3.1          5.6         2.4   Iris-virginica\n",
       "19           5.1         3.8          1.5         0.3      Iris-setosa\n",
       "147          6.5         3.0          5.2         2.0   Iris-virginica\n",
       "148          6.2         3.4          5.4         2.3   Iris-virginica\n",
       "149          5.9         3.0          5.1         1.8   Iris-virginica\n",
       "25           5.0         3.0          1.6         0.2      Iris-setosa\n",
       "39           5.1         3.4          1.5         0.2      Iris-setosa\n",
       "42           4.4         3.2          1.3         0.2      Iris-setosa\n",
       "46           5.1         3.8          1.6         0.2      Iris-setosa\n",
       "50           7.0         3.2          4.7         1.4  Iris-versicolor\n",
       "64           5.6         2.9          3.6         1.3  Iris-versicolor\n",
       "82           5.8         2.7          3.9         1.2  Iris-versicolor\n",
       "85           6.0         3.4          4.5         1.6  Iris-versicolor\n",
       "86           6.7         3.1          4.7         1.5  Iris-versicolor\n",
       "87           6.3         2.3          4.4         1.3  Iris-versicolor\n",
       "88           5.6         3.0          4.1         1.3  Iris-versicolor\n",
       "89           5.5         2.5          4.0         1.3  Iris-versicolor\n",
       "92           5.8         2.6          4.0         1.2  Iris-versicolor\n",
       "101          5.8         2.7          5.1         1.9   Iris-virginica\n",
       "103          6.3         2.9          5.6         1.8   Iris-virginica\n",
       "111          6.4         2.7          5.3         1.9   Iris-virginica\n",
       "112          6.8         3.0          5.5         2.1   Iris-virginica\n",
       "121          5.6         2.8          4.9         2.0   Iris-virginica\n",
       "127          6.1         3.0          4.9         1.8   Iris-virginica"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the left out indices for test data\n",
    "df_test = df_iris.loc[list(set(df_iris.index) - set(df_train.index)), :]\n",
    "df_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fce78bd",
   "metadata": {},
   "source": [
    "### Using train_test_split function from sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3fd24b99",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_iris.loc[:, [\"sepallength\", \"sepalwidth\", \"petallength\"]]\n",
    "y = df_iris.loc[:, \"petalwidth\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fce4cddc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>sepallength</th>\n",
       "      <th>sepalwidth</th>\n",
       "      <th>petallength</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>128 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepallength  sepalwidth  petallength\n",
       "0            5.1         3.5          1.4\n",
       "1            4.9         3.0          1.4\n",
       "3            4.6         3.1          1.5\n",
       "4            5.0         3.6          1.4\n",
       "5            5.4         3.9          1.7\n",
       "..           ...         ...          ...\n",
       "145          6.7         3.0          5.2\n",
       "146          6.3         2.5          5.0\n",
       "147          6.5         3.0          5.2\n",
       "148          6.2         3.4          5.4\n",
       "149          5.9         3.0          5.1\n",
       "\n",
       "[128 rows x 3 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "cbf89e7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0.2\n",
       "1      0.2\n",
       "3      0.2\n",
       "4      0.2\n",
       "5      0.4\n",
       "      ... \n",
       "145    2.3\n",
       "146    1.9\n",
       "147    2.0\n",
       "148    2.3\n",
       "149    1.8\n",
       "Name: petalwidth, Length: 128, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5415cdbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size = 0.8, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a1cfa083",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepallength</th>\n",
       "      <th>sepalwidth</th>\n",
       "      <th>petallength</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepallength  sepalwidth  petallength\n",
       "92           5.8         2.6          4.0\n",
       "100          6.3         3.3          6.0\n",
       "69           5.6         2.5          3.9\n",
       "0            5.1         3.5          1.4\n",
       "22           4.6         3.6          1.0\n",
       "..           ...         ...          ...\n",
       "128          6.4         2.8          5.6\n",
       "24           4.8         3.4          1.9\n",
       "114          5.8         2.8          5.1\n",
       "73           6.1         2.8          4.7\n",
       "124          6.7         3.3          5.7\n",
       "\n",
       "[102 rows x 3 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7834f1ca",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26, 3)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1d595aa9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "92     1.2\n",
       "100    2.5\n",
       "69     1.1\n",
       "0      0.2\n",
       "22     0.2\n",
       "      ... \n",
       "128    2.1\n",
       "24     0.2\n",
       "114    2.4\n",
       "73     1.2\n",
       "124    2.1\n",
       "Name: petalwidth, Length: 102, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "31a6db6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3176132e",
   "metadata": {},
   "source": [
    "### Model training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "430f6682",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "cc834841",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating the model object\n",
    "model_lr = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "22d4abaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_lr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "964d17b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9104513929181576"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_lr.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c76f9544",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__abstractmethods__',\n",
       " '__annotations__',\n",
       " '__class__',\n",
       " '__delattr__',\n",
       " '__dict__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__getstate__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__le__',\n",
       " '__lt__',\n",
       " '__module__',\n",
       " '__ne__',\n",
       " '__new__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__setattr__',\n",
       " '__setstate__',\n",
       " '__sizeof__',\n",
       " '__sklearn_clone__',\n",
       " '__str__',\n",
       " '__subclasshook__',\n",
       " '__weakref__',\n",
       " '_abc_impl',\n",
       " '_build_request_for_signature',\n",
       " '_check_feature_names',\n",
       " '_check_n_features',\n",
       " '_decision_function',\n",
       " '_estimator_type',\n",
       " '_get_default_requests',\n",
       " '_get_metadata_request',\n",
       " '_get_param_names',\n",
       " '_get_tags',\n",
       " '_more_tags',\n",
       " '_parameter_constraints',\n",
       " '_repr_html_',\n",
       " '_repr_html_inner',\n",
       " '_repr_mimebundle_',\n",
       " '_set_intercept',\n",
       " '_validate_data',\n",
       " '_validate_params',\n",
       " 'coef_',\n",
       " 'copy_X',\n",
       " 'feature_names_in_',\n",
       " 'fit',\n",
       " 'fit_intercept',\n",
       " 'get_metadata_routing',\n",
       " 'get_params',\n",
       " 'intercept_',\n",
       " 'n_features_in_',\n",
       " 'n_jobs',\n",
       " 'positive',\n",
       " 'predict',\n",
       " 'rank_',\n",
       " 'score',\n",
       " 'set_fit_request',\n",
       " 'set_params',\n",
       " 'set_score_request',\n",
       " 'singular_']"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(model_lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "13733cae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['sepallength', 'sepalwidth', 'petallength'], dtype=object)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_lr.feature_names_in_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "22b3f390",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.26107413,  0.24827545,  0.54700606])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_lr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a2b639b9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.09575419984582423"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_lr.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "09ea2b25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'sepallength': -0.2610741299844725,\n",
       " 'sepalwidth': 0.24827545186904953,\n",
       " 'petallength': 0.5470060612404106}"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict(zip(model_lr.feature_names_in_, model_lr.coef_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "25e27185",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.63490579, 1.07203126, 0.01155363, 1.22739587, 2.0152503 ,\n",
       "       1.51932711, 1.57274784, 1.60638655, 2.04271153, 0.25968118,\n",
       "       1.96062365, 0.18399263, 1.49563152, 0.39263006, 2.313833  ,\n",
       "       1.97553833, 1.41738323, 2.02278159, 1.79652183, 1.70719847,\n",
       "       0.17894711, 0.26849234, 1.98793697, 0.311896  , 1.73684966,\n",
       "       1.30308442])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prediction on test data\n",
    "y_pred_lr1 = model_lr.predict(X_test)\n",
    "y_pred_lr1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "79f8d0d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.7, 1. , 0.3, 1.3, 2.3, 1.5, 1.4, 1.8, 1.8, 0.2, 1.8, 0.2, 1.5,\n",
       "       0.4, 2.3, 2.3, 1.3, 2.1, 2. , 1.7, 0.3, 0.2, 2.4, 0.4, 1.8, 1. ])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "4e94430c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26,)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "4851707b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.17482930839177857"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(np.sum((y_test.values - y_pred_lr1)**2)/26) # RMSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "92f820bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.030565287072747614"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum((y_test.values - y_pred_lr1)**2)/26 #MSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "64589fa1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.030565287072747614"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "mean_squared_error(y_test.values, y_pred_lr1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "0a16cc54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.6, 1.1, 0. , 1.2, 2. , 1.5, 1.6, 1.6, 2. , 0.3, 2. , 0.2, 1.5,\n",
       "       0.4, 2.3, 2. , 1.4, 2. , 1.8, 1.7, 0.2, 0.3, 2. , 0.3, 1.7, 1.3])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.round(y_pred_lr1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "a5927139",
   "metadata": {},
   "outputs": [],
   "source": [
    "TSS = np.sum((y_test.values - np.round(y_test.values.mean(), 1))**2)\n",
    "ESS = np.sum((np.round(y_pred_lr1, 1) - np.round(y_test.values.mean(), 1))**2)\n",
    "RSS = np.sum((y_test.values - np.round(y_pred_lr1, 1))**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "4a99aea7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.18"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TSS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "938c77df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13.66"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ESS + RSS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7b66504",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "55e161c7",
   "metadata": {},
   "source": [
    "# Classification using kNN\n",
    "- Feature set (independent variables): 'sepallength', 'sepalwidth', 'petallength', 'petalwidth'\n",
    "- Target class (dependent variable): 'class'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "10f97cab",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_iris.loc[:, ['sepallength', 'sepalwidth', 'petallength', 'petalwidth']]\n",
    "y = df_iris.loc[:, 'class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "8bf1ce7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>sepallength</th>\n",
       "      <th>sepalwidth</th>\n",
       "      <th>petallength</th>\n",
       "      <th>petalwidth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>128 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepallength  sepalwidth  petallength  petalwidth\n",
       "0            5.1         3.5          1.4         0.2\n",
       "1            4.9         3.0          1.4         0.2\n",
       "3            4.6         3.1          1.5         0.2\n",
       "4            5.0         3.6          1.4         0.2\n",
       "5            5.4         3.9          1.7         0.4\n",
       "..           ...         ...          ...         ...\n",
       "145          6.7         3.0          5.2         2.3\n",
       "146          6.3         2.5          5.0         1.9\n",
       "147          6.5         3.0          5.2         2.0\n",
       "148          6.2         3.4          5.4         2.3\n",
       "149          5.9         3.0          5.1         1.8\n",
       "\n",
       "[128 rows x 4 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "e1c18115",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         Iris-setosa\n",
       "1         Iris-setosa\n",
       "3         Iris-setosa\n",
       "4         Iris-setosa\n",
       "5         Iris-setosa\n",
       "            ...      \n",
       "145    Iris-virginica\n",
       "146    Iris-virginica\n",
       "147    Iris-virginica\n",
       "148    Iris-virginica\n",
       "149    Iris-virginica\n",
       "Name: class, Length: 128, dtype: object"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb4bc7c7",
   "metadata": {},
   "source": [
    "### Performing train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "8e4d6c12",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train2, X_test2, y_train2, y_test2 = train_test_split(X, y, train_size = 0.8, random_state = 9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "e2bf50b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
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       "      <th>sepalwidth</th>\n",
       "      <th>petallength</th>\n",
       "      <th>petalwidth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
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       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>5.5</td>\n",
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       "      <td>4.0</td>\n",
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       "      <th>88</th>\n",
       "      <td>5.6</td>\n",
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       "      <td>1.3</td>\n",
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       "    <tr>\n",
       "      <th>124</th>\n",
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       "      <td>5.7</td>\n",
       "      <td>2.1</td>\n",
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       "      <th>...</th>\n",
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       "      <th>78</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
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       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>6.8</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.4</td>\n",
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       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepallength  sepalwidth  petallength  petalwidth\n",
       "143          6.8         3.2          5.9         2.3\n",
       "53           5.5         2.3          4.0         1.3\n",
       "89           5.5         2.5          4.0         1.3\n",
       "88           5.6         3.0          4.1         1.3\n",
       "124          6.7         3.3          5.7         2.1\n",
       "..           ...         ...          ...         ...\n",
       "78           6.0         2.9          4.5         1.5\n",
       "76           6.8         2.8          4.8         1.4\n",
       "139          6.9         3.1          5.4         2.1\n",
       "114          5.8         2.8          5.1         2.4\n",
       "148          6.2         3.4          5.4         2.3\n",
       "\n",
       "[102 rows x 4 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "0875cc3d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>sepallength</th>\n",
       "      <th>sepalwidth</th>\n",
       "      <th>petallength</th>\n",
       "      <th>petalwidth</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.1</td>\n",
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       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
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       "      <th>61</th>\n",
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       "      <td>1.8</td>\n",
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       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
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       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>4.9</td>\n",
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       "      <td>4.4</td>\n",
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       "      <td>2.3</td>\n",
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       "      <th>132</th>\n",
       "      <td>6.4</td>\n",
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       "      <td>2.2</td>\n",
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       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.3</td>\n",
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       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>7.0</td>\n",
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       "      <td>1.2</td>\n",
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       "      <td>5.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
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      ],
      "text/plain": [
       "     sepallength  sepalwidth  petallength  petalwidth\n",
       "17           5.1         3.5          1.4         0.3\n",
       "4            5.0         3.6          1.4         0.2\n",
       "61           5.9         3.0          4.2         1.5\n",
       "107          7.3         2.9          6.3         1.8\n",
       "20           5.4         3.4          1.7         0.2\n",
       "123          6.3         2.7          4.9         1.8\n",
       "43           5.0         3.5          1.6         0.6\n",
       "65           6.7         3.1          4.4         1.4\n",
       "93           5.0         2.3          3.3         1.0\n",
       "106          4.9         2.5          4.5         1.7\n",
       "137          6.4         3.1          5.5         1.8\n",
       "116          6.5         3.0          5.5         1.8\n",
       "47           4.6         3.2          1.4         0.2\n",
       "57           4.9         2.4          3.3         1.0\n",
       "42           4.4         3.2          1.3         0.2\n",
       "118          7.7         2.6          6.9         2.3\n",
       "132          6.4         2.8          5.6         2.2\n",
       "97           6.2         2.9          4.3         1.3\n",
       "50           7.0         3.2          4.7         1.4\n",
       "90           5.5         2.6          4.4         1.2\n",
       "92           5.8         2.6          4.0         1.2\n",
       "48           5.3         3.7          1.5         0.2\n",
       "104          6.5         3.0          5.8         2.2\n",
       "45           4.8         3.0          1.4         0.3\n",
       "125          7.2         3.2          6.0         1.8\n",
       "7            5.0         3.4          1.5         0.2"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "9abaff75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "143     Iris-virginica\n",
       "53     Iris-versicolor\n",
       "89     Iris-versicolor\n",
       "88     Iris-versicolor\n",
       "124     Iris-virginica\n",
       "            ...       \n",
       "78     Iris-versicolor\n",
       "76     Iris-versicolor\n",
       "139     Iris-virginica\n",
       "114     Iris-virginica\n",
       "148     Iris-virginica\n",
       "Name: class, Length: 102, dtype: object"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "54f224b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17         Iris-setosa\n",
       "4          Iris-setosa\n",
       "61     Iris-versicolor\n",
       "107     Iris-virginica\n",
       "20         Iris-setosa\n",
       "123     Iris-virginica\n",
       "43         Iris-setosa\n",
       "65     Iris-versicolor\n",
       "93     Iris-versicolor\n",
       "106     Iris-virginica\n",
       "137     Iris-virginica\n",
       "116     Iris-virginica\n",
       "47         Iris-setosa\n",
       "57     Iris-versicolor\n",
       "42         Iris-setosa\n",
       "118     Iris-virginica\n",
       "132     Iris-virginica\n",
       "97     Iris-versicolor\n",
       "50     Iris-versicolor\n",
       "90     Iris-versicolor\n",
       "92     Iris-versicolor\n",
       "48         Iris-setosa\n",
       "104     Iris-virginica\n",
       "45         Iris-setosa\n",
       "125     Iris-virginica\n",
       "7          Iris-setosa\n",
       "Name: class, dtype: object"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "00f47c5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26,)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test2.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2271a866",
   "metadata": {},
   "source": [
    "### Train a kNN classifier using the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "fd2382d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "d27132a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initializing the model object\n",
    "model_knn = KNeighborsClassifier(n_neighbors = 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "c6698115",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 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-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 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-6 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-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 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-6 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-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 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-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 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-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 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-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=4)</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=4)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=4)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the model on training data\n",
    "model_knn.fit(X_train2, y_train2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "da3e0e70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Iris-setosa', 'Iris-setosa', 'Iris-versicolor', 'Iris-virginica',\n",
       "       'Iris-setosa', 'Iris-virginica', 'Iris-setosa', 'Iris-versicolor',\n",
       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-virginica',\n",
       "       'Iris-virginica', 'Iris-setosa', 'Iris-versicolor', 'Iris-setosa',\n",
       "       'Iris-virginica', 'Iris-virginica', 'Iris-versicolor',\n",
       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
       "       'Iris-setosa', 'Iris-virginica', 'Iris-setosa', 'Iris-virginica',\n",
       "       'Iris-setosa'], dtype=object)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run predictions on test data\n",
    "y_pred_knn = model_knn.predict(X_test2)\n",
    "y_pred_knn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "60805dfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Iris-setosa', 'Iris-setosa', 'Iris-versicolor', 'Iris-virginica',\n",
       "       'Iris-setosa', 'Iris-virginica', 'Iris-setosa', 'Iris-versicolor',\n",
       "       'Iris-versicolor', 'Iris-virginica', 'Iris-virginica',\n",
       "       'Iris-virginica', 'Iris-setosa', 'Iris-versicolor', 'Iris-setosa',\n",
       "       'Iris-virginica', 'Iris-virginica', 'Iris-versicolor',\n",
       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
       "       'Iris-setosa', 'Iris-virginica', 'Iris-setosa', 'Iris-virginica',\n",
       "       'Iris-setosa'], dtype=object)"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Original targets\n",
    "y_test2.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "859945c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9], dtype=int64)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "incorrects = np.nonzero(model_knn.predict(X_test2) != y_test2)[0]\n",
    "incorrects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "de8c3c9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of y_test2: 26\n",
      "Length of incorrect_indices: 1\n",
      "Indices in incorrect_indices: [9]\n",
      "Original Value: Iris-virginica, Predicted Value: Iris-versicolor\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\python 3114\\Lib\\site-packages\\sklearn\\base.py:464: UserWarning: X does not have valid feature names, but KNeighborsClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "incorrect_indices = np.nonzero(model_knn.predict(X_test2) != y_test2)[0]\n",
    "\n",
    "print(\"Length of y_test2:\", len(y_test2))\n",
    "print(\"Length of incorrect_indices:\", len(incorrect_indices))\n",
    "print(\"Indices in incorrect_indices:\", incorrect_indices)\n",
    "\n",
    "for index in incorrect_indices:\n",
    "    if index < len(y_test2):\n",
    "        original_value = y_test2.iloc[index]  # Using iloc to access by integer index\n",
    "        predicted_value = model_knn.predict(X_test2.iloc[index].values.reshape(1, -1))[0]\n",
    "        print(f\"Original Value: {original_value}, Predicted Value: {predicted_value}\")\n",
    "    else:\n",
    "        print(f\"Index {index} is out of bounds for y_test2\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e683f43b",
   "metadata": {},
   "source": [
    "### Evaluating the kNN classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "02bf2e1c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9615384615384616"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_knn.score(X_test2, y_test2.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "cbcb40f7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9, 0, 0],\n",
       "       [0, 8, 0],\n",
       "       [0, 1, 8]], dtype=int64)"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confusion matrix for our test results\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "confusion_matrix(y_test2.values, y_pred_knn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "c7216e3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9615384615384616"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "25/26"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "a02cb5f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9615384615384616"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test2, y_pred_knn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "12625576",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9658119658119658"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score\n",
    "precision_score(y_test2, y_pred_knn, average=\"weighted\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f083b8a7",
   "metadata": {},
   "source": [
    "# Classification using Logistic regression\n",
    "- Feature set (independent variables): 'sepallength', 'sepalwidth', 'petallength', 'petalwidth'\n",
    "- Target class (dependent variable): 'class'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "4986d983",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "b76a0a56",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train3, X_test3, y_train3, y_test3 = train_test_split(X, y, train_size = 0.8, random_state = 9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "id": "80595f7c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Initializing the model object\n",
    "model_logistic1 = LogisticRegression(random_state = 12).fit(X_train3,y_train3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "id": "c1b27b2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-27 {color: black;}#sk-container-id-27 pre{padding: 0;}#sk-container-id-27 div.sk-toggleable {background-color: white;}#sk-container-id-27 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-27 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-27 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-27 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-27 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-27 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-27 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-27 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-27 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-27 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-27 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-27 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-27 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-27 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-27 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-27 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-27 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-27 div.sk-item {position: relative;z-index: 1;}#sk-container-id-27 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-27 div.sk-item::before, #sk-container-id-27 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-27 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-27 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-27 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-27 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-27 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-27 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-27 div.sk-label-container {text-align: center;}#sk-container-id-27 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-27 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-27\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(random_state=12)</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-27\" type=\"checkbox\" checked><label for=\"sk-estimator-id-27\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(random_state=12)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(random_state=12)"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the model on training data\n",
    "model_logistic1.fit(X_train3, y_train3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "id": "d5043242",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Iris-setosa', 'Iris-setosa', 'Iris-versicolor', 'Iris-virginica',\n",
       "       'Iris-setosa', 'Iris-virginica', 'Iris-setosa', 'Iris-versicolor',\n",
       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-virginica',\n",
       "       'Iris-virginica', 'Iris-setosa', 'Iris-versicolor', 'Iris-setosa',\n",
       "       'Iris-virginica', 'Iris-virginica', 'Iris-versicolor',\n",
       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
       "       'Iris-setosa', 'Iris-virginica', 'Iris-setosa', 'Iris-virginica',\n",
       "       'Iris-setosa'], dtype=object)"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run predictions on test data\n",
    "y_pred_lr2 = model_logistic1.predict(X_test3)\n",
    "y_pred_lr2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "4c9cb93f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9.55403239e-01, 4.45966892e-02, 7.14782839e-08],\n",
       "       [9.63467651e-01, 3.65323046e-02, 4.47828657e-08],\n",
       "       [1.89697290e-02, 8.98737115e-01, 8.22931564e-02],\n",
       "       [1.48171502e-06, 2.18108016e-02, 9.78187717e-01],\n",
       "       [8.86857701e-01, 1.13142008e-01, 2.90880537e-07],\n",
       "       [1.06999642e-03, 4.20181301e-01, 5.78748703e-01],\n",
       "       [9.40671448e-01, 5.93281564e-02, 3.95804966e-07],\n",
       "       [6.20026320e-03, 9.12660054e-01, 8.11396824e-02],\n",
       "       [1.40224320e-01, 8.57162866e-01, 2.61281388e-03],\n",
       "       [1.24958187e-02, 6.20995522e-01, 3.66508660e-01],\n",
       "       [1.41159992e-04, 1.35692875e-01, 8.64165965e-01],\n",
       "       [1.16110947e-04, 1.36947749e-01, 8.62936140e-01],\n",
       "       [9.64239902e-01, 3.57600427e-02, 5.56436090e-08],\n",
       "       [1.66740364e-01, 8.30700426e-01, 2.55920995e-03],\n",
       "       [9.75934964e-01, 2.40650057e-02, 3.04402572e-08],\n",
       "       [1.11054814e-08, 1.11892694e-03, 9.98881062e-01],\n",
       "       [2.18591285e-05, 3.49853332e-02, 9.64992808e-01],\n",
       "       [1.07072974e-02, 9.29942834e-01, 5.93498689e-02],\n",
       "       [2.55652293e-03, 8.45693000e-01, 1.51750477e-01],\n",
       "       [1.33055040e-02, 9.07123542e-01, 7.95709542e-02],\n",
       "       [2.25148990e-02, 9.52687865e-01, 2.47972359e-02],\n",
       "       [9.45765150e-01, 5.42347731e-02, 7.70498752e-08],\n",
       "       [1.02277379e-05, 2.21428882e-02, 9.77846884e-01],\n",
       "       [9.46953730e-01, 5.30461636e-02, 1.06780408e-07],\n",
       "       [9.31431761e-06, 5.05565925e-02, 9.49434093e-01],\n",
       "       [9.46611609e-01, 5.33883003e-02, 9.07041265e-08]])"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get probability values on test data\n",
    "model_logistic1.predict_proba(X_test3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2aaacfbc",
   "metadata": {},
   "source": [
    "### Evaluating the Logistic regression classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "id": "5601d220",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9615384615384616"
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_logistic1.score(X_test3, y_test3.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "id": "f5d5028f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9, 0, 0],\n",
       "       [0, 8, 0],\n",
       "       [0, 1, 8]], dtype=int64)"
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test3.values, y_pred_lr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "id": "7527538c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initializing the model object\n",
    "model_logistic2 = LogisticRegression(random_state = 12,solver='liblinear').fit(X_train3,y_train3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "id": "48a2932d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-28 {color: black;}#sk-container-id-28 pre{padding: 0;}#sk-container-id-28 div.sk-toggleable {background-color: white;}#sk-container-id-28 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-28 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-28 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-28 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-28 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-28 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-28 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-28 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-28 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-28 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-28 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-28 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-28 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-28 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-28 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-28 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-28 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-28 div.sk-item {position: relative;z-index: 1;}#sk-container-id-28 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-28 div.sk-item::before, #sk-container-id-28 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-28 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-28 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-28 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-28 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-28 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-28 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-28 div.sk-label-container {text-align: center;}#sk-container-id-28 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-28 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-28\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(random_state=12, solver=&#x27;liblinear&#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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-28\" type=\"checkbox\" checked><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(random_state=12, solver=&#x27;liblinear&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(random_state=12, solver='liblinear')"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the model on training data\n",
    "model_logistic2.fit(X_train3, y_train3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "id": "4bee8edc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Iris-setosa', 'Iris-setosa', 'Iris-versicolor', 'Iris-virginica',\n",
       "       'Iris-setosa', 'Iris-virginica', 'Iris-setosa', 'Iris-versicolor',\n",
       "       'Iris-versicolor', 'Iris-virginica', 'Iris-virginica',\n",
       "       'Iris-virginica', 'Iris-setosa', 'Iris-versicolor', 'Iris-setosa',\n",
       "       'Iris-virginica', 'Iris-virginica', 'Iris-versicolor',\n",
       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
       "       'Iris-setosa', 'Iris-virginica', 'Iris-setosa', 'Iris-virginica',\n",
       "       'Iris-setosa'], dtype=object)"
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run predictions on test data\n",
    "y_pred_lr3 = model_logistic2.predict(X_test3)\n",
    "y_pred_lr3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "id": "50eb0e76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7.91157011e-01, 2.08812269e-01, 3.07195466e-05],\n",
       "       [7.93944127e-01, 2.06031486e-01, 2.43867568e-05],\n",
       "       [3.97568242e-02, 6.37740348e-01, 3.22502828e-01],\n",
       "       [2.28191327e-04, 4.29320145e-01, 5.70451664e-01],\n",
       "       [7.16118825e-01, 2.83850034e-01, 3.11407430e-05],\n",
       "       [2.96986768e-03, 4.10284516e-01, 5.86745616e-01],\n",
       "       [8.34580169e-01, 1.65290863e-01, 1.28968121e-04],\n",
       "       [4.25757068e-02, 8.47805955e-01, 1.09618338e-01],\n",
       "       [8.77254436e-02, 7.91997733e-01, 1.20276824e-01],\n",
       "       [3.15090117e-03, 2.82535775e-01, 7.14313324e-01],\n",
       "       [1.41443573e-03, 3.42318459e-01, 6.56267105e-01],\n",
       "       [1.24767063e-03, 3.65982761e-01, 6.32769568e-01],\n",
       "       [7.48467160e-01, 2.51455239e-01, 7.76008103e-05],\n",
       "       [1.00964864e-01, 7.70051003e-01, 1.28984133e-01],\n",
       "       [7.67072192e-01, 2.32841144e-01, 8.66637757e-05],\n",
       "       [3.11758411e-05, 3.99787882e-01, 6.00180942e-01],\n",
       "       [4.95300431e-04, 2.70045896e-01, 7.29458803e-01],\n",
       "       [3.31458945e-02, 7.99774831e-01, 1.67079274e-01],\n",
       "       [2.76950607e-02, 8.63551784e-01, 1.08753155e-01],\n",
       "       [1.11143414e-02, 6.14840215e-01, 3.74045444e-01],\n",
       "       [3.55934778e-02, 7.95358492e-01, 1.69048030e-01],\n",
       "       [7.87380926e-01, 2.12602719e-01, 1.63540285e-05],\n",
       "       [4.49519684e-04, 2.55510690e-01, 7.44039790e-01],\n",
       "       [7.21238090e-01, 2.78668007e-01, 9.39032434e-05],\n",
       "       [7.28342350e-04, 4.20570451e-01, 5.78701207e-01],\n",
       "       [7.53648272e-01, 2.46312826e-01, 3.89023051e-05]])"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get probability values on test data\n",
    "model_logistic2.predict_proba(X_test3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "id": "b2fedb80",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_logistic2.score(X_test3, y_test3.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "id": "d87c35f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9, 0, 0],\n",
       "       [0, 8, 0],\n",
       "       [0, 0, 9]], dtype=int64)"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test3.values, y_pred_lr3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "887866a6",
   "metadata": {},
   "source": [
    "# Classification using Support Vector Machine\n",
    "- Feature set (independent variables): 'sepallength', 'sepalwidth', 'petallength', 'petalwidth'\n",
    "- Target class (dependent variable): 'class'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "91b6d986",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "d086fbc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initializing the model object\n",
    "model_svm = SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "939ab505",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-11 {color: black;}#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>SVC()</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the model on training data\n",
    "model_svm.fit(X_train2, y_train2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "f93c2b5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Iris-setosa', 'Iris-setosa', 'Iris-versicolor', 'Iris-virginica',\n",
       "       'Iris-setosa', 'Iris-virginica', 'Iris-setosa', 'Iris-versicolor',\n",
       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-virginica',\n",
       "       'Iris-virginica', 'Iris-setosa', 'Iris-versicolor', 'Iris-setosa',\n",
       "       'Iris-virginica', 'Iris-virginica', 'Iris-versicolor',\n",
       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
       "       'Iris-setosa', 'Iris-virginica', 'Iris-setosa', 'Iris-virginica',\n",
       "       'Iris-setosa'], dtype=object)"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run predictions on test data\n",
    "y_pred_svc = model_svm.predict(X_test2)\n",
    "y_pred_svc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2655c33e",
   "metadata": {},
   "source": [
    "### Evaluating the SVM classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "2027456e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9615384615384616"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_svm.score(X_test2, y_test2.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "714af22c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9, 0, 0],\n",
       "       [0, 8, 0],\n",
       "       [0, 1, 8]], dtype=int64)"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confusion matrix for our test results\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "confusion_matrix(y_test2.values, y_pred_svc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "dc6c3fcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Iris-setosa', 'Iris-setosa', 'Iris-versicolor', 'Iris-virginica',\n",
       "       'Iris-setosa', 'Iris-virginica', 'Iris-setosa', 'Iris-versicolor',\n",
       "       'Iris-versicolor', 'Iris-virginica', 'Iris-virginica',\n",
       "       'Iris-virginica', 'Iris-setosa', 'Iris-versicolor', 'Iris-setosa',\n",
       "       'Iris-virginica', 'Iris-virginica', 'Iris-versicolor',\n",
       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
       "       'Iris-setosa', 'Iris-virginica', 'Iris-setosa', 'Iris-virginica',\n",
       "       'Iris-setosa'], dtype=object)"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using linear kernel instead of rbf\n",
    "\n",
    "# Initializing the model object\n",
    "model_svm2 = SVC(kernel = \"linear\")\n",
    "\n",
    "# Fit the model on training data\n",
    "model_svm2.fit(X_train2, y_train2)\n",
    "\n",
    "# Run predictions on test data\n",
    "y_pred_svc2 = model_svm2.predict(X_test2)\n",
    "y_pred_svc2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "c08d7fa5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_svm2.score(X_test2, y_test2.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "ae32951f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9, 0, 0],\n",
       "       [0, 8, 0],\n",
       "       [0, 0, 9]], dtype=int64)"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test2.values, y_pred_svc2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1b012ac",
   "metadata": {},
   "source": [
    "# Classification using Decision Tree classifier\n",
    "- Feature set (independent variables): 'sepallength', 'sepalwidth', 'petallength', 'petalwidth'\n",
    "- Target class (dependent variable): 'class'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "a1918e8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "37e4e14c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initializing the model object\n",
    "model_dt = DecisionTreeClassifier(random_state = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "95229bda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-20 {color: black;}#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>DecisionTreeClassifier(random_state=10)</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" checked><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=10)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(random_state=10)"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the model on training data\n",
    "model_dt.fit(X_train2, y_train2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "fb33b95d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Iris-setosa', 'Iris-setosa', 'Iris-versicolor', 'Iris-virginica',\n",
       "       'Iris-setosa', 'Iris-virginica', 'Iris-setosa', 'Iris-versicolor',\n",
       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-virginica',\n",
       "       'Iris-virginica', 'Iris-setosa', 'Iris-versicolor', 'Iris-setosa',\n",
       "       'Iris-virginica', 'Iris-virginica', 'Iris-versicolor',\n",
       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
       "       'Iris-setosa', 'Iris-virginica', 'Iris-setosa', 'Iris-virginica',\n",
       "       'Iris-setosa'], dtype=object)"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run predictions on test data\n",
    "y_pred_dt = model_dt.predict(X_test2)\n",
    "y_pred_dt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3401bc2",
   "metadata": {},
   "source": [
    "### Evaluating the Decision Tree classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "1f1022ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9615384615384616"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_dt.score(X_test2, y_test2.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "e6a7cd69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9, 0, 0],\n",
       "       [0, 8, 0],\n",
       "       [0, 1, 8]], dtype=int64)"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confusion matrix for our test results\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "confusion_matrix(y_test2.values, y_pred_dt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1df43f75",
   "metadata": {},
   "source": [
    "# Classification using Random Forest classifier\n",
    "- Feature set (independent variables): 'sepallength', 'sepalwidth', 'petallength', 'petalwidth'\n",
    "- Target class (dependent variable): 'class'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "04545b26",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "9d120e8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initializing the model object\n",
    "model_rf = RandomForestClassifier(random_state = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "28999a17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-13 {color: black;}#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>RandomForestClassifier(random_state=10)</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" checked><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=10)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(random_state=10)"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the model on training data\n",
    "model_rf.fit(X_train2, y_train2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "8b6a9d2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Iris-setosa', 'Iris-setosa', 'Iris-versicolor', 'Iris-virginica',\n",
       "       'Iris-setosa', 'Iris-virginica', 'Iris-setosa', 'Iris-versicolor',\n",
       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-virginica',\n",
       "       'Iris-virginica', 'Iris-setosa', 'Iris-versicolor', 'Iris-setosa',\n",
       "       'Iris-virginica', 'Iris-virginica', 'Iris-versicolor',\n",
       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
       "       'Iris-setosa', 'Iris-virginica', 'Iris-setosa', 'Iris-virginica',\n",
       "       'Iris-setosa'], dtype=object)"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run predictions on test data\n",
    "y_pred_rf = model_rf.predict(X_test2)\n",
    "y_pred_rf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b574d5e",
   "metadata": {},
   "source": [
    "### Evaluating the Random Forest classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "242fc4e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9615384615384616"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_rf.score(X_test2, y_test2.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "1f754a07",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': True,\n",
       " 'ccp_alpha': 0.0,\n",
       " 'class_weight': None,\n",
       " 'criterion': 'gini',\n",
       " 'max_depth': None,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_leaf_nodes': None,\n",
       " 'max_samples': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'n_estimators': 100,\n",
       " 'n_jobs': None,\n",
       " 'oob_score': False,\n",
       " 'random_state': 10,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_rf.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "21495549",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9, 0, 0],\n",
       "       [0, 8, 0],\n",
       "       [0, 1, 8]], dtype=int64)"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test2.values, y_pred_rf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "307d384d",
   "metadata": {},
   "source": [
    "# Unsupervised ML"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f8695b5",
   "metadata": {},
   "source": [
    "## k-means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "cf11833b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "id": "ab5bdfc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initializing the model object\n",
    "model_kmeans = KMeans(n_clusters = 3, random_state=0, n_init='auto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "id": "80240d91",
   "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>sepallength</th>\n",
       "      <th>sepalwidth</th>\n",
       "      <th>petallength</th>\n",
       "      <th>petalwidth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>5.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>6.8</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepallength  sepalwidth  petallength  petalwidth\n",
       "143          6.8         3.2          5.9         2.3\n",
       "53           5.5         2.3          4.0         1.3\n",
       "89           5.5         2.5          4.0         1.3\n",
       "88           5.6         3.0          4.1         1.3\n",
       "124          6.7         3.3          5.7         2.1\n",
       "..           ...         ...          ...         ...\n",
       "78           6.0         2.9          4.5         1.5\n",
       "76           6.8         2.8          4.8         1.4\n",
       "139          6.9         3.1          5.4         2.1\n",
       "114          5.8         2.8          5.1         2.4\n",
       "148          6.2         3.4          5.4         2.3\n",
       "\n",
       "[102 rows x 4 columns]"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "id": "9f7657bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-26 {color: black;}#sk-container-id-26 pre{padding: 0;}#sk-container-id-26 div.sk-toggleable {background-color: white;}#sk-container-id-26 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-26 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-26 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-26 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-26 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-26 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-26 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-26 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-26 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-26 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-26 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-26 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-26 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-26 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-26 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-26 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-26 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-26 div.sk-item {position: relative;z-index: 1;}#sk-container-id-26 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-26 div.sk-item::before, #sk-container-id-26 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-26 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-26 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-26 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-26 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-26 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-26 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-26 div.sk-label-container {text-align: center;}#sk-container-id-26 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-26 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-26\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=3, n_init=&#x27;auto&#x27;, random_state=0)</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-26\" type=\"checkbox\" checked><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=3, n_init=&#x27;auto&#x27;, random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KMeans(n_clusters=3, n_init='auto', random_state=0)"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the model on training data\n",
    "model_kmeans.fit(X_train2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "id": "8d4b9087",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 2,\n",
       "       0, 1, 0, 0, 2, 0, 1, 2, 2, 1, 2, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2, 2,\n",
       "       1, 1, 0, 1, 0, 2, 0, 2, 2, 1, 2, 0, 0, 2, 0, 0, 2, 0, 2, 1, 0, 0,\n",
       "       2, 2, 1, 1, 0, 1, 2, 1, 2, 2, 1, 1, 0, 0, 2, 0, 2, 2, 0, 1, 0, 2,\n",
       "       0, 0, 2, 0, 0, 0, 2, 0, 1, 0, 0, 2, 0, 2])"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "id": "08ab372a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, 2, 1, 0, 1, 0, 0, 0, 2, 2, 1, 0, 1, 2, 2, 0, 0, 0, 0, 1,\n",
       "       2, 1, 2, 1])"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_kmeans.predict(X_test2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9f4449b",
   "metadata": {},
   "source": [
    "### Reverse engineering to validate the clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "id": "06a3ac0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 'Iris-versicolor', 1: 'Iris-setosa', 2: 'Iris-virginica'}"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a 0, 1, 2 mapping dictionary of the actual class labels\n",
    "y_test_dict = {0: \"Iris-versicolor\", 1: \"Iris-setosa\", 2: \"Iris-virginica\"}\n",
    "y_test_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "id": "1ae0e768",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Iris-setosa',\n",
       " 'Iris-setosa',\n",
       " 'Iris-versicolor',\n",
       " 'Iris-virginica',\n",
       " 'Iris-setosa',\n",
       " 'Iris-versicolor',\n",
       " 'Iris-setosa',\n",
       " 'Iris-versicolor',\n",
       " 'Iris-versicolor',\n",
       " 'Iris-versicolor',\n",
       " 'Iris-virginica',\n",
       " 'Iris-virginica',\n",
       " 'Iris-setosa',\n",
       " 'Iris-versicolor',\n",
       " 'Iris-setosa',\n",
       " 'Iris-virginica',\n",
       " 'Iris-virginica',\n",
       " 'Iris-versicolor',\n",
       " 'Iris-versicolor',\n",
       " 'Iris-versicolor',\n",
       " 'Iris-versicolor',\n",
       " 'Iris-setosa',\n",
       " 'Iris-virginica',\n",
       " 'Iris-setosa',\n",
       " 'Iris-virginica',\n",
       " 'Iris-setosa']"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List of predicted cluster lables mapped to the dictionary\n",
    "[y_test_dict[i] for i in model_kmeans.predict(X_test2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "id": "1a24a081",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17         Iris-setosa\n",
       "4          Iris-setosa\n",
       "61     Iris-versicolor\n",
       "107     Iris-virginica\n",
       "20         Iris-setosa\n",
       "123     Iris-virginica\n",
       "43         Iris-setosa\n",
       "65     Iris-versicolor\n",
       "93     Iris-versicolor\n",
       "106     Iris-virginica\n",
       "137     Iris-virginica\n",
       "116     Iris-virginica\n",
       "47         Iris-setosa\n",
       "57     Iris-versicolor\n",
       "42         Iris-setosa\n",
       "118     Iris-virginica\n",
       "132     Iris-virginica\n",
       "97     Iris-versicolor\n",
       "50     Iris-versicolor\n",
       "90     Iris-versicolor\n",
       "92     Iris-versicolor\n",
       "48         Iris-setosa\n",
       "104     Iris-virginica\n",
       "45         Iris-setosa\n",
       "125     Iris-virginica\n",
       "7          Iris-setosa\n",
       "Name: class, dtype: object"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Actual test labels\n",
    "y_test2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "id": "fd3b97e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17      True\n",
       "4       True\n",
       "61      True\n",
       "107     True\n",
       "20      True\n",
       "123    False\n",
       "43      True\n",
       "65      True\n",
       "93      True\n",
       "106    False\n",
       "137     True\n",
       "116     True\n",
       "47      True\n",
       "57      True\n",
       "42      True\n",
       "118     True\n",
       "132     True\n",
       "97      True\n",
       "50      True\n",
       "90      True\n",
       "92      True\n",
       "48      True\n",
       "104     True\n",
       "45      True\n",
       "125     True\n",
       "7       True\n",
       "Name: class, dtype: bool"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Comparing the mapped label names to the actual labels\n",
    "[y_test_dict[i] for i in model_kmeans.predict(X_test2)] == y_test2"
   ]
  }
 ],
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