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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e491bdfa",
+   "metadata": {},
+   "source": [
+    "## PCA based Dimensionality Reduction for Wine Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "930b8e3c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
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+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "        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>fixed acidity</th>\n",
+       "      <th>volatile acidity</th>\n",
+       "      <th>citric acid</th>\n",
+       "      <th>residual sugar</th>\n",
+       "      <th>chlorides</th>\n",
+       "      <th>free sulfur dioxide</th>\n",
+       "      <th>total sulfur dioxide</th>\n",
+       "      <th>density</th>\n",
+       "      <th>pH</th>\n",
+       "      <th>sulphates</th>\n",
+       "      <th>alcohol</th>\n",
+       "      <th>quality</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7.4</td>\n",
+       "      <td>0.70</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>0.076</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>0.9978</td>\n",
+       "      <td>3.51</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>9.4</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>7.8</td>\n",
+       "      <td>0.88</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>2.6</td>\n",
+       "      <td>0.098</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>67.0</td>\n",
+       "      <td>0.9968</td>\n",
+       "      <td>3.20</td>\n",
+       "      <td>0.68</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7.8</td>\n",
+       "      <td>0.76</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>0.092</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>0.9970</td>\n",
+       "      <td>3.26</td>\n",
+       "      <td>0.65</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>11.2</td>\n",
+       "      <td>0.28</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>0.075</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>0.9980</td>\n",
+       "      <td>3.16</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>7.4</td>\n",
+       "      <td>0.70</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>0.076</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>0.9978</td>\n",
+       "      <td>3.51</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>9.4</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
+       "0            7.4              0.70         0.00             1.9      0.076   \n",
+       "1            7.8              0.88         0.00             2.6      0.098   \n",
+       "2            7.8              0.76         0.04             2.3      0.092   \n",
+       "3           11.2              0.28         0.56             1.9      0.075   \n",
+       "4            7.4              0.70         0.00             1.9      0.076   \n",
+       "\n",
+       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
+       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
+       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
+       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
+       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
+       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
+       "\n",
+       "   alcohol  quality  \n",
+       "0      9.4        5  \n",
+       "1      9.8        5  \n",
+       "2      9.8        5  \n",
+       "3      9.8        6  \n",
+       "4      9.4        5  "
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn.decomposition import PCA\n",
+    "df = pd.read_csv(r'winequality-red.csv')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "939925c4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = df.pop('quality')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "7581d349",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>fixed acidity</th>\n",
+       "      <th>volatile acidity</th>\n",
+       "      <th>citric acid</th>\n",
+       "      <th>residual sugar</th>\n",
+       "      <th>chlorides</th>\n",
+       "      <th>free sulfur dioxide</th>\n",
+       "      <th>total sulfur dioxide</th>\n",
+       "      <th>density</th>\n",
+       "      <th>pH</th>\n",
+       "      <th>sulphates</th>\n",
+       "      <th>alcohol</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7.4</td>\n",
+       "      <td>0.70</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>0.076</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>0.9978</td>\n",
+       "      <td>3.51</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>9.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>7.8</td>\n",
+       "      <td>0.88</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>2.6</td>\n",
+       "      <td>0.098</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>67.0</td>\n",
+       "      <td>0.9968</td>\n",
+       "      <td>3.20</td>\n",
+       "      <td>0.68</td>\n",
+       "      <td>9.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7.8</td>\n",
+       "      <td>0.76</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>0.092</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>0.9970</td>\n",
+       "      <td>3.26</td>\n",
+       "      <td>0.65</td>\n",
+       "      <td>9.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>11.2</td>\n",
+       "      <td>0.28</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>0.075</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>0.9980</td>\n",
+       "      <td>3.16</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>9.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>7.4</td>\n",
+       "      <td>0.70</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>0.076</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>0.9978</td>\n",
+       "      <td>3.51</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>9.4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
+       "0            7.4              0.70         0.00             1.9      0.076   \n",
+       "1            7.8              0.88         0.00             2.6      0.098   \n",
+       "2            7.8              0.76         0.04             2.3      0.092   \n",
+       "3           11.2              0.28         0.56             1.9      0.075   \n",
+       "4            7.4              0.70         0.00             1.9      0.076   \n",
+       "\n",
+       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
+       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
+       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
+       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
+       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
+       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
+       "\n",
+       "   alcohol  \n",
+       "0      9.4  \n",
+       "1      9.8  \n",
+       "2      9.8  \n",
+       "3      9.8  \n",
+       "4      9.4  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f09c3df9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\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>fixed acidity</th>\n",
+       "      <th>volatile acidity</th>\n",
+       "      <th>citric acid</th>\n",
+       "      <th>residual sugar</th>\n",
+       "      <th>chlorides</th>\n",
+       "      <th>free sulfur dioxide</th>\n",
+       "      <th>total sulfur dioxide</th>\n",
+       "      <th>density</th>\n",
+       "      <th>pH</th>\n",
+       "      <th>sulphates</th>\n",
+       "      <th>alcohol</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-0.528360</td>\n",
+       "      <td>0.961877</td>\n",
+       "      <td>-1.391472</td>\n",
+       "      <td>-0.453218</td>\n",
+       "      <td>-0.243707</td>\n",
+       "      <td>-0.466193</td>\n",
+       "      <td>-0.379133</td>\n",
+       "      <td>0.558274</td>\n",
+       "      <td>1.288643</td>\n",
+       "      <td>-0.579207</td>\n",
+       "      <td>-0.960246</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-0.298547</td>\n",
+       "      <td>1.967442</td>\n",
+       "      <td>-1.391472</td>\n",
+       "      <td>0.043416</td>\n",
+       "      <td>0.223875</td>\n",
+       "      <td>0.872638</td>\n",
+       "      <td>0.624363</td>\n",
+       "      <td>0.028261</td>\n",
+       "      <td>-0.719933</td>\n",
+       "      <td>0.128950</td>\n",
+       "      <td>-0.584777</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.298547</td>\n",
+       "      <td>1.297065</td>\n",
+       "      <td>-1.186070</td>\n",
+       "      <td>-0.169427</td>\n",
+       "      <td>0.096353</td>\n",
+       "      <td>-0.083669</td>\n",
+       "      <td>0.229047</td>\n",
+       "      <td>0.134264</td>\n",
+       "      <td>-0.331177</td>\n",
+       "      <td>-0.048089</td>\n",
+       "      <td>-0.584777</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.654856</td>\n",
+       "      <td>-1.384443</td>\n",
+       "      <td>1.484154</td>\n",
+       "      <td>-0.453218</td>\n",
+       "      <td>-0.264960</td>\n",
+       "      <td>0.107592</td>\n",
+       "      <td>0.411500</td>\n",
+       "      <td>0.664277</td>\n",
+       "      <td>-0.979104</td>\n",
+       "      <td>-0.461180</td>\n",
+       "      <td>-0.584777</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-0.528360</td>\n",
+       "      <td>0.961877</td>\n",
+       "      <td>-1.391472</td>\n",
+       "      <td>-0.453218</td>\n",
+       "      <td>-0.243707</td>\n",
+       "      <td>-0.466193</td>\n",
+       "      <td>-0.379133</td>\n",
+       "      <td>0.558274</td>\n",
+       "      <td>1.288643</td>\n",
+       "      <td>-0.579207</td>\n",
+       "      <td>-0.960246</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1594</th>\n",
+       "      <td>-1.217796</td>\n",
+       "      <td>0.403229</td>\n",
+       "      <td>-0.980669</td>\n",
+       "      <td>-0.382271</td>\n",
+       "      <td>0.053845</td>\n",
+       "      <td>1.542054</td>\n",
+       "      <td>-0.075043</td>\n",
+       "      <td>-0.978765</td>\n",
+       "      <td>0.899886</td>\n",
+       "      <td>-0.461180</td>\n",
+       "      <td>0.072294</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1595</th>\n",
+       "      <td>-1.390155</td>\n",
+       "      <td>0.123905</td>\n",
+       "      <td>-0.877968</td>\n",
+       "      <td>-0.240375</td>\n",
+       "      <td>-0.541259</td>\n",
+       "      <td>2.211469</td>\n",
+       "      <td>0.137820</td>\n",
+       "      <td>-0.862162</td>\n",
+       "      <td>1.353436</td>\n",
+       "      <td>0.601055</td>\n",
+       "      <td>0.729364</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1596</th>\n",
+       "      <td>-1.160343</td>\n",
+       "      <td>-0.099554</td>\n",
+       "      <td>-0.723916</td>\n",
+       "      <td>-0.169427</td>\n",
+       "      <td>-0.243707</td>\n",
+       "      <td>1.255161</td>\n",
+       "      <td>-0.196679</td>\n",
+       "      <td>-0.533554</td>\n",
+       "      <td>0.705508</td>\n",
+       "      <td>0.542042</td>\n",
+       "      <td>0.541630</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1597</th>\n",
+       "      <td>-1.390155</td>\n",
+       "      <td>0.654620</td>\n",
+       "      <td>-0.775267</td>\n",
+       "      <td>-0.382271</td>\n",
+       "      <td>-0.264960</td>\n",
+       "      <td>1.542054</td>\n",
+       "      <td>-0.075043</td>\n",
+       "      <td>-0.676657</td>\n",
+       "      <td>1.677400</td>\n",
+       "      <td>0.305990</td>\n",
+       "      <td>-0.209308</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1598</th>\n",
+       "      <td>-1.332702</td>\n",
+       "      <td>-1.216849</td>\n",
+       "      <td>1.021999</td>\n",
+       "      <td>0.752894</td>\n",
+       "      <td>-0.434990</td>\n",
+       "      <td>0.203223</td>\n",
+       "      <td>-0.135861</td>\n",
+       "      <td>-0.666057</td>\n",
+       "      <td>0.511130</td>\n",
+       "      <td>0.010924</td>\n",
+       "      <td>0.541630</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1599 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
+       "0         -0.528360          0.961877    -1.391472       -0.453218  -0.243707   \n",
+       "1         -0.298547          1.967442    -1.391472        0.043416   0.223875   \n",
+       "2         -0.298547          1.297065    -1.186070       -0.169427   0.096353   \n",
+       "3          1.654856         -1.384443     1.484154       -0.453218  -0.264960   \n",
+       "4         -0.528360          0.961877    -1.391472       -0.453218  -0.243707   \n",
+       "...             ...               ...          ...             ...        ...   \n",
+       "1594      -1.217796          0.403229    -0.980669       -0.382271   0.053845   \n",
+       "1595      -1.390155          0.123905    -0.877968       -0.240375  -0.541259   \n",
+       "1596      -1.160343         -0.099554    -0.723916       -0.169427  -0.243707   \n",
+       "1597      -1.390155          0.654620    -0.775267       -0.382271  -0.264960   \n",
+       "1598      -1.332702         -1.216849     1.021999        0.752894  -0.434990   \n",
+       "\n",
+       "      free sulfur dioxide  total sulfur dioxide   density        pH  \\\n",
+       "0               -0.466193             -0.379133  0.558274  1.288643   \n",
+       "1                0.872638              0.624363  0.028261 -0.719933   \n",
+       "2               -0.083669              0.229047  0.134264 -0.331177   \n",
+       "3                0.107592              0.411500  0.664277 -0.979104   \n",
+       "4               -0.466193             -0.379133  0.558274  1.288643   \n",
+       "...                   ...                   ...       ...       ...   \n",
+       "1594             1.542054             -0.075043 -0.978765  0.899886   \n",
+       "1595             2.211469              0.137820 -0.862162  1.353436   \n",
+       "1596             1.255161             -0.196679 -0.533554  0.705508   \n",
+       "1597             1.542054             -0.075043 -0.676657  1.677400   \n",
+       "1598             0.203223             -0.135861 -0.666057  0.511130   \n",
+       "\n",
+       "      sulphates   alcohol  \n",
+       "0     -0.579207 -0.960246  \n",
+       "1      0.128950 -0.584777  \n",
+       "2     -0.048089 -0.584777  \n",
+       "3     -0.461180 -0.584777  \n",
+       "4     -0.579207 -0.960246  \n",
+       "...         ...       ...  \n",
+       "1594  -0.461180  0.072294  \n",
+       "1595   0.601055  0.729364  \n",
+       "1596   0.542042  0.541630  \n",
+       "1597   0.305990 -0.209308  \n",
+       "1598   0.010924  0.541630  \n",
+       "\n",
+       "[1599 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "scalar = StandardScaler()\n",
+    "df_scaled = pd.DataFrame(scalar.fit_transform(df), columns=df.columns)\n",
+    "df_scaled"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "86490ec6",
+   "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>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "      <th>9</th>\n",
+       "      <th>10</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-1.619530</td>\n",
+       "      <td>0.450950</td>\n",
+       "      <td>-1.774454</td>\n",
+       "      <td>0.043740</td>\n",
+       "      <td>0.067014</td>\n",
+       "      <td>-0.913921</td>\n",
+       "      <td>-0.161043</td>\n",
+       "      <td>-0.282258</td>\n",
+       "      <td>0.005098</td>\n",
+       "      <td>-0.267759</td>\n",
+       "      <td>0.048630</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-0.799170</td>\n",
+       "      <td>1.856553</td>\n",
+       "      <td>-0.911690</td>\n",
+       "      <td>0.548066</td>\n",
+       "      <td>-0.018392</td>\n",
+       "      <td>0.929714</td>\n",
+       "      <td>-1.009829</td>\n",
+       "      <td>0.762587</td>\n",
+       "      <td>-0.520707</td>\n",
+       "      <td>0.062833</td>\n",
+       "      <td>-0.138142</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.748479</td>\n",
+       "      <td>0.882039</td>\n",
+       "      <td>-1.171394</td>\n",
+       "      <td>0.411021</td>\n",
+       "      <td>-0.043531</td>\n",
+       "      <td>0.401473</td>\n",
+       "      <td>-0.539553</td>\n",
+       "      <td>0.597946</td>\n",
+       "      <td>-0.086857</td>\n",
+       "      <td>-0.187442</td>\n",
+       "      <td>-0.118229</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2.357673</td>\n",
+       "      <td>-0.269976</td>\n",
+       "      <td>0.243489</td>\n",
+       "      <td>-0.928450</td>\n",
+       "      <td>-1.499149</td>\n",
+       "      <td>-0.131017</td>\n",
+       "      <td>0.344290</td>\n",
+       "      <td>-0.455375</td>\n",
+       "      <td>0.091577</td>\n",
+       "      <td>-0.130393</td>\n",
+       "      <td>0.316714</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-1.619530</td>\n",
+       "      <td>0.450950</td>\n",
+       "      <td>-1.774454</td>\n",
+       "      <td>0.043740</td>\n",
+       "      <td>0.067014</td>\n",
+       "      <td>-0.913921</td>\n",
+       "      <td>-0.161043</td>\n",
+       "      <td>-0.282258</td>\n",
+       "      <td>0.005098</td>\n",
+       "      <td>-0.267759</td>\n",
+       "      <td>0.048630</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1594</th>\n",
+       "      <td>-2.150500</td>\n",
+       "      <td>0.814286</td>\n",
+       "      <td>0.617063</td>\n",
+       "      <td>0.407687</td>\n",
+       "      <td>-0.240936</td>\n",
+       "      <td>0.054835</td>\n",
+       "      <td>0.170812</td>\n",
+       "      <td>-0.355866</td>\n",
+       "      <td>-0.971524</td>\n",
+       "      <td>0.356851</td>\n",
+       "      <td>-0.053382</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1595</th>\n",
+       "      <td>-2.214496</td>\n",
+       "      <td>0.893101</td>\n",
+       "      <td>1.807402</td>\n",
+       "      <td>0.414003</td>\n",
+       "      <td>0.119592</td>\n",
+       "      <td>-0.674711</td>\n",
+       "      <td>-0.607970</td>\n",
+       "      <td>-0.247640</td>\n",
+       "      <td>-1.058135</td>\n",
+       "      <td>0.478879</td>\n",
+       "      <td>-0.241258</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1596</th>\n",
+       "      <td>-1.456129</td>\n",
+       "      <td>0.311746</td>\n",
+       "      <td>1.124239</td>\n",
+       "      <td>0.491877</td>\n",
+       "      <td>0.193716</td>\n",
+       "      <td>-0.506410</td>\n",
+       "      <td>-0.231082</td>\n",
+       "      <td>0.079382</td>\n",
+       "      <td>-0.808773</td>\n",
+       "      <td>0.242248</td>\n",
+       "      <td>-0.402910</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1597</th>\n",
+       "      <td>-2.270518</td>\n",
+       "      <td>0.979791</td>\n",
+       "      <td>0.627965</td>\n",
+       "      <td>0.639770</td>\n",
+       "      <td>0.067735</td>\n",
+       "      <td>-0.860408</td>\n",
+       "      <td>-0.321487</td>\n",
+       "      <td>-0.468876</td>\n",
+       "      <td>-0.612248</td>\n",
+       "      <td>0.779404</td>\n",
+       "      <td>0.040923</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1598</th>\n",
+       "      <td>-0.426975</td>\n",
+       "      <td>-0.536690</td>\n",
+       "      <td>1.628955</td>\n",
+       "      <td>-0.391716</td>\n",
+       "      <td>0.450482</td>\n",
+       "      <td>-0.496154</td>\n",
+       "      <td>1.189132</td>\n",
+       "      <td>0.042176</td>\n",
+       "      <td>0.404309</td>\n",
+       "      <td>0.779440</td>\n",
+       "      <td>-0.449781</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1599 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            0         1         2         3         4         5         6   \\\n",
+       "0    -1.619530  0.450950 -1.774454  0.043740  0.067014 -0.913921 -0.161043   \n",
+       "1    -0.799170  1.856553 -0.911690  0.548066 -0.018392  0.929714 -1.009829   \n",
+       "2    -0.748479  0.882039 -1.171394  0.411021 -0.043531  0.401473 -0.539553   \n",
+       "3     2.357673 -0.269976  0.243489 -0.928450 -1.499149 -0.131017  0.344290   \n",
+       "4    -1.619530  0.450950 -1.774454  0.043740  0.067014 -0.913921 -0.161043   \n",
+       "...        ...       ...       ...       ...       ...       ...       ...   \n",
+       "1594 -2.150500  0.814286  0.617063  0.407687 -0.240936  0.054835  0.170812   \n",
+       "1595 -2.214496  0.893101  1.807402  0.414003  0.119592 -0.674711 -0.607970   \n",
+       "1596 -1.456129  0.311746  1.124239  0.491877  0.193716 -0.506410 -0.231082   \n",
+       "1597 -2.270518  0.979791  0.627965  0.639770  0.067735 -0.860408 -0.321487   \n",
+       "1598 -0.426975 -0.536690  1.628955 -0.391716  0.450482 -0.496154  1.189132   \n",
+       "\n",
+       "            7         8         9         10  \n",
+       "0    -0.282258  0.005098 -0.267759  0.048630  \n",
+       "1     0.762587 -0.520707  0.062833 -0.138142  \n",
+       "2     0.597946 -0.086857 -0.187442 -0.118229  \n",
+       "3    -0.455375  0.091577 -0.130393  0.316714  \n",
+       "4    -0.282258  0.005098 -0.267759  0.048630  \n",
+       "...        ...       ...       ...       ...  \n",
+       "1594 -0.355866 -0.971524  0.356851 -0.053382  \n",
+       "1595 -0.247640 -1.058135  0.478879 -0.241258  \n",
+       "1596  0.079382 -0.808773  0.242248 -0.402910  \n",
+       "1597 -0.468876 -0.612248  0.779404  0.040923  \n",
+       "1598  0.042176  0.404309  0.779440 -0.449781  \n",
+       "\n",
+       "[1599 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pca = PCA()\n",
+    "df_pca = pd.DataFrame(pca.fit_transform(df_scaled))\n",
+    "df_pca"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "cfe6c451",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "pd.DataFrame(pca.explained_variance_ratio_).plot.bar()\n",
+    "plt.legend('')\n",
+    "plt.xlabel('Principal Components')\n",
+    "plt.ylabel('Explained Variance');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "aa67bc3b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_pca.hist()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ed6b994b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sbn\n",
+    "ax = sbn.lineplot(data=df_pca, palette=\"viridis\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Lab2/PCA-Wine-quality-classification.ipynb b/Lab2/PCA-Wine-quality-classification.ipynb
new file mode 100644
index 0000000..c6d5d2e
--- /dev/null
+++ b/Lab2/PCA-Wine-quality-classification.ipynb
@@ -0,0 +1,1171 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "74a1fd69",
+   "metadata": {},
+   "source": [
+    "## PCA - Wine Quality Classification"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "54e3b45b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import plotly.express as px"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "07298dd8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split # This function is used to split your dataset into a training set and a test set\n",
+    "from sklearn.ensemble import RandomForestClassifier # This is a machine learning model that uses an ensemble of decision trees to make predictions\n",
+    "from sklearn.neighbors import KNeighborsClassifier # This is another machine learning model that makes predictions based on the 'k' nearest neighbors to a given data point\n",
+    "from sklearn.metrics import accuracy_score # This function is used to calculate the accuracy of your model's predictions. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "c80110a0",
+   "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>fixed acidity</th>\n",
+       "      <th>volatile acidity</th>\n",
+       "      <th>citric acid</th>\n",
+       "      <th>residual sugar</th>\n",
+       "      <th>chlorides</th>\n",
+       "      <th>free sulfur dioxide</th>\n",
+       "      <th>total sulfur dioxide</th>\n",
+       "      <th>density</th>\n",
+       "      <th>pH</th>\n",
+       "      <th>sulphates</th>\n",
+       "      <th>alcohol</th>\n",
+       "      <th>quality</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7.4</td>\n",
+       "      <td>0.70</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>0.076</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>0.9978</td>\n",
+       "      <td>3.51</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>9.4</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>7.8</td>\n",
+       "      <td>0.88</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>2.6</td>\n",
+       "      <td>0.098</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>67.0</td>\n",
+       "      <td>0.9968</td>\n",
+       "      <td>3.20</td>\n",
+       "      <td>0.68</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7.8</td>\n",
+       "      <td>0.76</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>0.092</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>0.9970</td>\n",
+       "      <td>3.26</td>\n",
+       "      <td>0.65</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>11.2</td>\n",
+       "      <td>0.28</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>0.075</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>0.9980</td>\n",
+       "      <td>3.16</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>7.4</td>\n",
+       "      <td>0.70</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>0.076</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>0.9978</td>\n",
+       "      <td>3.51</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>9.4</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>7.4</td>\n",
+       "      <td>0.66</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.8</td>\n",
+       "      <td>0.075</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>0.9978</td>\n",
+       "      <td>3.51</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>9.4</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7.9</td>\n",
+       "      <td>0.60</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>1.6</td>\n",
+       "      <td>0.069</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>59.0</td>\n",
+       "      <td>0.9964</td>\n",
+       "      <td>3.30</td>\n",
+       "      <td>0.46</td>\n",
+       "      <td>9.4</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>7.3</td>\n",
+       "      <td>0.65</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.2</td>\n",
+       "      <td>0.065</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>0.9946</td>\n",
+       "      <td>3.39</td>\n",
+       "      <td>0.47</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>7.8</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.073</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>0.9968</td>\n",
+       "      <td>3.36</td>\n",
+       "      <td>0.57</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>7.5</td>\n",
+       "      <td>0.50</td>\n",
+       "      <td>0.36</td>\n",
+       "      <td>6.1</td>\n",
+       "      <td>0.071</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>102.0</td>\n",
+       "      <td>0.9978</td>\n",
+       "      <td>3.35</td>\n",
+       "      <td>0.80</td>\n",
+       "      <td>10.5</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
+       "0            7.4              0.70         0.00             1.9      0.076   \n",
+       "1            7.8              0.88         0.00             2.6      0.098   \n",
+       "2            7.8              0.76         0.04             2.3      0.092   \n",
+       "3           11.2              0.28         0.56             1.9      0.075   \n",
+       "4            7.4              0.70         0.00             1.9      0.076   \n",
+       "5            7.4              0.66         0.00             1.8      0.075   \n",
+       "6            7.9              0.60         0.06             1.6      0.069   \n",
+       "7            7.3              0.65         0.00             1.2      0.065   \n",
+       "8            7.8              0.58         0.02             2.0      0.073   \n",
+       "9            7.5              0.50         0.36             6.1      0.071   \n",
+       "\n",
+       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
+       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
+       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
+       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
+       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
+       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
+       "5                 13.0                  40.0   0.9978  3.51       0.56   \n",
+       "6                 15.0                  59.0   0.9964  3.30       0.46   \n",
+       "7                 15.0                  21.0   0.9946  3.39       0.47   \n",
+       "8                  9.0                  18.0   0.9968  3.36       0.57   \n",
+       "9                 17.0                 102.0   0.9978  3.35       0.80   \n",
+       "\n",
+       "   alcohol  quality  \n",
+       "0      9.4        5  \n",
+       "1      9.8        5  \n",
+       "2      9.8        5  \n",
+       "3      9.8        6  \n",
+       "4      9.4        5  \n",
+       "5      9.4        5  \n",
+       "6      9.4        5  \n",
+       "7     10.0        7  \n",
+       "8      9.5        7  \n",
+       "9     10.5        5  "
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(r'winequality-red.csv') # Reading data\n",
+    "df.head(10) # Displaying data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "4015310c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1599, 12)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape # Viewing shape of data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "877403fc",
+   "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>fixed acidity</th>\n",
+       "      <th>volatile acidity</th>\n",
+       "      <th>citric acid</th>\n",
+       "      <th>residual sugar</th>\n",
+       "      <th>chlorides</th>\n",
+       "      <th>free sulfur dioxide</th>\n",
+       "      <th>total sulfur dioxide</th>\n",
+       "      <th>density</th>\n",
+       "      <th>pH</th>\n",
+       "      <th>sulphates</th>\n",
+       "      <th>alcohol</th>\n",
+       "      <th>quality</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>1599.000000</td>\n",
+       "      <td>1599.000000</td>\n",
+       "      <td>1599.000000</td>\n",
+       "      <td>1599.000000</td>\n",
+       "      <td>1599.000000</td>\n",
+       "      <td>1599.000000</td>\n",
+       "      <td>1599.000000</td>\n",
+       "      <td>1599.000000</td>\n",
+       "      <td>1599.000000</td>\n",
+       "      <td>1599.000000</td>\n",
+       "      <td>1599.000000</td>\n",
+       "      <td>1599.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>8.319637</td>\n",
+       "      <td>0.527821</td>\n",
+       "      <td>0.270976</td>\n",
+       "      <td>2.538806</td>\n",
+       "      <td>0.087467</td>\n",
+       "      <td>15.874922</td>\n",
+       "      <td>46.467792</td>\n",
+       "      <td>0.996747</td>\n",
+       "      <td>3.311113</td>\n",
+       "      <td>0.658149</td>\n",
+       "      <td>10.422983</td>\n",
+       "      <td>5.636023</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>1.741096</td>\n",
+       "      <td>0.179060</td>\n",
+       "      <td>0.194801</td>\n",
+       "      <td>1.409928</td>\n",
+       "      <td>0.047065</td>\n",
+       "      <td>10.460157</td>\n",
+       "      <td>32.895324</td>\n",
+       "      <td>0.001887</td>\n",
+       "      <td>0.154386</td>\n",
+       "      <td>0.169507</td>\n",
+       "      <td>1.065668</td>\n",
+       "      <td>0.807569</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>4.600000</td>\n",
+       "      <td>0.120000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.900000</td>\n",
+       "      <td>0.012000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>0.990070</td>\n",
+       "      <td>2.740000</td>\n",
+       "      <td>0.330000</td>\n",
+       "      <td>8.400000</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>7.100000</td>\n",
+       "      <td>0.390000</td>\n",
+       "      <td>0.090000</td>\n",
+       "      <td>1.900000</td>\n",
+       "      <td>0.070000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>22.000000</td>\n",
+       "      <td>0.995600</td>\n",
+       "      <td>3.210000</td>\n",
+       "      <td>0.550000</td>\n",
+       "      <td>9.500000</td>\n",
+       "      <td>5.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>7.900000</td>\n",
+       "      <td>0.520000</td>\n",
+       "      <td>0.260000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>0.079000</td>\n",
+       "      <td>14.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>0.996750</td>\n",
+       "      <td>3.310000</td>\n",
+       "      <td>0.620000</td>\n",
+       "      <td>10.200000</td>\n",
+       "      <td>6.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>9.200000</td>\n",
+       "      <td>0.640000</td>\n",
+       "      <td>0.420000</td>\n",
+       "      <td>2.600000</td>\n",
+       "      <td>0.090000</td>\n",
+       "      <td>21.000000</td>\n",
+       "      <td>62.000000</td>\n",
+       "      <td>0.997835</td>\n",
+       "      <td>3.400000</td>\n",
+       "      <td>0.730000</td>\n",
+       "      <td>11.100000</td>\n",
+       "      <td>6.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>15.900000</td>\n",
+       "      <td>1.580000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>15.500000</td>\n",
+       "      <td>0.611000</td>\n",
+       "      <td>72.000000</td>\n",
+       "      <td>289.000000</td>\n",
+       "      <td>1.003690</td>\n",
+       "      <td>4.010000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>14.900000</td>\n",
+       "      <td>8.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
+       "count    1599.000000       1599.000000  1599.000000     1599.000000   \n",
+       "mean        8.319637          0.527821     0.270976        2.538806   \n",
+       "std         1.741096          0.179060     0.194801        1.409928   \n",
+       "min         4.600000          0.120000     0.000000        0.900000   \n",
+       "25%         7.100000          0.390000     0.090000        1.900000   \n",
+       "50%         7.900000          0.520000     0.260000        2.200000   \n",
+       "75%         9.200000          0.640000     0.420000        2.600000   \n",
+       "max        15.900000          1.580000     1.000000       15.500000   \n",
+       "\n",
+       "         chlorides  free sulfur dioxide  total sulfur dioxide      density  \\\n",
+       "count  1599.000000          1599.000000           1599.000000  1599.000000   \n",
+       "mean      0.087467            15.874922             46.467792     0.996747   \n",
+       "std       0.047065            10.460157             32.895324     0.001887   \n",
+       "min       0.012000             1.000000              6.000000     0.990070   \n",
+       "25%       0.070000             7.000000             22.000000     0.995600   \n",
+       "50%       0.079000            14.000000             38.000000     0.996750   \n",
+       "75%       0.090000            21.000000             62.000000     0.997835   \n",
+       "max       0.611000            72.000000            289.000000     1.003690   \n",
+       "\n",
+       "                pH    sulphates      alcohol      quality  \n",
+       "count  1599.000000  1599.000000  1599.000000  1599.000000  \n",
+       "mean      3.311113     0.658149    10.422983     5.636023  \n",
+       "std       0.154386     0.169507     1.065668     0.807569  \n",
+       "min       2.740000     0.330000     8.400000     3.000000  \n",
+       "25%       3.210000     0.550000     9.500000     5.000000  \n",
+       "50%       3.310000     0.620000    10.200000     6.000000  \n",
+       "75%       3.400000     0.730000    11.100000     6.000000  \n",
+       "max       4.010000     2.000000    14.900000     8.000000  "
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe() # Viewing statistical summary of all columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "60bb8fc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 1599 entries, 0 to 1598\n",
+      "Data columns (total 12 columns):\n",
+      " #   Column                Non-Null Count  Dtype  \n",
+      "---  ------                --------------  -----  \n",
+      " 0   fixed acidity         1599 non-null   float64\n",
+      " 1   volatile acidity      1599 non-null   float64\n",
+      " 2   citric acid           1599 non-null   float64\n",
+      " 3   residual sugar        1599 non-null   float64\n",
+      " 4   chlorides             1599 non-null   float64\n",
+      " 5   free sulfur dioxide   1599 non-null   float64\n",
+      " 6   total sulfur dioxide  1599 non-null   float64\n",
+      " 7   density               1599 non-null   float64\n",
+      " 8   pH                    1599 non-null   float64\n",
+      " 9   sulphates             1599 non-null   float64\n",
+      " 10  alcohol               1599 non-null   float64\n",
+      " 11  quality               1599 non-null   int64  \n",
+      "dtypes: float64(11), int64(1)\n",
+      "memory usage: 150.0 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info() # Viewing concise summary of DataFrame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "0dbcfbb6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "fixed acidity           0\n",
+       "volatile acidity        0\n",
+       "citric acid             0\n",
+       "residual sugar          0\n",
+       "chlorides               0\n",
+       "free sulfur dioxide     0\n",
+       "total sulfur dioxide    0\n",
+       "density                 0\n",
+       "pH                      0\n",
+       "sulphates               0\n",
+       "alcohol                 0\n",
+       "quality                 0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum() # Finding the number of missing or null values in each column of the DataFrame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "e3f20450",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x7f249973fcd0>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 511.111x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.catplot(x='quality', data=df, kind='count') # Plotting a count plot (x-axis, dataset, kind [type])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "997cf21f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='quality', ylabel='volatile acidity'>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot = plt.figure(figsize=(5,5)) # Creates a new figure with a specified size of 5x5 inches\n",
+    "sns.barplot(x='quality', y='volatile acidity', data=df) # Plots a bar plot for quality and volatile acidity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "ea02395b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='quality', ylabel='citric acid'>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot = plt.figure(figsize=(5,5)) # Creates a new figure with a specified size of 5x5 inches\n",
+    "sns.barplot(x='quality', y='citric acid', data=df) # Plots a bar plot for quality and citric acid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "f56c6048",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "correlation = df.corr() # Finding correlation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "5b34ae0d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,10))\n",
+    "sns.heatmap(correlation, cbar=True, annot=True, cmap='Reds') # Plotting a heatmap of the correlation DataFrame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "46ffdb1c",
+   "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>fixed acidity</th>\n",
+       "      <th>volatile acidity</th>\n",
+       "      <th>citric acid</th>\n",
+       "      <th>residual sugar</th>\n",
+       "      <th>chlorides</th>\n",
+       "      <th>free sulfur dioxide</th>\n",
+       "      <th>total sulfur dioxide</th>\n",
+       "      <th>density</th>\n",
+       "      <th>pH</th>\n",
+       "      <th>sulphates</th>\n",
+       "      <th>alcohol</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7.4</td>\n",
+       "      <td>0.70</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>0.076</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>0.9978</td>\n",
+       "      <td>3.51</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>9.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>7.8</td>\n",
+       "      <td>0.88</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>2.6</td>\n",
+       "      <td>0.098</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>67.0</td>\n",
+       "      <td>0.9968</td>\n",
+       "      <td>3.20</td>\n",
+       "      <td>0.68</td>\n",
+       "      <td>9.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7.8</td>\n",
+       "      <td>0.76</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>0.092</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>0.9970</td>\n",
+       "      <td>3.26</td>\n",
+       "      <td>0.65</td>\n",
+       "      <td>9.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>11.2</td>\n",
+       "      <td>0.28</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>0.075</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>0.9980</td>\n",
+       "      <td>3.16</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>9.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>7.4</td>\n",
+       "      <td>0.70</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>0.076</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>0.9978</td>\n",
+       "      <td>3.51</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>9.4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
+       "0            7.4              0.70         0.00             1.9      0.076   \n",
+       "1            7.8              0.88         0.00             2.6      0.098   \n",
+       "2            7.8              0.76         0.04             2.3      0.092   \n",
+       "3           11.2              0.28         0.56             1.9      0.075   \n",
+       "4            7.4              0.70         0.00             1.9      0.076   \n",
+       "\n",
+       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
+       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
+       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
+       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
+       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
+       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
+       "\n",
+       "   alcohol  \n",
+       "0      9.4  \n",
+       "1      9.8  \n",
+       "2      9.8  \n",
+       "3      9.8  \n",
+       "4      9.4  "
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = df.drop('quality', axis=1) # Dropping the quality column\n",
+    "x.head() # Viewing the DataFrame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "f91f368a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       0\n",
+       "1       0\n",
+       "2       0\n",
+       "3       0\n",
+       "4       0\n",
+       "       ..\n",
+       "1594    0\n",
+       "1595    0\n",
+       "1596    0\n",
+       "1597    0\n",
+       "1598    0\n",
+       "Name: quality, Length: 1599, dtype: int64"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y = df['quality'].apply(lambda y_value: 1 if y_value>=7 else 0)\n",
+    "# This applies the lambda function to each value in the 'quality' column.\n",
+    "# The lambda function takes a value y_value and returns 1 if y_value is\n",
+    "# greater than or equal to 7, and 0 otherwise.\n",
+    "\n",
+    "y # Display the result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "8c08611f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x,y, test_size=0.2, random_state=0) # Splits the data into training set and test set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "b2570d16",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1599,) (1279,) (320,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(y.shape, y_train.shape, y_test.shape) # Prints the splitted data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "90d5ebb0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = RandomForestClassifier()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "96b8a0a0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "RandomForestClassifier()"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.fit(x_train, y_train) # Training the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "66f1e3c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy =  92.8125\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_test_prediction = model.predict(x_test) # Making predictions on test data\n",
+    "test_data_accuracy = accuracy_score(x_test_prediction, y_test) # Calculating accuracy of predictions\n",
+    "\n",
+    "accuracy_rf = test_data_accuracy*100 # Converting the accuracy from a proportion (ranging from 0 to 1) \n",
+    "# to a percentage (ranging from 0 to 100)\n",
+    "\n",
+    "print('Accuracy = ', accuracy_rf) # Displays the accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "6948a736",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x,y, test_size=0.2, random_state=0)\n",
+    "\n",
+    "# train_test_split function from the sklearn.model_selection module is used to split the dataset into a training set and a test set.\n",
+    "\n",
+    "# x,y: These are the features and labels of the dataset. \n",
+    "# x is usually a DataFrame or 2D array-like object containing your features\n",
+    "# y is usually a Series or 1D array-like object containing your labels.\n",
+    "\n",
+    "# test_size=0.2: This means that 20% of the data will be used for the test set, and the remaining 80% will be used for the training set.\n",
+    "\n",
+    "# random_state=0: This sets the seed for the random number generator that shuffles the data before splitting it.\n",
+    "# Setting the seed to a fixed number (like 0) ensures that the output is reproducible,\n",
+    "# i.e., you'll get the same train/test split each time you run this code.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "6fe4bb20",
+   "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>KNeighborsClassifier(n_neighbors=30)</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\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=30)</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "KNeighborsClassifier(n_neighbors=30)"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "modell = KNeighborsClassifier(n_neighbors = 30) # Setting up a K-nearest neighbors classifier with 30 neighbors\n",
+    "modell.fit(x_train, y_train) # Training the model based on our data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "4d4ab37c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prediction = modell.predict(x_test) # Making predictions on the test data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "5f4e87b6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy =  90.3125\n"
+     ]
+    }
+   ],
+   "source": [
+    "accuracy2 = accuracy_score(y_test,prediction) # Calculate the accuracy of predictions\n",
+    "# 'y_test' is true labels and 'predictions' is the predicted labels\n",
+    "\n",
+    "accuracy_knn = accuracy2*100 # Converting the accuracy from a proportion (ranging from 0 to 1) to a percentage (ranging from 0 to 100)\n",
+    "print(\"Accuracy = \",accuracy_knn) # Displaying the accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "54555d59",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'models': ['RandomForestClassifier', 'KNeighborsClassifier'],\n",
+       " 'score': [92.8125, 90.3125]}"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "models = {\n",
+    "    \"models\": ['RandomForestClassifier','KNeighborsClassifier'], # This key-value pair is storing the names of two machine learning models \n",
+    "    # as a list of strings.\n",
+    "\n",
+    "    \"score\": [accuracy_rf, accuracy_knn]  # This key-value pair is storing the accuracy scores of the two models as a list of numbers.\n",
+    "}\n",
+    "models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "5057bc66",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_24261/1333390122.py:5: FutureWarning: \n",
+      "\n",
+      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+      "\n",
+      "  ax = sns.barplot(x=models['models'], y=models['score'], palette=colors) # Creating bar plot\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "colors = ['green', 'blue'] # Setting color for bar graph\n",
+    "sns.set_style('whitegrid') # Setting the style fo plot ('whitegrid' contains gridlines for easy comparision)\n",
+    "\n",
+    "plt.figure(figsize=(10,5)) # Creating a new figure with a specified size (10 units wide and 5 units tall).\n",
+    "ax = sns.barplot(x=models['models'], y=models['score'], palette=colors) # Creating bar plot\n",
+    "\n",
+    "# Labeling axis and title\n",
+    "plt.xlabel(\"Models\")\n",
+    "plt.ylabel(\"Score\")\n",
+    "plt.title(\"Model Selection\")\n",
+    "\n",
+    "# This loop adds the height of each bar as text above the bar.\n",
+    "# This is done using the text method of the Axes object ax.\n",
+    "# The height of the bar is obtained using the get_height method of the Patch object p,\n",
+    "# and the x-coordinate for the text is calculated as the x-coordinate of the bar plus\n",
+    "# half its width (p.get_x() + p.get_width() / 2.).\n",
+    "\n",
+    "for p in ax.patches:\n",
+    "  ax.text(x=p.get_x() + p.get_width() / 2.,\n",
+    "          y=p.get_height(),\n",
+    "          s='{:.2f}'.format(p.get_height()),\n",
+    "          ha='center')\n",
+    "\n",
+    "plt.show(); # Displaying the plot"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Lab2/PCA-using-alg-without-sk.ipynb b/Lab2/PCA-using-alg-without-sk.ipynb
new file mode 100644
index 0000000..c39b0d2
--- /dev/null
+++ b/Lab2/PCA-using-alg-without-sk.ipynb
@@ -0,0 +1,368 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8f87a67e",
+   "metadata": {},
+   "source": [
+    "## PCA using algorithm steps without using sklearn functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "dc3c31a6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "1b69d0a2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Here we are using inbuilt dataset of scikit learn\n",
+    "from sklearn.datasets import load_breast_cancer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "30cb3586",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# instantiating\n",
+    "cancer = load_breast_cancer(as_frame=True)\n",
+    "# creating dataframe\n",
+    "df = cancer.frame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "dfe4feb2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original Dataframe shape : (569, 31)\n",
+      "Inputs Dataframe shape   : (569, 30)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Checking shape\n",
+    "print('Original Dataframe shape :', df.shape)\n",
+    " \n",
+    "# Input features\n",
+    "X = df[cancer['feature_names']]\n",
+    "print('Inputs Dataframe shape   :', X.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2199ed46",
+   "metadata": {},
+   "source": [
+    "### Calculating mean, standard deviation, and standardizing it"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "6a466c27",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Calculating mean\n",
+    "X_mean = X.mean()\n",
+    "\n",
+    "# Calculating Standard deviation\n",
+    "X_std = X.std()\n",
+    "\n",
+    "# Standardization\n",
+    "Z = (X - X_mean) / X_std"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c5ac0c57",
+   "metadata": {},
+   "source": [
+    "### Finding covariance and calculating covariance matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "fff1096f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Calculating Covariance\n",
+    "c = Z.cov()\n",
+    "\n",
+    "# Plotting the Covariance matrix\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "sns.heatmap(c)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "45894ce1",
+   "metadata": {},
+   "source": [
+    "### Finding Eigen values and Eigen vectors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "681ad5f2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Eigen values:\n",
+      " [1.32816077e+01 5.69135461e+00 2.81794898e+00 1.98064047e+00\n",
+      " 1.64873055e+00 1.20735661e+00 6.75220114e-01 4.76617140e-01\n",
+      " 4.16894812e-01 3.50693457e-01 2.93915696e-01 2.61161370e-01\n",
+      " 2.41357496e-01 1.57009724e-01 9.41349650e-02 7.98628010e-02\n",
+      " 5.93990378e-02 5.26187835e-02 4.94775918e-02 1.33044823e-04\n",
+      " 7.48803097e-04 1.58933787e-03 6.90046388e-03 8.17763986e-03\n",
+      " 1.54812714e-02 1.80550070e-02 2.43408378e-02 2.74394025e-02\n",
+      " 3.11594025e-02 2.99728939e-02]\n",
+      "Eigen values Shape: (30,)\n",
+      "Eigen Vector Shape: (30, 30)\n"
+     ]
+    }
+   ],
+   "source": [
+    "eigenvalues, eigenvectors = np.linalg.eig(c) # Calculating Eigen values and Eigen vectors from Covariance\n",
+    "\n",
+    "print('Eigen values:\\n', eigenvalues)\n",
+    "print('Eigen values Shape:', eigenvalues.shape)\n",
+    "print('Eigen Vector Shape:', eigenvectors.shape)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "c02c840e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Indexing the Eigen values in descending order \n",
+    "idx = eigenvalues.argsort()[::-1]\n",
+    "\n",
+    "# Sorting the Eigen values in descending order \n",
+    "eigenvalues = eigenvalues[idx]\n",
+    "\n",
+    "# Sorting the corresponding eigenvectors\n",
+    "eigenvectors = eigenvectors[:,idx]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "c4bc34ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.44272026, 0.63243208, 0.72636371, 0.79238506, 0.84734274,\n",
+       "       0.88758796, 0.9100953 , 0.92598254, 0.93987903, 0.95156881,\n",
+       "       0.961366  , 0.97007138, 0.97811663, 0.98335029, 0.98648812,\n",
+       "       0.98915022, 0.99113018, 0.99288414, 0.9945334 , 0.99557204,\n",
+       "       0.99657114, 0.99748579, 0.99829715, 0.99889898, 0.99941502,\n",
+       "       0.99968761, 0.99991763, 0.99997061, 0.99999557, 1.        ])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "explained_var = np.cumsum(eigenvalues) / np.sum(eigenvalues)\n",
+    "explained_var\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "afed4d7a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n_components = np.argmax(explained_var >= 0.50) + 1\n",
+    "n_components\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "94ad1d29",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# PCA component or unit matrix\n",
+    "u = eigenvectors[:,:n_components]\n",
+    "pca_component = pd.DataFrame(u,\n",
+    "\t\t\t\t\t\t\tindex = cancer['feature_names'],\n",
+    "\t\t\t\t\t\t\tcolumns = ['PC1','PC2']\n",
+    "\t\t\t\t\t\t\t)\n",
+    "\n",
+    "# Plotting heatmap\n",
+    "plt.figure(figsize =(5, 7))\n",
+    "sns.heatmap(pca_component)\n",
+    "plt.title('PCA Component')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4e7f3a05",
+   "metadata": {},
+   "source": [
+    "### Finding PCA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "6ed0a4ad",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "          PCA1       PCA2\n",
+      "0     9.184755   1.946870\n",
+      "1     2.385703  -3.764859\n",
+      "2     5.728855  -1.074229\n",
+      "3     7.116691  10.266556\n",
+      "4     3.931842  -1.946359\n",
+      "..         ...        ...\n",
+      "564   6.433655  -3.573673\n",
+      "565   3.790048  -3.580897\n",
+      "566   1.255075  -1.900624\n",
+      "567  10.365673   1.670540\n",
+      "568  -5.470430  -0.670047\n",
+      "\n",
+      "[569 rows x 2 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Matrix multiplication or dot Product\n",
+    "Z_pca = Z @ pca_component\n",
+    "\n",
+    "# Renaming the columns\n",
+    "Z_pca.rename({'PC1': 'PCA1', 'PC2': 'PCA2'}, axis=1, inplace=True)\n",
+    "\n",
+    "# Print the Pricipal Component values\n",
+    "print(Z_pca)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "cb2c0982",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pca_component = pd.DataFrame(u,\n",
+    "\t\t\t\t\t\t\tindex = cancer['feature_names'],\n",
+    "\t\t\t\t\t\t\tcolumns = ['PCA1','PCA2']\n",
+    "\t\t\t\t\t\t\t)\n",
+    "\n",
+    "# Plotting heatmap\n",
+    "plt.figure(figsize =(5, 7))\n",
+    "sns.heatmap(pca_component)\n",
+    "plt.title('PCA Component')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Lab2/PCA-using-sklearn-Iris.ipynb b/Lab2/PCA-using-sklearn-Iris.ipynb
new file mode 100644
index 0000000..a17a273
--- /dev/null
+++ b/Lab2/PCA-using-sklearn-Iris.ipynb
@@ -0,0 +1,486 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## PCA using sklearn on the Iris dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "executionInfo": {
+     "elapsed": 2069,
+     "status": "ok",
+     "timestamp": 1677659865569,
+     "user": {
+      "displayName": "Dr. Suganya A",
+      "userId": "02497661006530203182"
+     },
+     "user_tz": -330
+    },
+    "id": "ByEptuu31nAB"
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\"\n",
+    "df = pd.read_csv(url, names=['sepal length','sepal width','petal length','petal width','target']) # Reading data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 206
+    },
+    "executionInfo": {
+     "elapsed": 34,
+     "status": "ok",
+     "timestamp": 1677659865571,
+     "user": {
+      "displayName": "Dr. Suganya A",
+      "userId": "02497661006530203182"
+     },
+     "user_tz": -330
+    },
+    "id": "1HIdQYtE3Cfq",
+    "outputId": "1ccd7d42-fcc9-4d21-b314-c908894eabe6"
+   },
+   "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>sepal length</th>\n",
+       "      <th>sepal width</th>\n",
+       "      <th>petal length</th>\n",
+       "      <th>petal width</th>\n",
+       "      <th>target</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",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   sepal length  sepal width  petal length  petal width       target\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"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head() # Viewing imported dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 27,
+     "status": "ok",
+     "timestamp": 1677659865572,
+     "user": {
+      "displayName": "Dr. Suganya A",
+      "userId": "02497661006530203182"
+     },
+     "user_tz": -330
+    },
+    "id": "knllFZEL2eE4",
+    "outputId": "35b38234-5804-4563-ec17-5c3b8e736493"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[-9.00681170e-01  1.03205722e+00 -1.34127240e+00 -1.31297673e+00]\n",
+      " [-1.14301691e+00 -1.24957601e-01 -1.34127240e+00 -1.31297673e+00]\n",
+      " [-1.38535265e+00  3.37848329e-01 -1.39813811e+00 -1.31297673e+00]\n",
+      " [-1.50652052e+00  1.06445364e-01 -1.28440670e+00 -1.31297673e+00]\n",
+      " [-1.02184904e+00  1.26346019e+00 -1.34127240e+00 -1.31297673e+00]\n",
+      " [-5.37177559e-01  1.95766909e+00 -1.17067529e+00 -1.05003079e+00]\n",
+      " [-1.50652052e+00  8.00654259e-01 -1.34127240e+00 -1.18150376e+00]\n",
+      " [-1.02184904e+00  8.00654259e-01 -1.28440670e+00 -1.31297673e+00]\n",
+      " [-1.74885626e+00 -3.56360566e-01 -1.34127240e+00 -1.31297673e+00]\n",
+      " [-1.14301691e+00  1.06445364e-01 -1.28440670e+00 -1.44444970e+00]\n",
+      " [-5.37177559e-01  1.49486315e+00 -1.28440670e+00 -1.31297673e+00]\n",
+      " [-1.26418478e+00  8.00654259e-01 -1.22754100e+00 -1.31297673e+00]\n",
+      " [-1.26418478e+00 -1.24957601e-01 -1.34127240e+00 -1.44444970e+00]\n",
+      " [-1.87002413e+00 -1.24957601e-01 -1.51186952e+00 -1.44444970e+00]\n",
+      " [-5.25060772e-02  2.18907205e+00 -1.45500381e+00 -1.31297673e+00]\n",
+      " [-1.73673948e-01  3.11468391e+00 -1.28440670e+00 -1.05003079e+00]\n",
+      " [-5.37177559e-01  1.95766909e+00 -1.39813811e+00 -1.05003079e+00]\n",
+      " [-9.00681170e-01  1.03205722e+00 -1.34127240e+00 -1.18150376e+00]\n",
+      " [-1.73673948e-01  1.72626612e+00 -1.17067529e+00 -1.18150376e+00]\n",
+      " [-9.00681170e-01  1.72626612e+00 -1.28440670e+00 -1.18150376e+00]\n",
+      " [-5.37177559e-01  8.00654259e-01 -1.17067529e+00 -1.31297673e+00]\n",
+      " [-9.00681170e-01  1.49486315e+00 -1.28440670e+00 -1.05003079e+00]\n",
+      " [-1.50652052e+00  1.26346019e+00 -1.56873522e+00 -1.31297673e+00]\n",
+      " [-9.00681170e-01  5.69251294e-01 -1.17067529e+00 -9.18557817e-01]\n",
+      " [-1.26418478e+00  8.00654259e-01 -1.05694388e+00 -1.31297673e+00]\n",
+      " [-1.02184904e+00 -1.24957601e-01 -1.22754100e+00 -1.31297673e+00]\n",
+      " [-1.02184904e+00  8.00654259e-01 -1.22754100e+00 -1.05003079e+00]\n",
+      " [-7.79513300e-01  1.03205722e+00 -1.28440670e+00 -1.31297673e+00]\n",
+      " [-7.79513300e-01  8.00654259e-01 -1.34127240e+00 -1.31297673e+00]\n",
+      " [-1.38535265e+00  3.37848329e-01 -1.22754100e+00 -1.31297673e+00]\n",
+      " [-1.26418478e+00  1.06445364e-01 -1.22754100e+00 -1.31297673e+00]\n",
+      " [-5.37177559e-01  8.00654259e-01 -1.28440670e+00 -1.05003079e+00]\n",
+      " [-7.79513300e-01  2.42047502e+00 -1.28440670e+00 -1.44444970e+00]\n",
+      " [-4.16009689e-01  2.65187798e+00 -1.34127240e+00 -1.31297673e+00]\n",
+      " [-1.14301691e+00  1.06445364e-01 -1.28440670e+00 -1.44444970e+00]\n",
+      " [-1.02184904e+00  3.37848329e-01 -1.45500381e+00 -1.31297673e+00]\n",
+      " [-4.16009689e-01  1.03205722e+00 -1.39813811e+00 -1.31297673e+00]\n",
+      " [-1.14301691e+00  1.06445364e-01 -1.28440670e+00 -1.44444970e+00]\n",
+      " [-1.74885626e+00 -1.24957601e-01 -1.39813811e+00 -1.31297673e+00]\n",
+      " [-9.00681170e-01  8.00654259e-01 -1.28440670e+00 -1.31297673e+00]\n",
+      " [-1.02184904e+00  1.03205722e+00 -1.39813811e+00 -1.18150376e+00]\n",
+      " [-1.62768839e+00 -1.74477836e+00 -1.39813811e+00 -1.18150376e+00]\n",
+      " [-1.74885626e+00  3.37848329e-01 -1.39813811e+00 -1.31297673e+00]\n",
+      " [-1.02184904e+00  1.03205722e+00 -1.22754100e+00 -7.87084847e-01]\n",
+      " [-9.00681170e-01  1.72626612e+00 -1.05694388e+00 -1.05003079e+00]\n",
+      " [-1.26418478e+00 -1.24957601e-01 -1.34127240e+00 -1.18150376e+00]\n",
+      " [-9.00681170e-01  1.72626612e+00 -1.22754100e+00 -1.31297673e+00]\n",
+      " [-1.50652052e+00  3.37848329e-01 -1.34127240e+00 -1.31297673e+00]\n",
+      " [-6.58345429e-01  1.49486315e+00 -1.28440670e+00 -1.31297673e+00]\n",
+      " [-1.02184904e+00  5.69251294e-01 -1.34127240e+00 -1.31297673e+00]\n",
+      " [ 1.40150837e+00  3.37848329e-01  5.35295827e-01  2.64698913e-01]\n",
+      " [ 6.74501145e-01  3.37848329e-01  4.21564419e-01  3.96171883e-01]\n",
+      " [ 1.28034050e+00  1.06445364e-01  6.49027235e-01  3.96171883e-01]\n",
+      " [-4.16009689e-01 -1.74477836e+00  1.37235899e-01  1.33225943e-01]\n",
+      " [ 7.95669016e-01 -5.87763531e-01  4.78430123e-01  3.96171883e-01]\n",
+      " [-1.73673948e-01 -5.87763531e-01  4.21564419e-01  1.33225943e-01]\n",
+      " [ 5.53333275e-01  5.69251294e-01  5.35295827e-01  5.27644853e-01]\n",
+      " [-1.14301691e+00 -1.51337539e+00 -2.60824029e-01 -2.61192967e-01]\n",
+      " [ 9.16836886e-01 -3.56360566e-01  4.78430123e-01  1.33225943e-01]\n",
+      " [-7.79513300e-01 -8.19166497e-01  8.03701950e-02  2.64698913e-01]\n",
+      " [-1.02184904e+00 -2.43898725e+00 -1.47092621e-01 -2.61192967e-01]\n",
+      " [ 6.86617933e-02 -1.24957601e-01  2.50967307e-01  3.96171883e-01]\n",
+      " [ 1.89829664e-01 -1.97618132e+00  1.37235899e-01 -2.61192967e-01]\n",
+      " [ 3.10997534e-01 -3.56360566e-01  5.35295827e-01  2.64698913e-01]\n",
+      " [-2.94841818e-01 -3.56360566e-01 -9.02269170e-02  1.33225943e-01]\n",
+      " [ 1.03800476e+00  1.06445364e-01  3.64698715e-01  2.64698913e-01]\n",
+      " [-2.94841818e-01 -1.24957601e-01  4.21564419e-01  3.96171883e-01]\n",
+      " [-5.25060772e-02 -8.19166497e-01  1.94101603e-01 -2.61192967e-01]\n",
+      " [ 4.32165405e-01 -1.97618132e+00  4.21564419e-01  3.96171883e-01]\n",
+      " [-2.94841818e-01 -1.28197243e+00  8.03701950e-02 -1.29719997e-01]\n",
+      " [ 6.86617933e-02  3.37848329e-01  5.92161531e-01  7.90590793e-01]\n",
+      " [ 3.10997534e-01 -5.87763531e-01  1.37235899e-01  1.33225943e-01]\n",
+      " [ 5.53333275e-01 -1.28197243e+00  6.49027235e-01  3.96171883e-01]\n",
+      " [ 3.10997534e-01 -5.87763531e-01  5.35295827e-01  1.75297293e-03]\n",
+      " [ 6.74501145e-01 -3.56360566e-01  3.07833011e-01  1.33225943e-01]\n",
+      " [ 9.16836886e-01 -1.24957601e-01  3.64698715e-01  2.64698913e-01]\n",
+      " [ 1.15917263e+00 -5.87763531e-01  5.92161531e-01  2.64698913e-01]\n",
+      " [ 1.03800476e+00 -1.24957601e-01  7.05892939e-01  6.59117823e-01]\n",
+      " [ 1.89829664e-01 -3.56360566e-01  4.21564419e-01  3.96171883e-01]\n",
+      " [-1.73673948e-01 -1.05056946e+00 -1.47092621e-01 -2.61192967e-01]\n",
+      " [-4.16009689e-01 -1.51337539e+00  2.35044910e-02 -1.29719997e-01]\n",
+      " [-4.16009689e-01 -1.51337539e+00 -3.33612130e-02 -2.61192967e-01]\n",
+      " [-5.25060772e-02 -8.19166497e-01  8.03701950e-02  1.75297293e-03]\n",
+      " [ 1.89829664e-01 -8.19166497e-01  7.62758643e-01  5.27644853e-01]\n",
+      " [-5.37177559e-01 -1.24957601e-01  4.21564419e-01  3.96171883e-01]\n",
+      " [ 1.89829664e-01  8.00654259e-01  4.21564419e-01  5.27644853e-01]\n",
+      " [ 1.03800476e+00  1.06445364e-01  5.35295827e-01  3.96171883e-01]\n",
+      " [ 5.53333275e-01 -1.74477836e+00  3.64698715e-01  1.33225943e-01]\n",
+      " [-2.94841818e-01 -1.24957601e-01  1.94101603e-01  1.33225943e-01]\n",
+      " [-4.16009689e-01 -1.28197243e+00  1.37235899e-01  1.33225943e-01]\n",
+      " [-4.16009689e-01 -1.05056946e+00  3.64698715e-01  1.75297293e-03]\n",
+      " [ 3.10997534e-01 -1.24957601e-01  4.78430123e-01  2.64698913e-01]\n",
+      " [-5.25060772e-02 -1.05056946e+00  1.37235899e-01  1.75297293e-03]\n",
+      " [-1.02184904e+00 -1.74477836e+00 -2.60824029e-01 -2.61192967e-01]\n",
+      " [-2.94841818e-01 -8.19166497e-01  2.50967307e-01  1.33225943e-01]\n",
+      " [-1.73673948e-01 -1.24957601e-01  2.50967307e-01  1.75297293e-03]\n",
+      " [-1.73673948e-01 -3.56360566e-01  2.50967307e-01  1.33225943e-01]\n",
+      " [ 4.32165405e-01 -3.56360566e-01  3.07833011e-01  1.33225943e-01]\n",
+      " [-9.00681170e-01 -1.28197243e+00 -4.31421141e-01 -1.29719997e-01]\n",
+      " [-1.73673948e-01 -5.87763531e-01  1.94101603e-01  1.33225943e-01]\n",
+      " [ 5.53333275e-01  5.69251294e-01  1.27454998e+00  1.71090158e+00]\n",
+      " [-5.25060772e-02 -8.19166497e-01  7.62758643e-01  9.22063763e-01]\n",
+      " [ 1.52267624e+00 -1.24957601e-01  1.21768427e+00  1.18500970e+00]\n",
+      " [ 5.53333275e-01 -3.56360566e-01  1.04708716e+00  7.90590793e-01]\n",
+      " [ 7.95669016e-01 -1.24957601e-01  1.16081857e+00  1.31648267e+00]\n",
+      " [ 2.12851559e+00 -1.24957601e-01  1.61574420e+00  1.18500970e+00]\n",
+      " [-1.14301691e+00 -1.28197243e+00  4.21564419e-01  6.59117823e-01]\n",
+      " [ 1.76501198e+00 -3.56360566e-01  1.44514709e+00  7.90590793e-01]\n",
+      " [ 1.03800476e+00 -1.28197243e+00  1.16081857e+00  7.90590793e-01]\n",
+      " [ 1.64384411e+00  1.26346019e+00  1.33141568e+00  1.71090158e+00]\n",
+      " [ 7.95669016e-01  3.37848329e-01  7.62758643e-01  1.05353673e+00]\n",
+      " [ 6.74501145e-01 -8.19166497e-01  8.76490051e-01  9.22063763e-01]\n",
+      " [ 1.15917263e+00 -1.24957601e-01  9.90221459e-01  1.18500970e+00]\n",
+      " [-1.73673948e-01 -1.28197243e+00  7.05892939e-01  1.05353673e+00]\n",
+      " [-5.25060772e-02 -5.87763531e-01  7.62758643e-01  1.57942861e+00]\n",
+      " [ 6.74501145e-01  3.37848329e-01  8.76490051e-01  1.44795564e+00]\n",
+      " [ 7.95669016e-01 -1.24957601e-01  9.90221459e-01  7.90590793e-01]\n",
+      " [ 2.24968346e+00  1.72626612e+00  1.67260991e+00  1.31648267e+00]\n",
+      " [ 2.24968346e+00 -1.05056946e+00  1.78634131e+00  1.44795564e+00]\n",
+      " [ 1.89829664e-01 -1.97618132e+00  7.05892939e-01  3.96171883e-01]\n",
+      " [ 1.28034050e+00  3.37848329e-01  1.10395287e+00  1.44795564e+00]\n",
+      " [-2.94841818e-01 -5.87763531e-01  6.49027235e-01  1.05353673e+00]\n",
+      " [ 2.24968346e+00 -5.87763531e-01  1.67260991e+00  1.05353673e+00]\n",
+      " [ 5.53333275e-01 -8.19166497e-01  6.49027235e-01  7.90590793e-01]\n",
+      " [ 1.03800476e+00  5.69251294e-01  1.10395287e+00  1.18500970e+00]\n",
+      " [ 1.64384411e+00  3.37848329e-01  1.27454998e+00  7.90590793e-01]\n",
+      " [ 4.32165405e-01 -5.87763531e-01  5.92161531e-01  7.90590793e-01]\n",
+      " [ 3.10997534e-01 -1.24957601e-01  6.49027235e-01  7.90590793e-01]\n",
+      " [ 6.74501145e-01 -5.87763531e-01  1.04708716e+00  1.18500970e+00]\n",
+      " [ 1.64384411e+00 -1.24957601e-01  1.16081857e+00  5.27644853e-01]\n",
+      " [ 1.88617985e+00 -5.87763531e-01  1.33141568e+00  9.22063763e-01]\n",
+      " [ 2.49201920e+00  1.72626612e+00  1.50201279e+00  1.05353673e+00]\n",
+      " [ 6.74501145e-01 -5.87763531e-01  1.04708716e+00  1.31648267e+00]\n",
+      " [ 5.53333275e-01 -5.87763531e-01  7.62758643e-01  3.96171883e-01]\n",
+      " [ 3.10997534e-01 -1.05056946e+00  1.04708716e+00  2.64698913e-01]\n",
+      " [ 2.24968346e+00 -1.24957601e-01  1.33141568e+00  1.44795564e+00]\n",
+      " [ 5.53333275e-01  8.00654259e-01  1.04708716e+00  1.57942861e+00]\n",
+      " [ 6.74501145e-01  1.06445364e-01  9.90221459e-01  7.90590793e-01]\n",
+      " [ 1.89829664e-01 -1.24957601e-01  5.92161531e-01  7.90590793e-01]\n",
+      " [ 1.28034050e+00  1.06445364e-01  9.33355755e-01  1.18500970e+00]\n",
+      " [ 1.03800476e+00  1.06445364e-01  1.04708716e+00  1.57942861e+00]\n",
+      " [ 1.28034050e+00  1.06445364e-01  7.62758643e-01  1.44795564e+00]\n",
+      " [-5.25060772e-02 -8.19166497e-01  7.62758643e-01  9.22063763e-01]\n",
+      " [ 1.15917263e+00  3.37848329e-01  1.21768427e+00  1.44795564e+00]\n",
+      " [ 1.03800476e+00  5.69251294e-01  1.10395287e+00  1.71090158e+00]\n",
+      " [ 1.03800476e+00 -1.24957601e-01  8.19624347e-01  1.44795564e+00]\n",
+      " [ 5.53333275e-01 -1.28197243e+00  7.05892939e-01  9.22063763e-01]\n",
+      " [ 7.95669016e-01 -1.24957601e-01  8.19624347e-01  1.05353673e+00]\n",
+      " [ 4.32165405e-01  8.00654259e-01  9.33355755e-01  1.44795564e+00]\n",
+      " [ 6.86617933e-02 -1.24957601e-01  7.62758643e-01  7.90590793e-01]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Selecting features and transforming it\n",
+    "\n",
+    "feature = ['sepal length', 'sepal width', 'petal length', 'petal width']\n",
+    "\n",
+    "x = df.loc[:,feature] # Selecting all the rows and only the columns specified in feature from the DataFrame\n",
+    "y = df.loc[:,'target'] # Setting a series that contains all rows from the 'target' column of the DataFrame\n",
+    "\n",
+    "x = StandardScaler().fit_transform(x) # Replacing x with a new array where each feature in x has been standardized.\n",
+    "\n",
+    "'''\n",
+    "StandardScaler(): This creates an instance of the StandardScaler class.\n",
+    "fit_transform(x): This method first fits the scaler to your data by calculating the mean and standard deviation,\n",
+    " and then it transforms your data by standardizing it. It returns a new array of the same shape as x but with the transformed values.\n",
+    "'''\n",
+    "\n",
+    "print(x)        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 23,
+     "status": "ok",
+     "timestamp": 1677659865573,
+     "user": {
+      "displayName": "Dr. Suganya A",
+      "userId": "02497661006530203182"
+     },
+     "user_tz": -330
+    },
+    "id": "TMIIwCE13W_P",
+    "outputId": "2623bed8-af59-4538-fca5-56a545d541a7"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "          pc1       pc2          target\n",
+      "0   -2.264542  0.505704     Iris-setosa\n",
+      "1   -2.086426 -0.655405     Iris-setosa\n",
+      "2   -2.367950 -0.318477     Iris-setosa\n",
+      "3   -2.304197 -0.575368     Iris-setosa\n",
+      "4   -2.388777  0.674767     Iris-setosa\n",
+      "..        ...       ...             ...\n",
+      "145  1.870522  0.382822  Iris-virginica\n",
+      "146  1.558492 -0.905314  Iris-virginica\n",
+      "147  1.520845  0.266795  Iris-virginica\n",
+      "148  1.376391  1.016362  Iris-virginica\n",
+      "149  0.959299 -0.022284  Iris-virginica\n",
+      "\n",
+      "[150 rows x 3 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.decomposition import PCA\n",
+    "\n",
+    "pca = PCA(n_components=2) # Specifying the no of principal components that I want to reduce the data to\n",
+    "pct = pca.fit_transform(x) # Fits model and applies dimensionality reduction\n",
+    "principal_df = pd.DataFrame(pct,columns=['pc1','pc2']) # Creating new Dataframe with transformed data\n",
+    "finaldf= pd.concat([principal_df,df[['target']]],axis=1) # Concatenates the DataFrame of principal components with the 'target' column from df\n",
+    "\n",
+    "print(finaldf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 523
+    },
+    "executionInfo": {
+     "elapsed": 2447,
+     "status": "ok",
+     "timestamp": 1677659868005,
+     "user": {
+      "displayName": "Dr. Suganya A",
+      "userId": "02497661006530203182"
+     },
+     "user_tz": -330
+    },
+    "id": "TeqPm9Pd3elv",
+    "outputId": "641fe083-09c6-4b5e-a915-ac471fc86ff6"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize = (8,8)) # Creating figure with 8x8 inches size\n",
+    "ax = fig.add_subplot(1,1,1) # Adding subplot to figure (1st row, 1st column, subplot is 1st one)\n",
+    "\n",
+    "# Setting Labels and title\n",
+    "ax.set_xlabel('Principal Component 1', fontsize = 15)\n",
+    "ax.set_ylabel('Principal Component 2', fontsize = 15)\n",
+    "ax.set_title('2 component PCA', fontsize = 20)\n",
+    "\n",
+    "# Defining categories in 'target' column and setting color of plot\n",
+    "targets = ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']\n",
+    "colors = ['r', 'g', 'b']\n",
+    "\n",
+    "# The for loop iterates over each target and color.\n",
+    "# For each target, it creates a boolean Series indicesToKeep where each value is True\n",
+    "#  if the corresponding row in finaldf['target'] is equal to the current target, and False otherwise.\n",
+    "#  It then plots the 'pc1' and 'pc2' values for the rows where indicesToKeep is True with the current color.\n",
+    "\n",
+    "for target, color in zip(targets,colors):\n",
+    "    indicesToKeep = finaldf['target'] == target\n",
+    "    ax.scatter(finaldf.loc[indicesToKeep, 'pc1']\n",
+    "               , finaldf.loc[indicesToKeep, 'pc2']\n",
+    "               , c = color\n",
+    "               , s = 50)\n",
+    "\n",
+    "ax.legend(targets) # Adding legend to plot with specified labels\n",
+    "ax.grid() # Adding grid lines to the plot"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Lab2/README.md b/Lab2/README.md
new file mode 100644
index 0000000..8930a49
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+++ b/Lab2/README.md
@@ -0,0 +1,52 @@
+# Principal Component Analysis
+
+- Principal Component Analysis is a
+popular unsupervised learning technique
+for reducing the dimensionality of large
+data sets.
+- It increases interpretability yet,
+at the same time, it minimizes information
+loss. It helps to find the most significant
+features in a dataset and makes the data
+easy for plotting in 2D and 3D. PCA helps
+in finding a sequence of linear combinations
+of variables.
+
+## Terms
+
+- **Principal Component:** They are a straight line that captures most
+of the variance of the data. They have a direction and
+magnitude. Principal components are orthogonal projections
+(perpendicular) of data onto lower-dimensional space.
+
+- **Dimensionality:** Quantity of features or variables
+used in the research.
+
+## Steps
+
+1. **Standardize the data**: PCA requires standardized data, so the first step is to standardize the
+data to ensure that all variables have a mean of 0 and a standard deviation of 1.
+
+2. **Calculate the covariance matrix**: The next step is to calculate the covariance matrix of the
+standardized data. This matrix shows how each variable is related to every other variable in
+the dataset.
+
+3. **Calculate the eigenvectors and eigenvalues**: The eigenvectors and eigenvalues of the
+covariance matrix are then calculated. The eigenvectors represent the directions in which
+the data varies the most, while the eigenvalues represent the amount of variation along
+each eigenvector.
+
+4. **Choose the principal components**: The principal components are the eigenvectors with the
+highest eigenvalues. These components represent the directions in which the data varies
+the most and are used to transform the original data into a lower-dimensional space.
+
+5. **Transform the data**: The final step is to transform the original data into the
+lower-dimensional space defined by the principal components.
+
+## Applications
+
+- Used to visualize multidimensional data.
+- Used to reduce the number of dimensions in healthcare data.
+- Can help resize an image.
+- Used in finance to analyze stock data and forecast returns.
+- Helps to find patterns in the high-dimensional datasets.
diff --git a/Lab2/winequality-red.csv b/Lab2/winequality-red.csv
new file mode 100644
index 0000000..d611beb
--- /dev/null
+++ b/Lab2/winequality-red.csv
@@ -0,0 +1,1600 @@
+fixed acidity,volatile acidity,citric acid,residual sugar,chlorides,free sulfur dioxide,total sulfur dioxide,density,pH,sulphates,alcohol,quality
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diff --git a/README.md b/README.md
index 7dfee95..e59d3a6 100644
--- a/README.md
+++ b/README.md
@@ -13,6 +13,12 @@ This repository contains the programs that I worked out in Machine Learning Labo
   - [Introduction to Numpy and Pandas package](https://github.com/kbdharun/ML-Lab/blob/main/Lab1/Numpy_&_Pandas.ipynb)
   - [Insurance Data Analysis](https://github.com/kbdharun/ML-Lab/blob/main/Lab1/ML_Lab1_Insurance.ipynb)
   - [Iris Data Analysis](https://github.com/kbdharun/ML-Lab/blob/main/Lab1/ML_Lab1_Iris.ipynb)
+- Lab 2: Principal Component Analysis
+  - [About PCA](https://github.com/kbdharun/ML-Lab/blob/main/Lab2/README.md)
+  - [PCA based dimensionality reduction on Wine dataset](https://github.com/kbdharun/ML-Lab/blob/main/Lab2/PCA-DR-Wine.ipynb)
+  - [PCA using algorithm steps without `sklearn`](https://github.com/kbdharun/ML-Lab/blob/main/Lab2/PCA-using-alg-without-sk.ipynb)
+  - [PCA using `sklearn` on Iris dataset](https://github.com/kbdharun/ML-Lab/blob/main/Lab2/PCA-using-sklearn-Iris.ipynb)
+  - [PCA - Wine Quality Classification](https://github.com/kbdharun/ML-Lab/blob/main/Lab2/PCA-Wine-quality-classification.ipynb)
 
 ## Prerequisites
 
diff --git a/requirements.txt b/requirements.txt
index bd53fd3..df3f791 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,6 +1,8 @@
-pandas
-numpy
 matplotlib
+notebook
+numpy
+pandas
+plotly
 seaborn
 scikit-learn
-notebook
+