{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e28d2301",
   "metadata": {},
   "source": [
    " A. To use PCA Algorithm for dimensionality reduction. You have a dataset that includes measurements for different variables on wine (alcohol, ash, magnesium, and so on). Apply PCA algorithm & transform this data so that most variations in the measurements of the variables are captured by a small number of principal components so that it is easier to distinguish between red and white wine by inspecting these principal components. Dataset Link: https://media.geeksforgeeks.org/wp-content/uploads/Wine.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e4bdc70c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import required libraries\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "af23f1e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset shape: (178, 14)\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>Malic_Acid</th>\n",
       "      <th>Ash</th>\n",
       "      <th>Ash_Alcanity</th>\n",
       "      <th>Magnesium</th>\n",
       "      <th>Total_Phenols</th>\n",
       "      <th>Flavanoids</th>\n",
       "      <th>Nonflavanoid_Phenols</th>\n",
       "      <th>Proanthocyanins</th>\n",
       "      <th>Color_Intensity</th>\n",
       "      <th>Hue</th>\n",
       "      <th>OD280</th>\n",
       "      <th>Proline</th>\n",
       "      <th>Customer_Segment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Alcohol  Malic_Acid   Ash  Ash_Alcanity  Magnesium  Total_Phenols  \\\n",
       "0    14.23        1.71  2.43          15.6        127           2.80   \n",
       "1    13.20        1.78  2.14          11.2        100           2.65   \n",
       "2    13.16        2.36  2.67          18.6        101           2.80   \n",
       "3    14.37        1.95  2.50          16.8        113           3.85   \n",
       "4    13.24        2.59  2.87          21.0        118           2.80   \n",
       "\n",
       "   Flavanoids  Nonflavanoid_Phenols  Proanthocyanins  Color_Intensity   Hue  \\\n",
       "0        3.06                  0.28             2.29             5.64  1.04   \n",
       "1        2.76                  0.26             1.28             4.38  1.05   \n",
       "2        3.24                  0.30             2.81             5.68  1.03   \n",
       "3        3.49                  0.24             2.18             7.80  0.86   \n",
       "4        2.69                  0.39             1.82             4.32  1.04   \n",
       "\n",
       "   OD280  Proline  Customer_Segment  \n",
       "0   3.92     1065                 1  \n",
       "1   3.40     1050                 1  \n",
       "2   3.17     1185                 1  \n",
       "3   3.45     1480                 1  \n",
       "4   2.93      735                 1  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the wine dataset\n",
    "# Keep Wine.csv in the same folder as this notebook\n",
    "df = pd.read_csv(\"Wine.csv\")\n",
    "\n",
    "print(\"Dataset shape:\", df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e67e1fe8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of features: 13\n",
      "Wine classes: [1, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "# Separate input features and class labels\n",
    "X = df.drop(\"Customer_Segment\", axis=1)\n",
    "y = df[\"Customer_Segment\"]\n",
    "\n",
    "print(\"Number of features:\", X.shape[1])\n",
    "print(\"Wine classes:\", sorted(y.unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2168bf77",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standardize the features\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "40e8fd61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Explained variance ratio: [0.36198848 0.1920749 ]\n",
      "Total variance captured: 55.41 %\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>Wine_Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.316751</td>\n",
       "      <td>1.443463</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.209465</td>\n",
       "      <td>-0.333393</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.516740</td>\n",
       "      <td>1.031151</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.757066</td>\n",
       "      <td>2.756372</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.008908</td>\n",
       "      <td>0.869831</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PC1       PC2  Wine_Class\n",
       "0  3.316751  1.443463           1\n",
       "1  2.209465 -0.333393           1\n",
       "2  2.516740  1.031151           1\n",
       "3  3.757066  2.756372           1\n",
       "4  1.008908  0.869831           1"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reduce the features to two principal components\n",
    "pca = PCA(n_components=2)\n",
    "X_pca = pca.fit_transform(X_scaled)\n",
    "\n",
    "pca_df = pd.DataFrame(X_pca, columns=[\"PC1\", \"PC2\"])\n",
    "pca_df[\"Wine_Class\"] = y.values\n",
    "\n",
    "# Show how much variance each component captures\n",
    "print(\"Explained variance ratio:\", pca.explained_variance_ratio_)\n",
    "print(\"Total variance captured:\", round(pca.explained_variance_ratio_.sum() * 100, 2), \"%\")\n",
    "pca_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5033b044",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the two principal components by wine class\n",
    "plt.figure(figsize=(8, 6))\n",
    "\n",
    "for wine_class in sorted(pca_df[\"Wine_Class\"].unique()):\n",
    "    data = pca_df[pca_df[\"Wine_Class\"] == wine_class]\n",
    "    plt.scatter(data[\"PC1\"], data[\"PC2\"], label=f\"Class {wine_class}\", alpha=0.8)\n",
    "\n",
    "plt.xlabel(\"First Principal Component (PC1)\")\n",
    "plt.ylabel(\"Second Principal Component (PC2)\")\n",
    "plt.title(\"PCA of Wine Dataset\")\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}