PCA-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8977265a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary libraries\n",
    "from sklearn import datasets # to retrieve the iris Dataset\n",
    "import pandas as pd # to load the dataframe\n",
    "from sklearn.preprocessing import StandardScaler # to standardize the features\n",
    "from sklearn.decomposition import PCA # to apply PCA\n",
    "import seaborn as sns # to plot the heat maps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9e628644",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0                5.1               3.5                1.4               0.2\n",
       "1                4.9               3.0                1.4               0.2\n",
       "2                4.7               3.2                1.3               0.2\n",
       "3                4.6               3.1                1.5               0.2\n",
       "4                5.0               3.6                1.4               0.2"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Load the Dataset\n",
    "iris = datasets.load_iris()\n",
    "#convert the dataset into a pandas data frame\n",
    "df = pd.DataFrame(iris['data'], columns = iris['feature_names'])\n",
    "#display the head (first 5 rows) of the dataset\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "72c3dc22",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.900681</td>\n",
       "      <td>1.019004</td>\n",
       "      <td>-1.340227</td>\n",
       "      <td>-1.315444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.143017</td>\n",
       "      <td>-0.131979</td>\n",
       "      <td>-1.340227</td>\n",
       "      <td>-1.315444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.385353</td>\n",
       "      <td>0.328414</td>\n",
       "      <td>-1.397064</td>\n",
       "      <td>-1.315444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.506521</td>\n",
       "      <td>0.098217</td>\n",
       "      <td>-1.283389</td>\n",
       "      <td>-1.315444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.021849</td>\n",
       "      <td>1.249201</td>\n",
       "      <td>-1.340227</td>\n",
       "      <td>-1.315444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>1.038005</td>\n",
       "      <td>-0.131979</td>\n",
       "      <td>0.819596</td>\n",
       "      <td>1.448832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>0.553333</td>\n",
       "      <td>-1.282963</td>\n",
       "      <td>0.705921</td>\n",
       "      <td>0.922303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>0.795669</td>\n",
       "      <td>-0.131979</td>\n",
       "      <td>0.819596</td>\n",
       "      <td>1.053935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>0.432165</td>\n",
       "      <td>0.788808</td>\n",
       "      <td>0.933271</td>\n",
       "      <td>1.448832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>0.068662</td>\n",
       "      <td>-0.131979</td>\n",
       "      <td>0.762758</td>\n",
       "      <td>0.790671</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1         2         3\n",
       "0   -0.900681  1.019004 -1.340227 -1.315444\n",
       "1   -1.143017 -0.131979 -1.340227 -1.315444\n",
       "2   -1.385353  0.328414 -1.397064 -1.315444\n",
       "3   -1.506521  0.098217 -1.283389 -1.315444\n",
       "4   -1.021849  1.249201 -1.340227 -1.315444\n",
       "..        ...       ...       ...       ...\n",
       "145  1.038005 -0.131979  0.819596  1.448832\n",
       "146  0.553333 -1.282963  0.705921  0.922303\n",
       "147  0.795669 -0.131979  0.819596  1.053935\n",
       "148  0.432165  0.788808  0.933271  1.448832\n",
       "149  0.068662 -0.131979  0.762758  0.790671\n",
       "\n",
       "[150 rows x 4 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Standardize the features\n",
    "#Create an object of StandardScaler which is present in sklearn.preprocessing\n",
    "scalar = StandardScaler()\n",
    "scaled_data = pd.DataFrame(scalar.fit_transform(df)) #scaling the data\n",
    "scaled_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fea09e28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Check the Co-relation between features without PCA\n",
    "sns.heatmap(scaled_data.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "62d9579f",
   "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>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>PC3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.264703</td>\n",
       "      <td>0.480027</td>\n",
       "      <td>-0.127706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2.080961</td>\n",
       "      <td>-0.674134</td>\n",
       "      <td>-0.234609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.364229</td>\n",
       "      <td>-0.341908</td>\n",
       "      <td>0.044201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.299384</td>\n",
       "      <td>-0.597395</td>\n",
       "      <td>0.091290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.389842</td>\n",
       "      <td>0.646835</td>\n",
       "      <td>0.015738</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PC1       PC2       PC3\n",
       "0 -2.264703  0.480027 -0.127706\n",
       "1 -2.080961 -0.674134 -0.234609\n",
       "2 -2.364229 -0.341908  0.044201\n",
       "3 -2.299384 -0.597395  0.091290\n",
       "4 -2.389842  0.646835  0.015738"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Applying PCA\n",
    "#Taking no. of Principal Components as 3\n",
    "pca = PCA(n_components = 3)\n",
    "pca.fit(scaled_data)\n",
    "data_pca = pca.transform(scaled_data)\n",
    "data_pca = pd.DataFrame(data_pca,columns=['PC1','PC2','PC3'])\n",
    "data_pca.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "600c6d4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Checking Co-relation between features after PCA\n",
    "sns.heatmap(data_pca.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f5d14b56",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}