1. Implementing and Visualisation PCA-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Principal Component Analysis\n",
    "\n",
    "Principal Component Analysis is a method that uses simple matrix operations from linear algebra and statistics to calculate a projection of the original data into the same number or fewer dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing the necessary libraries\n",
    "\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reading Dataset\n",
    "\n",
    "data=pd.read_csv('Iris.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\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>2</td>\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>3</td>\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</td>\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</td>\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": [
       "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
       "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
       "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
       "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
       "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
       "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking the first five rows\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dropping the unnecessary column ID\n",
    "\n",
    "data=data.drop('Id',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X is  (150, 4)\n",
      "Shape of Y is  (150,)\n"
     ]
    }
   ],
   "source": [
    "#Splitting the dependent and independent variables\n",
    "\n",
    "X=data.iloc[:,:-1]\n",
    "Y=data['Species']\n",
    "print(\"Shape of X is \",X.shape)\n",
    "print(\"Shape of Y is \",Y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X_train is  (120, 4)\n",
      "Shape of X_test is  (30, 4)\n",
      "Shape of Y_train is  (120,)\n",
      "Shape of Y_test is  (30,)\n"
     ]
    }
   ],
   "source": [
    "#Spiltting dataset into train and tests\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=0 )\n",
    "print('Shape of X_train is ', X_train.shape)\n",
    "print('Shape of X_test is ', X_test.shape)\n",
    "print('Shape of Y_train is ', Y_train.shape)\n",
    "print('Shape of Y_test is ', Y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Standardization\n",
    "\n",
    "Standardization makes the values of each feature in the data have zero-mean (when subtracting the mean in the numerator) and unit-variance. This method is widely used for normalization in many machine learning algorithms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Standardization\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "sc=StandardScaler()\n",
    "X_train_sc = sc.fit_transform(X_train)\n",
    "X_test_sc = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca=PCA(n_components=2)\n",
    "x_train_pca = pca.fit_transform(X_train_sc)\n",
    "x_test_pca = pca.transform(X_test_sc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.72226528, 0.96201323, 0.9953944 , 1.        ])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#This result the variance explained by the number of components taken into account\n",
    "\n",
    "pca.explained_variance_ratio_.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       [-2.30494738e+00, -2.43531797e-01,  8.47985836e-02,\n",
       "        -1.10877397e-01],\n",
       "       [ 2.32903683e+00,  1.34527758e-01, -7.21637492e-01,\n",
       "        -4.17851954e-02],\n",
       "       [-2.23740372e+00,  9.85846464e-02, -1.32682270e-01,\n",
       "         4.17649560e-02],\n",
       "       [ 1.52387687e+00,  1.00040377e+00,  9.29128163e-01,\n",
       "         4.36862233e-02],\n",
       "       [ 1.68242783e-01, -6.20802919e-01,  8.91851252e-02,\n",
       "        -6.65259686e-02],\n",
       "       [ 1.45049409e-01, -8.02274431e-01, -1.51770756e-01,\n",
       "         9.99059633e-03],\n",
       "       [-1.42474004e-01, -1.07405803e+00, -3.17916556e-01,\n",
       "         4.73628444e-02],\n",
       "       [ 1.52177374e+00,  6.01399011e-01,  6.21979337e-01,\n",
       "         1.91445395e-01],\n",
       "       [ 1.78400066e+00,  2.92706339e-01,  2.40020429e-01,\n",
       "         3.78348884e-01],\n",
       "       [ 1.11494102e+00, -1.00569413e+00, -3.71402982e-01,\n",
       "        -2.90529164e-02],\n",
       "       [ 1.27532483e+00,  2.59015027e-01, -2.58259982e-01,\n",
       "         5.19560535e-02],\n",
       "       [-2.00986734e+00, -5.51776815e-01, -2.88635129e-01,\n",
       "         3.11594200e-02],\n",
       "       [-9.36765222e-02, -1.59128467e+00, -2.44136189e-01,\n",
       "        -4.91419246e-02],\n",
       "       [ 1.93529805e+00,  5.17048547e-01,  4.11993963e-01,\n",
       "         2.41860625e-01],\n",
       "       [ 1.05608476e+00, -7.58640575e-01,  5.11966610e-01,\n",
       "        -4.29289250e-02],\n",
       "       [-2.19609621e+00,  3.58093278e-01, -1.52522063e-01,\n",
       "         2.83997703e-04],\n",
       "       [ 8.40144896e-01, -2.37005702e-02, -5.84815789e-01,\n",
       "        -4.93804229e-02],\n",
       "       [-2.04173388e-03, -2.28607127e-01,  2.90553362e-01,\n",
       "        -1.49039489e-01],\n",
       "       [ 6.76377991e-01,  3.61953799e-01,  6.08492032e-01,\n",
       "        -7.98013024e-02],\n",
       "       [ 9.10905621e-01, -1.45303558e+00, -6.89774464e-01,\n",
       "         1.09323587e-01],\n",
       "       [-2.06615479e+00,  7.42185110e-01, -4.56035655e-01,\n",
       "         1.82688000e-01],\n",
       "       [-2.21026982e+00,  1.02028205e+00,  1.80891193e-01,\n",
       "         5.98649153e-02],\n",
       "       [-2.23288987e+00, -3.82580267e-01, -2.33664840e-01,\n",
       "        -4.94658551e-02],\n",
       "       [ 1.35062418e+00, -1.17820187e-01,  1.59559394e-01,\n",
       "        -2.40560272e-01],\n",
       "       [ 6.30991357e-02, -8.12311123e-01, -2.95130373e-01,\n",
       "        -2.18451471e-01],\n",
       "       [ 2.39539989e+00,  2.45039375e+00, -1.11292026e-01,\n",
       "        -2.77396547e-01],\n",
       "       [-2.43305139e+00, -2.86402494e-01,  1.53780931e-01,\n",
       "        -5.09057032e-02]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame({'x_train_pca0':x_train_pca[:,0],'x_train_pca1':x_train_pca[:,1],'Species':Y_train})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>x_train_pca0</th>\n",
       "      <th>x_train_pca1</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>1.272282</td>\n",
       "      <td>0.357879</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>0.152232</td>\n",
       "      <td>-0.299835</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-2.187642</td>\n",
       "      <td>0.617154</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>0.941913</td>\n",
       "      <td>0.012181</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>1.762278</td>\n",
       "      <td>-0.271066</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-2.232890</td>\n",
       "      <td>-0.382580</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>1.350624</td>\n",
       "      <td>-0.117820</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>0.063099</td>\n",
       "      <td>-0.812311</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>2.395400</td>\n",
       "      <td>2.450394</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>-2.433051</td>\n",
       "      <td>-0.286402</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     x_train_pca0  x_train_pca1          Species\n",
       "137      1.272282      0.357879   Iris-virginica\n",
       "84       0.152232     -0.299835  Iris-versicolor\n",
       "27      -2.187642      0.617154      Iris-setosa\n",
       "127      0.941913      0.012181   Iris-virginica\n",
       "132      1.762278     -0.271066   Iris-virginica\n",
       "..            ...           ...              ...\n",
       "9       -2.232890     -0.382580      Iris-setosa\n",
       "103      1.350624     -0.117820   Iris-virginica\n",
       "67       0.063099     -0.812311  Iris-versicolor\n",
       "117      2.395400      2.450394   Iris-virginica\n",
       "47      -2.433051     -0.286402      Iris-setosa\n",
       "\n",
       "[120 rows x 3 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26568106fc8>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot( df['x_train_pca0'], df['x_train_pca1'],hue=df['Species'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}