Principle Component Analysis - Iris.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "077dd027",
   "metadata": {},
   "source": [
    "##### SNEHA 2213712080024"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7acb227c",
   "metadata": {},
   "source": [
    "# PRINCIPAL COMPONENT ANALYSIS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "248d9c41",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a4c2106a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "16fa9690",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = pd.read_csv(\"C:/Users/sneha/OneDrive/Desktop/ML DATASETS/Iris.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "56ab0860",
   "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>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",
       "  </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"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4a1fdc1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id               0\n",
       "SepalLengthCm    0\n",
       "SepalWidthCm     0\n",
       "PetalLengthCm    0\n",
       "PetalWidthCm     0\n",
       "Species          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4a776ad9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 6)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e6ab7e7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = iris.drop('Id', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "aa60ee5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = iris.iloc[:,:4]\n",
    "y = iris.iloc[:,4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bdf6a328",
   "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>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</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": [
       "   SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5ff78cd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "976ffb00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.68569351, -0.03926846,  1.27368233,  0.5169038 ],\n",
       "       [-0.03926846,  0.18800403, -0.32171275, -0.11798121],\n",
       "       [ 1.27368233, -0.32171275,  3.11317942,  1.29638747],\n",
       "       [ 0.5169038 , -0.11798121,  1.29638747,  0.58241432]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca = PCA()\n",
    "X = pca.fit_transform(x)\n",
    "pca.get_covariance()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2749f12",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a67a8399",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.92461621, 0.05301557, 0.01718514, 0.00518309])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "explained_variance = pca.explained_variance_ratio_\n",
    "explained_variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3d172c25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with plt.style.context('dark_background'):\n",
    "    plt.figure(figsize = (6,4))\n",
    "    plt.bar(range (4), explained_variance, alpha=0.5, align='center',label='Iris')\n",
    "    plt.ylabel('Explained Variance Ratio')\n",
    "    plt.xlabel('Principle Component')\n",
    "    plt.legend(loc ='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8ecde2c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components =3)\n",
    "X = pca.fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f601ee4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2d90bd84",
   "metadata": {},
   "outputs": [],
   "source": [
    "xtrain, xtest, ytrain, ytest = train_test_split(X,y,test_size =0.3, random_state =0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b7981400",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = KNeighborsClassifier(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "acbbc2b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.fit(xtrain,ytrain)\n",
    "y_pred =model.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "10616d51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[16,  0,  0],\n",
       "       [ 0, 17,  1],\n",
       "       [ 0,  0, 11]], dtype=int64)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(ytest, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30f54a6d",
   "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": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}