{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "T5lnzy-RLBSw"
},
"outputs": [],
"source": [
"# importing required libraries\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"source": [
"# importing or loading the dataset\n",
"dataset = pd.read_csv('Wine.csv')\n",
"\n",
"# distributing the dataset into two components X and Y\n",
"X = dataset.iloc[:, 0:13].values\n",
"y = dataset.iloc[:, 13].values"
],
"metadata": {
"id": "1tdRXj_yLGQ2"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Splitting the X and Y into the\n",
"# Training set and Testing set\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)"
],
"metadata": {
"id": "vED_BCHiLJt2"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# performing preprocessing part\n",
"from sklearn.preprocessing import StandardScaler\n",
"sc = StandardScaler()\n",
"\n",
"X_train = sc.fit_transform(X_train)\n",
"X_test = sc.transform(X_test)"
],
"metadata": {
"id": "yoFnQYYaLKsV"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Applying PCA function on training\n",
"# and testing set of X component\n",
"from sklearn.decomposition import PCA\n",
"\n",
"pca = PCA(n_components=2)\n",
"\n",
"X_train = pca.fit_transform(X_train)\n",
"X_test = pca.transform(X_test)\n",
"\n",
"explained_variance = pca.explained_variance_ratio_"
],
"metadata": {
"id": "GXNWZXlgLL16"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Fitting Logistic Regression To the training set\n",
"from sklearn.linear_model import LogisticRegression\n",
"\n",
"classifier = LogisticRegression(random_state=0)\n",
"classifier.fit(X_train, y_train)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 74
},
"id": "2NOsiylWLNzU",
"outputId": "dc964160-7692-47c8-b89f-72de8276f479"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"LogisticRegression(random_state=0)"
],
"text/html": [
"
LogisticRegression(random_state=0)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.