PCA.py

#!/usr/bin/env python
# coding: utf-8

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import pandas as pd
import matplotlib.pyplot as plt
import numpy as np


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dataset = pd.read_csv("Wine.csv")
X = dataset.iloc[:,0:13].values
Y = dataset.iloc[:,-1].values


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from sklearn.model_selection import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, train_size = 0.8, random_state=0)


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from sklearn.preprocessing import StandardScaler
SC = StandardScaler()
X_train_sc = SC.fit_transform(X_train)
X_test_sc = SC.transform(X_test)


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from sklearn.decomposition import PCA
pca = PCA(n_components=2)
X_train_sc_pca = pca.fit_transform(X_train_sc)
X_test_sc_pca = pca.transform(X_test_sc)


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from sklearn.linear_model import LogisticRegression
pca_log_reg = LogisticRegression(random_state=0)
pca_log_reg.fit(X_train_sc_pca, Y_train)


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from sklearn.metrics import confusion_matrix
from sklearn.metrics import plot_confusion_matrix
plot_confusion_matrix(pca_log_reg,X_test_sc_pca,Y_test)


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from sklearn.metrics import classification_report
print(classification_report(Y_test, pca_log_reg.predict(X_test_sc_pca)))


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