Principal Component Analysis (PCA) in Python – Step 7.) 3×3 Confusion Matrix

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# Import the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

# Import Data
dataset = pd.read_csv(‘PCA data.csv’)
X = dataset.iloc[:, 0:13].values
y = dataset.iloc[:, 13].values

# Split into the Training set and Test set
from sklearn.cross_validation import train_test_split
Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, test_size = 0.2)

# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
Xtrain = sc.fit_transform(Xtrain)
Xtest = sc.transform(Xtest)

# Applying PCA
from sklearn.decomposition import PCA
pca = PCA(n_components = 2)
Xtrain = pca.fit_transform(Xtrain)
Xtest = pca.transform(Xtest)
explained_variance = pca.explained_variance_ratio_

# Train Regression Model with PCA
from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression(random_state = 0)
classifier.fit(Xtrain, ytrain)

# Predict Results from PCA Model
ypred = classifier.predict(Xtest)

# Create Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(ytest, ypred)

 

Other Topics – Multivariate Analysis : 
Other Topics – Association Rule : 
Other Topics – Artifical Inteligent : 
  • Upper Confidence Bound
  • Thompson Sampling

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