Amazing technological breakthrough possible @S-Logix

Office Address

  • #5, First Floor, 4th Street Dr. Subbarayan Nagar Kodambakkam, Chennai-600 024 Landmark : Samiyar Madam
  • +91- 81240 01111

Social List

How to implement Principal Component Analysis (PCA) using sklearn in python?


To implement PCA using python.


Principal Component Analysis (PCA):

  • PCA is to reduce the dimensionality of a data set consisting of many variables correlated with each other.
  • If the number of columns in a data set is more than thousand, we cant do analysis for each and every column.
  • It reduces the dimension of data with the aim of retaining as much information as possible.

Data Re scaling:

  • Standardization is one of the data re scaling method.
  • Data re scaling is an important part of data preparation before applying machine learning algorithms.
  • Standardization refers to shifting the distribution of each attribute to have a mean of zero and a standard deviation of one (unit variance).

Eigen Values:

  • Eigenvalue is a number, it gives how much variance there is in the data in that direction.
  • Each feature has own eigen vectors and eigen values.
  • The eigen vector with the highest eigenvalue is therefore the principal component.
Sample Code

#import libraries

import pandas as pd
import numpy as np
import warnings
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA

#load data set URL
url = “”
names = [‘sepal-length’, ‘sepal-width’, ‘petal-length’, ‘petal-width’, ‘class’]
data = pd.read_csv(url, names=names)

x = data.drop(‘class’,1)

print(“Actual Features before standardizing\n\n”,x.head())

y = data[‘class’]

# Standardizing the features
x = StandardScaler().fit_transform(x)

print(“After standardizing the features\n\n”,x)

#covariance matrix
covar_matrix = PCA(n_components = 4)

variance = covar_matrix.explained_variance_ratio_

#Cumulative sum of variance
var=np.cumsum(np.round(variance, decimals=3)*100)

print(“Eigen values\n\n”,var)

#plot for variance explained
plt.ylabel(‘% Variance Explained’)
plt.xlabel(‘# of Features’)
plt.title(‘PCA Analysis’)

#fit PCA for 2 components
pca = PCA(n_components=2)
principalComponents = pca.fit_transform(x)
principalDf = pd.DataFrame(data = principalComponents
, columns = [‘principal component 1’, ‘principal component 2’])
print(“After Reduction of dimension into two components\n\n”,principalDf.head())

#concatenate with y variable
finalDf = pd.concat([principalDf, y], axis = 1)
print(“After concatenating with the target variable\n\n”,finalDf.head())

#Visualize the principal components
def visual(df):

df = df.sample(3)

ax = sns.countplot(x=”principal component 1″, data=df)

ax = sns.countplot(x=”principal component 2″, data=df)


#Scatter plot
ax = sns.scatterplot(x=”principal component 1″, y=”principal component 2″, data=finalDf)

#Explained variance
print(“The explained variance ratio is\n\n”,pca.explained_variance_ratio_*100)

implement Principal Component Analysis (PCA) using sklearn in python
import libraries
load data set URL
Standardizing the features
import StandardScaler
Actual Features before standardizing
Cumulative sum of variance
plot for variance explained
PCA Analysis
fit PCA for 2 components
principal component