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##### How to implement Linear Discriminant Analysis (LDA) using sklearn in python?
###### Description

To implement LDA using python.

#### Linear Discriminant Analysis:

• LDA is used mainly for dimension reduction of a data set.
• LDA tries to reduce dimensions of the feature set while retaining the information that discriminates output classes.
• LDA is a supervised dimensionality reduction technique.
• Its used to avoid overfitting.

#### 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 related to output classes.
• Each feature has own eigen vectors and eigen values.
• The eigen vector with the highest eigenvalue is therefore the principal component.

#### Explained Variance:

• It contains variance ratio for each linear discriminant.
• First discriminant having more variance data points.
• Second discriminant having less variance data points.
###### Sapmle Code

#import libraries
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
import warnings
warnings.filterwarnings(“ignore”)

#load data set URL
url = “https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data”
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_trans = StandardScaler().fit_transform(X)

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

#covariance matrix
covar_matrix = LDA(n_components = 4)

covar_matrix.fit(X_trans,y)

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(‘LDA Analysis’)
plt.ylim(30,100.5)
plt.style.context(‘seaborn-whitegrid’)
plt.plot(var)
plt.show()

#Fit LDA for two components
lda = LDA(n_components = 2)
LinearComponents = lda.fit_transform(X_trans, y)

#make it as data frame
finalDf = pd.DataFrame(data = LinearComponents
, columns = [‘linear discriminant 1’, ‘linear discriminant 2’])

print(“After transform X, the linear discriminants are\n\n”,finalDf.head())
print(“\n”)

#data visualizations
print(“2D LDA Visualization\n”)

def visual(df):
np.random.seed(1)
sample_size = 5
df = df.sample(sample_size)
plt.figure(figsize=(8,5))
sns.distplot(finalDf[‘linear discriminant 1’], hist = True, kde = False,kde_kws = {‘linewidth’: 3})
plt.show()
visual(finalDf)
print(“\n”)

def visual1(df):
np.random.seed(1)
sample_size = 5
plt.figure(figsize=(8,5))
sns.distplot(finalDf[‘linear discriminant 2’], hist = True, kde=False,
bins=int(180/5), color = ‘blue’,
hist_kws={‘edgecolor’:’black’})
plt.show()

visual1(finalDf)
print(“\n”)

#scatter plot
ax = sns.scatterplot(x=”linear discriminant 1″, y=”linear discriminant 2″, data=finalDf)
plt.show()
print(“\n”)

print(“The explained variance percentage is:”,lda.explained_variance_ratio_*100)