lda_10fold_cv.py

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.metrics import plot_confusion_matrix
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import RepeatedStratifiedKFold
from sklearn.naive_bayes import GaussianNB
from sklearn.pipeline import Pipeline
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
import seaborn as sns
import matplotlib.pyplot as plt

# Importing the dataset
dataset = pd.read_csv('data.csv')
dataset = dataset.fillna(0) #Filling NAN values with 0


#Check if the data is normally distributed or not
sns.distplot(dataset, hist = False, kde = True,
            kde_kws = {'shade': True, 'linewidth': 2}, 
            label = "Non-Normal", color ="green")
# adding legends to the subplots
plt.legend(loc = "upper right")

X = dataset.iloc[:,2:12].values
y = dataset.iloc[:,0].values

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)

from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

lda = LDA(n_components = 1)  #n_components=number of classes-1
X_train = lda.fit_transform(X_train, y_train)
X_test = lda.transform(X_test)

# Fitting Logistic Regression to the Training set
from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression(random_state = 0)
classifier.fit(X_train, y_train)

# Predicting the Test set results
y_pred = classifier.predict(X_test)

# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
plot_confusion_matrix(classifier, X_test, y_test) 
print(classification_report(y_test,y_pred))
score=cross_val_score(classifier,X_train,y_train,cv=10)
cross_val_error=1-score.mean()
print(cross_val_error*100)