ass6(gaussian)

 # Importing Libraries 

import numpy as np 

import pandas as pd 

import matplotlib.pyplot as plt 

from sklearn.naive_bayes import GaussianNB 

from sklearn.naive_bayes import MultinomialNB 

from sklearn.model_selection import train_test_split 

from sklearn.metrics import accuracy_score, f1_score 

from sklearn import preprocessing 

from sklearn.tree import DecisionTreeClassifier 

from google.colab import files 

uploaded = files.upload() 

data=pd.read_csv('pima-indians-diabetes.csv') 

raw = data.copy() 

data.describe() 

headers = ['preg', 'plas', 'pres', 'skin', 'insu', 'bmi', 'pedi', 'age', 'class'] 

data = pd.read_csv('pima-indians-diabetes.csv', names=headers) 

data.head() 

data.describe() 

data=data.drop_duplicates() 

feature_df=data[data.columns[0:-1]] 

x=np.asarray(feature_df) 

y=np.asarray(data[data.columns[-1]]) 

x1 = preprocessing.normalize(x, axis=0) 

x_train,x_test,y_train,y_test=train_test_split(x1,y,test_size=0.2,random_state=16) 

pd.DataFrame(data=x1, columns=headers[:-1]).hist() 

gnb = GaussianNB() 

y_pred = gnb.fit(x_train, y_train).predict(x_test) 

gnb_accuracy = accuracy_score(y_test, y_pred) 

gnb_f1 = f1_score(y_test, y_pred) 

print("Gaussian Naive Bayes:") 

print("Accuracy:", round(gnb_accuracy*100, 2)) 

print("F1-score:", round(gnb_f1*100, 2)) 

mnb = MultinomialNB() 

y_pred_mnb = mnb.fit(x_train, y_train).predict(x_test) 

mnb_accuracy = accuracy_score(y_test, y_pred_mnb) 

mnb_f1 = f1_score(y_test, y_pred) 

print("Multinomial Naive Bayes:") 

print("Accuracy:", round(mnb_accuracy*100, 2)) 

print("F1-score:", round(mnb_f1*100, 2))