naive_bayes.py

# Naive Bayes

# Importing the libraries
import numpy as np ## scientific comutaion
import matplotlib.pyplot as plt  ## Visulization
import pandas as pd  ## Reading data

# Importing the dataset
dataset = pd.read_csv('https://raw.githubusercontent.com/shivang98/Social-Network-ads-Boost/master/Social_Network_Ads.csv') ## Reading data from the url
X = dataset.iloc[:, [2, 3]].values  ## 
y = dataset.iloc[:, -1].values

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)

# Feature Scaling


# Training the Naive Bayes model on the Training set
from sklearn.naive_bayes import GaussianNB
classifier = GaussianNB()
classifier.fit(X_train, y_train)

# Predicting the Test set results
y_pred = classifier.predict(X_test)
print(y_pred)
# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix,accuracy_score
cm = confusion_matrix(y_test, y_pred)
score = accuracy_score(y_test,y_pred)
print(cm)
print(score*100)