Algorithm : Iris dataset with Naive Bayes / Research

iris.py

# Author: Remon Hasan , University of Asia Pacific
#Efficient process for Pycharm compiler for python
#Naive bayes algorithms for decting the real note of money

#python linear library pandas / cmd command line: pip install pandas
import pandas as pd

#python numpy library / cmd command line: pip install numpy
import numpy as np

# read the data file as csv
irisdata = pd.read_csv(r"C:\Users\Plab5 Pc27\Desktop\research\irish.csv")

# print data defined by objectirisdata
print(irisdata.shape)
print(irisdata.head())

# classify the levels from the data
x = bankdata.drop('Variety',axis=1)
y = bankdata['Variety']
X = bankdata.drop('Variety',axis=1)

# testing with 20% data for 80% data's 
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.20)

# python sklearn library / cmd command line: pip install sklearn
#python GaussianNB library / cmd command line: pip install GaussianNB
#using naive bayes 
from sklearn.naive_bayes import GaussianNB
clf = GaussianNB()
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
print(y_pred)