Play Tennis.py

# Load libraries
import numpy as np
import pandas as pd
from sklearn import metrics #Import scikit-learn metrics module for accuracy calculation
df=pd.read_csv("Play Tennis.csv")
from sklearn import preprocessing
string_to_int= preprocessing.LabelEncoder() #encode your data
df=df.apply(string_to_int.fit_transform) #fit and transform it
#To divide our data into attribute set and Label:
feature_cols = ['Outlook','Temprature','Humidity','Wind']
X = df[feature_cols ]        #contains the attribute 
y = df.Play_Tennis           #contains the label

#To divide our data into training and test sets:
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)

# perform training 
from sklearn.tree import DecisionTreeClassifier              # import the classifier
classifier = DecisionTreeClassifier(criterion="entropy")     # create a classifier object
classifier.fit(X_train, y_train)                             # fit the classifier with X and Y data 
#Predict the response for test dataset
y_pred= classifier.predict(X_test)  
print(y_pred)
pred= classifier.predict([[0,1,0,1]])  
print(pred)

# Model Accuracy, how often is the classifier correct?
from sklearn.metrics import accuracy_score
print("Accuracy:",metrics.accuracy_score(y_test, y_pred))