Decision Tree play tennis

 #numpy and pandas initialization

import numpy as np

from sklearn.preprocessing import LabelEncoder

from sklearn.model_selection import train_test_split

from sklearn.tree import DecisionTreeClassifier # Import Decision Tree Classifier

from sklearn.metrics import accuracy_score

import pandas as pd

# Read the data from the CSV file

data = pd.read_csv("tennis.csv")

# Encode categorical variables into numerical values

label_encoder = LabelEncoder()

data_encoded = data.copy()

for column in ['Outlook', 'Temperature', 'Humidity', 'Wind', 'Play Tennis']:

    data_encoded[column] = label_encoder.fit_transform(data[column])

# Split the data into features (X) and the target variable (y)

x = data_encoded.drop('Play Tennis', axis=1)

y = data_encoded['Play Tennis']

# Split the data into training and testing sets

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.4, random_state=1)

# Create Decision Tree classifer object

clf = DecisionTreeClassifier(criterion = 'entropy' , max_depth = 3)

# Train Decision Tree Classifer

clf = clf.fit(x_train , y_train)

#Predict the response for test dataset

y_pred = clf.predict(x_test)

# Calculate and print the accuracy of the model on the testing data

accuracy = accuracy_score(y_test, y_pred)

print("Accuracy on the testing data:",accuracy*100)

# from sklearn.preprocessing import LabelEncoder

# Le = LabelEncoder()

#

# PlayTennis['outlook'] = Le.fit_transform(PlayTennis['outlook'])

# PlayTennis['temp'] = Le.fit_transform(PlayTennis['temp'])

# PlayTennis['humidity'] = Le.fit_transform(PlayTennis['humidity'])

# PlayTennis['windy'] = Le.fit_transform(PlayTennis['wind'])

# PlayTennis['play'] = Le.fit_transform(PlayTennis['play'])

#

# PlayTennis

#

# y = PlayTennis['play']

# X = PlayTennis.drop(['play'],axis=1)

# # Fitting the model

#

# clf = DecisionTreeClassifier(criterion = 'entropy', max_depth = 5)

# clf = clf.fit(X, y)

#

# #Predict the response for test dataset

# y_pred = clf.predict(x_test)

#

# # Model Accuracy

# print("Accuracy:" , metrics.accuracy_score(y_test, y_pred)*100)