Write python program to implement decision tree whether or not to play tennis

# importing data

import numpy as np

import pandas as pd

df = pd.read_csv('H:\\ML_Practical\\CSV Files\\weather.csv')

# converting categorical variables into dummies/indicator variables

df_getdummy=pd.get_dummies(data=df, columns=['Temperature', 'Outlook', 'Windy','Humidity'])

# separating the training set and test set

from sklearn.model_selection import train_test_split

X = df_getdummy.drop('Played?',axis=1)

y = df_getdummy['Played?']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=101)

# visualising the decision tree diagram

from sklearn.tree import DecisionTreeClassifier

dtree = DecisionTreeClassifier(max_depth=3)

dtree.fit(X_train,y_train)

predictions = dtree.predict(X_test)

print(predictions)

from sklearn.tree import plot_tree

import matplotlib.pyplot as plt

fig = plt.figure(figsize=(6,6))

plot_tree(dtree, feature_names=df_getdummy.columns, fontsize=6, filled=True, class_names=['Not Play', 'Play'])

plt.show()