Machine Learning Project – Tennis Game in Decision Tree

Program 1

Tennis Dataset

import pandas as pd
import numpy as np
from sklearn import tree
from sklearn.preprocessing import LabelEncoder


df_tennis=pd.read_csv("D://scikit_data/tennisData/tennis.csv")

df_tennis


df_tennis.shape


df_tennis.info()


df_tennis.isnull().sum()


df_tennis


# Label Encoding 
le=LabelEncoder()
df_tennis['outlook_new']=le.fit_transform(df_tennis['outlook'])
df_tennis['temp_new']=le.fit_transform(df_tennis['temp'])
df_tennis['humidity_new']=le.fit_transform(df_tennis['humidity'])
df_tennis['windy_new']=le.fit_transform(df_tennis['windy'])
df_tennis['play_new']=le.fit_transform(df_tennis['play'])



df_tennis


df_tennis=df_tennis.drop(['outlook','temp','humidity','windy','play'],axis='columns')

 
df_tennis


# Independed Variables(input)
df_input=df_tennis.drop('play_new',axis='columns')


df_input


# Depended Variables
df_output=df_tennis.drop(['outlook_new','temp_new','humidity_new','windy_new'],axis='columns')


df_output


# Model Predication
model=tree.DecisionTreeClassifier()
type(model)


# Train 
model.fit(df_input,df_output)


model.predict([[0,1,0,0]])


model.score(df_input,df_output)

out1=int(input("Enter out look Weather(overcast-0,rainy-1,sunny-2) :"))
temp1=int(input("Enter Temperature(cool-0,hot-1,mild-2):"))
hum1=int(input("Enter Humidity:(high-0,normal-1)"))
wend1=int(input("Enter windy or not(TRUE-1,FALSE-0) :"))
result=model.predict([[out1,temp1,hum1,wend1]])
if(result==1):
    print("********PLayer Played a Tennis Game*******")
else:
    print(".......Player does not Played a Tennis Game.....")