decision Tree.py

#!/usr/bin/env python
# coding: utf-8

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import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import confusion_matrix,classification_report


# Load Dataset

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df=pd.read_csv("C:/Users/3yearb1/Desktop/ML_datasets/PlayTennis.csv")


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display(df)


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print(df.describe())


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outlook = df["Outlook"].str.get_dummies(" ")
print(outlook)


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temp = df["Temperature"].str.get_dummies(" ")
print(temp)


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hum = df["Humidity"].str.get_dummies(" ")
print(hum)


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wind = df["Wind"].str.get_dummies(" ")
print(wind)


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playtennis = df["Play Tennis"].str.get_dummies(" ")
print(playtennis)


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df.drop(['Outlook','Temperature','Humidity','Wind','Play Tennis'],axis=1,inplace=True)


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df=pd.concat([outlook,temp,hum,wind,playtennis],axis=1)       


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display(df)


# Prepare X and Y

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x=df.drop(['Yes','No'],axis=1)
y=df[['Yes']]


# Split X & Y into training and testing dataset

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X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=42)


# Building Model

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dt=DecisionTreeClassifier(criterion = 'entropy')


# Training Model

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dt.fit(X_train,y_train)


# Testing Model

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y_pred = dt.predict(X_test)


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print(y_pred)


# Calculating Confusion matrix and Classification report

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m=confusion_matrix(y_test,y_pred)


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m


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report = classification_report(y_test, y_pred)
display(report)


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