Dectree.py

# -*- coding: utf-8 -*-
"""ML_LAB_W10_DecTREE.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1G_fL2pYcYqd7Zcw6VEr0jJKr3nTxEEXH
"""

import numpy as np
import pandas as pd
from sklearn.datasets import load_iris
iris = load_iris()
df = pd.read_csv('/content/data4_19.csv')
df.head()

df = pd.DataFrame(iris.data,columns=iris.feature_names)
df.head()

df['target'] = iris.target
df.head()

df['flower_name'] =df.target.apply(lambda x: iris.target_names[x])
df.head()

X = df.drop(['target','flower_name'],axis=1)
y = df['target']
from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.25)
print(X_train.shape,X_test.shape)

from sklearn import tree
model = tree.DecisionTreeClassifier()

model.fit(X_train,y_train)

model.score(X_test,y_test)

flower = {0:'Iris-setosa',2:'Iris-virginica',1:'Iris-Versicolor'}
flower[model.predict([[5.2,3.1,1.4,0.2]])[0]]

pdf = pd.read_csv('/content/golf-dataset.csv')
pdf.head()

pdf['Play Golf'] = pdf['Play Golf'].map({'No':0,'Yes':1})

pdf.head()

from sklearn import tree
X = pdf.drop('Play Golf',axis=1)
y = pdf['Play Golf']
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.25)
model = tree.DecisionTreeClassifier()

model.fit(X_train,y_train)

model.score(X_test,y_test)

pdf.head()

pdf['TEMPERATUR'].unique()

pdf['Temp'] = pdf['Temp'].map({'Hot':0,'Mild':1,'Cool':2})

pdf['OUTLOOK'].unique()

pdf['Outlook'] = pdf['Outlook'].map({'Rainy':0,'Overcast':1,'Sunny':2})

pdf['WINDY'] = pdf['WINDY'].map({'False':0,'True':1})

pdf['HUMIDITY'].unique()

pdf['Humidity'] = pdf['Humidity'].map({'High':0,'Normal':1})

pdf['Windy'].unique

pdf.Windy = pdf.Windy.replace({True: 1, False: 0})