In this ML Algorithms course tutorial, we are going to learn “Decision Tree Regression in detail. we covered it by practically and theoretical intuition.
- What is Decision Tree?
- What are decision trees used for?
- How do Decision trees work?
- What is Decision Tree Regression?
- What is Gini impurity, entropy, cost function for CART algorithm?
- What is decision tree diagram?
- What is the difference between decision tree and random forest?
- How to implement Decision Tree Regression in python using sklearn?
Decision Tree Regression Source Code
# -*- coding: utf-8 -*-
"""Decision Tree Regression.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1-P0w3bewXTLPItdgVatdg8i5SzlIDX-L
## Business Problem - Predict the Price of Bangalore House
Using Decision Tree Regression - Supervised Machine Learning Algorithm
### Load Libraries
"""
import pandas as pd
"""### Load Data"""
path = r"https://drive.google.com/uc?export=download&id=1xxDtrZKfuWQfl-6KA9XEd_eatitNPnkB"
df = pd.read_csv(path)
df.head()
"""## Split Data"""
X = df.drop('price', axis=1)
y = df['price']
print('Shape of X = ', X.shape)
print('Shape of y = ', y.shape)
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=51)
print('Shape of X_train = ', X_train.shape)
print('Shape of y_train = ', y_train.shape)
print('Shape of X_test = ', X_test.shape)
print('Shape of y_test = ', y_test.shape)
"""##Decision Tree Regression - ML Model Training"""
from sklearn.tree import DecisionTreeRegressor
regressor = DecisionTreeRegressor(criterion='mse')
regressor.fit(X_train, y_train)
regressor.score(X_test, y_test)
"""## Predict the value of Home"""
X_test.iloc[-1, :]
regressor.predict([X_test.iloc[-1, :]])
y_test.iloc[-1]
pred = regressor.predict(X_test)
pred
y_test
"""Ab milenge next tutorial me,Tab tak ke liye SIKHATE SIKHATE kuch IMPLEMENT karte raho, Thank You.....-:)"""