· Machine Learning · 2 min read
📋 Prerequisites
- Basic understanding of data and machine learning
🎯 What You'll Learn
- Understand what regression analysis is and why it is important
- Learn the types of regression analysis
- Apply regression analysis to real-world data
- Interpret results from regression models
Introduction
Regression analysis is a statistical method used to examine the relationship between a dependent variable (target) and one or more independent variables (features).
It is widely used in machine learning for predicting continuous outcomes, such as predicting house prices, stock prices, or sales forecasts.
1️⃣ Why Use Regression Analysis?
✅ Understand how features influence the target variable.
✅ Predict future outcomes using existing data.
✅ Identify trends and patterns in your data.
2️⃣ Types of Regression Analysis
a) Simple Linear Regression
Uses one independent variable to predict a dependent variable by fitting a straight line.
Equation:
[ y = mx + c ]
b) Multiple Linear Regression
Uses two or more independent variables to predict the dependent variable.
c) Polynomial Regression
Uses polynomial terms to capture non-linear relationships.
3️⃣ Example: Predicting House Prices
Suppose you want to predict the price of a house based on its size.
✅ Independent variable (X): Size of the house in square feet.
✅ Dependent variable (y): Price of the house.
Using simple linear regression, you can find the best-fit line that describes how house size affects its price.
4️⃣ Using Regression in Python
from sklearn.linear_model import LinearRegression
import numpy as np
# Example data
X = np.array([[1000], [1500], [2000], [2500]])
y = np.array([200000, 300000, 400000, 500000])
model = LinearRegression()
model.fit(X, y)
# Predict price for a 1800 sq ft house
predicted_price = model.predict([[1800]])
print(predicted_price)5️⃣ Interpreting Regression Results
✅ Slope (Coefficient): Shows how much the target variable changes with a unit change in the feature.
✅ Intercept: The expected value of the target when all features are zero.
✅ R-squared (R²): Measures how well the model explains the variability in the target variable (closer to 1 means better fit).
Conclusion
Regression analysis is:
✅ A powerful tool for understanding and quantifying relationships in data.
✅ Widely used for predictive modeling in machine learning.
✅ An essential skill for data analysts and ML practitioners.
What’s Next?
✅ Try using multiple linear regression with multiple features (e.g., size, number of bedrooms, location).
✅ Explore polynomial regression to handle non-linear relationships.
✅ Continue your structured learning on superml.org.
Join the SuperML Community to discuss your regression projects and learn collaboratively.
Happy Learning! 📈