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🤖 Classification vs Regression in Machine Learning

This repository explores the two major types of supervised learning in Machine Learning: Classification and Regression. You'll find concise theoretical explanations, real-world use cases, and hands-on examples using Python (Scikit-learn, Pandas, Matplotlib).

📘 Overview

Aspect Classification Regression
Output Type Categories / Labels Continuous Values
Examples Spam Detection, Disease Diagnosis Price Prediction, Temperature Forecasting
Algorithms Logistic Regression, SVM, Decision Tree Linear Regression, SVR, Ridge, Lasso
Metrics Accuracy, Precision, Recall, F1 MAE, MSE, RMSE, R² Score

📌 Real-World Examples

✅ Classification

  • Iris Flower Classification: Predict species from petal/sepal dimensions.
  • Spam Email Detection: Predict whether an email is spam or not.

📈 Regression

  • House Price Prediction: Estimate house prices based on features.
  • Student Score Prediction: Predict marks based on study hours.

🔧 Technologies Used

  • Python 3
  • Pandas
  • NumPy
  • Scikit-learn
  • Matplotlib & Seaborn

📚 References

  • Scikit-learn documentation
  • "Hands-On ML" by Aurélien Géron
  • Kaggle Datasets

🙌 Contributions Welcome

If you'd like to add more notebooks, new datasets, or improve visualizations, feel free to fork and open a pull requests..