🧠 Machine Learning Problem Types – Learning Repository

This repository is organized by machine learning problem types, covering theory, code, datasets, and example use cases. It's designed for learning, experimenting, and showcasing understanding of different ML tasks.

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🔷 1. Supervised Learning Problems

Supervised learning involves labeled data. The model learns to map inputs to known outputs.

a. Classification

Goal: Predict discrete labels (e.g., categories)

Project	Description
Heart Disease Prediction	Predict heart disease from medical attributes
Customer Churn Prediction	Predicting which customers are likely to leave a telecom service using machine learning

b. Regression

Goal: Predict continuous numeric values

Project	Description
House Price Prediction	Predict house prices based on features like location and area
Fuel Consumption	Estimate fuel usage based on engine and vehicle metrics

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🔷 2. Unsupervised Learning Problems

Unsupervised learning finds patterns in unlabeled data.

a. Clustering

Goal: Group similar data points together

Project	Description
Customer Segmentation	Group customers by purchasing behavior
[Market Basket Analysis]	Identify product buying patterns

b. Dimensionality Reduction

Goal: Reduce features while preserving structure

Project	Description
[PCA on MNIST]	Visualize high-dimensional digit data
[t-SNE Visualization]	Nonlinear dimensionality reduction for complex patterns

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🔷 3. Semi-Supervised Learning

Use both labeled and a large amount of unlabeled data.

Project	Description
[Semi-supervised Medical Imaging]	Classify diseases with few labeled images

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🔷 4. Reinforcement Learning

Learn by interacting with an environment and maximizing reward.

Project	Description
[Grid World Q-Learning]	Simple RL agent learning to navigate grid

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🔷 5. Self-Supervised Learning

Learn representations using unlabeled data and pseudo-labels.

Project	Description
[SimCLR - Image Representations]	Contrastive learning for image features

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🔷 6. Time Series Forecasting

Predict future values from historical time-series data.

Project	Description
Sales Forecasting	Predict monthly sales using LSTM
[Weather Prediction]	Forecast weather conditions

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🔷 7. Anomaly Detection

Detect unusual patterns or outliers.

Project	Description
Credit Card Fraud Detection	Identify fraudulent transactions
[Network Intrusion Detection]	Detect suspicious network activity

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📌 Notes

• Projects will include: dataset, preprocessing, EDA, model training, evaluation, and documentation
• Ideal for interview preparation, portfolio, and deeper ML understanding

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📚 License

MIT License