Regression & Classification Models 📊

This repository contains machine learning projects focusing on foundational Regression and Classification techniques using popular datasets. It includes Jupyter Notebooks, datasets, and a detailed academic report.

Project Structure 📁

• Regreesion.ipynb: Jupyter Notebook demonstrating Regression algorithms predicting continuous variables.
• Classification.ipynb: Jupyter Notebook demonstrating Classification algorithms for categorical prediction.
• California_Houses.csv: The classic California Housing dataset used to predict house prices based on various demographic and geographical features (Regression).
• magic04.data: The MAGIC Gamma Telescope dataset, used to simulate the registration of high-energy gamma particles in an atmospheric Cherenkov telescope, predicting signal (gamma) vs. background (hadron) (Classification).
• ML_Assignment _1_Report.pdf: A comprehensive PDF report detailing the methodology, results, and evaluations of the models implemented in the notebooks.

Key Concepts Covered 🧠

• Data Preprocessing and Cleaning
• Model Training and Evaluation
• Overfitting vs Underfitting Analysis
• Regression Metrics (e.g., MSE, R-squared)
• Classification Metrics (e.g., Accuracy, Precision, Recall, F1-Score)

How to Follow Along 💻

1. Clone this repository:

   git clone https://github.com/BassemRamdan/Regression-Classification.git

2. Navigate to the project directory:

   cd Regression-Classification

3. Launch Jupyter Notebook or use an IDE like VSCode:

   jupyter notebook

4. Open the Regreesion.ipynb and Classification.ipynb notebooks inside your browser and run the cells sequentially!

Requirements 🛠️

To run the notebooks successfully, you'll generally need:

• python 3.x
• pandas
• numpy
• scikit-learn
• matplotlib / seaborn