Machine Learning Course Tasks

This repository contains assignments and projects for the Machine Learning course completed by Team 3.

Team Members

• AbdelRahman Hesham Zakaria (1210148)
• Shehab Tarek ElHadary (1210366)
• Omar Walid Mohamed (1210269)

Repository Structure

Assignment 1 - Data Preprocessing

• Preprocessing.ipynb - Initial data preprocessing techniques
• Preprocessing1.ipynb - Advanced preprocessing methods
• Datasets: Healthcare stroke data and medical insurance data

Assignment 4a - Loan Approval Prediction

• task4a updated.ipynb - Loan approval classification using:
  • Logistic Regression
  • Hard Margin SVM
  • Soft Margin SVM with RBF Kernel
• Dataset: Loan approval data

Assignment 5 - Customer Churn Analysis

• Assignment 5.ipynb - Customer churn prediction analysis
• Dataset: Churn dataset

Assignment 6 - CART (Classification and Regression Trees)

• CART ML Report.pdf - Comprehensive report on CART algorithm covering:
  • Binary recursive partitioning
  • Splitting criteria (Gini impurity, entropy)
  • Pruning techniques
  • Model strengths and limitations

Assignment 7 - Advanced Churn Prediction

• Assignment 7.ipynb - Enhanced customer churn analysis
• Dataset: Churn dataset

Technologies Used

• Python - Primary programming language
• Jupyter Notebook - Development environment
• scikit-learn - Machine learning library
• pandas - Data manipulation
• numpy - Numerical computing
• matplotlib & seaborn - Data visualization

Key Concepts Covered

• Data preprocessing and feature engineering
• Classification algorithms (Logistic Regression, SVM)
• Decision Trees and CART
• Model evaluation and comparison
• Confusion matrices and performance metrics
• Customer churn prediction
• Loan approval prediction

Notes

• Datasets should be placed in their respective assignment folders for notebooks to run correctly

How to Run

1. Clone the repository
2. Ensure you have Python 3.x installed with required libraries:

   pip install jupyter pandas numpy scikit-learn matplotlib seaborn

3. Place the required CSV datasets in the appropriate assignment folders
4. Open the desired notebook:

   jupyter notebook

License

This repository is for educational purposes as part of a university Machine Learning course.

---

Course: Machine Learning
Academic Year: 2024-2025 (First Term)