Machine Learning Project

Introduction

This repository contains a series of machine learning assignments that explore various aspects and techniques of ML. Each assignment focuses on different algorithms and real-world applications, demonstrating the versatility and power of machine learning across different domains.

Contents

1. Spam Detection Using Support Vector Machine (SVM)
2. Heart Disease Prediction
3. Linear Regression Analysis on World Happiness Report Data
4. Iris Species Prediction and Clustering

Project Highlights

1. Spam Detection Using Support Vector Machine (SVM)

• Goal: Classify text messages as spam or ham
• Techniques: Text preprocessing, TF-IDF vectorization, SVM
• Key Findings:
• High accuracy on test data
• Potential overfitting issues addressed
• Simplified model training process for better performance

2. Heart Disease Prediction

• Goal: Predict heart disease presence from patient data
• Techniques: Data cleaning, feature engineering, logistic regression, decision tree, random forest, voting classifiers
• Performance:
• Random Forest achieved highest accuracy (88.04%)
• All models showed good discrimination ability (ROC AUC 0.86-0.94)

3. Linear Regression Analysis on World Happiness Report Data

• Goal: Predict country happiness scores based on socio-economic factors
• Techniques: Data cleaning, correlation analysis, linear regression
• Performance:
• R-squared value of 0.83
• MSE of 0.19, indicating good predictive power

4. Iris Species Prediction and Clustering

• Goal: Predict iris flower species and perform clustering
• Techniques: Random Forest classification, KMeans clustering, PCA visualization
• Performance:
• Random Forest achieved 98% cross-validation accuracy
• KMeans clustering revealed 3 distinct clusters aligning with species

Setup and Installation

1. Clone this repository:

git clone https://github.com/mmalloul/Machine-Learning.git

2. Install the required dependencies:

pip install -r requirements.txt

Usage

Each Jupyter notebook (.ipynb file) contains a self-contained analysis. To run a notebook:

1. Start Jupyter Lab or Jupyter Notebook:

jupyter lab

or

jupyter notebook

2. Navigate to the desired notebook and run the cells in order.

Requirements

See requirements.txt for a list of required Python packages.

Conclusion

This project showcases the application of various machine learning techniques across different domains, ensuring high-quality AI implementations and demonstrating their practical utility in solving real-world problems. From text classification to regression analysis and clustering, these assignments cover a wide range of ML applications.

Future Work

• Explore more advanced techniques like deep learning for complex datasets
• Implement ensemble methods to potentially boost performance
• Investigate interpretability techniques for better model understanding
• Expand datasets or incorporate additional features for improved predictions

Contributors

• Mohammed Malloul - Linkedin