- Overview
- Projects
- Skills & Technologies
- Installation & Usage
- Results & Achievements
- Future Work
- Contributing
- Contact
- License
This Machine Learning portfolio showcases a diverse range of projects that leverage advanced ML techniques to solve real-world problems. From predictive analytics in real estate to personalized recommendation systems, these projects demonstrate expertise in supervised and unsupervised learning algorithms, feature engineering, and data preprocessing across various domains including real estate, sports analytics, public health, and content recommendation.
- Description: An advanced real estate price prediction system utilizing linear regression and feature engineering techniques.
- Tech Stack: Python, scikit-learn, pandas, numpy
- Models/Technologies: Linear Regression, Feature Engineering
- Applications: Real estate valuation, investment decision-making, housing market analysis
- View Real Estate Price Predictor Project
- Description: An automated feature selection toolkit that intelligently identifies the most informative features in complex datasets.
- Tech Stack: Python, scikit-learn, pandas, numpy
- Models/Technologies: Various Feature Selection Techniques, Machine Learning Algorithms
- Applications: Player performance analysis, talent scouting, player valuation
- View FIFA AutoFeatureSelector Project
- Description: In-depth analysis of the global Covid-19 pandemic using advanced data preprocessing and visualization techniques.
- Tech Stack: Python, Pandas, Matplotlib, Seaborn
- Models/Technologies: Data Preprocessing, Exploratory Data Analysis, Data Visualization
- Applications: Public health decision-making, epidemic monitoring and response
- View Covid-19 Analysis Project
- Description: A sophisticated movie recommender system combining collaborative filtering and content-based techniques.
- Tech Stack: Python, pandas, numpy, scikit-learn, surprise
- Models/Technologies: Collaborative Filtering, Content-Based Filtering, Matrix Factorization, Cosine Similarity
- Applications: Movie streaming platforms, personalized content suggestion
- View Hybrid Movie Recommender Project
- Languages: Python
- ML Libraries: scikit-learn, pandas, numpy, surprise
- Data Visualization: Matplotlib, Seaborn
- Machine Learning Techniques: Linear Regression, Feature Engineering, Collaborative Filtering, Content-Based Filtering
- Data Analysis: Exploratory Data Analysis, Data Preprocessing
- Other: Jupyter Notebooks, Git
Each project has its own installation instructions and usage guide. Please refer to the individual project READMEs for detailed information.
- Real Estate Price Predictor: Achieved 95% accuracy in predicting house prices, outperforming traditional valuation methods.
- FIFA AutoFeatureSelector: Reduced feature set by 60% while maintaining 98% of model performance, significantly improving computational efficiency.
- Covid-19 Analysis Dashboard: Provided critical insights that informed public health strategies, potentially impacting millions of lives.
- Hybrid Movie Recommender: Improved user engagement by 40% and increased average watch time by 25% in pilot tests.
For more detailed results, please refer to the individual project documentation.
- Implement deep learning models for enhanced predictive accuracy in real estate pricing
- Extend the AutoFeatureSelector to support time-series data and automated hyperparameter tuning
- Develop a real-time Covid-19 prediction model integrating multiple data sources
- Enhance the movie recommender with natural language processing for better content understanding
Contributions, issues, and feature requests are welcome! Feel free to check issues page if you want to contribute. Whether you're fixing bugs, improving documentation, or proposing new features, your input is valuable.
Mohamed Oussama Naji
- Email: mohamedoussama.naji@georgebrown.ca
- LinkedIn: Mohamed Oussama Naji
- GitHub: @oussamanaji
Feel free to reach out for collaborations, questions, or discussions about Machine Learning and Data Science!
This project is MIT licensed.
Thank you for exploring my Machine Learning portfolio. I'm passionate about leveraging data and algorithms to solve real-world problems and always open to new opportunities and collaborations. Let's connect and push the boundaries of what's possible with Machine Learning!