A hands-on machine learning repository where I build, experiment, and share real-world models, datasets, and insights to strengthen my understanding and practical skills.
This repository documents my journey in Machine Learning through practical implementation. Instead of focusing only on theory, I work on real-world problems, build models, and continuously improve my skills.
- End-to-end machine learning projects
- Data preprocessing and feature engineering
- Model building and evaluation
- Real-world datasets and problem statements
- Clear and structured code
Currently exploring and applying Linear Regression through multiple real-world projects:
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🏎️ F1 Race Prediction Predicting race outcomes based on performance and historical data
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🎓 Student Score Prediction Predicting student scores based on study hours and other factors
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🏠 House Price Prediction Estimating house prices using features like location, size, and amenities
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📦 Delivery Time Prediction Predicting delivery time based on distance, traffic, and order details
To build a strong foundation in Machine Learning and Data Science by consistently learning, applying concepts, and solving real-world problems.
- Python
- Pandas
- NumPy
- Matplotlib / Seaborn
- Scikit-learn
- Explore advanced ML algorithms
- Work on larger and more complex datasets
- Add model deployment projects
- Improve model performance with tuning techniques
This is a personal learning repository, but suggestions and ideas are always welcome!
⭐ If you find this helpful, feel free to star the repo!