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A collection of machine learning projects in Python, showcasing algorithms for classification, regression, and clustering using libraries like Scikit-learn and Pandas. Includes datasets, code, and tutorials for practical applications in data analysis and predictive modeling.

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📂 Machine Learning Projects

Welcome to the Machine Learning Projects repository! 🤖💡
This repo showcases a collection of ML projects across diverse domains like healthcare, finance, retail, and more.
Each project is crafted to boost your practical machine learning skills and help you master various algorithms and techniques. 🚀


📌 Project List

  1. Copy of Rock vs Mine Prediction 🪨
  2. Diabetes Prediction 🏥
  3. House Price Prediction 🏡
  4. Loan Status Prediction 💰
  5. Wine Quality Prediction 🍷
  6. Car Price Prediction 🚗
  7. Gold Price Prediction 📉
  8. Heart Disease Prediction ❤️
  9. Credit Card Fraud Detection 💳
  10. Medical Insurance Cost Prediction 📊
  11. Big Mart Sales Prediction 🏪
  12. Customer Segmentation using K-Means Clustering 👥
  13. Parkinson's Disease Detection 🧠
  14. Titanic Survival Prediction using ML 🚢
  15. Calories Burnt Prediction 🔥
  16. Spam Mail Prediction using ML 📧
  17. Movie Recommendation System using ML 🎥
  18. Breast Cancer Classification using ML 🏥
  19. Rainfall Prediction using ML
  20. Autism Prediction using ML 🧠
  21. Customer Churn Prediction using ML 📉
  22. Extra Learning Projects 🗂️

🚀 How to Use

Each project folder contains:

  • 📁 Dataset (if available)
  • 📄 Code (.ipynb or .py files)
  • 📃 README or brief explanation

To explore a project:

  1. Navigate to the linked folder above.
  2. Open the notebook or script.
  3. Follow the instructions inside.

🤝 Contributing

💡 Got improvements or new project ideas?
We welcome your contributions! Feel free to fork this repo and submit a pull request.


⭐ Show Your Support

If you find this repository helpful, star ⭐ it to help others discover it!
Happy Learning! 🚀🔥

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A collection of machine learning projects in Python, showcasing algorithms for classification, regression, and clustering using libraries like Scikit-learn and Pandas. Includes datasets, code, and tutorials for practical applications in data analysis and predictive modeling.

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