🧠 ML Projects Repository

A collection of machine learning projects built to explore different problem types, algorithms, and workflows — ranging from basic regression to more advanced classification and custom metric-based systems.

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📁 Repository Structure

Each project follows a consistent and scalable structure:

Project_Name/
│
├── Data_Viz_Data/                 # Data Visualization Data and EDAs 
├── Model_Source_Code/             # Jupyter notebooks & Dataset files
│   ├── electricity_data.csv
|   ├── initial.ipynb      # Experimental / testing notebook
│   └── model_.ipynb       # Finalized model implementation
└── README.md              # Project-specific documentation

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🔁 Workflow Strategy

Each project follows a structured development pipeline:

🧪 initial.ipynb

• Used for experimentation and testing
• Feature engineering, EDA, trying different models
• Safe space for breaking things and iterating

✅ model_.ipynb

• Clean, finalized version of the model
• Only stable and verified logic is included
• Represents the “production-ready” notebook

Any new idea or modification is first tested in initial.ipynb, and once validated, transferred to model_.ipynb.

Note: Underscore in model_.ipynb represents respective model folders.

💾 Datsets

• Each project includes a sample dataset (100–200 rows) to allow quick setup and testing without heavy downloads.
• Due to the large size of full datasets, they are not stored in this repository. Instead, Kaggle dataset links are provided in each model’s README for easy access.
• This approach ensures:
  • ⚡ Fast cloning of the repository
  • 🧪 Easy experimentation with sample data
  • 📦 Access to complete datasets when needed

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📊 Projects Included

• ⚡ Electricity Consumption Prediction
• 🌡️ CPU Temperature Prediction
• 📖 Student Marks Prediction
• 💵 Bank Fraud Prediction

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🧠 Skills & Concepts Covered

• Regression & Classification
• Feature Engineering
• Data Visualization (EDA)
• Model Evaluation Metrics
• Handling Different Dataset Types
• Iterative Model Development

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🚀 Future Improvements

• Add advanced models (ensemble, boosting, etc.)
• Hyperparameter tuning
• Model deployment (Flask / API)
• Performance comparison across models
• Centralized experiment tracking

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⚙️ Tools & Libraries

• Python
• NumPy, Pandas
• Matplotlib, Seaborn
• Scikit-learn
• Jupyter Notebook

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📌 Note

This repository is built with a strong focus on:

• Structured experimentation
• Clean separation between testing and final models
• Consistency across projects
• Kaggle knowledge

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👨‍💻 Author

Hardik Basu

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