MLLab is a unified home for multiple small ML/DL projects (regression, classification, NLP, recommender systems, time-series/stock movement, etc.). Originally, each project was a standalone notebook; now they are being refactored into a single, organized, Streamlit-based lab. The goal is both: learning (understanding different ML tasks) and portfolio (presenting them as one cohesive app).
- Start Date: 20th November 2025
- End Date: 23rd November 2025
- Total Time Required: 4 days
| Name | GitHub Profile | LinkedIn Profile |
|---|---|---|
| Yash Suhas Shukla | GitHub |
- Breast Cancer Prediction (binary classification with CatBoost)
- Handwritten Digit Detection (deep learning on image pixels)
- House Price Prediction (regression with multiple models)
- Iris Flower Classification (EDA + classical ML)
- Movie Recommendation System (correlation-based recommender)
- Predicting Diabetes (classification with multiple models)
- Sentiment Analysis on Movie Reviews (LSTM-based NLP)
- Spam Email Detection (LSTM-based NLP)
- Stock Price Prediction (up/down movement classification)
- Titanic Survival Prediction (Kaggle-style pipeline)
MLLab/
app/
main.py # Streamlit entry point
projects/ # Per-project logic & UI modules
notebooks/ # Original Jupyter notebooks for each mini-project
models/ # Saved trained models (pkl, h5, etc.)
data/
raw/ # Original datasets (CSV, Excel, etc.)
processed/ # Cleaned / feature-engineered datasets
utils/
common.py # Shared helpers (to be implemented)
requirements.txt
README.md
- Python 3.x
- Streamlit
- scikit-learn
- TensorFlow / Keras (for DL projects)
- XGBoost, CatBoost (where used)
- Pandas, NumPy, Matplotlib, Seaborn
-
Create and activate a virtual environment (optional but recommended):
# Windows python -m venv venv .\venv\Scripts\activate # Mac/Linux python3 -m venv venv source venv/bin/activate
-
Install dependencies:
pip install -r requirements.txt
-
Run the app:
streamlit run app/main.py
Note: Some models (like Handwritten Digits, Spam Detection) may take a moment to train on the first run. They will be saved to the
models/directory for faster loading in subsequent runs.
This application is for educational and demonstration purposes only.
- Medical predictions (Breast Cancer, Diabetes) are NOT medical advice.
- Financial/Survival predictions (Stock, Titanic) are based on historical data and are not guarantees of future outcomes.
Even though it's free to use, MLLab grows best when the community jumps in!
- 🧩 Add new mini-projects or ML experiments
- 🛠 Refactor code, clean notebooks, or optimize pipelines
- 📊 Improve visualizations, metrics, and explanations
- 🧪 Create exercises, challenges, or “try it yourself” sections
- 🐛 Report bugs or suggest new features via issues
Just fork the repo, push your changes, and open a pull request.
Every small improvement makes MLLab a better lab for everyone. ⚗️
This project is governed by the Creative Commons Zero (CC0 1.0 Universal) license.
That means:
🔓 You can use, modify, share, teach, remix, or build on this work freely — even for commercial purposes.
🎁 No permission or attribution required. This is my gift to the Python community.
🔗 View Full License Text → CC0 1.0 Universal
Machine Learning feels complex — but it doesn’t have to stay mysterious.
MLLab is here to turn scattered ML concepts into clear, hands-on mini-projects you can actually run, tweak, and learn from.
If you found this helpful, consider starring the repository ⭐,
sharing it with a friend, or contributing a new experiment.
Stay curious. Experiment boldly. Build ML projects that actually run. ⚗️🤖