A pure-Rust workspace for classical ML: scikit-learn-style preprocessing & models (datarust) plus one-call data profiling & quality reports (datarust-profile). Zero dependencies by default.
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Updated
Aug 1, 2026 - Rust
A pure-Rust workspace for classical ML: scikit-learn-style preprocessing & models (datarust) plus one-call data profiling & quality reports (datarust-profile). Zero dependencies by default.
📈 My personal 180-day machine learning challenge repository! Follow my journey as I complete daily exercises, mini-projects, and learning resources related to ML. 👨💻 This repository is open for everyone to see and use as inspiration for their own ML challenge. Fork this repository and join the challenge today! 🚀 Let's learn and grow together
ML-powered web app that predicts heart disease risk from clinical data using a KNN classifier, deployed with Streamlit.
This Flask app predicts house prices using a RandomForestRegressor model trained on a housing dataset. It includes data pre-processing with pipelines and imputers, stratified train-test splitting, and a user input form. Predictions are displayed on the web page, making it ideal for learning basic machine learning deployment with Flask.
End-to-end supervised ML project predicting student performance using study behavior data. Covers EDA, visualization, Linear Regression modeling, and evaluation metrics.
Resumable hyperparameter search for scikit-learn, backed by local SQLite. Restart interrupted experiments without losing completed trials.
LedgerGuard: Neo-bank Fraud Detection and Transaction Categorisation System || Tech Stack: Python 3.11, Django, pandas, scikit-learn, Matplotlib, sentence-transformers (MiniLM), PyTorch, LightGBM, XGBoost, imbalanced-learn, pytest, factory_boy, GitHub Actions, Docker, Gunicorn, Render
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