M

ML

VERA (Video Extraction and Recognition Analysis) is a fully on-premises video OCR pipeline for extracting and recognizing text (license plates, street signs, billboards) from dashcam/driving footage. Built with PaddleOCR 3.x, OpenCV, FastAPI, PostgreSQL, and MinIO, with a PySide6 desktop GUI. Consists of two components: vera-engine (OCR pipeline, backend API, CLI) and vera-desktop (GUI layer). No cloud inference, all processing runs locally.

FairLiebt Matching-Service — Python 3.12 / FastAPI (regelbasiert Phase 1 + ML Phase 2)

A dual-channel ML-based spelling correction engine for Movies and TV search, trained on TMDB vocabulary.

CVerify is a production-ready AI recruitment platform that analyses CVs through a 3-phase ML pipeline, recommends matching jobs from 20 000+ real job postings, and provides a full recruiter/candidate/admin web interface with an integrated AI chatbot.

Simple ML Project using regression models and KMeans clustering to predict Y from A and B, classify results, and expose predictions through a FastAPI API.

FastAPI-based microservice that predicts the risk level of a user session https://user-risk-detection-api.vercel.app

Predicts the risk level of a user session using behavioral signals

In real time inference simulator using the MVTec dataset, aproach via DINOv2 and Claude Sonnet 3.5 (Amazon Bedrock's API)

Slimme huishoudboekje-app met AI-gestuurde categorisering. Importeer je ING/Revolut transacties, krijg automatisch inzicht in je uitgaven en beheer je budget met NIBUD-referenties. Privacy-first: alle ML draait lokaal.

Volledige ge amp/vibe coded met Claude Code!

The AskAnna CLI offers a command-line interface to the AskAnna platform. With the Python SDK you can run AskAnna functions directly from your Python script. The CLI & Python SDK simplifies the communication with the AskAnna platform.

FOMC rate decision predictor using ML bronze->silver->gold methods

A production-oriented Machine Learning pipeline that predicts whether an active user session will result in a purchase.

Model: XGBoost Classifier optimized for class imbalance.

Performance: ROC AUC 0.936 | F1-score 0.71 (at 0.30 threshold).

Key Features: Reproducible environment (uv), modular CLI for training/inference, leakage-free preprocessing, and SHAP interpretability analysis.

Data: UCI Online Shoppers Purchasing Intention Dataset.

Tech Stack: Python, XGBoost, Scikit-learn, Pandas, SHAP.

A comprehensive Python toolkit for analyzing protein structures and small molecules using real datasets from RCSB PDB and FDA-approved drugs.

A comprehensive machine learning pipeline for classifying astronomy images into 6 categories of celestial objects, featuring advanced data preprocessing, exploratory data analysis, and deep learning classification models.

https://huggingface.co/spaces/Saqib772/Astronomy_image_classfication

Kaggle Notebook: https://www.kaggle.com/code/saqibiqbal2/astronomy-image-classification

A Computer Vision algorithm for Malaria parasite detection and classification in digital images of thick blood smears.

Here’s the source code for my exploratory data analysis and model training for a movie recommendation system. The main model deployment code is in this repository.

Deployment Repo: (https://gitlab.com/aydie/ml-model-netflix-recommendation-system)

Website: aydie.in Contact: business@aydie.in

House Prices Competition on Kaggle

Een MLFlow wrapper die het makkelijk maakt om samen in dezelfde MLFlow omgeving te werken met bijvoorbeeld Teams. Maakt het daarnaast makkelijker om met standaard machine learning packages (nu alleen nog scikit-learn) modellen en scores te loggen.

A comprehensive exploration of various machine learning algorithms, including supervised, unsupervised, and reinforcement learning methods. This project will implement, analyze, and optimize algorithms like decision trees, random forests, SVMs, and neural networks, providing hands-on experience in selecting and applying them for different use cases.