S

scikit-learn

Practical tasks on Deep Learning (DL) and Neural Networks (NN). 🤖

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.

A data mining project analyzing hate crime patterns in the United States from 2017 to 2025, using clustering, predictive modeling, and association rule mining.

Materials on various sections in Computer Science (CS). 🎓

A production-grade machine learning system for classifying celestial objects — stars, galaxies, and quasars — from 500,000 photometric observations sourced from the Sloan Digital Sky Survey (SDSS DR18). The pipeline covers LOF-based outlier detection, SMOTEENN class balancing, and SelectKBest feature selection, with six classifier configurations benchmarked against each other. Random Forest achieved the highest accuracy at ~99.51%, while LightGBM was selected for deployment due to its faster inference, smaller footprint, and clean ONNX export path. The system is fully containerized with Docker, backed by a CI/CD pipeline, and served live via a Gradio interface on Hugging Face Spaces.

Analisi della transizione energetica nell'UE27 (2000–2030) con Python, Power BI e Machine Learning. Tre indici sintetici originali (ITE, ICP, RGI) calcolati su dati Our World in Data e proiettati al 2030 tramite regressione lineare.

Progetto IFTS Data Analysis & AI - SIAM1838

Introduction to classification using machine learning and deep learning (PyTorch, TensorFlow, Keras)

Statistics for sci-kit learn.

Analysis of Kilter Board data, along with predictive models for V-grades based on holds and angle.

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!

Analysis of Tension Board 2 data, along with predictive models for V-grades based on holds and angle.

Fundamental theory and practice in Data Science (DS). 🧮

Supervised learning pipeline for rare event operational failure prediction, integrating leakage resistant preprocessing, class weighted modeling, precision recall threshold calibration, ROC AUC benchmarking, and permutation based feature importance to analyze production stress drivers.

API FastAPI permettant d’identifier des espèces de pingouins à partir de données tabulaires optionnelles et/ou d’une image.

L’API renvoie une liste d’espèces probables avec leurs probabilités et inclut :

Classification Machine Learning (RF, KNN, LR) Fusion multimodale (tabulaire + image) Journalisation des requêtes/réponses (SQLite) Interface web pour consulter les logs Interface web pour tester les prédictions Notebooks pour exploration et entraînement

Projet réalisé dans le cadre de la formation Développeur en Intelligence Artificielle (Simplon).

Classic ML + XGBoost + Streamlit (Thai movie reviews)

Training various machine learning models for NFLX stock price prediction with data collection, cleaning, and visualization tools.

Exercises and examples from the scikit learn mooc provided by Inria

Linear regression refresher

This project predicts house prices using machine learning models based on the King County House Sales dataset. It explores Simple Linear, Multiple Linear, Polynomial, and Ridge Regression models, comparing their performance in terms of accuracy. The best model identified is Polynomial Regression, achieving an R² score of 0.75.

examples of Applied ML