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aGrUM is a C++ library designed for easily building applications using graphical models such as Bayesian networks, influence diagrams, decision trees, GAI networks or Markov decision processes.

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

A LARA python-django app for managing projects and experiments in lab automation systems and scientific laboratories.

TheOneConf lets Python developers declare configuration variables in a single line of plain Python (name, type, default, help text) and then resolve values from CLI > env vars > config files > variable substitution > computed values > static defaults — without writing any manual parsing code.

ChemDataReader is a flexible python framework for reading and converting chemical data originating from webservers, including metadata and semantics.

A Python Django Persistent Identifier (PID) middleware to abstract from PID services, like handle, DOI, etc.

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.

Repository displaying the results of measurements of various Institute of Solar-Terrestrial Physics (ISTP) SB RAS instruments for the May 2024 geomagnetic storm

Le programme "Apprendre Python par des exemples" ou APPDE est une initiative du cabinet Kalamar visant à enseigner le langage Python. Suivez ce parcours pour perfectionner vos compétences en Python.

Repositorio que analiza problemáticas ambientales, sociales y administrativas en México, con un enfoque en el uso de datos, validación estructural y análisis geoespacial.

Praktisi Mengajar Teknik Informatika Universitas Nusa Putra 2024

This research presents a novel ensemble-based approach for detecting deepfake images using a combination of Convolutional Neural Networks (CNNs) and Vision Transformers (ViT). The system achieves 94.87% accuracy by leveraging three complementary architectures: a 12-layer CNN, a lightweight 6-layer CNN, and a hybrid CNN-ViT model. Our approach demonstrates robust performance in distinguishing between real and manipulated facial images.

Django API with actual and historical real estate data. It is accompanied by a scraper which collects the data and stores it in the SQLite database, and can be run on a daily basis

In this project, you will be provided with a real-world dataset, and you are required to implement the whole pipeline of building the data science pipeline on-premises and on-the-cloud. This includes understanding the business problem, preparing data, exploring the data, performing feature engineering, and building and deploying models

Machine learning techniques to classify water samples as drinkable or not drinkable.

Material that was used as part of a python course for beginners at the Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB)

Evaluation of various Machine learning models for sentiment analysis You are given the reviews dataset. These are 194439 amazon reviews for cell phones and accessories taken from https://jmcauley.ucsd.edu/data/amazon/ Use the “reviewText” and “overall” fields from this file. The goal is to predict the rating given the review by modeling it as a multi-class classification problem. • Take the first 70% dataset for train, next 10% for validation/development, and remaining 20% for test. • Traditional machine learning methods • Design some good linguistic features. You can start with basic TFIDF features. Use these classifiers: J48 decision trees, SVMs with linear/RBF kernel, logistic regression, xgboost, random forests and report accuracy on test set.

World's lightest toolkit to quickly and easily add a GUI to your Python programs and bring them online.