python3

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.

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.

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

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

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

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

This project aims to leverage the Ames Housing Price Dataset to develop an accurate predictive model for estimating residential property sale prices in Ames, Iowa. By analyzing various property features and their relationships to sale prices, the project seeks to uncover insights that can inform both buyers and sellers in the housing market.

This project explores the application of Support Vector Machines (SVM) in classifying human cell records to predict the malignancy of breast cancer samples. By training an SVM model on a dataset of breast cancer features, the goal is to accurately differentiate between benign and malignant cases. This research aims to contribute to the development of early detection and diagnosis methods for breast cancer.

This project aims to develop a deep learning model using Keras and TensorFlow to accurately predict a person's gender and age based on input data. The model will be trained on a dataset containing images and corresponding demographic information. By leveraging the power of deep learning, we seek to create a highly effective and reliable system for gender and age estimation.

Analyzing tennis matches to measure player speed, ball shot speed, and the number of shots using YOLO for object detection and convolutional neural networks (CNNs) for extracting court key points.

Provides in-depth insights into the historical trends, patterns, and achievements of the Summer Olympics. By leveraging a rich dataset, we explore various aspects of the games.

Praktisi Mengajar 2024 Teknik Informatika Universitas Nusa Putra

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.