D

data science

Bahn-Vorhersage - The best Train Delay Prediction System.

Recommend.Games blog: https://blog.recommend.games/

Mirrored to https://gitlab.b-data.ch/git/gsi and https://github.com/b-data/gsi.

Mirrored to https://gitlab.b-data.ch/docker/deployments/traefik and https://github.com/b-data/docker-deployment-traefik.

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

This repository holds all the code related to IOT Advanced Lab Book Tutorials. https://iotgarage.net/projects/IoTEducation.html

Data Science / Machine Learning Pipeline component for training and deploying ML models using CI

Documentos referentes al curso de Introducción a la bioestadística y programación:

Hojas de cálculo Bioestadística Programas bioinformáticos Bases de datos biosanitarias Lenguajes de programación Lenguaje de programación R

This project focuses on extracting and visualizing stock data using Python libraries such as yfinance for historical stock prices and web scraping techniques to gather company revenue data. It provides a comprehensive analysis by plotting both stock prices and revenues over time for companies like Tesla and GameStop.

Unified interface to connect common data sources in Data Science with common dataframe packages

Faire des analyses descriptives sur sur une base de donnée conséquente en résumer les grandes lignes

Mirrored to https://gitlab.b-data.ch/r/rsi and https://github.com/b-data/rsi.

Code and thesis generation for my PhD Thesis titled "A Genomic Medicine Approach to Identifying Novel Drugs"

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

Mirrored to https://gitlab.b-data.ch/docker/deployments/jupyter and https://github.com/b-data/docker-deployment-jupyter.

Mirrored to https://gitlab.b-data.ch/docker/deployments/gitlab-ce and https://github.com/b-data/docker-deployment-gitlab-ce.

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.