PiML (Python Interpretable Machine Learning) toolbox for model development & diagnostics

High Performance Asynchronous Workflow Scripting Library

Build an ML workflow using AWS Lambda and AWS step functions

Predicting price housing using a small data set. A project to understand the whole ML workflow.

ML scientific job orchestration platform: FastAPI API, Celery Worker, PostgreSQL DB, RabbitMQ broker, and React frontend for spectral analysis, data preprocessing, and active‐learning workflows 🪐

🏠House Price Prediction💰 | Multi-Pipelines and Models | 🌀 ZenML, MLflow | 🧪Streamlit

The project aims at understanding the pattern of the data and predicting customers who are going to churn based on multiple variables to help the company in retaining their existing customers.

predicts bike rental demand based on historical data from the Kaggle Bike Sharing Demand competition. The goal is to build an accurate regression model to forecast hourly rental counts using weather, calendar, and temporal data. We leverage AutoGluon’s TabularPredictor for rapid model development, feature engineering, hyperparameter optimization

Gathering Data (CSV, JSON, SQL, APIs & Web Scraping)

Collection of introductory ML-competions within regression, classification and more to come. Work in progress....

This is a one-day machine learning introductory course for beginners

This project is based on the case study of a telecommunication company, which is facing a customer churn issue. The project aims at understanding the pattern of the data and predicting customers who are going to churn based on multiple variables to help the company in retaining their existing customers.

Datacmp is a lightweight Python library for inspecting and preparing datasets. It offers summaries, column cleaning, and smart missing value handling - perfect for ML and data science workflows.

ML Workflow for a company called Scones Unlimited. Image classification model that can automatically detect which kind of vehicle delivery drivers have, in order to route them to the correct loading bay and orders.

End-to-end AWS ML project that builds and deploys an image classification model for Scones Unlimited to distinguish bicycles from motorcycles. Includes SageMaker training/deployment, Lambda-based inference services, Step Functions workflow orchestration, and monitoring for scalable, production-ready ML ops.

Classify the cell embryo development stages of the D. Melanogaster (fruit fly). Created by Noah Rizika, aiming to expedite research conducted by Bing He, Ph.D.