Example machine learning pipeline with MLflow and Hydra

Anomaly Detection Pipeline with Isolation Forest model and Kedro framework

This project is focused on the Deployment phase of machine learning. The Docker and FastAPI are used to deploy a dockerized server of trained machine learning pipeline.

Attendance prediction tool for NBA games using machine learning. Full pipeline implemented in Python from data ingestion to prediction. Attained mean absolute error of around 800 people (about 5% capacity) on test set.

This repo showcases a project that transforms ML model training into a simplified, production-ready Kedro Dockerized Pipeline. It emphasizes best MLOps practices, enabling easy training, evaluation, and deployment of models, including XGBoost, LightGBM and Random Forest, with built-in visualization and logging features for effective monitoring.

Explore a collection of Jupyter notebooks that guide you through various stages of the machine learning pipeline. From data analysis and feature engineering to model training and deployment, these notebooks provide practical insights for both beginners and experienced data enthusiasts. Let's dive into the world of data-driven decision-making! 📊🚀"

A machine learning pipeline taking you from raw data to fully trained machine learning model - from data to model (d2m).

Pipeline for augmenting sparse data for genetic optimisation

Developed a ETL pipeline, a ML training pipeline and a Flask web app that can classify disaster-related messages input by a user.

White and Red Wine classification using logistic regression

The code snippet cleans and analyzes a hotel bookings dataset, handling missing values, dropping unnecessary columns, and creating new features. It visualizes the data using various plots and performs feature encoding and selection. It then trains machine learning models to predict hotel booking cancellations.

Develop a machine learning model that can predict whether people have diabetes when their characteristics are specified

This repository lists one of my projects and findings as part of my Machine Learning DevOps Engineer Nanodegree.

Analyze disaster data from Figure Eight to build a model for an API that classifies disaster messages.

This repository contains a Machine Learning (ML) pipeline which predicts the response to messages in disaster situations. An ETL pipeline is also developed and everything is deployed with a web app based in Flask.

This repository has been created for Udacity Data Scientist Nanodegree Program - Data Engineering Part - Disaster Response Pipeline Project.

This repository shows the implementation of machine learning algorithms, data pipelines and data visualization with scikit-learn and python.

Pipeline for working with irregular search spaces in Platypus-Opt genetic optimisation

Framework3 is a super-simple and robust ML Pipeline for tabular and image competition. The purpose of this is to make the process not too abstract, so that the user can have full control over it.