A Python desktop application using CustomTkinter for data analysis and machine learning.

An advanced MLOps project featuring an end-to-end machine learning pipeline with a Random Forest Classifier. This repository automates data preprocessing, model training, hyperparameter tuning, and deployment using CI/CD, containerization, and cloud deployment. It includes real-time model monitoring, data versioning with DVC (Data Version Control)

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.

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

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.

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.

42 school project. Process EEG datas by cleaning, extracting, creating a ML pipeline implementing a dimensionality reduction algorithm before finding the right classifier and handling a real time data-stream with sklearn.

Clean and extract features from a large, scrapped data set from the Palestinian market. Deploying ML Pipeline

Classifying real messages that were sent during disaster events so that they can be sent to an appropriate disaster relief agency.

This is the first project of the ML-Ops Dicoding class. This project serves to classify whether a title is included in the clickbait category or not.

Automated ML pipeline for Iris dataset classification using Decision Tree. Features PCA dimensionality reduction and standard scaling.

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

Optimize and Enhance Your Search Quality

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