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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)
In this project I did Complete EDA, and Build a ML model that can accurately predict whether an Employee will be leave a company or not based on different factors.
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.
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.
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! 📊🚀"
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.
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.
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.
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 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.