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machine-learning

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

Project information This is the final source code for my model deployment—a Streamlit application for the Netflix Movie Recommendation System, or we can use the Flask framework. The complete model training, exploratory data analysis (EDA), and data preprocessing are available in my GitHub repository.

GitHub: github.com/aydiegithub/

Live Demo: aydie.in/ml/netflix-recommendation

Contact: business@aydie.in 9036469492 aydie.in

Ultralytics Python Project Template

MCP Cloudflare Server

A data mining project analyzing hate crime patterns in the United States from 2017 to 2025, using clustering, predictive modeling, and association rule mining.

A data science project focused on analyzing a car market dataset from Turkey in 2020. The goal is to explore the data, apply various analytical techniques, and derive insights. The specific direction of analysis will be determined through exploration, with potential for building predictive models or visualizing trends in the market.

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A "Lab" neural network library for controlled research into network-based machine learning.

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Introduction to classification using machine learning and deep learning (PyTorch, TensorFlow, Keras)

Moodle Plugin repository for recommending courses based on machine learning predicting, and a dashboard to monitor courses and users. Made by Diva Alfiah as a thesis project.

Scope create personal portfolio site by AI

Model Context Protocol in Go.

Fast Flexible Replay Buffer Library

This project is designed to analyze text for the presence of sarcasm. It uses machine learning models to classify input text and determine whether it contains sarcasm.

The project utilizes the following technology stack:

FastAPI - for creating the API interface and handling requests Docker - for packaging the application and its dependencies into a container Machine learning models for text sarcasm classification

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.