M

machine-learning

Classic ML + XGBoost + Streamlit (Thai movie reviews)

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[2025] European news intelligence platform with dual-layer AI sentiment analysis. Tracks media narrative shifts across 12 sources using VADER baseline + Gemini nuance, vector embeddings for semantic search, and interactive relationship mapping with real-time trend visualization.

[2025] Autonomous Claude Code plugin with pattern learning and skill auto-selection. Features 27 specialized agents across 4 collaborative groups, quality control automation, and intelligent task execution without human approval at each step.

A GUI-based Python utility engineered to remove image backgrounds using a suite of AI models. It streamlines testing by processing one image with multiple algorithms (rembg, mediapipe) and saving separate, transparent outputs for easy side-by-side comparison.

Comparison of different models and board representations to predict position advantage and to find the best next move.

API4AI is cloud-native computer vision & AI platform for startups, enterprises and individual developers. This repository contains sample mini apps that utilizes People Photo Background Removal API provided by API4AI.

pt_kmeans is a high-performance, pure PyTorch K-Means implementation for CPU/GPU, featuring K-Means++ initialization, hierarchical clustering, and cluster splitting, optimized for large-scale datasets.

Built a complete machine learning pipeline in SparkML using the Adult Census dataset (~48k rows, 14 features). Implemented data preprocessing, feature encoding, cross-validation, and model training with Logistic Regression and Random Forest. Evaluated models with metrics such as AUC and F1-score. Reflected on scalability trade-offs and optimizations in distributed ML.

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

A pure PyTorch implementation of the Local Outlier Probabilities (LoOP) algorithm for efficient anomaly detection.

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

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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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