A comprehensive end-to-end project demonstrating t-Distributed Stochastic Neighbor Embedding (t-SNE) for visualizing high-dimensional data using the MNIST handwritten digits dataset. Includes data preprocessing, parameter exploration, and insightful visualizations.

This repo contains two dimensionality reduction techniques for feature extraction: PCA and LDA and additionally t-SNE, which is a nonlinear feature extraction technique that can be used for visualizing data

HR Policy Clustering and Similarity Search Tool: Designed for processing and analyzing HR policy documents in Greek. Capabilities: text chunking, embedding generation, clustering, and semantic similarity detection, with the Qdrant vector database for data management. For identifying overlaps in policy content and generating recommendations.

Comparison of embedding-based models for electric guitar tone discrimination

Interactive 2D visualization of music genres using transformer-based embeddings.

A neural network-based text generation model using a vanilla MLP architecture to predict the next character in a sequence. The project includes a Streamlit app for customizing parameters like embedding size and block size, enabling real-time experimentation and optimization.

🐧 Clustering Antarctic Penguin Species | An unsupervised machine learning project using K-Means clustering to identify distinct penguin species from physical measurements. Implements the Elbow Method for optimal k selection and t-SNE for visualization.

p-SNE: Poisson Stochastic Neighbor Embedding for dimensionality reduction of sparse count data

Executed Fine-tuning & Benchmarking, optimizing 12+ LLMs (Gemma-family, Mistral, LLaMA, etc) across 6+ datasets (GSM8K, BoolQ, IMDB, Alpaca-GPT4 & more). Delivered a research-level contribution—model training, evaluation, insights, benchmark comparisons & documentation. Also crafting a custom dataset from open-sourced V0 system prompts.🛰

Data science project collecting and cleaning tweets on COVID‑19 vaccine conspiracies, applying LDA topic modeling, k‑means clustering, chi‑square and ANOVA tests.

Word2Vec implementation from scratch using Skip-Gram and Negative Sampling. Features interactive t-SNE/UMAP visualizations

Messing around with embeddings, and visualizations using OpenAI, numpy, sklearn and seaborn

ACL injury prediction with classical machine learning techniques (primarily addressing the extreme class imbalance).

Prepare Python script to calculate fingerprints on crystal structures and convert them into 2D space with t-SNE

Scan your data and get useful insights

🛍️ An interactive Streamlit app to explore, cluster, and generate smart recommendations on Amazon products. Includes preprocessing, KMeans clustering, visualizations (boxplots, t-SNE), and a data-driven suggestion system for sellers.

Word2Vec implementation from scratch using Skip-Gram and Negative Sampling. Features interactive t-SNE/UMAP visualizations

A friendly guide for understanding PCA, SNE, t-SNE, UMAP, and modern latent-space visualization.

End-to-end Data Science analysis of Glasdoor reviews of employees on companies with NLP tools and text mining.