S R Rayhan

A learning project for building Retrieval-Augmented Generation (RAG) pipelines using LangChain. Demonstrates document ingestion, processing, and retrieval-based question answering with support for multiple document formats including text files and PDFs.

A classification model built using Logistic Regression to predict survival outcomes on the Titanic Dataset. The project includes feature encoding, feature scaling, model evaluation, and ROC-AUC analysis using Python ML tools.

A beginner-friendly Machine Learning project that predicts house prices using the California Housing dataset from Kaggle. This project focuses on learning the core steps of an ML workflow: data analysis, preprocessing, model building, and evaluation. It marks the starting point of my journey into AI and Machine Learning.

This project implements a Bloom Filter to quickly check if a username already exists, drastically reducing database lookups. It uses multiple hash functions for accuracy and Redis for fast, scalable caching. Perfect for applications targeting thousands of users.