Where Ideas Come to Life

A collection of projects πŸš€

πŸš€ Building a Scalable Real-Time Chat Application from Scratch!

Websockets

πŸš€ Building a Scalable Real-Time Chat Application from Scratch!

Real-time communication is at the core of modern applications, and I wanted to build something that not only works but also scales. Drawing inspiration from our Modern Cryptography group project, where we focused on creating a secure communication channel between two users and developing a client-server application for secure LAN-based messaging.

Rahul Saini

Rahul Saini

Apr 4, 2025

Predicting Stock Prices with Machine Learning

Data Mining

Predicting Stock Prices with Machine Learning

This project involves predicting stock prices for multiple companies (e.g., TSLA, GOOGL, AAPL) using a time series forecasting model. The goal is to evaluate the model’s performance in accurately predicting next-day stock prices, measure deviations using key metrics, and analyze trends for profit or loss estimation. The project also highlights differences in predictive accuracy across stocks, possibly due to variations in market behavior or model fit.

Rahul Saini

Rahul Saini

Nov 21, 2024

Building a Movie Recommendation System With SVD

SVD

Building a Movie Recommendation System With SVD

Ever found yourself lost in the endless sea of movies, struggling to decide what to watch next? Our movie recommendation system is here to solve that problem! Leveraging Singular Value Decomposition (SVD) and a TF-IDF-based similarity model, we built an efficient way to serve personalized movie recommendations.

Rahul Saini

Rahul Saini

Nov 16, 2024

Sentiment Analysis with Transformers

Deep Learning

Sentiment Analysis with Transformers

Sentiment analysis, a subfield of natural language processing (NLP), focuses on determining the sentiment or opinion expressed in a piece of text. Transformers, powered by self-attention mechanisms, assign varying importance to different words, allowing them to effectively understand context. This makes them highly efficient for classifying text into positive, negative, or neutral sentiments based on underlying polarity.

Rahul Saini

Rahul Saini

Mar 20, 2024

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