This project is a French-to-English and Spanish-to-English translation application with 98% accuracy. Leveraged OCR to extract text from images and integrated a translation API for converting French text to English.

This project introduces a hybrid ResNet50-VGG16 model for real-time facial emotion detection, combining architectures to enhance recognition. It explores its effectiveness in conveying emotional insights, offering a comprehensive overview of emotion detection techniques for diverse applications.

Implemented a portfolio optimization strategy using mean-variance analysis. This method balances expected returns and risk by considering the mean (expected return) and variance (risk) of portfolio returns. Identified efficient portfolios that maximize returns for a given level of risk, enhancing investment decision-making and improving overall portfolio performance.

"Pokémon Doppelganger" is an innovative application that employs facial recognition technology and machine learning to find the Pokémon species most closely resembling a user's facial features. By analyzing facial embeddings extracted from user images and comparing them with pre-computed embeddings of Pokémon species, the system identifies the closest match using cosine similarity.

This project introduces Monte Carlo Simulation for stock prediction, utilizing stochastic modeling to forecast future stock prices. It explores its effectiveness in conveying predictive insights, offering a comprehensive overview of simulation techniques for financial analysis and decision-making.

This project presents a Python implementation of Real-Time Systems (RTS) scheduling algorithms, including Rate-Monotonic Scheduling (RMS), Earliest Deadline First (EDF), and Deadline Monotonic Scheduling (DMS). Through simulation and analysis, it evaluates these algorithms' performance in scheduling tasks with diverse deadlines and priorities in real-time systems, providing insights into their efficiency and suitability for different applications.

This project features encryption methods implemented in C++, encompassing a wide array of cipher techniques. With over 10 encryption algorithms, ranging from classical ciphers like Caesar cipher to modern ones like RSA, the code demonstrates diverse approaches to data encryption. Through these implementations, the project aims to explore encryption techniques thoroughly, providing insights into their functionality and application in securing sensitive information.

This project demonstrates various data visualization techniques such as Bar Charts, Waffle Charts, Maps, and Word Clouds. Each technique's unique application in conveying data insights is explored, providing a comprehensive overview of visualization methods for effective communication and analysis.

This Exploratory Data Analysis (EDA) delves into a dataset covering road accidents in India from 2016 to 2019. It includes various metrics such as accidents per lakh people, accidents per 10,000 vehicles, and more. Through statistical analysis and visualizations, this EDA aims to unveil patterns, trends, and factors contributing to road accidents, aiding in informed decision-making and policy formulation for road safety.

This project introduces Simon's Game, a web-based game developed using JavaScript, HTML5, and Tailwind CSS. Simon's Game challenges players' memory skills by presenting sequences of colors and sounds for them to repeat. With engaging user interfaces and interactive gameplay, this project offers an entertaining and nostalgic experience. It showcases the power of web technologies in creating immersive gaming experiences accessible across various devices.

This C++ project offers functions for adding and subtracting large integers, employing string manipulation techniques to ensure precision. Designed to handle arithmetic operations beyond standard integer limits, it's valuable for mathematical computations and data processing tasks requiring accurate treatment of extensive numeric values, enhancing computational capabilities.