I am a second year CSE Cybersecurity student with a keen interest in web development, cybersecurity and AI/ML

github

Yatin Annam  • Peerlist

The project is an interactive 3D Solar System Simulation built with React, Three.js, and React Three Fiber (R3F), featuring the Sun, eight planets, Earth’s moon, and an asteroid belt, all rendered in real-time 3D. All planets move at accurately calculated orbital speeds and are textured using NASA’s publicly available resources, providing both scientific accuracy and realistic visual detail. Smooth rotations, orbit paths, and lighting effects enhance the immersive experience. The simulation demonstrates 3D graphics in the browser, showcasing performance optimization, interactive controls, and the capabilities of React Three Fiber for creating educational and visually engaging content.

3D Solar System

Cafe Finder is a web application built using HTML, CSS, JavaScript, and Leaflet.js, designed to help users locate cozy cafes across any city in India. It features an interactive map with custom coffee icons, allowing users to instantly zoom into their chosen city and explore nearby cafes effortlessly. The project includes a sleek sidebar listing all cafes with animated cards, providing a smooth and engaging user experience. Each cafe card displays the name, address, and cuisine, and clicking on a card centers the map and opens a popup with detailed information. To make the interface intuitive and visually appealing, I integrated OpenStreetMap for the map and the Overpass API to fetch real-time cafe data. Small details like glowing titles, responsive design, and seamless sidebar-map synchronization make this one of my favorite and most polished projects to date.

Cafe Finder

Built a machine learning model using the UCI Adult dataset to predict whether an individual earns more than $50K annually based on features like age, education, occupation, and work hours. The model was trained using a Random Forest Classifier with label encoding and evaluated using accuracy and classification metrics. All preprocessing and training were done in Google Colab. The final model was deployed as an interactive web app using Streamlit, allowing real-time user input and predictions. The project is hosted on GitHub and publicly accessible via Streamlit Cloud.

Employee Salary Prediction

This project is a fully interactive web dashboard built using Streamlit, designed to visualize the career performance of Formula 1 drivers using real historical race data. It allows users to explore how drivers have performed over the years in terms of races, wins, points, and finishing positions. Users can select any driver from the sidebar and instantly view stats like total career races, best finish, total wins, nationality, and more. The dashboard features dynamic bar charts showing points per season and pie charts for race finish breakdowns, all within a sleek, F1-themed dark UI. The project was developed using Python, with Pandas for data processing and Plotly for interactive visualizations. It was deployed using Streamlit Cloud, allowing it to run entirely in the browser without installation. The goal was to blend data analysis with a real-world passion for Formula 1, making it informative, visually appealing, and easy to use.

F1 Driver Career Stats Dashboard

The GIF Maker is a Python-based project that uses the Pillow library to generate looping GIFs. It reads a folder of images (frames) and combines them into a smooth animation. The project supports adjustable frame duration, allowing control over the playback speed. It also includes infinite looping for seamless, continuous GIF output. Lightweight and simple to use, this tool provides an easy way to transform raw image sequences into professional GIFs. It can be applied in animations, demos, and creative projects.

GIF Generator

Built a To-Do List Application using React.js (Vite) as part of my learning journey in frontend development. The app allows users to add tasks, mark them as complete with a strike-through, and delete them with a single click, providing a simple yet effective task management experience. The project uses React hooks (useState) for state management and dynamic UI updates, along with clean CSS styling for a modern, responsive design. I also deployed it using GitHub Pages, making the app accessible online for anyone to try. This project helped me strengthen my understanding of React fundamentals like components, event handling, and conditional rendering, while also giving me hands-on experience with project setup, GitHub workflows, and deployment.

To-Do List Application

This project involved building a stock price prediction tool using Python and machine learning. Historical stock data was fetched using the yfinance API and processed to predict the next day's closing price. It focused on making short-term forecasts using real-world financial data. A Linear Regression model was trained on the closing prices and evaluated using Mean Squared Error to assess prediction accuracy. The dataset was split into training and testing sets to validate performance. Visualization was done using matplotlib to compare actual vs predicted prices. The project helped me understand time series forecasting, regression modeling, and data preprocessing. It also strengthened my skills in model evaluation and working with structured datasets. This task demonstrated how AI can support decision-making in financial applications.

AI for Predicting Stock Prices

Built an end-to-end machine learning model to detect fraudulent credit card transactions using a real-world dataset containing over 284,000 records. The project involved cleaning the dataset, balancing the data using undersampling techniques to address class imbalance, and applying Logistic Regression to classify transactions as legitimate or fraudulent. Achieved an accuracy of ~92% with strong performance metrics (precision, recall, F1-score). The entire pipeline was implemented using Google Colab and Python libraries like pandas, NumPy, and scikit-learn. Tech Framework: Python, pandas, NumPy, scikit-learn, Google Colab, GitHub This project helped me understand the real-world challenges in fraud detection like class imbalance and model evaluation, while also improving my data preprocessing and model-building skills.

Credit Card Fraud Detection us

Simple chatbot that responds to user queries like product info, support issues, return policies, and general help. Uses basic Python concepts like string manipulation, conditional statements (if-elif-else), and loops to simulate a help desk assistant. The chatbot takes the user's name, detects keywords from their input, and gives appropriate responses. It runs in a loop until the user exits by typing something like “bye” or “exit.”

Rule-Based Chatbot

Built a Secure File Sharing System using Python and Flask. The project allows users to securely upload and download files through AES encryption (CBC mode), ensuring data privacy. It features a clean and basic web interface, proper file separation (uploads, encrypted, decrypted), and uses a .env file to manage encryption keys securely. This project helped reinforce my understanding of Flask, file handling, and cryptographic principles in real-world applications.

Secure File Sharing System

Used Splunk SIEM to monitor and analyze simulated web access logs for potential security threats. Sample log files were ingested and queried to identify anomalies such as repeated timestamps and suspicious URI access patterns. Two incidents were documented: a possible reconnaissance attempt detected through clustered log entries with identical timestamps, and suspicious access behavior based on HTTP error codes and endpoint paths. The final report includes detailed search queries, incident classification, remediation suggestions, and screenshots from the Splunk dashboard.

Security Alert Monitoring & In

This project is a classic implementation of the Tic-Tac-Toe game using Python, where the player competes against an AI opponent. The game is played in the terminal, and users make their moves by selecting positions on a 3x3 board. The AI uses the Minimax algorithm to make optimal decisions, ensuring it either wins or forces a draw. The algorithm evaluates all possible outcomes recursively, choosing moves that minimize the player’s chances of winning. The project includes full game logic: input handling, board display, win/draw detection, and alternating turns between the player and the computer. It was built using core Python concepts like lists, loops, conditionals, and recursion.

Tic-Tac-Toe AI

Weather Forecast Webpage: A basic weather forecast webpage that fetches real-time weather data using the Open-Meteo API. It allows users to search for any city and view temperature, humidity, wind speed, and weather conditions. Tech Framework: HTML, CSS, JavaScript, Open-Meteo API, GitHub Pages This was my first attempt at building a web-based project while learning how to work with APIs. Excited to enhance and expand it further!

Weather Forecast Webpage

This project focused on identifying security vulnerabilities in the Damn Vulnerable Web Application (DVWA), a deliberately insecure web app used for learning and practice. It was hosted on Kali Linux using Docker for safe local testing. The vulnerabilities tested included SQL Injection, Reflected Cross-Site Scripting (XSS), Broken Authentication through brute-force login, and Cross-Site Request Forgery (CSRF). Each attack was simulated manually through the browser interface. All findings were mapped to the OWASP Top 10 categories. Each vulnerability was documented with screenshots, technical details, and suggested mitigation strategies to improve application security.