ML Engineer

github

Rishabh Bhartiya  • Peerlist

Collected and Classified the bit from the British Birdsong Dataset, Computed the MFCC features and Spectrogram with the help of Librosa Library; also the concept of clustering was being well defined.

Bird Voice Recognition using MFCC and CNN

Project Overview: Color Palette Generator Objective: Develop a web-based application that enables users to generate diverse color palettes based on a provided hex color code. Features: User Input: Allows users to input a hex color code. Color Schemes Generated: Monochrome Colors: Different shades of the input color. Analogous Colors: Colors adjacent to the input color on the color wheel. Complementary Colors: Colors opposite the input color on the color wheel. Triadic Colors: Three colors evenly spaced around the color wheel. Tetradic Colors: Four colors forming a rectangle on the color wheel. Technologies Used: Frontend: Developed using HTML, CSS, and JavaScript to create an interactive and responsive user interface. Backend: Implemented with Node.js and Express.js to handle user inputs and process color computations. Deployment: Hosted on Render, a cloud platform for web applications. Development Process: Color Computation: Utilized color theory algorithms to calculate various color schemes from the input hex code. User Interface: Designed a clean and intuitive interface to display the generated color palettes effectively. Testing: Conducted thorough testing to ensure accurate color generation and a seamless user experience. Outcome: The Color Palette Generator provides users with a straightforward tool to create cohesive color schemes, aiding in design and development tasks that require color harmonization. This project demonstrates the application of color theory in web development and showcases the integration of user-centric design with functional programming.

COLOR Palette Generator

Project represents all the risk associated with the business. It also consists the Entity Relationship Model and Value Model.

Food Vending Machine Business Model Processin

GitCity turns your GitHub contribution history into an interactive isometric 3D city. Every commit grows a building. The more consistent you are, the taller your skyline.Then you switch to simulation mode and drive through it.v2 now has rain, BGM, real accident scenarios with a prone zone that slows nearby vehicles, and fully ambient visuals.No login. Free. Open source. Just enter any GitHub username.

GITCITY

A Manim-based Python library for visualizing LLM and Transformer architectures programmatically. Designed for researchers and educators who want to animate attention mechanisms, token flows, and model internals through code rather than manually.

LLManim — Python Library for LLM & Transforme

A Manim-based Python library for animating statistical concepts — distributions, hypothesis testing, and data plots — through a declarative Python API.

manim_stats — Python Library for Statistical 

I have studied the random forest and its parameter. The thesis also consists of python code for implementing the random forest algorithm.

Master’s Thesis : Study of Random Forest Algo

It is an internship assingned task. Majorly focused on Decision Tree Algorithm.

Prediction Using Decision Tree Algorithm

Edge-rendered GitHub README component generator built on Next.js 15 Vercel Edge Runtime. 44 metallic themes, 16 component types, 20+ SVG text animations. Zero cold starts.

READMEFORGE

An application-oriented survey entirely dedicated to Face recognition with the aspect of Neural Networks. This project contains literature review of 20 scientific papers.

Survey Project- Face Recognition in Neural Ne

Project Overview The TMKOC Episode Finder is a web application designed to help fans of Taarak Mehta Ka Ooltah Chashmah find episodes based on character names. With over 4,100+ episodes in the dataset, this app allows users to search for any character and instantly retrieve relevant episodes. Key Features ✅ Character-Based Search – Find episodes by entering a character’s name ✅ 4,100+ Episodes Indexed – A vast dataset covering years of TMKOC history ✅ Intelligent Keyword Matching – Efficient search functionality for precise results ✅ User-Friendly Interface – Simple and intuitive design for easy navigation ✅ Deployed on Render – Accessible from anywhere, anytime! Tech Stack 🛠 Python (Flask) | Pandas | Data Processing | HTML/CSS | Render Deployment My Contribution 💡 Data Collection & Processing: Extracted and structured data from multiple sources 💡 Backend Development: Built a Flask-based search system for quick episode retrieval 💡 Frontend & UI Design: Designed a simple and user-friendly interface 💡 Deployment: Hosted the app on Render for public access Why I Built This? As a data enthusiast and Python developer, I wanted to create something meaningful that blends data science with entertainment. Being a TMKOC fan, I realized how difficult it was to find episodes based on specific characters. This inspired me to develop an intelligent episode search tool that makes finding your favorite moments effortless!

TMKOC Episode Finder

Visual representation of real-time scenario of 4-Lane interchange, designed on Anylogic Simulation Software.

Traffic Light Management with Pedestrian Move

Analyzed the MFCC features and train a model with the renowned Urban Sound 8k dataset with a target to classify the sound bit with its correctly associated label. Also represented a visual comparison of different classifier to prove which one is getting most accuracy.