As a proactive Campus Ambassador for eDC IIT Delhi, I excelled in promoting entrepreneurial spirit among peers through innovative outreach and event facilitation, showcasing my adeptness in Machine Learning and leadership. My technical proficiency spans C, Java, and Python, underpinned by strong problem-solving and team collaboration skills.
Available upon request.
Member of Coding Club/Tech Society Name, where I organize and participate in coding events. Volunteered for Event Name/Organization, contributing to specific task. Participated in inter-college debate competitions and won specific award.
I hereby declare that the above information is true to the best of my knowledge and belief.
eDC (Entrepreneurship Development Cell) IIT Delhi, Campus Ambassador, 01/24 - 03/24, Worked as a Student Ambassador for eDC IIT Delhi, promoted entrepreneurial initiatives by engaging peers, organizing outreach activities, and facilitating participation in events like hackathons, workshops, and competitions.
1. Grocery Store Management System
Description: Developed a comprehensive grocery store management system that streamlined inventory
tracking, order processing, and sales reporting using a database-driven approach, improving
operational efficiency and customer service.
Methodology :
Backend Technology Stack: Implemented using Python and FastAPI to manage inventory,
sales, and user authentication with efficient API endpoints.
Frontend Technology Stack: Developed a simple user interface using HTML, CSS, and
JavaScript for basic product management, transaction tracking, and report viewing.
Database: Used MongoDB (version 6.0) for flexible storage and management of product
details, inventory data, and transaction logs.
Purpose
Designed to simplify grocery store operations by enabling inventory tracking, recording sales
and purchases, and providing essential insights for store management.
2. Lychee Yield Predictor
Description:Designed a machine learning model to predict lychee crop yield using climatic, soil, and
agricultural data. Improved accuracy through feature engineering and model optimization,
providing actionable insights for farmers to optimize production and manage risks.
Methodology
Data Collection: Compiled a dataset from 1987 to 2024, including climatic, soil, and
agricultural data, along with historical yield records.
Data Preprocessing: Handled missing values, normalized numerical features, and performed
feature selection to improve model performance.
Exploratory Data Analysis (EDA): Analyzed correlations and trends to identify key factors
affecting lychee yield.
Model Development: Built a Random Forest Regression model for yield prediction,
optimizing hyperparameters using grid search.
Model Validation: Evaluated model performance using cross-validation, achieving a
prediction accuracy of 92%.
Deployment: Developed a Flask backend to serve predictions and a JavaScript frontend for
visualization, with PostgreSQL for data storage.
3. Calories Burnt Predictor: (Undergoing)
Developed a machine learning model to estimate calories burned during physical activities
based on user data such as age, weight, duration, and activity type. Improved prediction
accuracy through feature engineering and model tuning, enabling personalized fitness
insights.
English Teacher at EDCEnglish Teacher at EDC
Research intern at IIT DELHIResearch intern at IIT DELHI
Research Development Officer at Indian Institute of Technology Delhi (IIT Delhi)Research Development Officer at Indian Institute of Technology Delhi (IIT Delhi)