Open to opportunities
Hi, I'm Jibek Gupta
Data, Machine Learning & Software Systems
Skills across data analytics, machine learning, and AI to build impactful, data-driven software solutions.
About
About Me
Learning, Building, and Solving with Data
I am a Computer Science student at Howard University with a strong interest in data analytics, machine learning, and software systems. I enjoy working with data to understand problems, build practical solutions, and develop systems that are reliable, scalable, and easy to use.
I'm eager to connect with professionals and organizations seeking to drive impact
with data science and software solutions.
Let's create something meaningful together!
Skills
Python
Data analysis, automation, and ML workflows using Pandas and NumPy.
Machine Learning
Modeling with Scikit-learn and PyTorch for predictive insights.
SQL
Querying, joins, and data validation across relational databases.
Power BI
Dashboards, KPIs, and storytelling for stakeholders.
Git & GitHub
Version control, collaboration, and clean code history.
Professional Experience
-
2025
–
2025
Data Science Research Assistant
Data Science Institute, University of Chicago · Chicago, IL -
2024
–
Present
Data Analysis Research Assistant
Quantitative Histories Lab, Howard University · Washington, DC
Education
-
2022
–
2026
Bachelor of Science
Howard UniversityCGPA: 3.77/4.0
Certifications & Recognition
Learning & Building
Featured Projects
Customer Purchase Behavior Analysis
A customer analytics project examining retail purchase behavior and spending patterns to inform data-driven decisions.
CNN-Based Image Classification System
Implemented and analyzed a CNN-based image classifier in a Jupyter Notebook, focusing on model architecture, training workflow, and evaluation using deep learning techniques in Python.
A/B Testing: Landing Page Conversion Analysis
An end-to-end A/B test analyzing whether a new landing page improves user conversion using statistical hypothesis testing and effect size analysis.
MNIST Digit Classification - Machine Learning Implementation
Implemented a K-Nearest Neighbors classifier for handwritten digit recognition achieving 97%+ accuracy on the MNIST dataset. Applied systematic hyperparameter optimization using grid search and cross-validation techniques.
Hybrid Movie Recommender System
A hybrid movie recommendation system using content-based and collaborative filtering techniques.
Automated Web Scraping Tool
Built a Python web scraping pipeline using BeautifulSoup and Selenium to extract structured data from 100+ dynamic product pages with high reliability.
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