PhD in CS (Human-AI Interaction, Human-Centered AI, Information Visualization) @ Khoury College - Northeastern University, Boston. Previously worked at QuantUniversity, Amazon, Persistent Systems (4 years of experience in data science, machine learning, and analytics).
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Projects in Data Science and Machine Learning
SnowCast: Lake-Effect Snow Prediction with Hybrid Multimodal CNN-LSTM Network
- Developed a hybrid model combining 3D-CNN and LSTM networks that fused meteorological data and radar images to
predict snow over Lake Michigan with an F1 score of 0.87 - a 117% improvement over the baseline decision tree model
- Created a novel data preparation function to transform the numerical data into sliding window of observations to predict
snow 3 days in advance based on 5 days of historical data
![](https://img.shields.io/badge/Scikitlearn-white?logo=Scikitlearn)
View code on Github
StockSense: NLP based Stock Market Sentiment Analysis Web App using Streamlit and FastAPI
- Developed a specialized web-crawler to extract stock market specific news from diverse web sources daily
- Utilized AWS Lambda functions to deploy and operationalize large-scale deep learning models, including HuggingFace T5 and BERT, for stock news summarization and sentiment analysis
- Orchestrated workflow pipelines using Apache Airflow and hosted the application on Google App Engine for daily updates
- Enabled user logging in BigQuery to analyse user engagement statistics through an interactive dashboard on Data Studio
![](https://img.shields.io/badge/GCP-white?logo=GCP)
View code on Github
Demographic Clustering: Unleashing Insights Through Unsupervised Algorithms
- Utilized unsupervised learning techniques including k-means, hierarchical clustering, and DBSCAN to segment customers into four key demographic groups
- Leveraged principal component analysis to reduce feature dimensions and boost model performance
- Assessed models via silhouette analysis, determining agglomerative hierarchical clustering as the top performer with a silhouette score of 0.42
![](https://img.shields.io/badge/Scikitlearn-white?logo=Scikitlearn)
View code on Github
Generating Storm Nowcast Images from Archival Imagery: Web App for Visualizing Predicted Storm Patterns
- Designed and implemented a Python app with Streamlit using SEVIR (Storm EVent ImageRy) dataset to enable a deep-learning-based Nowcasting API built with FastAPI web framework
- Built an API that generated and cached the GIF of the near-term forecasted storm images to Google Cloud Storage
- Deployed large-scale deep learning HuggingFace T5 model on service endpoint using AWS Lambda functions to summarize the lat-long specific storm event narrative from the NOAA dataset
- Enabled batch requests for generating images for multiple locations for hourly updates using Apache Airflow, and cached the output to Google Cloud Storage
- Enabled user logging in BigQuery to analyse user engagement statistics through an interactive dashboard on Google DataStudio, and hosted the application on Google App Engine
![](https://img.shields.io/badge/GCP-white?logo=GCP)
View repo - 1 on Github
View repo - 2 on Github
WebApp for Visual Search using DeepFashion Dataset
- Implemented Similarity Search Algorithm built by Ilya Katsov
- Enabled data ingestion and search on Elastic Cloud using Python Client and Kibana Console
- Build a Streamlit web interface to allow users to choose the product of their liking and view k-similar images that they might like
![](https://img.shields.io/badge/Tensorflow-white?logo=Tensorflow)
View code on Github
Hotel Database Management System
- Designed and implemented a relational database using SQL to build a unified system for hotel businesses to enable
reservation management of rooms, restaurants, and events
- Created triggers to compute membership status based on total points earned by customer, and final amount due on invoice
for each reservation
- Built a dashboard in Tableau to visualize the performance of various revenue segments, such as memberships and events
![](https://img.shields.io/badge/Tableau-white?logo=Tableau)
View code on Github
Product Recommendation based on Market Basket Analysis and Customer Segmentation using RFM Analysis
- Built a web app using Streamlit to provide product recommendations based on association rules generated using Apriori Algorithm for similar customers
- Performed Market Basket Analysis to develop more effective product placement, pricing, cross-sell, and up-sell strategies on retail dataset
- Identified target customer segments using RFM analysis to build promotional strategies
- Provide an interactive dashboard to analyze country-level performance of products, customers, and sales revenue statistics on a monthly, weekly, and daily level
![](https://img.shields.io/badge/Streamlit-white?logo=Streamlit)
View code on Github
Other projects