Data Analyst & MSBA candidate at Northeastern University. Turning complex datasets into clear, impactful business solutions using Python, SQL, Alteryx, and machine learning.
I'm a Master's student in Business Analytics at Northeastern University, passionate about using data to drive real business decisions. My work sits at the intersection of analytics, machine learning, and storytelling through data.
Currently interning as a Data Analyst at HubSpot, I work on customer experience datasets — uncovering the patterns behind user frustration and building visualizations that translate insights into action.
I've built end-to-end ML systems for churn prediction, revenue leakage detection, and NLP-based fake review detection. My research was published by Springer Nature (ICISD, 2025) and won the Best Paper Award.
End-to-end ML system to identify at-risk customers and estimate revenue leakage for subscription businesses. Combined CLV estimation, predictive modeling, and interactive Streamlit dashboards.
AI-powered churn prediction that analyzes customer behavior and generates personalized retention recommendations. Interactive Streamlit dashboard with AWS deployment experimentation.
NLP system detecting fake reviews, classifying emotional tone, and categorizing themes using LSTM, TF-IDF, and multi-task learning. Best Paper Award at ICISD 2025, published by Springer Nature.
Focusing on data analytics, statistical modeling, machine learning, and business intelligence.
Foundation in algorithms, data structures, software engineering, and statistical analysis.
Presents ReviewGuard, a comprehensive NLP-based system to detect fake reviews, analyze emotional tone, and classify thematic relevance using LSTM networks, TF-IDF, semantic embeddings, and multi-task learning. The LSTM model achieved 95.4% classification accuracy on Amazon datasets. Awarded Best Paper at ICISD 2025.
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