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Data Processing
My Projects

RoveMiles — Data Analysis + Financial Modeling Work

Goal:

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Built end-to-end predictive analytics and financial modeling system for RoveMiles.

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Problem Statement:​​

​Analyzed large transactional datasets, engineered financial metrics, and contributed to dashboards for revenue modeling and key business KPIs.

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Result:

Delivered a comprehensive financial model, identified high-impact churn and profitability drivers, and built dashboards for revenue and unit-economics analysis.

Approach / Methodology:​

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  • Built a multi-sheet financial forecasting model covering GMV, take rate, redemption cost, partner payouts, revenue curves, and profit margins.

  • Performed scenario and sensitivity analysis on mile issuance/redemption behavior to evaluate profitability.

  • Analyzed partner, redemption, and customer behavior datasets to identify churn factors and optimize pricing.

  • Created dashboards and tools for KPI tracking and revenue modeling.

Key Learnings:​

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  • Business-focused machine learning

  • Working with ambiguous real-world datasets

  • Integrating financial models with predictive analytics

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Skills:

​Excel financial modeling (GMV, take rate, unit economics), data analysis, scenario forecasting, partner-pricing modeling, revenue/cost analysis, dashboard creation​

     Real-Time Customer Behavior Analytics Pipeline (AWS)

Goal:

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Turning live data into instant business insight.

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Problem Statement: 

Businesses need immediate visibility into customer interactions, but traditional batch-processing systems cause delays in detecting anomalies, trends, and engagement patterns.

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Results / Visuals:

  • Cut event-to-dashboard latency to under 2 minutes

  • Fixed Redshift cost spike (~$1.3k) and optimized compute use

  • Enabled BI team to visualize customer trends in near-real-time

  • Fully fault-tolerant streaming architecture

Approach / Methodology:​

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  • Designed full architecture: Kinesis → Lambda → S3 → Glue Crawler → Glue ETL → Glue DQ → Redshift → QuickSight

  • Built PySpark ETL scripts for cleaning, partitioning, transforming event data

  • Set up Glue Data Catalog tables, schema inference, and automated crawlers

  • Configured Redshift clusters, IAM roles, networking, VPC, subnets

  • Implemented AWS Budgets + cost controls after diagnosing Redshift overages

  • Created QuickSight dashboards for funnels, anomalies, activity timelines

Key Learnings:

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  • Deep practical understanding of cloud data engineering architecture

  • Schema evolution, Glue DQ, IAM policy debugging, Redshift performance tuning

  • How real-time ETL differs from batch pipelines

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Skills:

AWS Kinesis, AWS Lambda, AWS Glue, Glue DQ, Redshift, S3, Athena, CloudWatch, PySpark, VPC, IAM

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GitHub  I  View Report

     AI-Powered Retrieval-Augmented Generation System

Goal:

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Offline AI for secure, collaborative insight.

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Problem Statement: 

Teams handling sensitive or confidential data need a locally hosted LLM solution that runs fully offline while providing accurate retrieval, high-quality responses, and support for multi-agent collaboration.​

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Results / Visuals:

● Reduced API cost by 100% via full offline inference

● Achieved faster hybrid retrieval through graph + vector search

● Delivered a modern UI suitable for internal research workflows

Approach / Methodology:

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  • Built a full RAG architecture using Microsoft GraphRAG for structured retrieval

  • Integrated AutoGen agents for multi-agent reasoning

  • Used Ollama to run local models (Mistral, Nomic embeddings)

  • Added Lite-LLM proxy to enable OpenAI-style function calling with local models

  • Designed an interactive Chainlit UI supporting multi-threaded chats

  • Generated embeddings and stored them in local vector stores

Key Learnings:

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  • Differences between graph-based and dense retrieval

  • Offline inference tradeoffs, local model memory/latency handling

  • Multi-agent orchestration patterns

 

​Skills:

Python, Chainlit, Microsoft GraphRAG, AutoGen, Lite-LLM, Ollama, Mistral, Nomic Embeddings, FAISS

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​GitHub  

     ChurnGuard — Customer Churn Prediction ML Platform

Goal:

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ML-based churn prediction with +15% accuracy improvement.

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Problem Statement: 

Businesses need tools to forecast customer churn and proactively address retention weaknesses

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Results / Visuals:

● 15% accuracy increase after tuning

● Clear ranking of features affecting churn risk

● Delivered a dashboard-ready dataset of churn probabilities

Approach / Methodology:

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  • Cleaned and normalized customer datasets

  • Feature engineering + outlier detection

  • Used Logistic Regression, Random Forests, Neural Networks, Gradient Boosting

  • Tuned hyperparameters to maximize predictive score

  • Evaluated with accuracy, ROC-AUC, precision-recall

Key Learnings:

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  • ML model experimentation workflows

  • Importance of preprocessing and data quality

  • Tradeoffs between different classification models

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Skills:

Python, Pandas, Numpy, Scikit-Learn, Matplotlib, Jupyter

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GitHub 

Gene Expression Visualizer- RNA-seq & Microarray Analysis

Goal:
Simplify gene-expression visualization for high-dimensional datasets.

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Problem Statement:
RNA-seq/microarray data contain thousands of genes, requiring clustering, dimensionality reduction, QC checks, and publication-quality plots — tasks that otherwise need extensive coding.​

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Approach / Methodology:

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  • Loaded & normalized expression matrices

  • Performed PCA & hierarchical clustering

  • Generated correlation and QC plots

  • Automated report creation via unified API

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Results / Visuals:

  • Clustered heatmaps

  • PCA sample-relationship plots

  • Correlation matrices

  • Expression distribution box plots* One-command automated reporting​

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Key Learnings:

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  • Handling high-dimensional biological data

  • Normalization & QC strategies

  • Accessible visualization design

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Skills:

Python, Pandas, NumPy, Matplotlib, Seaborn, Scikit-Learn

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GitHub 

     DNA Analysis Toolkit — Comprehensive Sequence Analysis 

Goal:

Provide an all-in-one DNA analysis tool for students and researchers.

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Problem Statement:

DNA analysis typically requires multiple tools for GC content, ORFs, codon usage, translation, restriction sites, and molecular properties — making workflows slow and fragmented.

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Approach / Methodology:

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  • FASTA parsing and standardized structure

  • GC, ORF, codon usage, translation pipelines

  • Restriction-site scanning

  • Visualization with Matplotlib/Seaborn

  • Memory-efficient batch workflows

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Results / Visuals:

  • GC-content plots, ORF maps

  • Codon-usage heatmaps

  • Restriction-site visualizations

  • Molecular weight/Tm calculations

  • Batch processing for 1000+ FASTA sequences

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Key Learnings:

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  • DNA analysis fundamentals

  • BioPython workflows

  • Scientific visualization best practices

 

​Skills:

Python, Biopython, Pandas, Matplotlib, Seaborn

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GitHub 

     BLAST Result Parser — Automated Sequence Alignment Analysis
 

Goal:
Automate BLAST alignment parsing and filtering for faster biological insight.

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Problem Statement:

​BLAST outputs are large and difficult to interpret manually. Researchers need simpler tools to filter hits by e-value, identity, and bit score, extract top matches, and export results for downstream analysis.​​​

Approach / Methodology:

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  • Parsed XML/tabular BLAST with Biopython

  • Standardized alignment fields

  • Built chainable filtering pipeline

  • Computed statistics via pandas

  • Provided multi-format export

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Results / Visuals:​

  • Clean filtering by e-value, identity %, bit score

  • Summary statistics for alignment quality

  • Query-grouped and top-hit reports

  • CSV/JSON exports for further analysis

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Key Learnings:

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  • BLAST metric interpretation

  • Scientific data-pipeline design

  • Usable API design for researchers

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Skills:

Python, Biopython, Pandas, JSON/CSV

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GitHub 

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