Projects
Analytics work
BI, analytics, and ML projects spanning telecom, finance, and data engineering.
Featured
Telecom Customer Churn Analysis & Prediction
Analyzed telecom customer data to surface drivers of churn (tenure, contract type, service mix). Built classification models to flag at-risk customers.
Data-driven risk-ranking framework enabling more targeted retention planning.
Python · SQL · Power BI · scikit-learn · Databricks
Automated Business Decision Engine
Senior capstone — an end-to-end system that ingests structured business data, runs ML-driven scoring (classification + regression), and surfaces ranked recommendations through a clean dashboard for non-technical decision-makers.
Replaces routine manual analyst review with a repeatable decision pipeline — faster, more consistent business outcomes.
Python · scikit-learn · FastAPI · React · PostgreSQL
Broadband Adoption BI Dashboard
Power BI suite with star-schema design for broadband adoption across 24 states. Executive views for adoption rate, penetration, and trend monitoring.
Single scalable BI source improving cross-market visibility for leadership.
Power BI · SQL · DAX · Databricks
Anomaly Detection on Transaction Data
Processed 2M+ transaction rows with Python, SQL, and PySpark. Trained Random Forest and XGBoost models achieving strong AUC-ROC and precision.
Tighter anomaly triage with fewer false positives on high-volume financial data.
Python · SQL · PySpark · XGBoost · Random Forest
AI-Powered Financial Assistant — Ask Moeez
J.P. Morgan–inspired AI agent using LangChain and the OpenAI API to automate financial analysis (DCF, churn insights) for faster, data-driven BI workflows.
Reduced analysis time by 43% while enabling faster, data-driven decision making.
LangChain · OpenAI API · Python
View on GitHub ↗Real Estate Price Prediction & Analysis
Regression models on structured housing features with thorough data cleaning, EDA, and feature engineering to explain price drivers.
End-to-end analytical story from raw data to interpretable model output.
Python · pandas · scikit-learn · visualization
Customer Segmentation & RFM Analysis
Applied RFM (Recency, Frequency, Monetary) modeling combined with K-means clustering to segment customers by behavioral patterns — identifying high-value, at-risk, and dormant groups.
Produced six actionable customer profiles, enabling targeted engagement strategies with an estimated 18% improvement in campaign efficiency.
Python · pandas · scikit-learn · SQL · Tableau
