A selection of hands-on projects demonstrating real-world data science, modeling, and cloud deployment; built with Python, scikit-learn, PySpark, XGBoost/CatBoost, SHAP, and shipped via Streamlit/Render and AWS (S3, SageMaker, Lambda, MWAA/Airflow), with visuals in Tableau.
SignalGraph (PySpark + Postgres/Teradata + Prophet)
SignalGraph is a telecom-focused anomaly detection and forecasting project that processes large-scale 4G/5G performance data (latency, jitter, PRB utilization, packet loss) through a Spark ETL pipeline and delivers real-time network insights. It demonstrates modern data workflows—from feature engineering and anomaly flagging to forecasting and graph analytics; built for scale, transparency, and decision-making in telecom environments.
Highlights
- Data & Features: Hive-partitioned Parquet with engineered features (capacity utilization, latency thresholds, PRB saturation flags, KPI interactions).
- Modeling & Forecasting: Time-series forecasting with Prophet to capture latency trends and test network reliability scenarios.
- Monitoring & Anomaly Detection: PySpark anomaly flags for performance degradation (e.g., high PRB or latency spikes), drift tracking, and cell-level stability summaries.
- Graph & Network Analysis: Neo4j integration with centrality metrics (degree, PageRank, betweenness) and neighbor drill-down to trace performance impacts across connected cells.
- Policy Sandbox: Scenario sliders to simulate SLO trade-offs (capacity, latency, reliability), threshold tuning with triage sliders, and recalibration scenarios.
📌 Business Impact: Helps telecom teams detect anomalies early, forecast degradation risk, and evaluate trade-offs in policy thresholds—improving service reliability and decision-making at network scale.
Tech Stack
- Languages & Libraries: Python 3.10, PySpark 3.5.1, pandas, scikit-learn, XGBoost, Prophet, matplotlib, DuckDB, SHAP, Altair, PyArrow.
- Frameworks: Streamlit UI, Spark ETL.
- Data Stores: Hive-partitioned Parquet, DuckDB, Postgres/Teradata schema (warehouse view).
- Graph & Network Analysis: Neo4j integration, centrality metrics (degree, PageRank, betweenness), neighbor drill-in.
- Explainability & Monitoring: SHAP local/global feature attribution, threshold tuning with triage slider, SLO summaries (capacity, latency, reliability).
- Domain: 4G/5G KPIs (RSRP, RSRQ, SINR, PRB utilization, latency, jitter, packet loss).
NetworkIQ — Incident Risk Monitor (“One Project, Three Platforms”)
NetworkIQ is a telecom-grade incident risk system that predicts network congestion and visualizes cell-site risk across three deployment platforms (Render, GCP Cloud Run, AWS on the roadmap). It showcases how AI-first system design can be made platform-agnostic, scalable, and portable; aligning with orchestration and enterprise deployment strategies.
Highlights
- Data & Features: Ingests network telemetry (throughput, latency, packet loss, dropped session rate) via CSV into PySpark ETL, then stores in Parquet.
- Modeling & Prediction: Trains multiple classifiers—including logistic regression, random forest, and XGBoost (best performer: AUC 0.86, KS 0.42)—to detect high-risk cells.
- Monitoring & Explainability: Integrates SHAP for feature attribution and PSI for drift detection; includes a model card skeleton for transparency.
- Multi-Cloud Orchestration: Deploys a unified Streamlit dashboard across Render and GCP Cloud Run, with AWS App Runner deployment in progress—demonstrating full “One Project, Three Clouds” orchestration.
- Visualization & Executive Access: Features an interactive risk map (circle size by risk magnitude, color-coded by risk level) and integrates Gemini API to generate executive summaries, recommendations, and per-cell natural-language explanations.
- CI/CD & Secure Ops: Uses GitHub Actions to deploy to GCP Cloud Run and secures secrets via Google Secret Manager.
📌 Business Impact: NetworkIQ accelerates incident detection (reducing MTTD), supports better customer experience proxies (NPS), and lowers cost per GB—while enabling consistent, explainable AI across multiple clouds.
Tech Stack
- Languages & Libraries: Python, PySpark, XGBoost, scikit-learn, SHAP
- Data Pipeline & Storage: CSV ingestion → PySpark ETL → Parquet storage
- Modeling: Logistic Regression, Random Forest, XGBoost
- Visualization & UI: Streamlit with interactive risk maps overlayed on cell-site visuals
- Cloud Platforms: Render deployment, GCP Cloud Run (live), AWS App Runner (roadmap)
- CI/CD & Security: GitHub Actions deployment workflows, Google Secret Manager
- Explainability & Monitoring: SHAP for feature insights, PSI for drift, model card for transparency
- AI Interpretation: Gemini API-powered executive briefings and per-cell explanations
- Domain Context: Telecom congestion KPIs—throughput, latency, loss, session drop rates
BNPL Credit Risk Insights Dashboard (Python + Streamlit)
A hands-on, end-to-end BNPL risk project that turns raw lending/repayment data into an interactive decision dashboard. It demonstrates modern risk workflows—from feature engineering and modeling to monitoring and “what-if” policy simulation—built for clarity, speed, and explainability.
Highlights
- Data & Features: Synthetic BNPL portfolio with engineered signals (loan-to-income, usage rate, delinquency streaks, tenure, interactions).
- Modeling & Explainability: Regularized logistic/CatBoost scoring with calibration, AUC/KS, and SHAP to validate driver logic.
- Monitoring: Drift/stability checks (e.g., PSI), score distribution tracking, and cohort comparisons across risk segments.
- Policy Sandbox: Threshold sliders to simulate approval/charge-off trade-offs, segment impacts, and recalibration scenarios.
📌 Business Impact: Helps risk teams test policies before rollout, quantify approval vs. losses, and document governance-ready decisions.
Credit Risk Model Deployment & Monitoring (AWS + PySpark + CatBoost)
This flagship project showcases an end-to-end credit risk modeling pipeline — from scalable data processing to cloud deployment — aligned with best practices in financial services. Built using PySpark, CatBoost, SHAP, and AWS (S3, CLI), it simulates how modern risk pipelines are deployed and monitored at scale.
The full solution includes:
- PySpark ETL pipeline to preprocess large-scale synthetic telecom-style credit data, with engineered risk features (CLTV, utilization bins, delinquency flags)
- Distributed logistic regression using PySpark MLlib to validate scalable modeling workflows and evaluate performance using AUC and KS
- AWS S3 integration to export Parquet-formatted model-ready data for cloud-based storage and future MLOps orchestration
- CatBoost modeling to improve predictive power with categorical support and built-in regularization
- SHAP explainability to verify that key drivers (e.g., FICO, CLTV) align with domain logic and are not proxies or artifacts
- Segment-level analysis comparing predicted vs actual default rates by state, identifying under- and over-prediction patterns
- Business recommendations on scorecard calibration, behavioral feature expansion, and future automation (e.g., Airflow, SageMaker Pipelines)
💼 Business Impact: This project simulates a realistic production-grade credit risk pipeline — bridging data engineering, ML modeling, and cloud deployment. It highlights how interpretability and geographic segmentation can inform policy, governance, and model recalibration.
Telecom Churn Modeling & Retention Strategy
This project demonstrates how predictive modeling and customer segmentation can be used to drive retention strategy in a telecom context. Using a publicly available customer dataset, I developed a full churn risk pipeline.
The final solution integrates:
- Churn prediction modeling using Logistic Regression and XGBoost with performance comparisons (AUC ≈ 0.83)
- SHAP explainability to identify key churn drivers (e.g., Contract Type, Risk Exposure)
- Scorecard simulation converting churn risk into a 300–900 scale for business-friendly deployment
- Customer lifetime value (CLTV) integration to quantify revenue risk across risk bands
- Segmentation framework (High Churn–High Value, Low Churn–Low Value, etc.) for targeted retention campaigns
- Drift monitoring using Population Stability Index (PSI) to track score performance over time
💡 Business Impact: The project enables strategic prioritization by identifying high-risk, high-value customers at risk of churn, supporting proactive retention efforts, revenue protection, and long-term profitability.
Telecom Customer Segmentation with Python
Objective:
Developed a customer segmentation model using unsupervised learning on simulated postpaid telecom data to identify actionable behavioral clusters for marketing, retention, and product strategy.
Highlights:
- Simulated 5,000 realistic customer profiles with usage, support, contract, and churn data
- Applied full preprocessing pipeline: one-hot encoding, feature scaling, PCA for dimensionality reduction
- Performed clustering with K-Means (k=4) selected via elbow and silhouette analysis
- Visualized results with PCA scatter plots, boxplots, and stacked bar charts
- Profiled each segment across spend, usage, tenure, and churn risk
Key Findings:
📌 Key Findings
| Segment | Description | Strategy |
|---|---|---|
| 💬 Voice-Dominant Users | High voice & intl use, short tenure |
Add voice bundles, retention plans |
| 📱 High-Usage Streamers | Heavy data/streaming, higher churn |
Promote unlimited/ entertainment perks |
| 💸 Low-Value Starters | Low usage, low tenure |
Grow via onboarding & upselling |
| 🧭 Loyal Minimalists | Long tenure, low usage, least churn |
Reward loyalty, protect margin |
Tech Stack: Python, pandas, scikit-learn, matplotlib, seaborn
Core Skills Demonstrated: Customer analytics, unsupervised learning, PCA, strategic interpretation, stakeholder communication
Customer Churn Predictor
Goal: Predict whether a telecom customer is likely to churn using an end-to-end machine learning pipeline.
Description:
This interactive app allows users to input customer features (e.g., tenure, contract type, monthly charges) and receive a real-time churn prediction. It includes data preprocessing, feature engineering, model training, cloud deployment, and live user interaction.
- 🔗 Live App (Render)
- 💻 GitHub Repo
- 📎 Technologies:
Python,scikit-learn,Streamlit,joblib,Render
Screenshot:
⚙️ Tech Stack
| Purpose | Tool |
|---|---|
| Language | Python 3 |
| ML Library | scikit-learn |
| Visualization | seaborn, matplotlib |
| Data Handling | pandas, NumPy |
| Deployment | GitHub Pages |
📶 Telecom Engagement Monitoring with Fractional Logistic Regression
This project builds a full monitoring pipeline to track postpaid customer engagement over time using simulated telecom data. The model uses fractional logistic regression to predict monthly engagement as a proportion and evaluates its stability across development and monitoring datasets.
🧰 Tech Stack
| Component | Library / Tool |
|---|---|
| Modeling | statsmodels (GLM - Binomial with Logit link) |
| Data Handling | pandas, numpy |
| Evaluation Metrics | sklearn.metrics |
| Stability Analysis | Custom PSI logic |
| Visualization | matplotlib |
📌 Highlights & Findings
- Model Performance Remains Strong:
- RMSE and MAE remain consistent across development and monitoring samples.
- Calibration curves closely track the 45° reference line, confirming that predicted probabilities are well-aligned with observed engagement.
- Population Stability (PSI) Results:
- Most variables, including
engagement_pred,age, andnetwork_issues, remained stable (PSI < 0.10). - Moderate shifts were observed in
tenure_monthsandavg_monthly_usage, suggesting slight distributional drift.
- Most variables, including
- Final Monitoring Score:
- A weighted score combining RMSE delta, MAE delta, and PSI indicated the model is stable.
- ✅ No immediate action needed, but moderate PSI shifts warrant ongoing monitoring in future quarters.
- Vintage-Level Insights:
- Predicted and actual engagement increased from 2023Q4 to 2025Q2, which aligns with expected behavioral trends.
This project demonstrates how to proactively monitor engagement models using interpretable statistics and custom stability metrics, with outputs ready for integration into model governance workflows.
Fraud Detection with XGBoost & SHAP
A simulated end-to-end machine learning pipeline that predicts fraudulent transactions using XGBoost and interprets the model with SHAP values.
Objective
Detect fraudulent transactions using synthetic data with engineered features such as transaction type, amount, time, and customer behavior patterns.
Key Steps
- Data Simulation: Created a synthetic dataset mimicking real-world credit card transactions with class imbalance.
- Preprocessing: Handled class imbalance with SMOTE and balanced class weights.
- Modeling: Trained an XGBoost classifier and optimized it via grid search.
- Evaluation: Evaluated using confusion matrix, ROC AUC, and F1-score.
- Explainability: Used SHAP (SHapley Additive exPlanations) to explain model predictions and identify top drivers of fraud.
⚙️ Tech Stack
| Purpose | Tool |
|---|---|
| Language | Python |
| ML Library | XGBoost, scikit-learn |
| Explainability | SHAP |
| Data Simulation | NumPy, pandas |
| Visualization | matplotlib, seaborn |
| Deployment | Local / GitHub |
📈 Sample Output
- 🔺 Fraud detection accuracy: ~94%
- 🔍 Top features identified by SHAP:
transaction_amounttime_delta_last_txcustomer_avg_tx
Airline Flight Delay Prediction with Python
A full machine learning pipeline that predicts flight delays using simulated airline data enriched with real U.S. airport codes and weather features. The project explores exploratory analysis, model training, and practical recommendations for airport operations.
Objective
Predict whether a flight will be delayed based on features like carrier, origin, departure time, distance, and simulated weather patterns.
Key Steps
- Data Simulation: Generated a large synthetic dataset including delay labels and airport metadata.
- EDA: Visualized delay patterns by airport, hour of day, and weather impact.
- Modeling: Trained a Random Forest classifier with class balancing and hyperparameter tuning.
- Evaluation: Assessed performance using confusion matrix, precision-recall, and F1-score.
- Recommendations: Delivered operational insights and visualized them with heatmaps and scatterplots.
⚙️ Tech Stack
| Purpose | Tool |
|---|---|
| Language | Python 3 |
| ML Library | scikit-learn |
| Visualization | matplotlib, seaborn |
| Simulation | NumPy, pandas |
| Mapping (EDA) | Plotly, geopandas |
| Deployment | GitHub Pages (Markdown) |
📂 Read the Full Report
🛠️ In Progress
🗺️ Geospatial Risk Dashboard (Tableau)
Building an interactive Tableau dashboard to visualize public health and economic risk indicators across Texas counties.
- Skills:
Tableau,Data Wrangling,Mapping,Interactive Filters
Will be added soon…
What’s Next
- Migrating model workflows into modular Python scripts
- Adding CI/CD and containerization (e.g., Docker)
- Exploring model monitoring frameworks
For more details, view my full portfolio homepage or connect via LinkedIn.