Abdallah Alameer Ali

Predictive Modeling: Developed an end-to-end Python platform using Scikit-learn, deploying a Random Forest Regressor for performance forecasting and a Gradient Boosting Classifier (with SMOTE) to predict promotion readiness. o Explainable AI (XAI) & Diagnostics: Implemented SHAP values with Logistic Regression to uncover core behavioral drivers of employee attrition, and applied Linear Regression to detect pay equity disparities. o Prescriptive Analytics: Built an automated recommendation engine that translates complex predictive outputs into actionable HR policies, enabling data-driven retention and talent development strategies.

Impact: Provides predictive insights for HR, including attrition, performance, and pay equity analysis.

This project demonstrates the engineering of a Snowflake cloud data warehouse to analyze SaaS customer behavior. The goal was to process 280K+ records into a structured analytical schema and extract retention metrics, LTV, and churn drivers using advanced SQL.

Impact: Uncovered core churn drivers (17.04% due to support bottlenecks) and engineered proactive BI views to identify $1.97M in cumulative lost revenue.

Developed a Power BI dashboard to transform complex logistics data into operational insights, designing an optimized Star Schema and authoring complex DAX for performance correlations.

Impact: Visualized critical KPIs like On-Time Delivery (79.6%) and CSAT (84.4%) to drive preventive maintenance.

Conducted an end-to-end customer behavior analysis and A/B testing using Python (Pandas, SciPy). Developed a custom algorithm for control store selection combining Pearson Correlation and Magnitude Distance. Translated complex statistical findings into actionable C-level commercial recommendations to optimize shelf space and promotional strategies.

he objective was to build a Power BI dashboard that provides: Clear KPI tracking Risk segmentation Demographic and behavioral analysis Interactive dynamic reporting 📊 KPI Metrics The dashboard includes the following key performance indicators: Total Policies – Measures the size of the customer base Total Claim Amount – Overall financial exposure Claim Frequency – Indicates how often claims occur Average Claim Amount – Measures claim severity Gender-wise Policy Distribution – Customer segmentation

Impact: An insurance company needed a centralized interactive dashboard to better understand its policyholder base and claim patterns

The goal to answer: Where is the revenue coming from How efficient is the cash collection? Where are the financial risks

Impact: This project focuses on analyzing business performance, cash flow, and loan risk to support data-driven decision-making.

This project demonstrates the design and implementation of a SQL-based Data Warehouse using Swiggy order data. The goal was to transform raw transactional data into a structured Star Schema and extract business insights using analytical SQL queries.