Data Analytics • Data Scientist

Building scalable solutions that help businesses make smarter decisions through Data Analytics, Machine Learning, and Business Intelligence.

• Building data-driven solutions through Data Analytics, Machine Learning, and Business Intelligence.
• Worked on projects involving statistical analysis, demand forecasting, predictive analytics, business intelligence, fraud detection, and election analytics.
• Built dashboards and analytical solutions inspired by the operational needs of local businesses and educational institutions to deliver actionable insights.
• Data becomes valuable when it helps people make informed decisions.
• Every dataset tells a story that can be translated into practical solutions and measurable impact.

• Python • SQL

• Pandas • NumPy • Microsoft Excel • Statistics

• Scikit-learn • XGBoost • Regression • Classification • Deep Learning

• Power BI • Matplotlib • Seaborn

• Git • GitHub • Jupyter Notebook • VS Code

A data storytelling project developed for AtliQ Media to analyze the Tamil Nadu Assembly Elections using Python, SQL and Power BI. The objective is to transform complex election data into meaningful insights that help editorial teams explain how voting patterns, turnout, regional dynamics, and electoral outcomes changed between the 2021 and 2026 Assembly Elections.

Key Analysis

• Compared the 2021 and 2026 Assembly Elections to analyze changes in voter turnout, regional seat distribution, and winning margins.
• Identified constituency-level seat flips, regional strongholds, and shifts in voting patterns across Tamil Nadu.
• Examined the electoral impact of emerging political parties, including TVK, on constituency-level competition and regional performance.
• Performed SQL- and Python-based analysis to uncover trends behind election outcomes and support data-driven storytelling.
• Built interactive Power BI dashboards that enable journalists and editorial teams to communicate election insights through data rather than opinion.

Business Value

Helped transform large-scale election data into clear, evidence-based stories, enabling audiences to better understand voter behaviour, regional trends, and the factors influencing election results.

Technologies

Python • SQL • Power BI

Forecasted future product demand and identified stock-out risks using Machine Learning to support inventory planning and replenishment decisions.

Project Highlights

• Achieved an R² Score of 82.7% using XGBoost for demand forecasting.
• Compared multiple regression and classification models to identify the best-performing approach.
• Identified products with high stock-out risk through inventory trend and replenishment analysis.
• Visualized model performance and feature importance to improve interpretability and business understanding.

Key Business Insights

Supported data-driven inventory planning by improving demand visibility, reducing stock-out risks, and enabling proactive replenishment decisions.

Technologies

Python • Pandas • Scikit-learn • XGBoost • Matplotlib

Built an interactive Power BI dashboard to help pharmacy businesses monitor inventory, reduce stock-related losses, and support faster operational decision-making. The dashboard transforms sales and inventory data into actionable insights, enabling pharmacists to identify stock shortages, monitor medicine expiry, and prioritize timely replenishment.

Key Analysis

• Monitored inventory health by tracking low-stock, out-of-stock, and expiring medicines.
• Identified medicines requiring immediate replenishment through reorder-level analysis.
• Analyzed sales performance, customer purchasing behaviour, payment trends, and executive KPIs.
• Assessed inventory risk by measuring the potential financial impact of expired and unavailable medicines.
• Delivered actionable recommendations through interactive Power BI dashboards and KPI-driven insights.

Key Business Insights

• Identified 29 out-of-stock medicines and 1,000 low-stock medicines requiring immediate replenishment.
• Detected 5,000 expired medicines, including 130 expiring within 7 days and 344 within 30 days, supporting proactive inventory planning.
• Highlighted an estimated Inventory Loss of ₹308 Million and Revenue at Risk of ₹414 Million, demonstrating the business impact of inventory inefficiencies.
• Recommended urgent replenishment of Amoxicillin, Azithromycin, and Vitamin C based on inventory levels and customer demand.
• Identified Sunday as the highest revenue-generating day, with 33.65% of transactions completed through UPI, providing insights into customer purchasing behaviour.

Technologies

Power BI • Microsoft Excel • DAX

Applying Data Analytics, Machine Learning, Deep Learning and Artificial Intelligence to develop practical solutions for business problems.

• Predictive Analytics
• Business Intelligence
• Decision Intelligence
• Data Storytelling
• AI Applications