Project Details

The Bank Analytics Project dives into the financial world, examining financial datasets to uncover the dynamics of loans and borrower behavior. With a focus on total loan amounts, interest rates, income profiles, and loan statuses, the analysis paints a detailed picture of the lending landscape.

Key insights emerge: a significant 80.35% of loans are fully paid, with debt consolidation leading as the top borrowing reason, while California takes the crown for the highest loan amounts. The analysis delves deeper into loan performance, identifying crucial KPIs like DTI ratio and recovery rates, offering a clearer understanding of borrower risk and repayment behavior.

Actionable recommendations follow, including tightening risk management for higher-risk loan grades, improving verification processes, and deploying targeted regional strategies to optimize the lending portfolio.

Live Interactive Dashboard

Project Details

In this HR Attrition Analysis project using Power BI, I was tasked with identifying factors driving employee turnover. The company faced high attrition, and management needed insights to take corrective action and improve retention.

I analyzed HR data focusing on demographics, turnover trends, and employee wellness. Key KPIs, including attrition rates by job role, gender, age group, and work-life balance, were tracked.

The analysis revealed that employees in Life Sciences and Medical fields, married individuals, and those aged 31-45 showed the highest attrition. Roles like Laboratory Technicians and Sales Executives, along with employees working overtime, experienced significant turnover.

Based on these findings, I recommended improving compensation, offering career development opportunities, and implementing work-life balance policies. These actions resulted in a 10% reduction in employee attrition, strengthening workforce stability.

Live Interactive Dashboard

Project Details

Analyzed a large sales dataset with 209,000 units sold and $34.492M in revenue to enhance sales and marketing decisions.

Develop a Power BI dashboard to visualize key metrics, including total quantity (209,000 units), total revenue ($34.492M), and a profit margin of 58.83%. Identify top-selling products and analyze sales trends and regional performance.

Cleaned data using Power Query. Created visualizations including:
Line Charts for monthly trends, highlighting peaks in October and December.
Bar Charts for the top 5 products, with Product A leading at 30,000 units.
Maps for regional sales distribution, focusing on high-performing cities like New York and San Francisco.
Analyzed weekly patterns and regional performance.

Delivered an interactive dashboard offering real-time insights. Identified peak sales periods and top cities, which supported improved inventory management and targeted marketing strategies, enhancing sales performance and profitability.

Live Interactive Dashboard

Project Details

For AtliQ Hardwares, I analyzed over 500,000 records of sales data from 2019 to 2021 to optimize sales strategies and improve net profit margins. Using ETL processes, I cleaned and rectified missing values, duplicates, and errors to ensure data accuracy. I built a dynamic Star Schema to connect all tables for comprehensive analysis, and integrated the data into Power Pivot for efficient processing.

With DAX, I created key measures like Net Sales, Gross Margin, and COGS, allowing in-depth analysis. Pivot Tables and Conditional Formatting helped identify key trends and quarterly insights aligned with AtliQ’s fiscal year.

The analysis showed Amazon as the top customer, with India leading in net sales at $161.3M in 2021. Sales surged from October to December, and 16 new products were launched, with AQ Qwerty leading at $22M. These insights empowered AtliQ to refine their sales strategies and make data-driven decisions for future growth.

Project Details

In this project, I analyzed player progression and engagement in a gaming environment using SQL. The goal was to uncover insights into key gameplay aspects such as player progression, kill counts, difficulty level performance, multi-day engagement, and top scorer identification. By leveraging advanced SQL techniques, including window functions, stored procedures, and user-defined functions, I performed cumulative analysis, tracked game counts, and evaluated login behaviors.

This project not only enhanced my proficiency in SQL but also allowed me to streamline repetitive tasks, create reusable solutions, and perform complex data analysis. The insights derived from this analysis can help improve game design and user experience, providing valuable feedback for enhancing player retention and optimizing gameplay mechanics.