Welcome To My Data Analyst Portfolio!
This is a machine learning project to predict customer churn. It uses predictive models like Random Forest to identify at-risk customers and key churn drivers, such as age and estimated salary, to help businesses build effective retention strategies.
This project provides an in-depth analysis of employee attrition at a technology company. The goal was to identify the key drivers of turnover and create a dashboard that HR leaders can use to monitor, predict, and reduce attrition.
In this project, I conducted a comprehensive analysis of Blinkit’s retail sales data using SQL. The process involved importing, cleaning, and transforming the dataset, followed by generating key performance indicators (KPIs) segmented by item type, fat content, and outlet characteristics. These analyses provided data-driven insights to support strategic decision-making.
This project utilizes Python to analyze loan data, calculate key metrics like total applications, funded amounts, interest rates, and debt-to-income ratios, and create visualizations showing trends, regional patterns, loan terms, purposes, and the impact of factors like employment length and home ownership.
In this project, I built a modern front-end password generator using HTML, CSS, and JavaScript. I made it so users can create secure, random passwords by customizing the length and selecting character types like uppercase, lowercase, numbers, and symbols. I also implemented a responsive design and added a convenient one-click copy-to-clipboard feature.
This project analyzes Shopify sales data in Power BI to uncover insights into transaction performance, customer purchasing behavior, and long-term customer value. It delivers an interactive dashboard that allows stakeholders to explore sales trends, customer retention, and revenue patterns dynamically.
