Hospital database system built with Oracle APEX and SQL, featuring an interactive dashboard for real-time insights into patient distribution and doctor availability. Designed to optimize resource management and support hospital administration in data-driven decision-making.

This project presents an interactive Power BI dashboard for analyzing Blinkit sales performance across multiple dimensions such as product categories, outlet size, fat content, and year-wise trends.

This repository contains materials for a Data Analysis Hackathon I designed to provide learners with practical experience in analyzing e-commerce sales and customer insights. Participants utilize tools such as Excel, SQL, Python, and Power BI to explore datasets, uncover trends, answer key business questions, and generate actionable insights.

Premier Data Scout - CETYS MBA 2025

A time series forecasting project using ARIMA and Python to predict Netflix’s quarterly subscription growth, aiding data-driven decisions in content strategy and business planning.

Advanced Excel dashboard designed to analyze product category performance and provide interactive insights into monthly sales trends. Uses tools like regression analysis and combo boxes to enhance decision-making with key metrics on sales and profit.

Modeling Agri-finance Credit Risk with Data & Machine Learning using Python (Jupyter Notebook)

📊 End-to-end data analytics project analyzing a bank’s loan portfolio to identify profitable segments, high-risk borrowers, and strategic insights using Python, Jupyter Notebook, and data visualization.

The Hospitality-Domain-Project is a Power BI dashboard for AtliQ Grands, enabling them to track key metrics, make data-driven decisions, and achieve a 20% efficiency boost and 15% revenue growth.

A data-driven approach to designing an optimal Data Science curriculum. This project extracts skills from job postings, applies NLP and clustering techniques (K-Means, Hierarchical, DBSCAN), and maps industry demands to educational recommendations. Uses Python, Scikit-learn, OpenAI embeddings, and Seaborn for visualization.

This project involves data cleaning and exploratory data analysis (EDA) of a fictional retail clothing store dataset. The focus of this analysis is to identify trends, correct inconsistencies, and derive insights in order to make data-driven decisions.

This repository contains a Power BI dashboard designed to analyze Amazon's global sales performance. It provides dynamic, interactive visualizations of sales by region, segment, and customer, helping to uncover insights and guide data-driven business decisions.

This project analyzes AtliQ Grands' hotel revenue, occupancy trends, and profitability using Power BI. The interactive dashboard provides insights into RevPAR, ADR, market share, and customer behavior to help the management make data-driven decisions. 🔗 Live Power BI Dashboard

This project uses vehicle auction data to identify key factors affecting sale prices and auction timelines, uncovering insights and strategies to optimize auction efficiency, inventory management, and overall sales performance.

Python CLI tool that automates Earthworks specification from lab data (Dry Density, CBR, Su, MCV). Generates validated plots and PDF reports for data-driven geotechnical design.

Developed an interactive Power BI dashboard by integrating transaction and customer data from a SQL database to provide real-time insights. Streamlined data processing to monitor key performance metrics and trends, sharing actionable insights with stakeholders to support data-driven decision-making.

Data-driven analysis inspired by the Moneyball approach, identifying affordable replacements for key Oakland A's players using R and sabermetrics to support cost-effective recruitment.

Analyzed lead quality trends to identify top-performing sources and optimize marketing spend. Explored factors like widgets, campaigns, and geography using Python-based analysis and visualizations. Provided actionable insights to improve conversion rates and justify CPL adjustments.

A Python script to analyze sales performance using Pandas and Matplotlib. Loads, cleans, and visualizes sales data, calculating key metrics (total sales, average order value, unique customers) and generating trends (monthly sales, product category performance). Includes error handling for robust execution.