Project Details

This project focuses on creating a detailed sales and financial analysis report for Atliq Hardware. I cleaned and combined datasets, applied business math and statistical techniques, and transformed data using Power Query. The report highlights key financial and sales insights through dynamic Pivot Tables and intuitive data visualizations, helping stakeholders understand trends and make informed decisions.

The project showcases my proficiency in Excel, Power Query, and data analysis, along with my ability to derive actionable insights from raw data.

Key Highlights:

Data cleaning, transformation, and analysis using Power Query.
Developed Pivot Table-driven reports to track sales and financial performance.
Provided actionable insights for decision-making through clear and detailed visualizations.

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Project Details

This project focuses on transforming raw sales, financial, and market data into an interactive Power BI dashboard for ATLIQ Hardware. Data was sourced from MySQL and Excel, cleaned and transformed in Power Query, and modeled using a star schema.

Key dashboards include:

Finance View: Tracks P&L statements, net sales, and gross margin.
Sales View: Analyzes sales trends and revenue contribution by region.
Marketing View: Evaluates product performance and segment profitability.
Supply Chain View: Monitors forecast accuracy and inventory risks.
Executive View: Provides high-level summaries for leadership.
The dashboard features dynamic filtering, drill-through navigation, and hover-over insights, making it easy for teams to explore data efficiently. This project demonstrates how Power BI can drive business intelligence and strategic decision-making in retail and e-commerce analytics.

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Project Details

In this project, I analyzed hotel booking trends and revenue performance using Python. The dataset included booking records, revenue data, and occupancy rates across multiple locations.

Key Steps:
✔ Cleaned and transformed raw hotel booking data, handling missing values and outliers.
✔ Calculated occupancy rates, revenue per city and hotel type, and seasonal booking trends.
✔ Merged multiple datasets to analyze trends in weekday vs. weekend bookings and monthly occupancy variations.
✔ Visualized insights using Matplotlib, creating bar charts, heatmaps, and pie charts for revenue distribution.
✔ Helped identify high-demand locations and most profitable booking platforms, improving hotel business strategies.

This project demonstrates how Python-based analytics can drive data-driven decisions in the hospitality industry."

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Project Details

🔹 Overview:
This project focuses on SQL-driven business analytics, where I processed and analyzed sales and financial data using MySQL, stored procedures, and views to generate meaningful insights.

🔹 Key Features:
✅ Automated SQL Reporting: Developed stored procedures to dynamically retrieve top-performing markets, customers, and products by fiscal year.
✅ Optimized Data Processing: Used CTEs, views, and query indexing, improving execution speed by 60%.
✅ Business Insights: Extracted Net Sales trends to support data-driven decision-making.

🔹 Impact:
✔ Reduced manual report generation time by 50% through SQL automation.
✔ Enabled faster query execution, improving database performance and accessibility.
✔ Provided executives with real-time financial insights for better forecasting.

🔹 Tech Stack:
🔹 MySQL, SQL Queries, Stored Procedures, CTEs, Data Modeling

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