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Developed a Sales Analytics project using Python to analyze relationships between key business metrics through correlation analysis and data visualization. The project identified critical patterns between sales, profitability, discounts, and operational costs to support data-driven business decision-making.
Tools Used: Python, Pandas, Matplotlib, Seaborn, Business Analytics.
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Amazing work
Building an Advanced AI-Driven Analytics System using Python. This project focuses on processing large datasets and generating automated insights. I utilized Pandas and NumPy for data manipulation and integrated LLM capabilities to provide natural language summaries of the data.
A store made $2.3M in revenue and was still losing money. Here’s where it was going wrong.
I built a Sales & Profit Analysis dashboard covering 4 years of data
And the numbers told a story revenue alone would never show.
The findings:
📍Furniture had $740K in sales but only $20K in profit
📍Tables and Bookcases had negative margins which means selling more meant losing more
📍The South region was bleeding compared to every other region
📍Technology was quietly carrying the entire business
The fix wasn’t complicated once the data was clear:
✅Reduce Furniture discounts and renegotiate supplier costs
✅Invest more in Technology since it’s already winning
✅Study the West region and replicate it in the South
That’s what a good dashboard does.
Not just show numbers
It shows you exactly where to act.
This dashboard was built with Power BI.
The hardest part was knowing which questions to ask before touching the tool.
What’s one business decision in your industry that data could make obvious but most people still make on gut feeling?
Thank you
Challenges
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