Predictive Analytics Dashboard
ML-Driven Revenue Forecasting for Executive Decision-Making
Project Overview
A regional distribution company's finance team was building quarterly budgets from last year's numbers and intuition. The CFO wanted ML-driven forecasts so the board could allocate resources with confidence. The challenge: three years of messy, inconsistent CSV exports and eight product lines with different seasonal patterns.
The Challenges
- 1
Three years of raw CSV exports with inconsistent column schemas and missing values
- 2
Seasonal patterns varied significantly across 8 distinct product lines
- 3
Executive team needed a zero-learning-curve UI — no data science background
- 4
Model needed to explain its predictions, not just output numbers
Our Approach
We built a data cleaning and feature engineering pipeline in pandas, trained gradient boosting models (XGBoost) on the cleaned historical data, and wrapped everything in a Next.js dashboard served by FastAPI. Each forecast includes confidence intervals and a plain-language summary of the top contributing factors.
Key Features & Metrics
Gradient boosting model trained on 36 months of cleaned sales history
Per-SKU and category-level quarterly forecasts with confidence intervals
Plain-language AI commentary explaining each forecast's key drivers
One-click CSV export formatted for board presentation
Automated weekly model retraining as new sales data comes in
Forecast accuracy improved from 62% baseline to 89%
Results & Business Outcome
Forecast accuracy rose from 62% to 89%. Inventory over-ordering dropped 31%, freeing $240K in working capital. The CFO now presents ML-generated projections to the board every quarter.
When business decisions are built on accurate predictions instead of assumptions, every resource allocation becomes a competitive advantage rather than a calculated gamble.
Let's Build Something Intelligent Together
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