🧠 AI Sales Forecasting Dashboard

Powered by Linear Regression & Scikit-learn  |  Data: 2022–2024

Growth Rate

vs previous period

Total Sales

forecast period

Daily Average

per day

Model MAE

average error

About this model: A Linear Regression model was trained on sales data from 2022–2024 to forecast the next 6 months. It identifies the underlying trend in historical data and projects it forward. The Mean Absolute Error (MAE) of means predictions are off by roughly that amount on average ( of a typical month's sales) useful context when setting budgets.

📈 6-Month Sales Forecast Chart

🔮 6-Month Sales Forecast

Month Forecasted Sales Status
* Forecasts carry an average error of ±. Use these as planning guides, not exact predictions.

📖 What does this forecast mean for your business?

Based on sales data from 2022 to 2024, the model predicts total revenue of over the next six months a growth rate compared to the previous period.

March is the peak month at $38,752. Inventory should be stocked and extra staff hired before then. April shows a significant dip likely seasonal followed by a recovery through May and June.

How to act on this: Use March's figure to set maximum stock orders. Trigger promotions in April to cushion the dip. Re-train the model monthly with new actuals to continuously improve accuracy.

💡 Business Recommendations

📦

Stock Inventory

Stock up before March — the peak sales month at $38,752

📣

Marketing Budget

Increase marketing spend in April to recover from the sales dip

👥

Staffing Plan

Hire extra staff for March and June peak demand periods

📊

Review Monthly

Update forecast every month with new data to improve accuracy

Built by Trisha  |  Linear Regression Model  |  Python & Scikit-learn  |  Data: 2022–2024