Sales Forecast Model
Model Description
This model predicts Product_Store_Sales_Total (revenue) based on product and store attributes.
Best Model: Random Forest
Performance Metrics
| Metric | Train | Test |
|---|---|---|
| R² | 0.9714 | 0.9312 |
| RMSE | 179.95 | 280.16 |
| MAE | 65.18 | 107.08 |
Features Used
- Product_Weight
- Product_Allocated_Area
- Product_MRP
- Store_Age
- Product_Sugar_Content_Encoded
- Product_Type_Encoded
- Store_Size_Encoded
- Store_Location_City_Type_Encoded
- Store_Type_Encoded
Usage
from huggingface_hub import hf_hub_download
import joblib
# Download model
model_path = hf_hub_download(repo_id="Abhik19/sales-forecast-model", filename="best_model.joblib")
model = joblib.load(model_path)
# Make predictions
predictions = model.predict(X)
Business Context
Accurate sales forecasting helps:
- Plan sales operations by region
- Optimize supply chain procurement
- Reduce sales pipeline risks
- Establish benchmarks for trend analysis
- Downloads last month
- -
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support