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cef44ca | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | # 📦 Random Forest Model for Inventory Optimization
This is a trained **Random Forest Regressor** model for predicting **stockout risks** and **optimizing inventory levels** based on supplier lead time and demand fluctuations.
## Model Overview
- **Algorithm Used**: Random Forest Regressor
- **Purpose**: Forecasting inventory demand & optimizing reorder points
- **Key Features**:
- Supplier lead times
- Order quantities
- Shipment modes
- Regional logistics data
- Demand fluctuations
## 📊 Training Details
- **Dataset**: Historical e-commerce inventory data (orders, shipments, supplier info)
- **Feature Engineering**: Handled missing values, removed outliers, and normalized data
- **Performance Metrics**:
- **Mean Absolute Error (MAE):** *XYZ*
- **Root Mean Squared Error (RMSE):** *XYZ*
- **R² Score:** *XYZ*
## 🔧 How to Use the Model
To load and use the model in Python:
```python
import joblib
from huggingface_hub import hf_hub_download
# Download the model
model_path = hf_hub_download(repo_id="sohnikaavisakula/inventory-optimization", filename="inventory_model.pkl")
# Load the model
model = joblib.load(model_path)
# Example input (adjust based on your dataset)
X_test = [[5.2, 1.3, 7.8, 3.1]] # Replace with real data
prediction = model.predict(X_test)
print("Predicted stockout risk:", prediction)
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