README.md

README.md

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+# 📦 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)