sohnikaavisakula/inventory-optimization

📦 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:

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)