ecommerce-spending-predictor

E-commerce Spending Predictor

A Linear Regression model that predicts yearly customer spending based on user behavior metrics from an e-commerce platform.

Model Description

• Model Type: Linear Regression (scikit-learn)
• Task: Tabular Regression
• Language: Python
• License: Apache 2.0

Features

Target Variable

• Yearly Amount Spent - Total yearly spending in USD

Performance Metrics

Quick Start

from huggingface_hub import hf_hub_download
import joblib

# Download and load model
model_path = hf_hub_download(
    repo_id="Srikanth-Karthi/ecommerce-spending-predictor",
    filename="model.joblib"
)
model = joblib.load(model_path)

# Predict
features = [[33.0, 12.0, 37.0, 3.5]]
# [Avg Session Length, Time on App, Time on Website, Membership Years]

prediction = model.predict(features)
print(f"Predicted Yearly Spending: ${prediction[0]:.2f}")

Training Details

• Algorithm: Linear Regression
• Library: scikit-learn
• Train/Test Split: 70/30
• Random State: 42

Use Cases

• Customer lifetime value prediction
• Marketing budget allocation
• Customer segmentation
• Revenue forecasting

Limitations

• Model assumes linear relationships between features and target
• Trained on a specific e-commerce dataset (500 samples)
• May not generalize to different industries or customer bases

Citation

@misc{ecommerce-spending-predictor,
  author = {Srikanth-Karthi},
  title = {E-commerce Spending Predictor},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/Srikanth-Karthi/ecommerce-spending-predictor}
}