🛍️ AI-Powered Customer Return Prediction System

Predict high-risk product returns before they happen using Machine Learning and Ensemble Learning.

Overview

Product returns are a major challenge for e-commerce companies, leading to:

• Increased logistics costs
• Refund processing expenses
• Inventory management issues
• Reduced operational efficiency

This project uses Machine Learning to predict the probability of a customer returning a purchased product based on customer behavior, product characteristics, pricing information, and transaction history.

The system enables retailers to proactively identify high-risk orders and take preventive actions such as targeted customer support, product recommendations, or fraud investigation.

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Live Application

https://hcl-hackathon-retailreturnclassification.streamlit.app/

Key Features

✅ End-to-End Machine Learning Pipeline

✅ Ensemble Stacking Architecture

✅ Advanced Feature Engineering

✅ Probability-Based Risk Scoring

✅ Interactive Streamlit Web Application

✅ Production-Ready Model Serialization

✅ Real-Time Predictions

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Business Problem

Returns cost online retailers billions of dollars annually.

Most companies react after a return occurs.

This system shifts the process from:

Reactive Returns Management → Predictive Returns Prevention

By identifying return-prone purchases beforehand, businesses can:

• Reduce return-related losses
• Improve customer satisfaction
• Optimize logistics operations
• Improve inventory planning

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Solution Architecture

User Input
     │
     ▼
Streamlit Web App
     │
     ▼
Feature Engineering Layer
     │
     ▼
Stacked Ensemble Model
     │
     ▼
Return Probability Prediction
     │
     ▼
Business Decision Support

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Dataset Features

Customer Features

• Age
• Gender
• Income Bracket
• Loyalty Program
• Membership Duration
• Customer Support Calls
• Days Since Last Purchase

Product Features

• Category
• Subcategory
• Brand
• Price
• Discount
• Product Rating
• Review Count
• Historical Product Return Rate

Transaction Features

• Quantity
• Total Items Purchased
• Season
• Weekend Indicator
• Holiday Season Indicator

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Data Preprocessing

Missing Value Handling

• Numerical Features → Median Imputation
• Categorical Features → Most Frequent / Unknown

Feature Encoding

• One-Hot Encoding
• Boolean Standardization

Data Cleaning

• Duplicate Removal
• Category Standardization
• Outlier Validation

Feature Scaling

• Numerical Feature Normalization

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Feature Engineering

Several domain-specific features were created to improve predictive performance:

Pricing Features

final_price = price - discount
discount_ratio = discount / price

Customer Features

is_loyal = 1 if loyalty_program == "Yes"

Purchase Features

value_per_item = final_price / quantity

These engineered features improved the model's ability to capture purchasing patterns associated with returns.

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Machine Learning Models

Base Models

Logistic Regression

• Captures global linear relationships
• Interpretable baseline model

Random Forest

• Handles non-linear interactions
• Robust against overfitting

XGBoost

• Captures complex decision boundaries
• Strong performance on tabular datasets

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Stacking Ensemble

The final prediction is generated using a Stacking Ensemble Architecture.

               Logistic Regression
                        │
                        ▼
               Random Forest
                        │
                        ▼
                    XGBoost
                        │
                        ▼
             Meta Logistic Regression
                        │
                        ▼
                 Final Prediction

The meta-model learns the optimal combination of predictions from all base learners, resulting in better performance than individual models.

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Model Evaluation

Evaluation metrics used:

• Accuracy
• Precision
• Recall
• F1 Score
• ROC-AUC
• Confusion Matrix

Why Recall Matters

In return prediction systems, missing an actual return is often more costly than a false alarm.

Therefore, Recall was treated as a primary optimization metric.

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Streamlit Application

The application provides a simple interface where users can enter:

• Customer Age
• Product Price
• Discount
• Product Return Rate
• Quantity
• Loyalty Program Status

The system then predicts:

Output

Prediction: Returned / Not Returned

Return Probability: 84%

Risk Classification

• ⚠️ High Return Risk
• ✅ Low Return Risk

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Tech Stack

Programming

• Python

Machine Learning

• Scikit-Learn
• XGBoost

Data Processing

• Pandas
• NumPy

Deployment

• Streamlit

Model Persistence

• Joblib

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Installation

Clone Repository

git clone https://github.com/yourusername/customer-return-prediction.git

cd customer-return-prediction

Install Dependencies

pip install -r requirements.txt

Run Application

streamlit run app.py

Future Improvements

Explainable AI

• SHAP Value Analysis
• Feature Importance Dashboard

MLOps

• Docker Containerization
• CI/CD Pipeline
• Automated Retraining

Deployment

• FastAPI Backend
• AWS/GCP Deployment
• Real-Time Prediction API

Monitoring

• Data Drift Detection
• Model Performance Tracking
• Prediction Monitoring Dashboard

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Hackathon Project

Developed as part of an ML Hackathon challenge focused on solving real-world retail analytics problems using machine learning and predictive modeling.

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Author

Siddhi Jadhav

Aspiring Data Scientist | Machine Learning Engineer

• LinkedIn: Siddhi Jadhav
• GitHub: Siddhi-Jadhav01