https://github.com/newking9088/orderreturnclassifier
orderReturnClassifier is a machine learning project that predicts whether an order will be returned. By analyzing various order features using classification algorithms, it aims to help businesses reduce return rates and improve customer satisfaction.
https://github.com/newking9088/orderreturnclassifier
classification data-visualization ecommerce machine-learning
Last synced: 3 months ago
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orderReturnClassifier is a machine learning project that predicts whether an order will be returned. By analyzing various order features using classification algorithms, it aims to help businesses reduce return rates and improve customer satisfaction.
- Host: GitHub
- URL: https://github.com/newking9088/orderreturnclassifier
- Owner: newking9088
- License: mit
- Created: 2024-08-25T23:09:34.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2024-08-28T01:40:22.000Z (9 months ago)
- Last Synced: 2025-01-05T06:43:03.128Z (5 months ago)
- Topics: classification, data-visualization, ecommerce, machine-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 8.75 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# orderReturnClassifier
orderReturnClassifier is a machine learning project that predicts whether an order will be returned. By analyzing various order features using classification algorithms, it aims to help businesses reduce return rates and improve customer satisfaction.
# Predicting Product Returns: A Business Use Case
## Introduction
In this project, we focus on understanding the reasons behind product returns and predicting whether a product will be returned. Our primary goal is to provide actionable insights for retailers rather than achieving the highest possible model accuracy. By identifying patterns and factors that lead to returns, we aim to help businesses reduce return rates and associated costs.
## Business Context
Product returns pose a significant challenge for retailers, leading to substantial financial losses. In 2023, U.S. retailers faced $743 billion in merchandise returns, accounting for 14.5% of total sales [1]. For every $1 billion in sales, the average retailer incurs $145 million in returns [1]. Additionally, return fraud contributes to $101 billion in losses annually [1]. These statistics highlight the importance of understanding and mitigating product returns to improve profitability.
## Methodology
### Data Collection and Preprocessing
The data on product sales and returns, including features such as product ID, purchase date, return date etc. were from a retailer company between 2012-2015. The data was cleaned and preprocessed to handle missing values and outliers.
### Feature Engineering
To enhance our model's predictive power, we engineered several features, including:
- **Product Return Count**: The number of times a product has been returned.
- **Product Return Ratio**: The ratio of returns to total sales for each product.
- **Profit Ratio**: The margin of profits on a product.
We will talk about downside of engineering some of these features in the whole data set.
### Model Training and Evaluation
We used a logistic and random forest classifier model to predict product returns. The model was trained on historical data and evaluated using the ROC-AUC metric, which is robust to class imbalance. Our focus was on understanding the factors influencing returns rather than maximizing model accuracy.
### Temporal Data Splitting
To ensure our model's predictions are valid and applicable in real-world scenarios, we suggest splitting the data into training and testing sets based on different time periods. This approach mimics the real-life situation where future data is unknown at the time of model training. For more details on splitting temporal data, refer to this important read [2].
## Discussion
By focusing on the business use case of understanding and predicting product returns, we aim to provide valuable insights for retailers to reduce return rates and associated costs. This project highlights the importance of feature engineering and appropriate data splitting techniques in building robust predictive models.
## References
[1]: [NRF and Appriss Retail Report: $743 Billion in Merchandise Returned in 2023](https://nrf.com/media-center/press-releases/nrf-and-appriss-retail-report-743-billion-merchandise-returned-2023)
[2]: [How (and why) to create a good validation set](https://www.fast.ai/posts/2017-11-13-validation-sets.html)