Project: Product Returns Analysis and ML Time-Series Forecasting

Predicting product returns using machine learning to help e-commerce businesses minimize losses and optimize logistics.

Objective:

A company seeks to analyze product returns for three products after experiencing a high volume of returns. They aim to determine whether this trend is driven by customer behavior or potential issues with the products. Therefore, this project focuses on analyzing product returns and implementing ML-based forecasting to:

• Identify Potential Product Defects.
• Predict Product Return Rates through Machine Learning.
• Reduce Return Rate.
• Improve Supply Chain Efficiency.
• Enhance Customer Satisfaction.
• Cost Reduction.

Tech Stacks:

• Python
• Numpy & Pandas
• Matplotlib & Seaborn
• Prophet
• pmdarima
• sklearn

ML Models:

• Prophet
• ARIMA
• Random Forest

Project Flow:

• Exploratory Data Analysis (EDA)
• Data Processing
• Data Analysis
• ML Model Diagnostics
• Dicky-Fuller Test (Hypothesis Testing)
• Prophet Time-Series Forecasting Model
• Model Evaluation and Cross-Validation
• Prophet Model Conclusion
• ARIMA Forescasting Model
• Model Evaluation
• ARIMA Model Conclusion
• Random Forest Forecasting Model
• Random Forest Model Evaluation
• Random Forest Conclusion
• Models Comparison

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Full Project can be accessed through this link: Product_Returns_ML_Forecasting.ipynb

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