Project information
- Category: Data Science, Product Design, AI, Predictive Modeling
- Client: E-Commerce
- Project date: 20 January, 2024
- Project URL: https://github.com/Aidanszwec/Portfolio
Credit:
Building off the open source data from https://Kaggle.com thank you so much to Laura Fink who created the Initial analysis creating a foundation for the dashboard and analysis report. Credit: https://www.kaggle.com/allunia
Full project description coming soon.
In this project, I undertook the challenge of optimizing warehouse operations and predicting daily product sales for an online retailer. Leveraging advanced machine learning and AI modeling techniques, the primary objectives were to streamline warehouse processes, enhance inventory management, and empower the business with actionable insights.
Key Achievements:
- 1. Granular Sales Forecasting:
- 2. Exploratory Data Analysis: (EDA):
- 3. Data-Driven Decision-Making:
- 4. Operational Efficiency:
- 5. Strategic Positioning:
Developed a robust forecasting model utilizing the CatBoost Regressor, enabling accurate predictions of daily product sales. This strategic insight serves as a cornerstone for adaptive inventory planning.
: Conducted in-depth EDA to unravel patterns in customer behavior, product popularity, and transaction dynamics. The findings contributed to informed decisions for optimizing warehouse operations.
Emphasized interpretability through Shapley values, ensuring transparent and understandable models. Stakeholders can confidently make decisions based on a clear understanding of the factors influencing sales predictions.
Identified opportunities for cost-effective warehouse management, from storage optimization to streamlined order fulfillment. The project aimed at enhancing overall operational efficiency for sustainable growth.
By translating data insights into actionable strategies, the project aimed to position the business strategically in the competitive e-commerce landscape. The focus on adaptive strategies provides a competitive edge in a rapidly changing market.
Business Impact:
- Proactive Inventory Planning:
- Cost Savings:
Accurate sales forecasts enable proactive inventory planning, minimizing stockouts and overstock situations.
Streamlined warehouse operations contribute to cost-effective management, translating to tangible cost savings.
Conclusion:
In summary, this project showcases a data-driven approach to e-commerce analytics, emphasizing the importance of predictive modeling in driving strategic decision-making, optimizing operations, and ultimately contributing to the success of the business in the dynamic world of online retail.