Sales prediction is a critical component in the planning and management of the supply chain for retail companies. An accurate forecast allows companies to better manage their inventories, optimize production, and reduce costs. In this post, we will explore a case study where data science techniques were applied to predict sales in a chain of retail stores.
Data Collection and Cleaning
The first step in this project was the collection of historical sales data, which included information on products, dates, store locations, and promotional data. Additionally, external data such as weather conditions and local events, which could influence sales, were collected. Once gathered, the data was cleaned to remove outliers and correct errors.
Modeling and Choosing Prediction Algorithms
Several machine learning algorithms were used to predict sales, including linear regression models, decision trees, and time series models such as ARIMA. Each model was evaluated using a cross-validation technique to determine its accuracy and generalization ability. The random forest model proved to be the most accurate, with a significantly lower root mean squared error (RMSE) than the other models.
Model Evaluation and Fine-Tuning
The random forest model was fine-tuned by adjusting hyperparameters and improving feature selection. Different combinations of hyperparameters were tested using grid search and cross-validation. Additionally, feature engineering was applied to include additional variables that could improve the model’s accuracy, such as seasonal promotions and holidays.
Results and Recommendations
The final model achieved high accuracy in sales prediction, allowing the company to adjust its inventories and significantly reduce excess stock. Moreover, the predictions helped improve promotion planning and optimize product distribution across different locations.
Project Impact and Lessons Learned
This project demonstrated the value of using advanced data science techniques for sales prediction. Lessons learned include the importance of data quality, proper feature selection, and rigorous model evaluation. In the future, additional approaches such as deep learning could be explored to further improve prediction accuracy.
