Intern(Chow Jun Hoe)

 31/7/2025 Today, my focus is to verify the impact of dummying future data's time-category variables on the accuracy of the XGBoost model. After multiple rounds of testing, the results show that this adjustment indeed improves model performance during certain time periods. In particular, the discrepancy between the predicted curve and actual sales data is significantly reduced, indicating that the model is better able to capture sales trends after price changes.

 30/7/2025 This morning, I attended the Financial Year 2025 presentation meeting, which detailed Eco-Shop's achievements over the past year and outlined key goals for 2026. In the afternoon, I returned to my XGBoost model research, experimenting with future data. This time, I tested whether adding get_dummies (one-hot encoding of categorical variables) to future data could further improve prediction accuracy. Specifically, encoding holiday, month, and day of the week information for future dates would allow the model to better identify future time characteristics.

 29/7/2025 This morning, I had planned to continue testing and optimizing the XGBoost model, specifically adjusting parameters to account for holiday factors and lag features. However, my work schedule suddenly changed when my company informed me that I needed to assist with the handling of supplies for a charity event sponsored by Eco-Shop. As a result, I spent almost the entire day supporting this effort.

 28/7/2025 Today, my work focuses on re-adjusting the time range around festivals to further optimize the model's performance in Eco-Shop sales forecasting, with the goal of reducing MAPE and making the forecast results closer to actual sales trends. In the previous version, the model was already able to capture the sales on Hari Raya, but after observing the results, it was found that Awal Ramadan would also cause an increase in sales, so I tried to add this factor in.

 25/7/2025 Today's model adjustment work has achieved interim results. Through carefully designed holiday-related variables, I successfully made the XGBoost model's prediction trend during Hari Raya closer to the actual sales data. Not only did it show a significant expected increase before the festival, but it also correctly captured the peak during the festival and the decline after the festival. The overall accuracy (such as MAPE) has also improved. However, when further observing the model's prediction performance at other key points (such as after the product price increase), it was found that the model's perception of "sales volume changes after the price increase" is insufficient. Further adjustments are needed.

 24/5/2025 In the previous model building, I found that after adding lag_365, lag_730 and other lag features, the model predicted well in normal time periods, but around holidays, especially during peak consumption periods like Hari Raya, the prediction effect was biased. I strengthened the dummy features related to festivals in the model, such as marking the day of Hari Raya (is_raya), the day before (is_raya_before) and the day after (is_raya_after) as independent variables to help the model clearly distinguish between festival periods.

 23/7/2025 In terms of the forecast model, today I further added the lag feature (lagging sales). The experimental results show that the forecast line is closer to the actual sales, especially for non-holiday sales. However, an important problem was also found: the effect of the lag feature will mask the impact of external factors such as holidays. For example, even if there are promotions on the eve of certain holidays, the model will refer to the previous "non-holiday" sales value, resulting in insufficient prediction of the peak period. At 10:30 this morning, the Prime Minister made an important statement. At the request of the manager, an impact analysis was conducted on the news and presented to him in the afternoon.