<![CDATA[Sales forecasting in the Micro, Small, and Medium Enterprises (MSME) sector faces challenges due to the fluctuating (noisy) nature of the data and the absence of class labels (unlabeled) required for training supervised learning models. This study proposes a sequential hybrid architecture in which the K-Means algorithm is employed as a Self-Labeling mechanism to automatically transform raw transaction data into class labels (“Low” and “High”). The resulting synthetic labels are then used to train an Ensemble Voting Classifier model that aggregates predictions from XGBoost, LightGBM, and CatBoost. The experimental evaluation results show that although the single XGBoost model achieves a slightly higher accuracy (96.24%) compared to the Ensemble model (96.07%), the hybrid Ensemble Voting model proves superior in terms of probability calibration, achieving the lowest Loss value of 0.1532. This value outperforms XGBoost (0.1646) and LightGBM (0.1772), indicating more reliable and stable prediction confidence. The model also demonstrates excellent balance with an F1-Score of 0.95 and a Recall of 0.96 for the majority class. This study confirms that the hybrid approach is effective in reducing uncertainty in MSME stock management.]]>
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