Effectively addressing missing data is vital in data mining to ensure accurate analysis and reliable decision-making. This study presents the applied C-B intermediate procedure as a practical approach to restoring randomly missing data in datasets. Using a dataset on beverage consumption in India, categorized by type, the method's ability to estimate and recover missing values was evaluated. After applying the C-B technique, the mean values for Tea, Milk, and Coffee, initially recorded as 450, 606, and 301, respectively, were successfully adjusted to 454, 607, and 302, demonstrating the procedure's effectiveness in improving data integrity. Statistical analyses, including standard deviation, coefficient of variation, and one-way ANOVA, showed minimal discrepancies from the original values, confirming the robustness of the proposed method. These findings emphasize the technique's potential to improve data completeness and reliability, making it a valuable solution for managing large datasets with missing entries.