Data quality is crucial in machine learning (ML) applications, as errors in the data can significantly impact the prediction accuracy of the underlying ML model. Therefore, data cleaning is an integral component of any ML pipeline. However, in practical scenarios, data cleaning incurs significant costs, as it often involves domain experts for configuring and executing the cleaning process. Thus, efficient resource allocation during data cleaning can enhance ML prediction accuracy while controlling expenses. This paper presents COMET, a system designed to optimize data cleaning efforts for ML tasks. COMET gives step-by-step recommendations on which feature to clean next, maximizing the efficiency of data cleaning under resource constraints. We evaluated COMET across various datasets, ML algorithms, and data error types, demonstrating its robustness and adaptability. Our results show that COMET consistently outperforms feature importance-based, random, and another well-known cleaning method, achieving up to 52 and on average 5 percentage points higher ML prediction accuracy than the proposed baselines.