The image displays a structured workflow diagram for a data science project, detailing the process from dataset acquisition to model accuracy assessment. It begins with dataset splitting and preprocessing, which includes steps like noise removal and normalization. Following data preprocessing, there is an exploratory data analysis phase, then model selection, leading to predictive modeling. The diagram continues with a feedback loop from vocabulary lookup to token ID mapping, and features a step for feature hashing. The final step is the detection of accuracies, indicating the evaluation of the model’s predictive performance.
