Unless you have a huge dataset (“Big Data”), it is generally not worth attempting machine learning or data science projects on your problem.1 pointTrueFalse

The statement is False.

While it's often true that large datasets can enhance the effectiveness of machine learning and data science projects, smaller datasets can still yield valuable insights and allow for successful modeling, especially if the data is well-structured and relevant. Techniques such as transfer learning, feature engineering, and specialized algorithms can help improve performance even with limited data. Additionally, exploratory data analysis can provide significant insights regardless of the dataset size, helping inform decisions and strategies. Therefore, it's not exclusively about having a "huge dataset"; quality, relevance, and the right methodologies can also lead to meaningful outcomes in machine learning projects.