ML is all about working with data. The quality of the training data and labels is crucial to the success of an ML model. High-quality data leads to a more accurate ML model and the right prediction. Often in the real world, your data has multiple issues such as missing values, noise, bias, outliers, and so on. Part of data science is the cleaning and preparing of your data to get it ready for ML.
The first thing about data preparation is to understand business problems. Data scientists are often very eager to jump into the data directly, start coding, and start producing insights. However, without a clear understanding of the business problem, any insights you develop have a high chance of becoming a solution which is unable to address a problem. It makes much more sense to start with a clear user story and business objectives before getting lost in the data. After building a solid understanding of the business problem, you can begin to narrow down the...
