For Quantitive Analysis for Decision Making, we are working on a case study regarding Capital Bikeshare: Analyzing Bike Rental Demand . My part is to " Describe the best approach to handle missing and incorrect information given the context of the problem and be sure to support your argument with scholarly research."

There are also several metrics related to bike-sharing that you need to know in order to gauge the launching of your new helmet with the timing of optimal bike-sharing usage. So, starting with the database you have been given for this purpose, you will first have to clean the data to ensure its integrity. In many analytics projects, the data may have issues, such as missing information, incorrect information, and so on. Before building a model, the data analyst must decide about how to handle missing or incorrect information. Describe the best approach to handle missing and incorrect information given the context of the problem and be sure to support your argument with scholarly research.