Handling Missing Values using Smarten Augmented Analytics!

1.    Missing Data – Why does it matter so much?

Ever worked upon an analytical project and noticed the presence of blank or NAN or undefined values in the records representing the data and being in need of correctly dealing with them? This might be a routine situation while working with real world data. It becomes a crucial step to execute fair technique to handle these missing values after understanding the analysis required from the data as often data for one party can be a noise to another party. Data can be missing owing to corrupt data, incomplete data extraction process, data entry errors or simply the data is rare and is actually missing! But handling such data is of great challenge in order to make right decisions and generate robust predictive models or reports. This article sums up key steps to handle missing values using Smarten Augmented Analytics and further explains its utility from the Employee Salary Prediction dataset.