Handling Missing Values in Data
Datasets are not perfect. Use these techniques to deal with missing data points in your dataset
After starting a machine learning or a data science project you begin your EDA or exploratory data analysis hoping to find interesting patterns and insights about the data before you go on to extract features and build your model. But it is very common to find a lot of values missing in your data. These missing values arise due to many factors not in your direct control. Sometimes due to the ways the data was captured. In some cases the values are not available at all for observation. Nevertheless you will need to handle those missing values before you move further. Lets look at the ways to do that. To be honest there isn’t a single standard technique or a general solution to handle missing values but there are a few ways which you can use depending upon your use case to help you deal with missing values in your data. But before that lets see what are the types of missing data.
Types of missing values
We can classify the missing values in different types. Each type of missing value require slightly…
