[FIXED] Missing data in Dataframe using Python

Issue

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Hi ,

Attached is the data, can you please help me to handle the missing data in the "Outlet_Size" column. So that i can use this complete data for preparing the datascience models.

Thanks,

Solution

These are one of the major challenges of Data Mining problems (or Machine Learning). YOU decide what to do with the missing data based on PURE EXPERIENCE. You mustn't look at Data Science as a blackbox that follows a series of steps to be successful at it!

Some guidelines about missing data.

A. If more than 40% of the data is missing from a column, drop it! (Again, the 40% depends on what type of problem you're working with! If the data is super crucial or its very trivial that you can ignore it).

B. Check if there is someway you can impute the missing data from the internet. You're looking at item weight! If there is anyway you could know which product you're dealing with instead of hashed coded Item_Identifier, then you can always literally Google it and figure it out.

C. Missing data can be classified into two types:

MCAR: missing completely at random. This is the desirable scenario in case of missing data.

MNAR: missing not at random. Missing not at random data is a more serious issue and in this case it might be wise to check the data gathering process further and try to understand why the information is missing. For instance, if most of the people in a survey did not answer a certain question, why did they do that? Was the question unclear? Assuming data is MCAR, too much missing data can be a problem too. Usually a safe maximum threshold is 5% of the total for large datasets. If missing data for a certain feature or sample is more than 5% then you probably should leave that feature or sample out. We therefore check for features (columns) and samples (rows) where more than 5% of the data is missing using a simple function

D. As posted in the comments, you can simply drop the rows using df.dropna() or fill them with infinity, or fill them with mean using df["value"] = df.groupby("name").transform(lambda x: x.fillna(x.mean())) This groups the column value from dataframe df by category name, finds the mean in each category and fills the missing value in value with the corresponding mean of that category!

E. Apart from just either dropping missing values, replacing with mean or median, there are other advanced regression techniques you can use that has a way to predict missing values and fill it, E.G (mice: Multivariate Imputation by Chained Equations), you should browse and read more about where advanced imputation technique will be helpful.

Answered By - user8508347