Скачать или смотреть Loan Prediction | EDA | Handling Missing Values | Part - 2

Hi, So this video is about Handling Missing Values in the dataset. This video is in continuation with our previous video, which was on Variable Categorization and Variable Encoding.

Link to Part-1 -    • Loan Prediction | EDA | Handling Missing V...  

There are many methods by which you can handle missing values, such as -

1.) Deleting the rows or columns with missing values.
2.) Imputing the missing cells with the mean/median/mode of the non-missing values.
3.) Predicting missing values using regression techniques.
4.) Using KNN imputation.

There isn’t any best or worst method in handling missing values, it depends on the data we have and the problem we are currently trying to solve. The only thing is, we want highly accurate machine learning model at the end.

So here, in the current problem, we'd be using Regression techniques to predict the missing values. We might discuss about other methods in later videos.

Link to the Dataset - https://www.kaggle.com/zaurbegiev/my-...

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Have a very nice day!!!!!

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