How can I Properly Handle Missing Values when Training and Evaluating a Model?

I’m getting ready to build a machine learning model, and my dataset contains missing values. Here’s the part of the code where I’m working with the dataset:

Is there anyone who can aid me in addressing the issue of missing values within the data, enabling me to proceed with the model training?

Your code structure is well-organized, but it’s crucial to address the presence of missing values in your dataset before proceeding with further preprocessing. One effective approach to handling missing values is mean imputation, which involves replacing missing values with the mean of the respective feature. Here’s how you can adapt your code to incorporate mean imputation:

In this code, mean imputation is utilized to handle missing values before proceeding with the subsequent preprocessing steps.

While this is a good start, it’s important to ensure that missing values are handled effectively during preprocessing. An alternative method to address missing values is to employ ‘most frequent’ imputation, which replaces missing values with the most frequent value in the respective feature. Here’s a revised version of your code that integrates ‘most frequent’ imputation:

Incorporating 'most frequent' imputation in this code helps handle missing values before proceeding with subsequent preprocessing steps.