Common pitfalls which occur during data representation in Scikit-Learn using Python

When working with scikit-learn for data representation, people make a few common mistakes. Here are some examples of these mistakes, along with the corresponding code snippets:

1. Not converting categorical variables to numerical representations:

One common mistake is not converting categorical variables to numerical representations before fitting the model. Scikit-learn’s algorithms generally require numerical inputs. An incorrect approach is shown in the example code below.
The correct approach involves converting the categorical variables into numerical variables and then performing the fitting.

2. Not scaling features appropriately:

Scaling features is often necessary to ensure that all variables are on a similar scale, which can help improve the performance of many machine learning algorithms. Here’s an example code that loads a sample dataset and scales its features before training using StandardScaler:

3. Not handling missing values:

Handling missing values is crucial to prevent errors during model training and you may forget to perform this step or perform it inefficiently. Scikit-learn algorithms generally do not handle missing values automatically and we either have to drop them or perform imputation techniques to deal with them. The incorrect and the correct approaches are shown in the example code below.