Technical requirements

In this chapter, we will use the pandas, NumPy, and Matplotlib Python libraries, as well as scikit-learn and Feature-engine. For guidelines on how to obtain these libraries, visit the Technical requirements section of Chapter 1, Imputing Missing Data.

We will also use the open-source Category Encoders Python library, which can be installed using pip:

pip install category_encoders

To learn more about Category Encoders, visit the following link: https://contrib.scikit-learn.org/category_encoders/.

We will also use the Credit Approval dataset, which is available in the UCI Machine Learning Repository at https://archive.ics.uci.edu/ml/datasets/credit+approval.

To prepare the dataset, follow these steps:

1. Visit http://archive.ics.uci.edu/ml/machine-learning-databases/credit-screening/ and click on crx.data to download the data:

Figure 2.1 – The index directory for the Credit Approval dataset

2. Save crx.data to the folder where you will run the following commands.

After downloading the data, open up a Jupyter Notebook and run the following commands.

3. Import the required libraries:

   import random
   import numpy as np
   import pandas as pd

4. Load the data:

   data = pd.read_csv("crx.data", header=None)

5. Create a list containing the variable names:

   varnames = [f"A{s}" for s in range(1, 17)]

6. Add the variable names to the DataFrame:

   data.columns = varnames

7. Replace the question marks in the dataset with NumPy NaN values:

   data = data.replace("?", np.nan)

8. Cast some numerical variables as float data types:

   data["A2"] = data["A2"].astype("float")
   data["A14"] = data["A14"].astype("float")

9. Encode the target variable as binary:

   data["A16"] = data["A16"].map({"+": 1, "-": 0})

10. Rename the target variable:

    data.rename(columns={"A16": "target"}, inplace=True)

11. Make lists that contain categorical and numerical variables:

    cat_cols = [
        c for c in data.columns if data[c].dtypes=="O"] 
    num_cols = [
        c for c in data.columns if data[c].dtypes!= "O"]

12. Fill in the missing data:

    data[num_cols] = data[num_cols].fillna(0)
    data[cat_cols] = data[cat_cols].fillna("Missing")

13. Save the prepared data:

    data.to_csv("credit_approval_uci.csv", index=False)

You can find a Jupyter Notebook that contains these commands in this book’s GitHub repository at https://github.com/PacktPublishing/Python-Feature-Engineering-Cookbook-Second-Edition/blob/main/ch02-categorical-encoding/donwload-prepare-store-credit-approval-dataset.ipynb.

Note

Some libraries require that you have already imputed missing data, for which you can use any of the recipes from Chapter 1, Imputing Missing Data.