The user guide and examples included in Feature-engine’s documentation are based on these 3 datasets:

Titanic dataset#

We use the dataset available in openML which can be downloaded from here.

Ames House Prices dataset#

We use the data set created by Professor Dean De Cock: * Dean De Cock (2011) Ames, Iowa: Alternative to the Boston Housing * Data as an End of Semester Regression Project, Journal of Statistics Education, Vol.19, No. 3.

The examples are based on a copy of the dataset available on Kaggle.

The original data and documentation can be found here:

Credit Approval dataset#

We use the Credit Approval dataset from the UCI Machine Learning Repository:

Dua, D. and Graff, C. (2019). UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science.

To download the dataset visit this website and click on “” to download the data set.

To prepare the data for the examples:

import random
import pandas as pd
import numpy as np

# load data
data = pd.read_csv('', header=None)

# create variable names according to UCI Machine Learning information
varnames = ['A'+str(s) for s in range(1,17)]
data.columns = varnames

# replace ? by np.nan
data = data.replace('?', np.nan)

# re-cast some variables to the correct types
data['A2'] = data['A2'].astype('float')
data['A14'] = data['A14'].astype('float')

# encode target to binary
data['A16'] = data['A16'].map({'+':1, '-':0})

# save the data
data.to_csv('creditApprovalUCI.csv', index=False)