Preparing a dataset for machine learning with scikit-learn

The first step to implementing any machine learning algorithm with scikit-learn is data preparation. Scikit-learn comes with a set of constraints to implementation that will be discussed later in this section. The dataset that we will be using is based on mobile payments and is found on the world's most popular competitive machine learning website – Kaggle.

You can download the dataset from: https://www.kaggle.com/ntnu-testimon/paysim1.

Once downloaded, open a new Jupyter Notebook by using the following code in Terminal (macOS/Linux) or Anaconda Prompt/PowerShell (Windows):

Jupyter Notebook

The fundamental goal of this dataset is to predict whether a mobile transaction is fraudulent. In order to do this, we need to first have a brief understanding of the contents of our data. In order to explore the dataset, we will use the pandas package in Python. You can install pandas by using the following code in Terminal (macOS/Linux) or PowerShell (Windows):

pip3 install pandas

Pandas can be installed on Windows machines in an Anaconda Prompt by using the following code:

conda install pandas

We can now read in the dataset into our Jupyter Notebook by using the following code: 

#Package Imports

import pandas as pd

#Reading in the dataset

df = pd.read_csv('PS_20174392719_1491204439457_log.csv')

#Viewing the first 5 rows of the dataset

df.head()

This produces an output as illustrated in the following screenshot: