Explore the Dataset
Learn various data exploration and data visualization techniques using pandas, Matplotlib, and Seaborn libraries.
We'll cover the following...
In this lesson, we’ll start working on a machine learning problem where we have to predict the amount of money an individual customer will spend in the coming year. To work on this problem, we’ll be using a dataset that contains the transaction records of an online retail shop.
Here is some basic information about the dataset.
Feature Details
Feature | Data Type | Details |
InvoiceNo | Integer | Invoice number of the transaction |
StockCode | String | Unique code of individual product |
Description | String | Description of a product |
Quantity | Integer | Quantity sold in a transaction |
InvoiceDate | String | Date and time of the transaction |
UnitPrice | Integer | Unit price of a product |
CustomerID | String | Unique identifier of a customer |
Country | String | Country of residence of customer |
Let’s familiarize ourselves with the dataset through exploratory data analysis (EDA).
Data exploration
Import the dataset first. We should always check the dimensions and the data types of each ...