2. Unsupervised Learning – Real-Life Applications

Activity 2.01: Using Data Visualization to Aid the Pre-processing Process

Solution:

1. Import all the required elements to load the dataset and pre-process it:

   import pandas as pd
   import matplotlib.pyplot as plt
   import numpy as np

2. Load the previously downloaded dataset by using pandas' read_csv() function. Store the dataset in a pandas DataFrame named data:

   data = pd.read_csv("wholesale_customers_data.csv")

3. Check for missing values in your DataFrame. Using the isnull() function plus the sum() function, count the missing values of the entire dataset at once:

   data.isnull().sum()

   The output is as follows:

   Channel             0
   Region              0
   Fresh               0
   Milk                0
   Grocery             0
   Frozen              0
   Detergents_Paper    0
   Delicassen          0
   dtype: int64

   As you can see from the preceding screenshot, there are no missing values in the dataset.

4. Check for outliers...