Chapter 2: Unsupervised Learning: Real-life Applications

Activity 3: Using Data Visualization to Aid the Preprocessing Process

1. Load the previously downloaded dataset by using the Pandas function read_csv(). Store the dataset in a Pandas DataFrame named data:

   import pandas as pd
   import matplotlib.pyplot as plt
   import numpy as np
   np.random.seed(0)

   First, import the required libraries. Then, feed the dataset path to the Pandas function's read_csv():

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

2. 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()

   Figure 2.16: A screenshot showing the number of missing values in the DataFrame

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

3. Check for outliers in your DataFrame. Using the technique you learned in the previous chapter, label those values that...