Jupyter Guide

To get started, open the file on the right entitled "Step 1...". You'll complete each task for Step 1 in that Jupyter Notebook file. Remember, you must run the cells (ctrl/cmd(⌘) + Enter) for each task before moving onto the next task in the Jupyter Notebook. Continue until you have completed all tasks in this step.

Practice Encoding Categorical Data

To review the concepts covered in this step, please refer to the One-hot Encoding module of the Convert Categorical Variables into Quantitative Variables course.

Encoding categorical variables is important because it allows us to convert these variables into a format that can be understood by machine learning algorithms. In this step, we will practice encoding nominal and ordinal data using an ordinal encoding and one-hot encoding.

In this hands-on practice, we will be encoding nominal and ordinal data using Pandas's Series.replace() method. We'll also use one-hot encoding. We will use Pandas's Pandas.get_dummies() function to perform one-hot encoding.

Task 1.1: Load the Dataset and Visualize

Load the dataset 'Qualitative_Data.csv' into a pandas DataFrame and display the first 5 rows of the data.

import pandas as pd

df = pd.read_csv('Qualitative_Data.csv')
print(df.head())

Task 1.2: Ordinal Encoding without Overwriting

Encode the 'Product Feedback' column using ordinal encoding and store the encoded values in a new column named 'Product Feedback Encoded'. The categories in the column are 'Positive', 'Neutral', and 'Negative'. Encode these as 2, 1, and 0 respectively.

feedback_dict = {'Positive': 2, 'Neutral': 1, 'Negative': 0}
df['Product Feedback Encoded'] = df['Product Feedback'].replace(feedback_dict)

Task 1.3: In-Place One-hot Encoding with Prefix

Perform one-hot encoding on the 'Social Media Activity' column in the df DataFrame itself, adding a prefix to the new columns to identify them. Use the pd.get_dummies() function with an in-place update.

df = pd.get_dummies(df, columns=['Social Media Activity'], prefix='SMA')

Task 1.4: Inspect the Updated DataFrame

Print the first 5 rows of the updated DataFrame df with the one-hot encoded columns.

print(df.head())