Why engineered features improve analysis in Data Analysis Python - See It in Action

Why Engineered Features Improve Analysis

📖 Scenario: Imagine you work in a small online store. You have data about customers and their purchases. You want to understand which customers are likely to buy more. Sometimes, the original data is not enough. You can create new data from the old data. This is called feature engineering. It helps to find better answers.

🎯 Goal: You will create a new feature from existing data and see how it helps to understand customer behavior better.

📋 What You'll Learn

Create a dictionary with customer names and their total purchase amounts

Create a threshold variable to separate high and low spenders

Create a new dictionary with a feature showing if a customer is a high spender

Print the new dictionary to see the result

💡 Why This Matters

🌍 Real World

In real business, creating new features from raw data helps to find useful insights faster and improves decision making.

💼 Career

Data scientists and analysts often engineer features to improve models and reports, making their work more effective.

1

Create the initial customer purchase data

Create a dictionary called customer_purchases with these exact entries: 'Alice': 120, 'Bob': 75, 'Charlie': 200, 'Diana': 50, 'Evan': 180

Data Analysis Python

# Create the customer_purchases dictionary with exact values
# Your code here

Hint

Use curly braces {} to create a dictionary. Separate each customer and purchase amount with a colon and commas.

2

Set a threshold to identify high spenders

Create a variable called high_spender_threshold and set it to 100

Data Analysis Python

customer_purchases = {'Alice': 120, 'Bob': 75, 'Charlie': 200, 'Diana': 50, 'Evan': 180}
# Create the high_spender_threshold variable and set it to 100
# Your code here

Hint

Just assign the number 100 to the variable high_spender_threshold.

3

Create a new feature to mark high spenders

Create a new dictionary called customer_features using a dictionary comprehension. Use for customer, amount in customer_purchases.items(). For each customer, set the value to True if amount >= high_spender_threshold, else False

Data Analysis Python

customer_purchases = {'Alice': 120, 'Bob': 75, 'Charlie': 200, 'Diana': 50, 'Evan': 180}
high_spender_threshold = 100
# Create customer_features dictionary with true/false for high spenders
# Your code here

Hint

Use a dictionary comprehension with {customer: (amount >= high_spender_threshold) for customer, amount in customer_purchases.items()}.

4

Print the new features dictionary

Write a print statement to display the customer_features dictionary

Data Analysis Python

customer_purchases = {'Alice': 120, 'Bob': 75, 'Charlie': 200, 'Diana': 50, 'Evan': 180}
high_spender_threshold = 100
customer_features = {customer: (amount >= high_spender_threshold) for customer, amount in customer_purchases.items()}
# Print the customer_features dictionary
# Your code here

Hint

Use print(customer_features) to show the dictionary.