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.

Practice

1. Why do engineered features often improve data analysis results?

easy

A. They highlight important patterns not obvious in raw data

B. They reduce the size of the dataset by removing rows

C. They always increase the number of features without filtering

D. They replace the need for any data cleaning

Solution

1. Step 1: Understand the role of engineered features

   Engineered features transform raw data into forms that better reveal patterns.

2. Step 2: Compare with other options

   Options B, C, and D describe unrelated or incorrect effects of engineered features.

3. Final Answer:

   They highlight important patterns not obvious in raw data -> Option A

4. Quick Check:

   Engineered features = highlight patterns [OK]

Hint: Engineered features reveal hidden patterns in data [OK]

Common Mistakes:

• Thinking engineered features reduce dataset size
• Assuming engineered features always add more features
• Believing engineered features replace data cleaning

2. Which of the following is the correct way to create a new feature age_group from age in pandas using a condition?

easy

A. df['age_group'] = df['age'].filter(lambda x: 'adult' if x >= 18 else 'child')

B. df['age_group'] = df['age'].map('adult' if df['age'] >= 18 else 'child')

C. df['age_group'] = df['age'].apply(lambda x: 'adult' if x >= 18 else 'child')

D. df['age_group'] = df['age'].groupby(lambda x: 'adult' if x >= 18 else 'child')

Solution

1. Step 1: Identify correct pandas method for conditional feature creation

   The apply method with a lambda function correctly applies a condition to each value.

2. Step 2: Check other options for errors

   map expects a mapping dict or function, filter is for filtering rows, groupby is for grouping data, so they are incorrect here.

3. Final Answer:

   df['age_group'] = df['age'].apply(lambda x: 'adult' if x >= 18 else 'child') -> Option C

4. Quick Check:

   Use apply with lambda for conditional feature [OK]

Hint: Use apply with lambda for conditional new features [OK]

Common Mistakes:

• Using map with a conditional expression incorrectly
• Confusing filter with apply for feature creation
• Using groupby instead of apply for new column

3. Given the code below, what will be the output of print(df)?

import pandas as pd
df = pd.DataFrame({'height_cm': [170, 160, 180], 'weight_kg': [70, 60, 80]})
df['bmi'] = df['weight_kg'] / (df['height_cm'] / 100) ** 2
print(df)

medium

A. SyntaxError due to incorrect operator precedence

B. {'height_cm': [170, 160, 180], 'weight_kg': [70, 60, 80], 'bmi': [24.22, 23.44, 24.69]}

C. DataFrame with bmi column calculated as weight_kg multiplied by height_cm

D. DataFrame with bmi column calculated as weight_kg divided by height_cm squared in meters

Solution

1. Step 1: Understand the BMI calculation in code

   BMI is weight in kg divided by height in meters squared. The code correctly converts height_cm to meters and squares it.

2. Step 2: Check output format

   The print shows a DataFrame with a new 'bmi' column calculated as described, not a dictionary or error.

3. Final Answer:

   DataFrame with bmi column calculated as weight_kg divided by height_cm squared in meters -> Option D

4. Quick Check:

   BMI = weight / (height/100)^2 [OK]

Hint: Check formula carefully for correct calculation [OK]

Common Mistakes:

• Confusing multiplication with division in BMI formula
• Expecting dictionary output instead of DataFrame
• Thinking code causes syntax error

4. The following code tries to create a new feature income_level but raises an error. What is the error and how to fix it?

df['income_level'] = df['income'].apply(lambda x: 'high' if x > 50000 else 'low' if x <= 50000)

medium

A. No error; code runs fine

B. SyntaxError due to missing else clause; fix by adding final else

C. KeyError because 'income' column does not exist

D. TypeError because apply cannot use lambda functions

Solution

1. Step 1: Identify the lambda function structure

   The lambda uses nested if-else but misses a final else clause, causing SyntaxError.

2. Step 2: Fix the lambda expression

   Add a final else clause to cover all cases, e.g., lambda x: 'high' if x > 50000 else 'low'.

3. Final Answer:

   SyntaxError due to missing else clause; fix by adding final else -> Option B

4. Quick Check:

   Lambda if-else must cover all cases [OK]

Hint: Ensure lambda if-else has final else clause [OK]

Common Mistakes:

• Assuming apply cannot use lambda
• Ignoring missing else causing syntax error
• Thinking income column is missing without checking

5. You have a dataset with columns temperature_c and humidity. You want to create a new feature heat_index that combines both to better predict discomfort. Which approach best explains why engineered features like heat_index improve analysis?

hard

A. Combining temperature and humidity into heat_index captures interaction effects raw features miss

B. Using only temperature or humidity separately is always better for simplicity

C. Heat_index replaces raw data so original features are no longer needed

D. Engineered features like heat_index reduce dataset size by removing columns

Solution

1. Step 1: Understand interaction effects in features

   Heat index combines temperature and humidity to capture how they jointly affect discomfort, which single features miss.

2. Step 2: Evaluate other options

   Options A, C, and D misunderstand feature engineering benefits or effects on data.

3. Final Answer:

   Combining temperature and humidity into heat_index captures interaction effects raw features miss -> Option A

4. Quick Check:

   Engineered features = capture interactions [OK]

Hint: Engineered features capture combined effects of raw data [OK]

Common Mistakes:

• Thinking single features are always better
• Believing engineered features replace raw data completely
• Assuming engineered features reduce dataset size