Exploratory data analysis in ML Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ data_output

intermediate

Calculate the mean and median of a dataset

You have a dataset of ages: [22, 25, 29, 24, 30, 22, 28]. What are the mean and median values?

ML Python

import numpy as np
ages = [22, 25, 29, 24, 30, 22, 28]
mean_age = np.mean(ages)
median_age = np.median(ages)
print(f"Mean: {mean_age}, Median: {median_age}")

A Mean: 25.0, Median: 25.71

B Mean: 25.71, Median: 24.0

C Mean: 24.0, Median: 25.71

D Mean: 25.71, Median: 25.0

💡 Hint

Mean is the average, median is the middle value when sorted.

Mean is sum of all ages divided by count: (22+25+29+24+30+22+28)/7 = 25.71. Median is the middle value after sorting: [22,22,24,25,28,29,30], middle is 25.

❓ visualization

intermediate

Identify the correct histogram plot for given data

Given the data points: [1, 2, 2, 3, 3, 3, 4, 4, 5], which histogram correctly shows the frequency of each number?

ML Python

import matplotlib.pyplot as plt
import numpy as np
data = [1, 2, 2, 3, 3, 3, 4, 4, 5]
plt.hist(data, bins=5, edgecolor='black')
plt.show()

A Bar heights: 1=1, 2=2, 3=3, 4=2, 5=1

B Bar heights: 1=2, 2=1, 3=3, 4=1, 5=2

C Bar heights: 1=1, 2=3, 3=2, 4=2, 5=1

D Bar heights: 1=1, 2=2, 3=2, 4=3, 5=1

💡 Hint

Count how many times each number appears.

Number 1 appears once, 2 appears twice, 3 appears three times, 4 appears twice, 5 appears once.

🧠 Conceptual

advanced

Understanding correlation coefficients

Which statement correctly describes a Pearson correlation coefficient of -0.85 between two variables?

A Strong positive linear relationship

B Weak negative linear relationship

C Strong negative linear relationship

D No linear relationship

💡 Hint

Correlation values near -1 or 1 indicate strong relationships.

A value of -0.85 means a strong negative linear relationship: as one variable increases, the other tends to decrease strongly.

🔧 Debug

advanced

Identify the error in this code for calculating variance

What error will this code raise?

import numpy as np
data = [1, 2, 3, 4, 5]
variance = np.var(data, ddof=1)
print(variance)

ML Python

import numpy as np
data = [1, 2, 3, 4, 5]
variance = np.var(data, ddof=1)
print(variance)

A SyntaxError due to missing parenthesis

B TypeError because ddof is invalid

C NameError because variance is undefined

D No error, prints variance

💡 Hint

Check the parentheses in the function call.

The np.var function call is missing a closing parenthesis, causing a SyntaxError.

🚀 Application

expert

Determine the number of clusters from a silhouette score plot

You run k-means clustering on a dataset with k values from 2 to 6. The silhouette scores are: {2: 0.45, 3: 0.52, 4: 0.48, 5: 0.40, 6: 0.35}. Which k should you choose for best cluster separation?

A k = 2

B k = 3

C k = 4

D k = 5

💡 Hint

Higher silhouette score means better cluster separation.

The highest silhouette score is 0.52 at k=3, indicating best cluster separation.

Practice

1. What is the main goal of exploratory data analysis (EDA) in machine learning?

easy

A. To train the machine learning model

B. To understand the main features and quality of the data

C. To deploy the model to production

D. To write the final report without looking at data

Solution

1. Step 1: Understand the purpose of EDA

   EDA is used to explore and understand the data before modeling.

2. Step 2: Differentiate EDA from other steps

   Training and deployment happen after EDA, so they are not the main goal of EDA.

3. Final Answer:

   To understand the main features and quality of the data -> Option B

4. Quick Check:

   EDA = Understand data [OK]

Hint: EDA means exploring data, not modeling or deploying [OK]

Common Mistakes:

• Confusing EDA with model training
• Thinking EDA is for deployment
• Skipping data understanding step

2. Which Python library is commonly used for basic exploratory data analysis tasks like viewing data samples and summary statistics?

easy

A. matplotlib

B. tensorflow

C. pandas

D. scikit-learn

Solution

1. Step 1: Identify libraries for EDA

   Pandas provides functions like head() and describe() for data samples and summary stats.

2. Step 2: Exclude other libraries

   Matplotlib is for plotting, scikit-learn for modeling, tensorflow for deep learning, not basic EDA.

3. Final Answer:

   pandas -> Option C

4. Quick Check:

   pandas = data samples and stats [OK]

Hint: Use pandas for quick data overview and stats [OK]

Common Mistakes:

• Using matplotlib for data samples
• Confusing modeling libraries with EDA
• Trying tensorflow for simple stats

3. Given the following Python code using pandas, what will print(df.describe()) output?

import pandas as pd
data = {'age': [25, 30, 22, 40, 28], 'score': [88, 92, 85, 95, 90]}
df = pd.DataFrame(data)
print(df.describe())

medium

A. Summary statistics including count, mean, std, min, max for 'age' and 'score'

B. The first 5 rows of the dataframe

C. A plot showing distribution of 'age' and 'score'

D. An error because describe() needs parameters

Solution

1. Step 1: Understand what describe() does

   It returns summary statistics like count, mean, std, min, max for numeric columns.

2. Step 2: Check the code output

   Since 'age' and 'score' are numeric, describe() will show stats for both columns.

3. Final Answer:

   Summary statistics including count, mean, std, min, max for 'age' and 'score' -> Option A

4. Quick Check:

   describe() = summary stats [OK]

Hint: describe() shows stats, head() shows rows [OK]

Common Mistakes:

• Thinking describe() shows data rows
• Expecting a plot from describe()
• Assuming describe() needs arguments

4. You run this code but get an error:

import pandas as pd
data = {'age': [25, 30, None, 40], 'score': [88, 92, 85, 95]}
df = pd.DataFrame(data)
print(df.mean())

What is the likely cause and fix?

medium

A. Error due to None value; fix by using df.mean(skipna=True)

B. Error due to missing import; fix by importing numpy

C. Error due to wrong data type; fix by converting all to strings

D. No error; code runs fine as is

Solution

1. Step 1: Check how pandas handles None in numeric columns

   Pandas treats None as NaN and by default mean() skips NaN values.

2. Step 2: Verify if code runs without error

   The code runs fine and calculates mean ignoring None.

3. Final Answer:

   No error; code runs fine as is -> Option D

4. Quick Check:

   pandas mean() skips None by default [OK]

Hint: pandas mean() ignores None/NaN by default [OK]

Common Mistakes:

• Assuming None causes error in mean()
• Thinking numpy import is needed here
• Trying to convert numbers to strings

5. You have a dataset with columns age, income, and purchased (Yes/No). Which EDA step helps you find if income affects purchased?

hard

A. Plotting boxplots of income grouped by purchased categories

B. Calculating mean age of all customers

C. Checking for missing values in age column

D. Sorting data by income in ascending order

Solution

1. Step 1: Identify relationship to explore

   We want to see if income differs between those who purchased and those who did not.

2. Step 2: Choose appropriate EDA method

   Boxplots grouped by purchased categories show income distribution differences clearly.

3. Final Answer:

   Plotting boxplots of income grouped by purchased categories -> Option A

4. Quick Check:

   Boxplots show group differences [OK]

Hint: Use grouped boxplots to compare numeric by category [OK]

Common Mistakes:

• Ignoring grouping by purchased
• Checking unrelated columns
• Just sorting without visualization