NumPy with machine learning libraries - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of NumPy array shape after sklearn train_test_split

What is the shape of X_train after running the following code?

NumPy

import numpy as np
from sklearn.model_selection import train_test_split

X = np.arange(20).reshape(10, 2)
y = np.arange(10)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
print(X_train.shape)

A (7, 2)

B (3, 2)

C (10, 2)

D (14, 2)

💡 Hint

Remember test_size=0.3 means 30% data is for testing, rest for training.

The original data has 10 samples. 30% of 10 is 3 samples for testing, so training has 7 samples. Each sample has 2 features, so shape is (7, 2).

❓ data_output

intermediate

Result of NumPy array after StandardScaler transform

What is the output array after applying StandardScaler to the data below?

NumPy

import numpy as np
from sklearn.preprocessing import StandardScaler

data = np.array([[1, 2], [3, 4], [5, 6]])
scaler = StandardScaler()
scaled_data = scaler.fit_transform(data)
print(np.round(scaled_data, 2))

A

[[1. 1.]
 [3. 4.]
 [5. 6.]]

B

[[-1.22 -1.22]
 [ 0.   0.  ]
 [ 1.22  1.22]]

C

[[0. 0.]
 [0. 0.]
 [0. 0.]]

D

[[-1.  -1.]
 [ 0.   0.]
 [ 1.   1.]]

💡 Hint

StandardScaler centers data to mean 0 and scales to unit variance.

StandardScaler subtracts mean and divides by standard deviation for each feature. The rounded output matches option B.

🔧 Debug

advanced

Identify the error when using NumPy array with sklearn LinearRegression

What error will this code raise when fitting the model?

NumPy

import numpy as np
from sklearn.linear_model import LinearRegression

X = np.array([1, 2, 3, 4, 5])
y = np.array([2, 4, 6, 8, 10])
model = LinearRegression()
model.fit(X, y)

A ValueError: Expected 2D array, got 1D array instead

B TypeError: unsupported operand type(s) for +: 'int' and 'str'

C No error, model fits successfully

D IndexError: index out of bounds

💡 Hint

Check the shape of X passed to fit method.

sklearn expects X to be 2D (samples, features). Passing 1D array causes ValueError.

❓ visualization

advanced

Interpret the plot of PCA components from NumPy data

Given the PCA plot below, which statement is true about the data?

NumPy

import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA

np.random.seed(0)
data = np.dot(np.random.rand(2, 2), np.random.randn(2, 200)).T
pca = PCA(n_components=2)
components = pca.fit_transform(data)
plt.scatter(components[:, 0], components[:, 1])
plt.xlabel('PC1')
plt.ylabel('PC2')
plt.title('PCA of Data')
plt.show()

A PCA components are identical to original features.

B The data has no variance along the first principal component.

C The first principal component explains more variance than the second.

D The second principal component explains more variance than the first.

💡 Hint

PCA orders components by explained variance descending.

PCA sorts components by variance explained. The first component always explains the most variance.

🧠 Conceptual

expert

Why use NumPy arrays with scikit-learn instead of Python lists?

Which is the main reason scikit-learn prefers NumPy arrays over Python lists for input data?

A NumPy arrays automatically normalize data before training.

B Python lists cannot store numerical data.

C Python lists are immutable and cannot be changed.

D NumPy arrays provide efficient memory usage and fast numerical operations required by scikit-learn.

💡 Hint

Think about performance and data structure requirements.

NumPy arrays are optimized for numerical computations and use less memory, which helps scikit-learn run faster.

Practice

1. Why do machine learning libraries often use NumPy arrays for data input?

easy

A. Because NumPy arrays can only store text data

B. Because NumPy arrays are fast and easy to manipulate for numerical data

C. Because NumPy arrays automatically train machine learning models

D. Because NumPy arrays are the only data type in Python

Solution

1. Step 1: Understand NumPy array properties

   NumPy arrays store numerical data efficiently and support fast operations.

2. Step 2: Connect to machine learning needs

   Machine learning needs fast numerical data processing, which NumPy arrays provide.

3. Final Answer:

   Because NumPy arrays are fast and easy to manipulate for numerical data -> Option B

4. Quick Check:

   NumPy arrays speed up numerical data handling = A [OK]

Hint: Remember: ML needs fast number crunching, NumPy fits well [OK]

Common Mistakes:

• Thinking NumPy arrays store only text
• Believing NumPy trains models automatically
• Assuming Python has only one data type

2. Which of the following is the correct way to convert a Python list [1, 2, 3] into a NumPy array?

easy

A. np.toarray([1, 2, 3])

B. np.list([1, 2, 3])

C. np.convert([1, 2, 3])

D. np.array([1, 2, 3])

Solution

1. Step 1: Recall NumPy array creation syntax

   The function to create arrays from lists is np.array().

2. Step 2: Check each option

   Only np.array([1, 2, 3]) is valid syntax; others are not NumPy functions.

3. Final Answer:

   np.array([1, 2, 3]) -> Option D

4. Quick Check:

   Use np.array() to convert lists = A [OK]

Hint: Use np.array() to convert lists to arrays [OK]

Common Mistakes:

• Using non-existent functions like np.list()
• Confusing array creation with conversion functions
• Forgetting to import NumPy as np

3. What will be the output of this code snippet?

import numpy as np
from sklearn.preprocessing import StandardScaler

X = np.array([[1, 2], [3, 4], [5, 6]])
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
print(np.round(X_scaled, 2))

medium

A. [[-1.22 -1.22]
[ 0. 0. ]
[ 1.22 1.22]]

B. [[1 2]
[3 4]
[5 6]]

C. [[0. 0.]
[0. 0.]
[0. 0.]]

D. Error: StandardScaler not imported

Solution

1. Step 1: Understand StandardScaler effect

   StandardScaler centers data to mean 0 and scales to unit variance.

2. Step 2: Calculate scaled values

   For each column, mean is 3 and 4; std dev approx 1.63. Values transform to approx -1.22, 0, 1.22.

3. Final Answer:

   [[-1.22 -1.22] [ 0. 0. ] [ 1.22 1.22]] -> Option A

4. Quick Check:

   StandardScaler centers and scales data = D [OK]

Hint: StandardScaler centers data to mean 0 and scales variance 1 [OK]

Common Mistakes:

• Expecting original data output
• Confusing StandardScaler with MinMaxScaler
• Missing import causing runtime error

4. Identify the error in this code that uses NumPy with scikit-learn:

import numpy as np
from sklearn.linear_model import LinearRegression

X = [[1, 2], [3, 4], [5, 6]]
y = [1, 2, 3]
model = LinearRegression()
model.fit(X, y)
print(model.coef_)

medium

A. X should be a NumPy array, not a list

B. y should be a 2D array, not a list

C. LinearRegression requires data scaling before fitting

D. model.fit() is missing required parameters

Solution

1. Step 1: Check data types for model.fit()

   scikit-learn expects NumPy arrays or similar, not plain Python lists.

2. Step 2: Identify the issue in X

   X is a list of lists; converting to np.array is needed for proper fitting.

3. Final Answer:

   X should be a NumPy array, not a list -> Option A

4. Quick Check:

   Use NumPy arrays for input data = B [OK]

Hint: Always convert input data to NumPy arrays before fitting models [OK]

Common Mistakes:

• Thinking y must be 2D array
• Believing scaling is mandatory before fitting
• Assuming fit() needs extra parameters

5. You have a dataset as a Python list of lists representing features. You want to prepare it for a machine learning model that requires normalized NumPy arrays. Which steps should you perform?

hard

A. Convert the list to a Python tuple, then normalize manually with loops

B. Directly pass the list to the model without conversion or scaling

C. Convert the list to a NumPy array, then use a scaler like MinMaxScaler to normalize

D. Use pandas DataFrame without converting to NumPy arrays

Solution

1. Step 1: Convert list to NumPy array

   Machine learning models expect NumPy arrays for efficient computation.

2. Step 2: Normalize data using scaler

   Use MinMaxScaler or similar to scale features between 0 and 1 for better model performance.

3. Final Answer:

   Convert the list to a NumPy array, then use a scaler like MinMaxScaler to normalize -> Option C

4. Quick Check:

   Convert then scale data before modeling = C [OK]

Hint: Always convert data to NumPy arrays and scale before modeling [OK]

Common Mistakes:

• Passing raw lists directly to models
• Using tuples which are not efficient for ML
• Skipping normalization hurting model accuracy