Python ML ecosystem overview in ML Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Key libraries for data manipulation in Python ML

Which Python library is primarily used for handling and analyzing structured data like tables?

A NumPy

B Pandas

C Scikit-learn

D Matplotlib

💡 Hint

Think about the library that works well with rows and columns, like a spreadsheet.

Pandas is designed for working with structured data in tables, making it easy to manipulate and analyze data.

❓ Predict Output

intermediate

Output of a simple NumPy array operation

What is the output of this code?

ML Python

import numpy as np
arr = np.array([1, 2, 3])
result = arr * 2
print(result.tolist())

A [2, 4, 6]

B [1, 2, 3, 1, 2, 3]

C [1, 4, 9]

D TypeError

💡 Hint

Multiplying a NumPy array by a number multiplies each element.

NumPy arrays support element-wise operations, so each element is multiplied by 2.

❓ Model Choice

advanced

Choosing the right library for deep learning

You want to build a deep neural network with GPU support and easy model building. Which Python library is best suited?

A TensorFlow

B Scikit-learn

C Pandas

D Matplotlib

💡 Hint

Look for a library designed for deep learning and GPU acceleration.

TensorFlow is a popular deep learning library with GPU support and high-level APIs for building models.

❓ Metrics

advanced

Understanding model evaluation metrics in scikit-learn

Which metric from scikit-learn is best to evaluate a classification model's ability to balance precision and recall?

A Accuracy Score

B Mean Squared Error

C F1 Score

D R-squared

💡 Hint

Think about a metric that combines precision and recall into one number.

F1 Score is the harmonic mean of precision and recall, useful for imbalanced classes.

🔧 Debug

expert

Identifying the error in a scikit-learn pipeline code

What error does this code raise?

ML Python

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression

pipe = Pipeline([
    ('scaler', StandardScaler()),
    ('clf', LogisticRegression())
])

pipe.fit([[1, 2], [3, 4]], [0, 1])

A ValueError: Found input variables with inconsistent numbers of samples

B TypeError: fit() missing 1 required positional argument

C AttributeError: 'Pipeline' object has no attribute 'fit'

D No error, code runs successfully

💡 Hint

Check if the number of samples in features and labels match.

The features and labels have matching sample counts, so the code runs without error.

Practice

1. Which Python library is best known for simple and easy-to-use machine learning algorithms like classification and regression?

easy

A. Matplotlib

B. scikit-learn

C. Pandas

D. TensorFlow

Solution

1. Step 1: Identify libraries for ML algorithms

   scikit-learn is designed for easy access to classic ML algorithms like classification and regression.

2. Step 2: Differentiate from other libraries

   TensorFlow is for deep learning, Pandas is for data handling, and Matplotlib is for plotting.

3. Final Answer:

   scikit-learn -> Option B

4. Quick Check:

   Simple ML algorithms = scikit-learn [OK]

Hint: Classic ML? Think scikit-learn first [OK]

Common Mistakes:

• Confusing TensorFlow with scikit-learn
• Choosing Pandas as ML library
• Picking Matplotlib for modeling

2. Which of the following is the correct way to import the Pandas library in Python?

easy

A. import pandas as pd

B. import pandas as pn

C. from pandas import numpy

D. import pd from pandas

Solution

1. Step 1: Recall standard import syntax for Pandas

   The common convention is to import pandas as pd for easy reference.

2. Step 2: Check other options for syntax errors or wrong aliases

   import pandas as pn uses wrong alias, C tries to import numpy from pandas which is invalid, D has incorrect import syntax.

3. Final Answer:

   import pandas as pd -> Option A

4. Quick Check:

   Standard Pandas import = import pandas as pd [OK]

Hint: Pandas is always imported as pd [OK]

Common Mistakes:

• Using wrong alias like pn
• Incorrect import syntax
• Confusing pandas with numpy

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

from sklearn.linear_model import LinearRegression
model = LinearRegression()
print(type(model))

medium

A. TypeError

B. <class 'LinearRegression'>

C. NameError

D. <class 'sklearn.linear_model._base.LinearRegression'>

Solution

1. Step 1: Understand the import and object creation

   The code imports LinearRegression from sklearn.linear_model and creates an instance named model.

2. Step 2: Check what print(type(model)) outputs

   It prints the full class path including module name, which is <class 'sklearn.linear_model._base.LinearRegression'>.

3. Final Answer:

   <class 'sklearn.linear_model._base.LinearRegression'> -> Option D

4. Quick Check:

   Type of model = sklearn.linear_model._base.LinearRegression [OK]

Hint: type() shows full class path in sklearn [OK]

Common Mistakes:

• Expecting just class name without module path
• Confusing with error outputs
• Missing import causing NameError

4. Identify the error in this code snippet:

import numpy as np
from sklearn.model_selection import train_test_split
X = np.array([[1, 2], [3, 4], [5, 6]])
y = np.array([1, 2, 3])
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)
print(X_train.shape, X_test.shape)

medium

A. train_test_split is not imported correctly

B. Shapes printed are incorrect due to wrong test_size

C. No error, code runs correctly

D. X and y arrays have mismatched lengths

Solution

1. Step 1: Check imports and data arrays

   numpy and train_test_split are imported correctly; X has 3 samples, y has 3 labels matching in length.

2. Step 2: Verify train_test_split usage and output

   test_size=0.25 splits 3 samples into 2 train and 1 test samples; shapes printed are correct.

3. Final Answer:

   No error, code runs correctly -> Option C

4. Quick Check:

   Matching array lengths and correct split = no error [OK]

Hint: Check array lengths and import correctness first [OK]

Common Mistakes:

• Assuming train_test_split import is wrong
• Thinking test_size must be integer
• Ignoring matching lengths of X and y

5. You want to build a deep learning model for image classification. Which Python library should you choose to start with, considering ease of use and community support?

hard

A. TensorFlow with Keras

B. scikit-learn

C. Pandas

D. Matplotlib

Solution

1. Step 1: Identify libraries suited for deep learning

   TensorFlow with Keras is widely used for deep learning and image classification tasks.

2. Step 2: Compare with other options

   scikit-learn is for classic ML, Pandas is for data handling, Matplotlib is for plotting, none are designed for deep learning.

3. Final Answer:

   TensorFlow with Keras -> Option A

4. Quick Check:

   Deep learning + images = TensorFlow with Keras [OK]

Hint: Deep learning? Think TensorFlow + Keras [OK]

Common Mistakes:

• Choosing scikit-learn for deep learning
• Confusing Pandas or Matplotlib as modeling tools
• Ignoring Keras as high-level API