ML workflow (collect, prepare, train, evaluate, deploy) in ML Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Identify the correct order of ML workflow steps

Which of the following lists the steps of a typical machine learning workflow in the correct order?

A Collect data, Prepare data, Train model, Evaluate model, Deploy model

B Prepare data, Collect data, Train model, Deploy model, Evaluate model

C Train model, Collect data, Prepare data, Evaluate model, Deploy model

D Collect data, Train model, Prepare data, Evaluate model, Deploy model

💡 Hint

Think about what you need before training a model and what comes after training.

The typical ML workflow starts with collecting data, then preparing it for training. After that, the model is trained, evaluated for performance, and finally deployed for use.

❓ Predict Output

intermediate

Output of data preparation step

Given the following Python code snippet for data preparation, what is the output of the variable prepared_data?

ML Python

raw_data = [10, 20, None, 30, None, 40]
prepared_data = [x for x in raw_data if x is not None]

A [10, 20, 30, 40]

B SyntaxError

C [None, None]

D [10, 20, None, 30, None, 40]

💡 Hint

Look at the list comprehension filtering out None values.

The list comprehension keeps only values that are not None, so the output excludes None values.

❓ Model Choice

advanced

Choosing the right model for classification

You have a dataset with labeled images of cats and dogs. Which model type is best suited for this classification task?

A Linear Regression

B Convolutional Neural Network (CNN)

C K-Means Clustering

D Principal Component Analysis (PCA)

💡 Hint

Think about which model type works well with images and classification.

CNNs are designed to work with image data and are excellent for classification tasks like distinguishing cats and dogs.

❓ Metrics

advanced

Evaluating model performance with accuracy

A model predicted 80 correct labels out of 100 total samples. What is the accuracy of the model?

A 0.2

B 80

C 0.8

D 1.25

💡 Hint

Accuracy is the ratio of correct predictions to total samples.

Accuracy = Number of correct predictions / Total number of samples = 80/100 = 0.8.

🔧 Debug

expert

Debugging deployment code error

You have this Python code to deploy a trained model, but it raises an error. What error does it raise?

ML Python

def deploy_model(model):
    model.save('model_path')

trained_model = None
deploy_model(trained_model)

A NameError: name 'model' is not defined

B TypeError: deploy_model() missing 1 required positional argument

C SyntaxError: invalid syntax

D AttributeError: 'NoneType' object has no attribute 'save'

💡 Hint

Check what happens if you call a method on None.

The variable trained_model is None, so calling save on it raises an AttributeError.

Practice

1. Which step in the ML workflow involves gathering raw data from various sources?

easy

A. Deploy

B. Train

C. Evaluate

D. Collect

Solution

1. Step 1: Understand the meaning of each ML workflow step

   Collect means to gather raw data needed for the model.

2. Step 2: Match the step to the description

   Since gathering data is the first step, it matches 'Collect'.

3. Final Answer:

   Collect -> Option D

4. Quick Check:

   Gathering data = Collect [OK]

Hint: Collect means get data first before anything else [OK]

Common Mistakes:

• Confusing 'Train' with data gathering
• Thinking 'Evaluate' means collecting data
• Mixing 'Deploy' with data collection

2. Which of the following is the correct order of the ML workflow steps?

easy

A. Collect, Prepare, Train, Evaluate, Deploy

B. Train, Collect, Prepare, Deploy, Evaluate

C. Prepare, Collect, Train, Evaluate, Deploy

D. Deploy, Evaluate, Train, Prepare, Collect

Solution

1. Step 1: Recall the standard ML workflow order

   The correct order is: Collect data, Prepare data, Train model, Evaluate model, Deploy model.

2. Step 2: Compare each option to the correct order

   Only Collect, Prepare, Train, Evaluate, Deploy matches this exact sequence.

3. Final Answer:

   Collect, Prepare, Train, Evaluate, Deploy -> Option A

4. Quick Check:

   Correct ML workflow order = Collect, Prepare, Train, Evaluate, Deploy [OK]

Hint: Remember: Collect first, then Prepare, Train, Evaluate, Deploy [OK]

Common Mistakes:

• Mixing up Collect and Prepare order
• Putting Deploy before Evaluate
• Starting with Train before data collection

3. Given this Python code snippet for training a model, what will be printed after evaluation?

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score

iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.3, random_state=42)
model = DecisionTreeClassifier()
model.fit(X_train, y_train)
predictions = model.predict(X_test)
accuracy = accuracy_score(y_test, predictions)
print(f"Accuracy: {accuracy:.2f}")

medium

A. Accuracy: 0.98

B. Accuracy: 0.85

C. Accuracy: 0.70

D. Accuracy: 1.00

Solution

1. Step 1: Understand the code flow

   The code loads iris data, splits it, trains a Decision Tree, predicts, and calculates accuracy.

2. Step 2: Recall typical Decision Tree accuracy on iris test split

   With random_state=42 and 30% test size, accuracy is 0.98.

3. Final Answer:

   Accuracy: 0.98 -> Option A

4. Quick Check:

   Decision Tree on iris test = 0.98 accuracy [OK]

Hint: Decision Trees on iris usually score 98% accuracy [OK]

Common Mistakes:

• Assuming accuracy is always 1.00
• Confusing train and test accuracy
• Ignoring random_state effect on split

4. You wrote this code to train a model but get an error. What is the main problem?

from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(X_train, y_train)
print(model.predict(X_test))

Assuming X_train, y_train, and X_test are defined but you forgot to prepare data properly.

medium

A. Model was not imported correctly

B. Data was not scaled or cleaned before training

C. Predict method is called before fit

D. X_test has different features than X_train

Solution

1. Step 1: Analyze the error cause

   Logistic Regression often requires data preparation like scaling or cleaning.

2. Step 2: Identify missing preparation step

   Since data preparation is missing, model may fail or give poor results.

3. Final Answer:

   Data was not scaled or cleaned before training -> Option B

4. Quick Check:

   Missing data prep causes training issues [OK]

Hint: Always prepare data before training models [OK]

Common Mistakes:

• Thinking import error caused failure
• Assuming predict before fit error
• Ignoring feature mismatch possibility

5. You want to deploy a model that predicts house prices. Which step should you do right after evaluating the model's accuracy?

hard

A. Collect more data

B. Prepare the data again

C. Deploy the model to a real environment

D. Train the model with new parameters

Solution

1. Step 1: Understand the ML workflow sequence

   After evaluating the model, the next step is to deploy it if accuracy is good.

2. Step 2: Match the step after evaluation

   Deploy means putting the model into real use for predictions.

3. Final Answer:

   Deploy the model to a real environment -> Option C

4. Quick Check:

   Evaluation done, next is Deploy [OK]

Hint: Deploy comes right after evaluation if model is good [OK]

Common Mistakes:

• Repeating data collection unnecessarily
• Preparing data again without new data
• Retraining without evaluation feedback