ML workflow (collect, prepare, train, evaluate, deploy) in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to start the ML workflow by collecting data.

ML Python

data = [1]('data.csv')

Drag options to blanks, or click blank then click option'

Aload_data

Bpd.read_csv

Ctrain_model

Devaluate

Attempts:

3 left

💡 Hint

Common Mistakes

Using functions like train_model or evaluate before loading data.

Trying to load data without specifying a function.

We use pd.read_csv to load data from a CSV file, which is the first step in the ML workflow: collecting data.

2fill in blank

medium

Complete the code to prepare the data by filling missing values.

ML Python

data['age'] = data['age'].[1](data['age'].mean())

Drag options to blanks, or click blank then click option'

Afillna

Bdropna

Creplace

Dmean

Attempts:

3 left

💡 Hint

Common Mistakes

Using dropna removes rows instead of filling missing values.

Using mean directly does not fill missing values.

The fillna method replaces missing values with a specified value, here the mean age, which is part of data preparation.

3fill in blank

hard

Fix the error in training the model by choosing the correct method to fit the data.

ML Python

model = LogisticRegression()
model.[1](X_train, y_train)

Drag options to blanks, or click blank then click option'

Afit

Btransform

Cscore

Dpredict

Attempts:

3 left

💡 Hint

Common Mistakes

Using predict before training causes errors.

Using score or transform does not train the model.

The fit method trains the model using the training data, which is essential before making predictions.

4fill in blank

hard

Fill both blanks to evaluate the model's accuracy.

ML Python

from sklearn.metrics import [1]
accuracy = [2](y_test, y_pred)

Drag options to blanks, or click blank then click option'

Aaccuracy_score

Bmean_squared_error

Dr2_score

Attempts:

3 left

💡 Hint

Common Mistakes

Using mean_squared_error for classification accuracy.

Using r2_score which is for regression.

We import accuracy_score and use it to calculate how often the model's predictions match the true labels.

5fill in blank

hard

Fill all three blanks to deploy the trained model by saving it to a file.

ML Python

import joblib

joblib.[1](model, '[2]')
loaded_model = joblib.[3]('[2]')

Drag options to blanks, or click blank then click option'

Adump

Bmodel.pkl

Cload

Dsave

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'save' instead of 'dump' causes errors.

Using wrong file extensions or names.

Confusing load and dump methods.

We use joblib.dump to save the model to a file named model.pkl, then joblib.load to load it back for deployment.

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