Machine learning concept in Intro to Computing - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the machine learning library.

Intro to Computing

import [1]

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

Anumpy

Btensorflow

Cpandas

Dmatplotlib

Attempts:

3 left

💡 Hint

Common Mistakes

Importing numpy instead of tensorflow

Importing pandas which is for data manipulation

Importing matplotlib which is for plotting

TensorFlow is a popular library used for machine learning tasks.

2fill in blank

medium

Complete the code to create a simple linear regression model using scikit-learn.

Intro to Computing

from sklearn.linear_model import [1]
model = [1]()

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

ALinearRegression

BLogisticRegression

CDecisionTreeClassifier

DKMeans

Attempts:

3 left

💡 Hint

Common Mistakes

Using LogisticRegression which is for classification

Using DecisionTreeClassifier which is for classification

Using KMeans which is for clustering

LinearRegression is the class used to create a linear regression model in scikit-learn.

3fill in blank

hard

Fix the error in the code to split data into training and testing sets with test_size=0.2.

Intro to Computing

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=[1], random_state=42)

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

A0.5

B50

C0.2

D'0.2'

Attempts:

3 left

💡 Hint

Common Mistakes

Using an integer like 50 instead of a float

Passing the number as a string

Using a value greater than 1

The test_size parameter expects a float between 0 and 1 representing the proportion of the dataset to include in the test split.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths only if the length is greater than 3.

Intro to Computing

{word: [1] for word in words if [2]

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

Alen(word)

Bword

Clen(word) > 3

Dword > 3

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as the value

Checking if the word is greater than 3 (which is invalid)

Not using len(word) in the condition

The dictionary comprehension maps each word to its length using len(word). The condition filters words with length greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their frequencies only if frequency is greater than 1.

Intro to Computing

{ [1]: [2] for [3], [2] in word_counts.items() if [2] > 1 }

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

Aword.upper()

Bcount

Cword

Dfreq

Attempts:

3 left

💡 Hint

Common Mistakes

Using the original word as key without uppercasing

Using inconsistent variable names for count

Not filtering counts greater than 1

The comprehension maps the uppercase version of each word to its count, iterating over word_counts items where count is greater than 1.

Practice

1. What is the main purpose of machine learning in data science?

easy

A. To write complex code manually for every task

B. To replace all human decision-making instantly

C. To store large amounts of data without analysis

D. To let computers learn from data and make predictions

Solution

1. Step 1: Understand machine learning goal

   Machine learning helps computers learn patterns from data instead of hard coding rules.

2. Step 2: Compare options to goal

   Only To let computers learn from data and make predictions describes learning from data to predict or decide, which matches the goal.

3. Final Answer:

   To let computers learn from data and make predictions -> Option D

4. Quick Check:

   Machine learning = learning from data [OK]

Hint: Machine learning means learning from data, not manual coding [OK]

Common Mistakes:

• Thinking machine learning is just storing data
• Believing it replaces all human decisions immediately
• Confusing it with writing manual code

2. Which of the following is the correct order of steps in a typical machine learning process?

easy

A. Create model, train model, predict results

B. Predict results, train model, create model

C. Train model, create model, predict results

D. Predict results, create model, train model

Solution

1. Step 1: Recall machine learning workflow

   First, create a model structure, then train it with data, finally use it to predict.

2. Step 2: Match steps to options

   Create model, train model, predict results correctly orders: create model, train model, predict results.

3. Final Answer:

   Create model, train model, predict results -> Option A

4. Quick Check:

   Order = create, train, predict [OK]

Hint: Model must be created before training and predicting [OK]

Common Mistakes:

• Trying to train before creating the model
• Predicting before training the model
• Mixing the order of steps

3. Consider this Python code snippet using scikit-learn:

from sklearn.linear_model import LinearRegression
model = LinearRegression()
X_train = [[1], [2], [3], [4]]
y_train = [2, 4, 6, 8]
model.fit(X_train, y_train)
print(model.predict([[5]]))

What will be the output?

medium

A. [10.]

B. [5.]

C. [8.]

D. Error: model not trained

Solution

1. Step 1: Understand training data pattern

   X_train and y_train show y = 2 * x (e.g., 1->2, 2->4, 3->6, 4->8).

2. Step 2: Predict for input 5

   Using the learned pattern, prediction for 5 is 2 * 5 = 10.

3. Final Answer:

   [10.] -> Option A

4. Quick Check:

   Prediction = 2 * 5 = 10 [OK]

Hint: Check training pattern, then apply to new input [OK]

Common Mistakes:

• Ignoring training data pattern
• Confusing input and output values
• Assuming model is untrained

4. Identify the error in this machine learning code snippet:

from sklearn.tree import DecisionTreeClassifier
model = DecisionTreeClassifier()
X_train = [[0], [1], [2]]
y_train = [0, 1, 1]
model.fit(X_train)
print(model.predict([[1]]))

medium

A. Incorrect import statement

B. Missing target labels in fit method

C. Predict method called before fit

D. X_train has wrong data type

Solution

1. Step 1: Check fit method parameters

   fit() requires both features (X_train) and labels (y_train), but only X_train is given.

2. Step 2: Identify error cause

   Missing y_train causes error because model doesn't know target labels to learn.

3. Final Answer:

   Missing target labels in fit method -> Option B

4. Quick Check:

   fit() needs X and y [OK]

Hint: fit() always needs features and labels [OK]

Common Mistakes:

• Forgetting to pass y_train to fit()
• Thinking predict can run before fit
• Assuming import is wrong

5. You have a dataset with missing values and want to build a machine learning model. Which step should you take before training the model?

hard

A. Only use the missing values as input features

B. Train the model directly on raw data

C. Remove or fill missing values to clean the data

D. Ignore missing values as models handle them automatically

Solution

1. Step 1: Understand impact of missing data

   Missing values can confuse models and reduce accuracy if not handled.

2. Step 2: Choose proper data cleaning step

   Removing or filling missing values (imputation) prepares data for better training.

3. Final Answer:

   Remove or fill missing values to clean the data -> Option C

4. Quick Check:

   Clean data before training [OK]

Hint: Always clean missing data before training [OK]

Common Mistakes:

• Training on raw data with missing values
• Assuming models ignore missing data
• Using missing values as features without cleaning