First ML prediction (linear regression) in ML Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of simple linear regression prediction

What is the predicted value of y for x = 4 using the model y = 2x + 1?

ML Python

def predict(x):
    return 2 * x + 1

result = predict(4)
print(result)

A 8

B 7

C 9

D 10

💡 Hint

Multiply x by 2 and then add 1.

The formula y = 2x + 1 means for x=4, y = 2*4 + 1 = 9.

❓ Model Choice

intermediate

Choosing the right model for linear data

You have data points that form a straight line. Which model is best to predict new points?

A Neural network with many layers

B Decision tree

C K-means clustering

D Linear regression

💡 Hint

Think about the simplest model that fits a straight line.

Linear regression fits a straight line to data, ideal for linear relationships.

❓ Metrics

advanced

Interpreting mean squared error (MSE)

If a linear regression model has an MSE of 0.01 on test data, what does this mean?

A The average squared difference between predicted and actual values is 0.01

B The model predicts perfectly with no errors

C The model's predictions are off by 1 on average

D The model's accuracy is 1%

💡 Hint

MSE measures average squared errors, not accuracy percentage.

MSE is the average of squared differences between predicted and true values; 0.01 means small errors.

🔧 Debug

advanced

Why does this linear regression code fail?

What error will this code produce? from sklearn.linear_model import LinearRegression model = LinearRegression() X = [1, 2, 3, 4] y = [2, 4, 6, 8] model.fit(X, y)

ML Python

from sklearn.linear_model import LinearRegression
model = LinearRegression()
X = [1, 2, 3, 4]
y = [2, 4, 6, 8]
model.fit(X, y)

A No error, code runs fine

B ValueError because X must be 2D array

C TypeError because y is not a numpy array

D NameError because LinearRegression is not imported

💡 Hint

Check the shape of X expected by sklearn.

sklearn expects X to be 2D (like [[1], [2], [3], [4]]), not 1D list.

🧠 Conceptual

expert

Effect of learning rate in linear regression training

What happens if the learning rate in gradient descent for linear regression is set too high?

A The model may fail to converge and loss can increase or oscillate

B The model will converge faster without any issues

C The model will ignore the learning rate and converge normally

D The model will always find the global minimum immediately

💡 Hint

Think about what happens when steps are too big in a hill-climbing problem.

A too high learning rate causes overshooting the minimum, preventing convergence.

Practice

1. What is the main goal of linear regression in machine learning?

easy

A. To classify data into categories

B. To find a line that best fits the data points

C. To cluster data points into groups

D. To reduce the number of features

Solution

1. Step 1: Understand linear regression purpose

   Linear regression aims to find a straight line that best fits the relationship between input and output data.

2. Step 2: Compare with other options

   Classification, clustering, and feature reduction are different tasks, not the goal of linear regression.

3. Final Answer:

   To find a line that best fits the data points -> Option B

4. Quick Check:

   Linear regression = line fitting [OK]

Hint: Linear regression fits a line, not groups or categories [OK]

Common Mistakes:

• Confusing regression with classification
• Thinking linear regression clusters data
• Believing it reduces features

2. Which of the following is the correct Python syntax to create a simple linear regression model using scikit-learn?

easy

A. model = LinearRegression()

B. model = LinearRegression.fit()

C. model = LinearRegression.fit(X, y)

D. model = LinearRegression(X, y)

Solution

1. Step 1: Recall model creation syntax

   In scikit-learn, you first create the model object with LinearRegression(), without arguments.

2. Step 2: Understand fitting method

   The fit method is called on the model object with data, e.g., model.fit(X, y), not during creation.

3. Final Answer:

   model = LinearRegression() -> Option A

4. Quick Check:

   Model creation uses class name with parentheses [OK]

Hint: Create model with class(), fit separately [OK]

Common Mistakes:

• Calling fit during model creation
• Passing data to constructor
• Using fit as a class method

3. Given the code below, what will be the predicted value for X_new = [[5]]?

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

medium

A. [9.]

B. [12.]

C. [8.]

D. [10.]

Solution

1. Step 1: Understand the training data pattern

   The output y doubles the input X: y = 2 * X.

2. Step 2: Predict for X_new = 5

   Using the pattern, prediction = 2 * 5 = 10.

3. Final Answer:

   [10.] -> Option D

4. Quick Check:

   2 x 5 = 10 [OK]

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

Common Mistakes:

• Ignoring the pattern and guessing
• Using last y value as prediction
• Confusing input and output

4. What is wrong with the following code snippet for training a linear regression model?

from sklearn.linear_model import LinearRegression
X = [1, 2, 3, 4]
y = [2, 4, 6, 8]
model = LinearRegression()
model.fit(X, y)

medium

A. X should be a 2D array, not a 1D list

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

C. LinearRegression() requires parameters in constructor

D. fit() method should be called with no arguments

Solution

1. Step 1: Check input shape requirements

   scikit-learn expects X as a 2D array (list of lists), but X is a 1D list here.

2. Step 2: Confirm y shape and method usage

   y can be 1D list, constructor needs no parameters, fit requires X and y arguments.

3. Final Answer:

   X should be a 2D array, not a 1D list -> Option A

4. Quick Check:

   X must be 2D for fit() [OK]

Hint: X must be 2D array for sklearn fit() [OK]

Common Mistakes:

• Passing 1D list for X
• Thinking y must be 2D
• Adding parameters to LinearRegression()

5. You trained a linear regression model on data where y = 3x + 2. After training, you want to predict the output for x = 10. Which code snippet correctly predicts and prints the result?

hard

A. model.predict([10])\nprint(prediction)

B. prediction = model.predict([10, 2])\nprint(prediction)

C. prediction = model.predict([[10]])\nprint(prediction[0])

D. prediction = model.predict(10)\nprint(prediction)

Solution

1. Step 1: Understand input shape for prediction

   scikit-learn expects a 2D array for prediction input, so [[10]] is correct.

2. Step 2: Access prediction output correctly

   predict returns an array; to print the single value, use prediction[0].

3. Final Answer:

   prediction = model.predict([[10]]) print(prediction[0]) -> Option C

4. Quick Check:

   Input 2D array, print first prediction [OK]

Hint: Use 2D input for predict, print first element [OK]

Common Mistakes:

• Passing 1D list or scalar to predict
• Printing prediction array directly without indexing
• Passing wrong shaped input with extra values