Linear regression basics in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the linear regression model from scikit-learn.

Data Analysis Python

from sklearn.linear_model import [1]

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

ADecisionTreeRegressor

BLinearRegression

CLogisticRegression

DKNeighborsClassifier

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing LogisticRegression which is for classification, not regression.

Using DecisionTreeRegressor which is a different model type.

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

2fill in blank

medium

Complete the code to create a linear regression model object.

Data Analysis Python

model = [1]()

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

ALinearRegression

BLogisticRegression

CRandomForestRegressor

DKMeans

Attempts:

3 left

💡 Hint

Common Mistakes

Using LogisticRegression which is for classification.

Using clustering models like KMeans.

We create a linear regression model by calling LinearRegression().

3fill in blank

hard

Fix the error in the code to fit the model with features X and target y.

Data Analysis Python

model.fit([1], y)

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

Ay

Bfit

CX

Dmodel

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the target variable y as the first argument.

Passing the model object itself.

The fit method requires the features as the first argument, which is X.

4fill in blank

hard

Fill both blanks to predict target values using the model and features X.

Data Analysis Python

predictions = model.[1]([2])

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Apredict

BX

Cfit

Dscore

Attempts:

3 left

💡 Hint

Common Mistakes

Using fit instead of predict.

Passing the model object instead of features.

Use predict method with features X to get predictions.

5fill in blank

hard

Fill all three blanks to create a dictionary of feature names and their coefficients from the model.

Data Analysis Python

coef_dict = { [1]: [2] for [3], coef in zip(feature_names, model.coef_) }

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

Aname

Bcoef

Dfeature

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong variable names that don't match the loop variables.

Mixing up keys and values in the dictionary comprehension.

We create a dictionary where keys are feature names and values are coefficients. The loop variable for names is name and for coefficients is coef.

Practice

1. What is the main goal of linear regression in data analysis?

easy

A. To find a straight line that best fits the data points

B. To group data points into clusters

C. To calculate the average of all data points

D. To sort data points in ascending order

Solution

1. Step 1: Understand the purpose of linear regression

   Linear regression aims to find a straight line that best represents the relationship between two variables.

2. Step 2: Compare options with the goal

   Only To find a straight line that best fits the data points describes finding a straight line that fits data points, which matches the goal of linear regression.

3. Final Answer:

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

4. Quick Check:

   Linear regression = best fit line [OK]

Hint: Linear regression fits a line to data points [OK]

Common Mistakes:

• Confusing regression with clustering
• Thinking regression sorts or averages data
• Mixing up regression with classification

2. Which of the following is the correct way to import the LinearRegression class from scikit-learn in Python?

easy

A. from sklearn.linear_model import LinearRegression

B. import LinearRegression from sklearn.linear_model

C. from sklearn import LinearRegression

D. import linear_model.LinearRegression

Solution

1. Step 1: Recall correct import syntax in Python

   Python uses 'from module import class' to import specific classes.

2. Step 2: Match with scikit-learn syntax

   Scikit-learn's LinearRegression is in sklearn.linear_model, so the correct import is 'from sklearn.linear_model import LinearRegression'.

3. Final Answer:

   from sklearn.linear_model import LinearRegression -> Option A

4. Quick Check:

   Correct import = from sklearn.linear_model import LinearRegression [OK]

Hint: Use 'from module import class' for specific imports [OK]

Common Mistakes:

• Using wrong import order
• Trying to import class directly from sklearn
• Using incorrect module names

3. What will be the output of this Python code using scikit-learn's LinearRegression?

from sklearn.linear_model import LinearRegression
import numpy as np

X = np.array([[1], [2], [3], [4]])
y = np.array([2, 4, 6, 8])
model = LinearRegression()
model.fit(X, y)
pred = model.predict([[5]])
print(round(pred[0], 1))

medium

A. 12.0

B. 9.0

C. 10.0

D. 8.0

Solution

1. Step 1: Understand the data and model fitting

   X values are 1 to 4, y values are 2, 4, 6, 8, which is y = 2 * x perfectly.

2. Step 2: Predict for X=5 using the fitted model

   Since y = 2 * x, prediction for 5 is 2 * 5 = 10. The code rounds to 1 decimal place, so output is 10.0.

3. Final Answer:

   10.0 -> Option C

4. Quick Check:

   Prediction = 2 * 5 = 10.0 [OK]

Hint: Check if data fits y=mx+b, then predict easily [OK]

Common Mistakes:

• Ignoring rounding in print
• Confusing input shape for predict
• Assuming wrong slope or intercept

4. Identify the error in this code snippet for fitting 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. LinearRegression cannot be imported from sklearn.linear_model

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

C. model.fit requires no arguments

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

Solution

1. Step 1: Check input shapes for model.fit

   LinearRegression expects X as 2D array (samples, features). Here X is 1D list, which causes error.

2. Step 2: Confirm y shape is acceptable

   y can be 1D array/list for target values, so y is fine.

3. Final Answer:

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

4. Quick Check:

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

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

Common Mistakes:

• Passing 1D X array causes shape error
• Thinking y must be 2D
• Misunderstanding import statements

5. You have data of house sizes (in square meters) and their prices (in thousands). You want to predict the price of a house with 120 square meters using linear regression. Given these data points:

sizes = [50, 80, 100, 150]
prices = [150, 200, 250, 350]

Which code snippet correctly fits the model and predicts the price for 120 square meters?

hard

A. from sklearn.linear_model import LinearRegression
X = sizes
y = prices
model = LinearRegression()
model.fit(X, y)
pred = model.predict([120])
print(pred)

B. from sklearn.linear_model import LinearRegression
import numpy as np
X = np.array(sizes).reshape(-1,1)
y = np.array(prices)
model = LinearRegression()
model.fit(X, y)
pred = model.predict([[120]])
print(round(pred[0], 2))

C. import LinearRegression
X = sizes
model = LinearRegression()
model.fit(X, prices)
pred = model.predict(120)
print(pred)

D. from sklearn.linear_model import LinearRegression
import numpy as np
X = np.array(sizes)
y = np.array(prices)
model = LinearRegression()
model.fit(X, y)
pred = model.predict(np.array([120]))
print(pred)

Solution

1. Step 1: Prepare data in correct shape for fitting

   LinearRegression requires X as 2D array, so sizes must be reshaped with .reshape(-1,1).

2. Step 2: Fit model and predict correctly

   from sklearn.linear_model import LinearRegression import numpy as np X = np.array(sizes).reshape(-1,1) y = np.array(prices) model = LinearRegression() model.fit(X, y) pred = model.predict([[120]]) print(round(pred[0], 2)) reshapes X, fits model, predicts with [[120]] (2D), and rounds output, which is correct usage.

3. Final Answer:

   from sklearn.linear_model import LinearRegression import numpy as np X = np.array(sizes).reshape(-1,1) y = np.array(prices) model = LinearRegression() model.fit(X, y) pred = model.predict([[120]]) print(round(pred[0], 2)) -> Option B

4. Quick Check:

   Reshape X and predict with 2D input [OK]

Hint: Always reshape X to 2D before fitting and predicting [OK]

Common Mistakes:

• Not reshaping X to 2D array
• Passing 1D array to predict
• Incorrect import or usage of LinearRegression