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]

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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]()

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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)

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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_) }

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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.