Predict Prices Using Two Features | Multiple Linear Regression
Linear Regression with Python

Зміст курсу

Linear Regression with Python

# Predict Prices Using Two Features

For this challenge, the same housing dataset will be used. However, now it has two features: age and area of the house (columns `age` and `square_feet`).

Your task is to build a Multiple Linear Regression model using the `OLS` class. Also, you will print the summary table to look at the p-values of each feature.

Завдання

1. Assign the `'age'` and `'square_feet'` columns of `df` to `X`.
2. Preprocess the `X` for the `OLS`'s class constructor.
3. Build and train the model using the `OLS` class.
4. Preprocess the `X_new` array the same as `X`.
5. Predict the target for `X_new`.
6. Print the model's summary table.

If you did everything right, you got the p-values close to zero. That means all our features are significant for the model.

Все було зрозуміло?

Секція 2. Розділ 5

# Predict Prices Using Two Features

For this challenge, the same housing dataset will be used. However, now it has two features: age and area of the house (columns `age` and `square_feet`).

Your task is to build a Multiple Linear Regression model using the `OLS` class. Also, you will print the summary table to look at the p-values of each feature.

Завдання

1. Assign the `'age'` and `'square_feet'` columns of `df` to `X`.
2. Preprocess the `X` for the `OLS`'s class constructor.
3. Build and train the model using the `OLS` class.
4. Preprocess the `X_new` array the same as `X`.
5. Predict the target for `X_new`.
6. Print the model's summary table.

If you did everything right, you got the p-values close to zero. That means all our features are significant for the model.

Все було зрозуміло?

Секція 2. Розділ 5