Зміст курсу

Linear Regression with Python

1. Simple Linear Regression

What is Linear Regression Finding the Parameters Building Linear Regression Using NumPy Building Linear Regression Using Statsmodels Predict House Prices

2. Multiple Linear Regression

Linear Regression with Two Features Linear Regression with n Features Building Multiple Linear Regression Choosing the Features Predict Prices Using Two Features

3. Polynomial Regression

Quadratic Regression Polynomial Regression Building Polynomial Regression Interpolation vs Extrapolation Evaluate the Model

4. Choosing The Best Model

Metrics Overfitting R-squared Predict Prices Using Polynomial Regression

Evaluate the Model

In this challenge, you are given the good old housing dataset, but this time only with the 'age' feature.


              1234
            
import pandas as pd

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b22d1166-efda-45e8-979e-6c3ecfc566fc/houses_poly.csv')
print(df.head())

Let's build a scatterplot of this data.


              1234567
            
import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b22d1166-efda-45e8-979e-6c3ecfc566fc/houses_poly.csv')
X = df['age']
y = df['price']
plt.scatter(X, y, alpha=0.4)

Fitting a straight line to this data may not be a great choice. The price gets higher for either brand-new or really old houses. Fitting a parabola looks like a better choice. And that's what you will do in this challenge.

But before you start, recall the PolynomialFeatures class.

The fit_transform(X) method requires X to be a 2-D array (or a DataFrame).
Using X = df[['column_name']] will get your X suited for fit_transform().
And if you have a 1-D array, use .reshape(-1, 1) to make a 2-D array with the same contents.

The task is to build a Polynomial Regression of degree 2 using PolynomialFeatures and OLS.

Завдання

Assign the X variable to a DataFrame containing column 'age'.
Create an X_tilde matrix using the PolynomialFeatures class.
Build and train a Polynomial Regression model.
Print the model's parameters.
Reshape X_new to be a 2-D array.
Preprocess X_new the same way as X.

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів

Все було зрозуміло?

Дякуємо за ваш відгук!

Секція 3. Розділ 5

Evaluate the Model

In this challenge, you are given the good old housing dataset, but this time only with the 'age' feature.


              1234
            
import pandas as pd

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b22d1166-efda-45e8-979e-6c3ecfc566fc/houses_poly.csv')
print(df.head())

Let's build a scatterplot of this data.


              1234567
            
import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b22d1166-efda-45e8-979e-6c3ecfc566fc/houses_poly.csv')
X = df['age']
y = df['price']
plt.scatter(X, y, alpha=0.4)

But before you start, recall the PolynomialFeatures class.

The task is to build a Polynomial Regression of degree 2 using PolynomialFeatures and OLS.

Завдання

Assign the X variable to a DataFrame containing column 'age'.
Create an X_tilde matrix using the PolynomialFeatures class.
Build and train a Polynomial Regression model.
Print the model's parameters.
Reshape X_new to be a 2-D array.
Preprocess X_new the same way as X.

Все було зрозуміло?

Дякуємо за ваш відгук!

Секція 3. Розділ 5

Evaluate the Model

In this challenge, you are given the good old housing dataset, but this time only with the 'age' feature.


              1234
            
import pandas as pd

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b22d1166-efda-45e8-979e-6c3ecfc566fc/houses_poly.csv')
print(df.head())

Let's build a scatterplot of this data.


              1234567
            
import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b22d1166-efda-45e8-979e-6c3ecfc566fc/houses_poly.csv')
X = df['age']
y = df['price']
plt.scatter(X, y, alpha=0.4)

But before you start, recall the PolynomialFeatures class.

The task is to build a Polynomial Regression of degree 2 using PolynomialFeatures and OLS.

Завдання

Assign the X variable to a DataFrame containing column 'age'.
Create an X_tilde matrix using the PolynomialFeatures class.
Build and train a Polynomial Regression model.
Print the model's parameters.
Reshape X_new to be a 2-D array.
Preprocess X_new the same way as X.

Все було зрозуміло?

Дякуємо за ваш відгук!

In this challenge, you are given the good old housing dataset, but this time only with the 'age' feature.


              1234
            
import pandas as pd

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b22d1166-efda-45e8-979e-6c3ecfc566fc/houses_poly.csv')
print(df.head())

Let's build a scatterplot of this data.


              1234567
            
import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b22d1166-efda-45e8-979e-6c3ecfc566fc/houses_poly.csv')
X = df['age']
y = df['price']
plt.scatter(X, y, alpha=0.4)

But before you start, recall the PolynomialFeatures class.

The task is to build a Polynomial Regression of degree 2 using PolynomialFeatures and OLS.

Завдання

Assign the X variable to a DataFrame containing column 'age'.
Create an X_tilde matrix using the PolynomialFeatures class.
Build and train a Polynomial Regression model.
Print the model's parameters.
Reshape X_new to be a 2-D array.
Preprocess X_new the same way as X.

Секція 3. Розділ 5