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

Predict House Prices

Let's build a real-world example regression model. We have a file, houses_simple.csv, that holds information about housing prices with its area as a 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_simple.csv')
print(df.head())

Let's assign variables and visualize our dataset!


              12345678
            
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_simple.csv')
X = df['square_feet']
y = df['price']

plt.scatter(X, y, alpha=0.5)

In the example with a person's height, it was much easier to imagine a line fitting the data well.
But now our data has much more variance since the target highly depends on many other things like age, location, interior, etc.
Anyway, the task is to build the line that best fits the data we have; it will show the trend. The OLS class should be used for that. Soon we will learn how to add more features, it will make the prediction better!

Завдання

Swipe to show code editor

Assign the 'price' column of df to y.
Create the X_tilde matrix using the add_constant() function from statsmodels(imported as sm).
Initialize the OLS object and train it.
Preprocess X_new array the same way as X.
Predict the target for X_new_tilde matrix.

_{Once you've completed this task, click the button below the code to check your solution.}

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 1. Розділ 5

Predict House Prices

Let's build a real-world example regression model. We have a file, houses_simple.csv, that holds information about housing prices with its area as a 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_simple.csv')
print(df.head())

Let's assign variables and visualize our dataset!


              12345678
            
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_simple.csv')
X = df['square_feet']
y = df['price']

plt.scatter(X, y, alpha=0.5)

Завдання

Swipe to show code editor

Assign the 'price' column of df to y.
Create the X_tilde matrix using the add_constant() function from statsmodels(imported as sm).
Initialize the OLS object and train it.
Preprocess X_new array the same way as X.
Predict the target for X_new_tilde matrix.

_{Once you've completed this task, click the button below the code to check your solution.}

Все було зрозуміло?

Дякуємо за ваш відгук!

Секція 1. Розділ 5

Predict House Prices

Let's build a real-world example regression model. We have a file, houses_simple.csv, that holds information about housing prices with its area as a 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_simple.csv')
print(df.head())

Let's assign variables and visualize our dataset!


              12345678
            
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_simple.csv')
X = df['square_feet']
y = df['price']

plt.scatter(X, y, alpha=0.5)

Завдання

Swipe to show code editor

Assign the 'price' column of df to y.
Create the X_tilde matrix using the add_constant() function from statsmodels(imported as sm).
Initialize the OLS object and train it.
Preprocess X_new array the same way as X.
Predict the target for X_new_tilde matrix.

_{Once you've completed this task, click the button below the code to check your solution.}

Все було зрозуміло?

Дякуємо за ваш відгук!

Let's build a real-world example regression model. We have a file, houses_simple.csv, that holds information about housing prices with its area as a 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_simple.csv')
print(df.head())

Let's assign variables and visualize our dataset!


              12345678
            
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_simple.csv')
X = df['square_feet']
y = df['price']

plt.scatter(X, y, alpha=0.5)

Завдання

Swipe to show code editor

Assign the 'price' column of df to y.
Create the X_tilde matrix using the add_constant() function from statsmodels(imported as sm).
Initialize the OLS object and train it.
Preprocess X_new array the same way as X.
Predict the target for X_new_tilde matrix.

_{Once you've completed this task, click the button below the code to check your solution.}

Секція 1. Розділ 5