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Linear Regression with Python
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Linear Regression with Python

Linear Regression with Python

1. Simple Linear Regression
What is Linear RegressionFinding the ParametersBuilding Linear Regression Using NumPyBuilding Linear Regression Using StatsmodelsPredict House Prices
2. Multiple Linear Regression
Linear Regression with Two FeaturesLinear Regression with n FeaturesBuilding Multiple Linear RegressionChoosing the FeaturesPredict Prices Using Two Features
3. Polynomial Regression
Quadratic RegressionPolynomial RegressionBuilding Polynomial RegressionInterpolation vs ExtrapolationEvaluate the Model
4. Choosing The Best Model
MetricsOverfittingR-squaredPredict 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())
copy

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)
copy

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

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

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

Завдання

  1. Assign the 'price' column of df to y.
  2. Create the X_tilde matrix using the add_constant() function from statsmodels(imported as sm).
  3. Initialize the OLS object and train it.
  4. Preprocess X_new array the same way as X.
  5. 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?

Секція 1. Розділ 5
toggle bottom row

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())
copy

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)
copy

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

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

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

Завдання

  1. Assign the 'price' column of df to y.
  2. Create the X_tilde matrix using the add_constant() function from statsmodels(imported as sm).
  3. Initialize the OLS object and train it.
  4. Preprocess X_new array the same way as X.
  5. 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?

Секція 1. Розділ 5
toggle bottom row

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())
copy

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)
copy

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

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

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

Завдання

  1. Assign the 'price' column of df to y.
  2. Create the X_tilde matrix using the add_constant() function from statsmodels(imported as sm).
  3. Initialize the OLS object and train it.
  4. Preprocess X_new array the same way as X.
  5. 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?

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())
copy

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)
copy

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

  1. Assign the 'price' column of df to y.
  2. Create the X_tilde matrix using the add_constant() function from statsmodels(imported as sm).
  3. Initialize the OLS object and train it.
  4. Preprocess X_new array the same way as X.
  5. 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
Секція 1. Розділ 5
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів
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