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Linear Regression with Python
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Зміст курсу

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

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())
copy

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)
copy

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

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

Завдання

  1. Assign the X variable to a DataFrame containing column 'age'.
  2. Create an X_tilde matrix using the PolynomialFeatures class.
  3. Build and train a Polynomial Regression model.
  4. Print the model's parameters.
  5. Reshape X_new to be a 2-D array.
  6. 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?

Секція 3. Розділ 5
toggle bottom row

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())
copy

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)
copy

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

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

Завдання

  1. Assign the X variable to a DataFrame containing column 'age'.
  2. Create an X_tilde matrix using the PolynomialFeatures class.
  3. Build and train a Polynomial Regression model.
  4. Print the model's parameters.
  5. Reshape X_new to be a 2-D array.
  6. 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?

Секція 3. Розділ 5
toggle bottom row

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())
copy

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)
copy

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

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

Завдання

  1. Assign the X variable to a DataFrame containing column 'age'.
  2. Create an X_tilde matrix using the PolynomialFeatures class.
  3. Build and train a Polynomial Regression model.
  4. Print the model's parameters.
  5. Reshape X_new to be a 2-D array.
  6. 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?

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())
copy

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)
copy

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

  1. Assign the X variable to a DataFrame containing column 'age'.
  2. Create an X_tilde matrix using the PolynomialFeatures class.
  3. Build and train a Polynomial Regression model.
  4. Print the model's parameters.
  5. Reshape X_new to be a 2-D array.
  6. 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
Секція 3. 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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