Зміст курсу

Linear Regression for ML

1. Simple Linear Regression

What is Linear Regression Finding the Parameters Building the Linear Regression with scikit-learn Challenge Cheatsheet

2. Multiple Linear Regression

Linear Regression with Two Features Linear Regression with n Features Building Multiple Linear Regression Challenge Cheatsheet

3. Polynomial Regression

Quadratic Regression Polynomial Regression PolynomialFeatures Building the Polynomial Regression Interpolation vs Extrapolation Challenge Cheatsheet

4. Evaluating and Comparing Models

Metrics

Challenge

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.

The task is to build a Polynomial Regression of degree 2 using a pipeline, as was shown in a previous chapter. Here is a list of the classes and functions from sklearn that you will need.

Завдання

Create a model using the make_pipeline function.
As function arguments, pass the instances of classes that:
- adds polynomial features of a degree n (don't forget to set the include_bias to False).
- performs Linear Regression.
Train the model.
Predict the target for X_new.

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 6

Challenge

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)

The task is to build a Polynomial Regression of degree 2 using a pipeline, as was shown in a previous chapter. Here is a list of the classes and functions from sklearn that you will need.

Завдання

Create a model using the make_pipeline function.
As function arguments, pass the instances of classes that:
- adds polynomial features of a degree n (don't forget to set the include_bias to False).
- performs Linear Regression.
Train the model.
Predict the target for X_new.

Все було зрозуміло?

Дякуємо за ваш відгук!

Секція 3. Розділ 6

Challenge

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)

The task is to build a Polynomial Regression of degree 2 using a pipeline, as was shown in a previous chapter. Here is a list of the classes and functions from sklearn that you will need.

Завдання

Create a model using the make_pipeline function.
As function arguments, pass the instances of classes that:
- adds polynomial features of a degree n (don't forget to set the include_bias to False).
- performs Linear Regression.
Train the model.
Predict the target for X_new.

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)

The task is to build a Polynomial Regression of degree 2 using a pipeline, as was shown in a previous chapter. Here is a list of the classes and functions from sklearn that you will need.

Завдання

Create a model using the make_pipeline function.
As function arguments, pass the instances of classes that:
- adds polynomial features of a degree n (don't forget to set the include_bias to False).
- performs Linear Regression.
Train the model.
Predict the target for X_new.

Секція 3. Розділ 6