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Linear Regression for ML
course content

Course Content

Linear Regression for ML

Linear Regression for ML

1. Simple Linear Regression
What is Linear RegressionFinding the ParametersBuilding the Linear Regression with scikit-learnChallengeCheatsheet
2. Multiple Linear Regression
Linear Regression with Two FeaturesLinear Regression with n FeaturesBuilding Multiple Linear RegressionChallengeCheatsheet
3. Polynomial Regression
Quadratic RegressionPolynomial RegressionPolynomialFeaturesBuilding the Polynomial RegressionInterpolation vs ExtrapolationChallengeCheatsheet
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())
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.

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.

carousel-imgcarousel-imgcarousel-img

Task

  1. 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.
  2. Train the model.
  3. Predict the target for X_new.

Task

  1. 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.
  2. Train the model.
  3. Predict the target for X_new.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Section 3. Chapter 6
toggle bottom row

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())
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.

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.

carousel-imgcarousel-imgcarousel-img

Task

  1. 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.
  2. Train the model.
  3. Predict the target for X_new.

Task

  1. 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.
  2. Train the model.
  3. Predict the target for X_new.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Section 3. Chapter 6
toggle bottom row

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())
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.

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.

carousel-imgcarousel-imgcarousel-img

Task

  1. 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.
  2. Train the model.
  3. Predict the target for X_new.

Task

  1. 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.
  2. Train the model.
  3. Predict the target for X_new.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

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.

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.

carousel-imgcarousel-imgcarousel-img

Task

  1. 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.
  2. Train the model.
  3. Predict the target for X_new.

Switch to desktop for real-world practiceContinue from where you are using one of the options below
Section 3. Chapter 6
Switch to desktop for real-world practiceContinue from where you are using one of the options below
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