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Linear Regression for ML
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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

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 at least show the trend. The LinearRegression class should be used for that. Soon we will learn how to add more features to improve the predictions!

Task

  1. Import the LinearRegression class from sklearn.linear_model.
  2. Assign the 'square_feet' column to X.
    Make sure you assign pandas DataFrame with a single column instead of pandas Series (refer to hint if needed).
  3. Initialize the LinearRegression model.
  4. Train the model.
  5. Predict the target for the X_new array.

Task

  1. Import the LinearRegression class from sklearn.linear_model.
  2. Assign the 'square_feet' column to X.
    Make sure you assign pandas DataFrame with a single column instead of pandas Series (refer to hint if needed).
  3. Initialize the LinearRegression model.
  4. Train the model.
  5. Predict the target for the X_new array.

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

Everything was clear?

Section 1. Chapter 4
toggle bottom row

Challenge

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 at least show the trend. The LinearRegression class should be used for that. Soon we will learn how to add more features to improve the predictions!

Task

  1. Import the LinearRegression class from sklearn.linear_model.
  2. Assign the 'square_feet' column to X.
    Make sure you assign pandas DataFrame with a single column instead of pandas Series (refer to hint if needed).
  3. Initialize the LinearRegression model.
  4. Train the model.
  5. Predict the target for the X_new array.

Task

  1. Import the LinearRegression class from sklearn.linear_model.
  2. Assign the 'square_feet' column to X.
    Make sure you assign pandas DataFrame with a single column instead of pandas Series (refer to hint if needed).
  3. Initialize the LinearRegression model.
  4. Train the model.
  5. Predict the target for the X_new array.

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

Everything was clear?

Section 1. Chapter 4
toggle bottom row

Challenge

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 at least show the trend. The LinearRegression class should be used for that. Soon we will learn how to add more features to improve the predictions!

Task

  1. Import the LinearRegression class from sklearn.linear_model.
  2. Assign the 'square_feet' column to X.
    Make sure you assign pandas DataFrame with a single column instead of pandas Series (refer to hint if needed).
  3. Initialize the LinearRegression model.
  4. Train the model.
  5. Predict the target for the X_new array.

Task

  1. Import the LinearRegression class from sklearn.linear_model.
  2. Assign the 'square_feet' column to X.
    Make sure you assign pandas DataFrame with a single column instead of pandas Series (refer to hint if needed).
  3. Initialize the LinearRegression model.
  4. Train the model.
  5. Predict the target for the X_new array.

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

Everything was clear?

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 at least show the trend. The LinearRegression class should be used for that. Soon we will learn how to add more features to improve the predictions!

Task

  1. Import the LinearRegression class from sklearn.linear_model.
  2. Assign the 'square_feet' column to X.
    Make sure you assign pandas DataFrame with a single column instead of pandas Series (refer to hint if needed).
  3. Initialize the LinearRegression model.
  4. Train the model.
  5. Predict the target for the X_new array.

Switch to desktop for real-world practiceContinue from where you are using one of the options below
Section 1. Chapter 4
Switch to desktop for real-world practiceContinue from where you are using one of the options below
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