Course Content
Linear Regression with Python
Linear Regression with Python
Predict Prices Using Two Features
For this challenge, the same housing dataset will be used. However, now it has two features: age and area of the house (columns age
and square_feet
).
import pandas as pd df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b22d1166-efda-45e8-979e-6c3ecfc566fc/houseprices.csv') print(df.head())
Your task is to build a Multiple Linear Regression model using the OLS
class. Also, you will print the summary table to look at the p-values of each feature.
Task
- Assign the
'age'
and'square_feet'
columns ofdf
toX
. - Preprocess the
X
for theOLS
's class constructor. - Build and train the model using the
OLS
class. - Preprocess the
X_new
array the same asX
. - Predict the target for
X_new
. - Print the model's summary table.
Task
- Assign the
'age'
and'square_feet'
columns ofdf
toX
. - Preprocess the
X
for theOLS
's class constructor. - Build and train the model using the
OLS
class. - Preprocess the
X_new
array the same asX
. - Predict the target for
X_new
. - Print the model's summary table.
If you did everything right, you got the p-values close to zero. That means all our features are significant for the model.
Everything was clear?
Predict Prices Using Two Features
For this challenge, the same housing dataset will be used. However, now it has two features: age and area of the house (columns age
and square_feet
).
import pandas as pd df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b22d1166-efda-45e8-979e-6c3ecfc566fc/houseprices.csv') print(df.head())
Your task is to build a Multiple Linear Regression model using the OLS
class. Also, you will print the summary table to look at the p-values of each feature.
Task
- Assign the
'age'
and'square_feet'
columns ofdf
toX
. - Preprocess the
X
for theOLS
's class constructor. - Build and train the model using the
OLS
class. - Preprocess the
X_new
array the same asX
. - Predict the target for
X_new
. - Print the model's summary table.
Task
- Assign the
'age'
and'square_feet'
columns ofdf
toX
. - Preprocess the
X
for theOLS
's class constructor. - Build and train the model using the
OLS
class. - Preprocess the
X_new
array the same asX
. - Predict the target for
X_new
. - Print the model's summary table.
If you did everything right, you got the p-values close to zero. That means all our features are significant for the model.
Everything was clear?
Predict Prices Using Two Features
For this challenge, the same housing dataset will be used. However, now it has two features: age and area of the house (columns age
and square_feet
).
import pandas as pd df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b22d1166-efda-45e8-979e-6c3ecfc566fc/houseprices.csv') print(df.head())
Your task is to build a Multiple Linear Regression model using the OLS
class. Also, you will print the summary table to look at the p-values of each feature.
Task
- Assign the
'age'
and'square_feet'
columns ofdf
toX
. - Preprocess the
X
for theOLS
's class constructor. - Build and train the model using the
OLS
class. - Preprocess the
X_new
array the same asX
. - Predict the target for
X_new
. - Print the model's summary table.
Task
- Assign the
'age'
and'square_feet'
columns ofdf
toX
. - Preprocess the
X
for theOLS
's class constructor. - Build and train the model using the
OLS
class. - Preprocess the
X_new
array the same asX
. - Predict the target for
X_new
. - Print the model's summary table.
If you did everything right, you got the p-values close to zero. That means all our features are significant for the model.
Everything was clear?
For this challenge, the same housing dataset will be used. However, now it has two features: age and area of the house (columns age
and square_feet
).
import pandas as pd df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b22d1166-efda-45e8-979e-6c3ecfc566fc/houseprices.csv') print(df.head())
Your task is to build a Multiple Linear Regression model using the OLS
class. Also, you will print the summary table to look at the p-values of each feature.
Task
- Assign the
'age'
and'square_feet'
columns ofdf
toX
. - Preprocess the
X
for theOLS
's class constructor. - Build and train the model using the
OLS
class. - Preprocess the
X_new
array the same asX
. - Predict the target for
X_new
. - Print the model's summary table.
If you did everything right, you got the p-values close to zero. That means all our features are significant for the model.