Multivariable/Multiple Linear Regression in Scikit Learn?
28,764
If your code above works for univariate, try this
import pandas as pd
from sklearn import linear_model
dataTrain = pd.read_csv("dataTrain.csv")
dataTest = pd.read_csv("dataTest.csv")
# print df.head()
x_train = dataTrain[['Temperature(K)', 'Pressure(ATM)']].to_numpy().reshape(-1,2)
y_train = dataTrain['CompressibilityFactor(Z)']
x_test = dataTest[['Temperature(K)', 'Pressure(ATM)']].to_numpy().reshape(-1,2)
y_test = dataTest['CompressibilityFactor(Z)']
ols = linear_model.LinearRegression()
model = ols.fit(x_train, y_train)
print model.predict(x_test)[0:5]
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Author by
Drizzer Silverberg
Updated on July 09, 2022Comments
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Drizzer Silverberg almost 2 years
I have a dataset (dataTrain.csv & dataTest.csv) in .csv file with this format:
Temperature(K),Pressure(ATM),CompressibilityFactor(Z) 273.1,24.675,0.806677258 313.1,24.675,0.888394713 ...,...,...
And able to build a regression model and prediction with this code:
import pandas as pd from sklearn import linear_model dataTrain = pd.read_csv("dataTrain.csv") dataTest = pd.read_csv("dataTest.csv") # print df.head() x_train = dataTrain['Temperature(K)'].reshape(-1,1) y_train = dataTrain['CompressibilityFactor(Z)'] x_test = dataTest['Temperature(K)'].reshape(-1,1) y_test = dataTest['CompressibilityFactor(Z)'] ols = linear_model.LinearRegression() model = ols.fit(x_train, y_train) print model.predict(x_test)[0:5]
However, what I want to do is multivariable regression. So, the model will be
CompressibilityFactor(Z) = intercept + coef*Temperature(K) + coef*Pressure(ATM)
How to do that in scikit-learn?
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rtk22 over 7 yearsJust include both Temperature and Pressure in your xtrain, xtest.
x_train = dataTrain[["Temperature(K)", "Pressure(ATM)"]]
and then the same for x_test.
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Warren O'Neill over 5 yearsDataFrames don't have a
reshape
function. To run the above code I have to usevalues
first, egx_train = dataTrain[['Temperature(K)', 'Pressure(ATM)']].values.reshape(-1,2)
.