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?
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]
That's correct you need to use .values.reshape(-1,2)
In addition if you want to know the coefficients and the intercept of the expression:
CompressibilityFactor(Z) = intercept + coefTemperature(K) + coefPressure(ATM)
you can get them with:
Coefficients = model.coef_
intercept = model.intercept_