Finding final regression equation in python

Question

How to find final regression model equation including coefficients with all variables? is there any method?

Answer 1

Show you an Example

I show you an example with OLS using boston house price data set.

Code:

# load a dataset and regression function
from sklearn import linear_model,datasets
import pandas as pd
# I use boston dataset to show you 
full_data = datasets.load_boston()

# get a regressor, fit intercept
reg = linear_model.LinearRegression(fit_intercept=True)
# data is our explanatory, target is our response
reg.fit(full_data['data'],full_data['target'])

# we have 1 intercept and  11 variables' coef
reg.intercept_,reg.coef_

# get the name of features
full_data.feature_names
# append to get a new list
coef = np.append(reg.intercept_,reg.coef_)
feature_names = np.append(['Intercept'], full_data.feature_names)
# output a dataframe contains coefficients you want
pd.DataFrame({"feature_names":feature_names,"coef":coef})

Output:

   feature_names       coef
0      Intercept  36.459488
1           CRIM  -0.108011
2             ZN   0.046420
3          INDUS   0.020559
4           CHAS   2.686734
5            NOX -17.766611
6             RM   3.809865
7            AGE   0.000692
8            DIS  -1.475567
9            RAD   0.306049
10           TAX  -0.012335
11       PTRATIO  -0.952747
12             B   0.009312
13         LSTAT  -0.524758

Some Advice

You can use dir(object) to see what's in your fitted model, like using dir(full_data) and dir(reg) to see atrributes and methods of an instance.

As for sklearn, here is an official guide about it. You can find functions and datasets on the guide.