get names of featrues from a lasso regression in python

To get the names of the selected features (non-zero coefficients) from a Lasso regression in Python, you can use the following code:

main.py
from sklearn.linear_model import Lasso
lasso = Lasso(alpha=0.1) # initialize the Lasso model with the desired alpha value
lasso.fit(X, y) # fit the model with X and y
selected_features = X.columns[lasso.coef_ != 0] # get the names of non-zero coefficient features
print(selected_features) # print the selected feature names
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Here, X is your feature matrix and y is your target variable. The alpha parameter controls the strength of the L1 penalty in the Lasso regression, and you can set it to a value of your choice.

Once you fit the Lasso model, you can obtain the names of the selected features by using the coefficients that are non-zero (i.e., the features with non-zero coefficients are the ones that were selected). In the code, we obtain the column names of the feature matrix X where the coefficients are non-zero using the condition lasso.coef_ != 0.

Note: Make sure to preprocess your feature matrix appropriately before fitting the Lasso regression.