![]() |
Recommendation after running regression (approach) - Printable Version +- Python Forum (https://python-forum.io) +-- Forum: Python Coding (https://python-forum.io/forum-7.html) +--- Forum: General Coding Help (https://python-forum.io/forum-8.html) +--- Thread: Recommendation after running regression (approach) (/thread-11592.html) |
Recommendation after running regression (approach) - danishzmalik - Jul-17-2018 Hey all, Im new to python and data science. Ive generated a regression model using sklearns LinearRegression(). i.e LinearRegression(Train_features, train_label) Train Features: x1,x2,x3 Train Label: y My model works accurately with low mse scores. My problem is that now i have to make a recommendation based on the model, and have to recommend a value for x1 (x1 is the only controllable feature), such that y = a required value. What approach should i use for this? I tried setting x1 as the response variable and and y as a predictor variable and then ran the regression but it gave me really high values for mse. I know i can do it manually using the generated regression equation, but is there any way to do it on python? |