# LinearRegression

As noted by some fellow kagglers, LinearRegression can achieve good ranking on the Leaderboard.
Solution:
Remove single value and duplicate features.
Using 29 features from X752 to X780, train 12 LinearRegression models in sklearn 1.7.x, each by reserving one month of data as validation. Public: 0.10630, Private: 0.10664.
Feature selection is done by removing one feature at a time from the given features (according to order from the train.parquet file) and fit with multiple LinearRegression, each using data of one month and validation is the data of the next month except for the final month where validation becomes the data of the first month. Validation score (PearsonR) from all these models are averaged, set of features that gave the best score is selected.
Remarks:
There is a discrepancy between the prediction output for LinearRegression model of scikit-learn 1.2.2 (default on kaggle notebook) and 1.7.x. Version 1.2.2 performs worse.
Using only the last 50 features in the given order of train.parquet file and training one LinearRegression model on the whole train dataset, gives a better score on private leaderboard. Public score: 0.09877, Private score: 0.11127.
Thanks everyone!