A Smart Approach to Feature Selection Recursive Feature Elimination (RFE) is the best method for feature selection in a large dataset. This is because it systematically reduces the dataset by considering smaller and smaller sets of features recursively. In this method, the features are ranked based on their importance by a machine learning model (like Random Forest or SVM), which eliminates the least significant features iteratively. Doing so is effective as it improves model performance by enhancing generalisation and reducing overfitting. Apart from this, RFE also helps in understanding every feature, which, in turn, makes the model more interpretable.
Model building does need good amount of domain experience to be able to effectively collect data and create training data after doing some data munging. If you have a very large dataset meaning there are a lot of fields in the dataset which can lead to an even higher number of features, it is important to run dimensionality reduction on large datasets. One simple yet highly effective method that I often start with is identifying multicollinearity among features. The idea of multicollinearity is that the variables or features should be highly related to the target or label but should not be related among each other. If there is a high correlation among two independent variables then they are not really independent and if we use both as features then our prediction is deemed to be less accurate. I have found Pearson coefficient of correlation to do a great job in identifying multicollinearity among features. Pandas and NumPy libraries have inbuilt methods for creating correlation matrix which can be used to identify correlation between any two features. Any correlation coefficient value greater than 0.5 indicates high correlation. After identifying two correlated features, we can eliminate the one which has lesser correlation with the target value.