Feature selection is one of the most important step to make your model works well. In data mining, feature selection is the first step and it effects to all of process. Feature selection help model on some points: - The model will be trained faster - Reduce overfitting - Simplifying model - Reduce the dimension of data Hence, feature selection is kick-off step and it effects overall, especially in model. There are 3 type of feature selection: Filter methods, wrapper methods and embedded methods. Filter methods: this methods "filter" data based on correlation score. Normally, our data have many features, and a label. We calculate the correlations between features and label. After that, we only retrain the features that have a good (relevant) correlated score and remove others. In this type of method we have some ways to calculate the correlation. - Pearson correlation: this one is based on covariance between 2 continuous variables. $$ p_{X,Y} = \frac {Cov(X, Y)...