Logistic regression using Python sklearn
In Logistic Regression model we can implement variables which meet 3 conditions:
- independent variables have to be correlated with dependent variable
- independent variables have NOT to be correlated with each other
- only relevant variables can be added to the model, otherwise it does not make sense
and both 1. and 2. point we can achieve using Pearson correlation for example:
-- correlation between independent variables and dependent variable (targetVariable
1. CORREL = data.corr().sort_values('TargetVariable')
--heatmap which shows lvl of Pearson correlation between independent variables (the closer 0 the lower the correlation)
2. sns.heatmap (data.corr(), cmap="coolwarm", annot=true)
But when I choose appropriate variables after 1. and 2. step how can I meet the 3. step and choose which variables (which are correlated with independent variable and not correlated with other independent variable) are important and I should use it in model and which are not important and I should out them? Maybe p-value and delete all with p-value higher than 0,05? But how?
Please give me the example code to meet 3. step.