statistical-iv 0.2.8

Statistical IV

Statistical_IV: J-Divergence Hypothesis Test for the Information Value (IV). Calculation of the Information Value with specific limits to the predictive power.

Using optimalBinning, We created a specific way to calculate a predicitive power for each particular variable.

0. Import package

   from statistical_iv import api
   

1. Provide a DataFrame as Input:

   • Supply a DataFrame df containing your data for IV calculation.

2. Specify Predictor Variables:

   • Prived a list of predictor variable names (variables_names) to analyze.

3. Define the Target Variable:

   • Specify the name of the target variable (var_y) in your DataFrame.

4. Indicate Variable Types:

   • Define the type of your predictor variables as 'categorical' or 'numerical' using the type_vars parameter.

5. Optional: Set Maximum Bins:

   • Adjust the maximum number of bins for discretization (optional) using the max_bins parameter.

6. Call the statistical_iv Function:

   • Calculate IV by calling the statistical_iv function with the specified parameters (That is used for OptimalBinning package).

   result_df = statistical_iv(df, variables_names, var_y, type_vars, max_bins)
   

Example Result: