python code for Confidence interval and prediction reliability for regression using keras deep NN

for tabular data for regression it should work for any data file: features both are categorical and continues data is not extremely big for example 5 millions of rows 30 features, each categorical feature has 12000 values data you create by yourself or you existing data set but both categorical and continues features you need some special way to find reliability for each predictions for example see [login to view URL] [login to view URL] but this approach is weak one you need to find better one maybe [login to view URL] [login to view URL] [login to view URL]@qucit/a-simple-technique-to-estimate-prediction-intervals-for-any-regression-model-2dd73f630bcb [login to view URL] [login to view URL]:1472065/[login to view URL] page 14 2.4.4 Prediction Interval for Artificial Neural Networks Several methods to estimate prediction intervals for neural networks exist. These include dropout [4], bootstrap [23], mean variance estimation [23] and upper lower bound estimation [24]. [login to view URL] [login to view URL]@qucit/a-simple-technique-to-estimate-prediction-intervals-for-any-regression-model-2dd73f630bcb algorithm from these links maybe not good enough, you need to find theory and develop code to provide good solution