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1. Offline Model Training

1. Offline Model Training
OFFLINE MODEL TRAINING- Model training with feedforward neural network- Data is normalize during the training to get more accurate predicition DATA PARTITIONING- Data partitioning to split data to train, validation and test set before undergo model training.- Data split ratio are:- 1. train:validation : 80:20 2. validation:test : 50:50- Save the csv files in dedicated folder particularly in the same folder of the project Dataset:https://www.kaggle.com/datasets/dhanushnarayananr/credit-card-fraud read csv filesplit train:validation(80:20)Model trainingconvert input data to 3-D arrayconvert input datato 3-D arrayscore the actual fraudto predicted fraudconvert prediction to(>=0.5 to 1)(<0.5 to 0)Evaluate thetrained modelwith test.csvsave trained modelfor deploymentwrite test.csvsplit validation:test(50:50)save validation.csvsave test.csvread train.csvread validation.csvread test.csvtest dataset will further split for model deploymentsaved as"real-life-data"join with test table CSV Reader Partitioning Keras NetworkLearner Train DataTransformation Validation DataTransformation Model Architecture Scorer Rule Engine Keras NetworkExecutor Keras NetworkWriter CSV Writer Partitioning CSV Writer CSV Writer CSV Reader CSV Reader CSV Reader Partitioning CSV Writer Joiner OFFLINE MODEL TRAINING- Model training with feedforward neural network- Data is normalize during the training to get more accurate predicition DATA PARTITIONING- Data partitioning to split data to train, validation and test set before undergo model training.- Data split ratio are:- 1. train:validation : 80:20 2. validation:test : 50:50- Save the csv files in dedicated folder particularly in the same folder of the project Dataset:https://www.kaggle.com/datasets/dhanushnarayananr/credit-card-fraud read csv filesplit train:validation(80:20)Model trainingconvert input data to 3-D arrayconvert input datato 3-D arrayscore the actual fraudto predicted fraudconvert prediction to(>=0.5 to 1)(<0.5 to 0)Evaluate thetrained modelwith test.csvsave trained modelfor deploymentwrite test.csvsplit validation:test(50:50)save validation.csvsave test.csvread train.csvread validation.csvread test.csvtest dataset will further split for model deploymentsaved as"real-life-data"join with test table CSV Reader Partitioning Keras NetworkLearner Train DataTransformation Validation DataTransformation Model Architecture Scorer Rule Engine Keras NetworkExecutor Keras NetworkWriter CSV Writer Partitioning CSV Writer CSV Writer CSV Reader CSV Reader CSV Reader Partitioning CSV Writer Joiner

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Created by: suhaimi
Created at: 2022-06-24
On NodePit since: 2022-08-21
Last update: 2024-11-17
Created with KNIME version: v4.6.1
Tags: Deep LearningClassification

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