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Deploying the Diabetes predictor

Diabetes Prediction - Deployement

The deployment phase in machine learning is the stage where a trained model is put into production and used to make predictions on new data. This phase involves taking the model that was developed during the training phase and integrating it into a larger system or application.
Notice the three basic data prep steps: missing value imputation, type conversion, and outlier.

ClassifierTo enable its reuse at any time, the trained model was saved and reloaded in this workflow. InducerNode used to create a prediction model from training data. The resulting model can then be used to make predictions on new data. Data PrepocessingData preprocessing has been performed to adaptthe data to the machine learning model used.Specifically, some variables have been modifiedand missing values have been removed to makethe data consistent with the model's specifications. Upload your Test set here Analysis on predictorsIn the last part of the workflow, the LogLossmetric cannot be used as the target variable isnot available. However, a visual analysis of themodel's predictions was carried out to evaluatethe probability of diabetes. To do this, a DataAppwas used to explore and visualize the data inorder to identify possible diabetes cases obtainedfrom the model. Diabetes Prediction - Deployement The deployment phase in machine learning is the stage where a trained model is put into production and used to make predictions on new data. This phase involvestaking the model that was developed during the training phase and integrating it into a larger system or application.Notice the three basic data prep steps: missing value imputation, type conversion, and outlier.Input attributes type rulesString: Sex, HighChol, CholCheck, BMI, Smoker, HeartDiseaseorAttack, PhysActivity, Fruits, Veggies, HyAlcoholConsump, DiffWalk, Hypertension, Stroke, DiabetesNumber (Integer): age, GenHlth, MentHlth, PhysHlth Read new dataGeneratepredictionsCleaning test data to achieve better performanceRead modelAnalysis on diabetes predictionsExcel Reader Gradient BoostedTrees Predictor VariableTransformation Model Reader Analysis ClassifierTo enable its reuse at any time, the trained model was saved and reloaded in this workflow. InducerNode used to create a prediction model from training data. The resulting model can then be used to make predictions on new data. Data PrepocessingData preprocessing has been performed to adaptthe data to the machine learning model used.Specifically, some variables have been modifiedand missing values have been removed to makethe data consistent with the model's specifications. Upload your Test set here Analysis on predictorsIn the last part of the workflow, the LogLossmetric cannot be used as the target variable isnot available. However, a visual analysis of themodel's predictions was carried out to evaluatethe probability of diabetes. To do this, a DataAppwas used to explore and visualize the data inorder to identify possible diabetes cases obtainedfrom the model. Diabetes Prediction - Deployement The deployment phase in machine learning is the stage where a trained model is put into production and used to make predictions on new data. This phase involvestaking the model that was developed during the training phase and integrating it into a larger system or application.Notice the three basic data prep steps: missing value imputation, type conversion, and outlier.Input attributes type rulesString: Sex, HighChol, CholCheck, BMI, Smoker, HeartDiseaseorAttack, PhysActivity, Fruits, Veggies, HyAlcoholConsump, DiffWalk, Hypertension, Stroke, DiabetesNumber (Integer): age, GenHlth, MentHlth, PhysHlth Read new dataGeneratepredictionsCleaning test data to achieve better performanceRead modelAnalysis on diabetes predictionsExcel Reader Gradient BoostedTrees Predictor VariableTransformation Model Reader Analysis

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Created by: lucap
Created at: 2023-02-15
On NodePit since: 2023-04-21
Last update: 2024-05-01
Created with KNIME version: v4.7.0
Tags: Customer IntelligenceCIDiabetesDeploymentData visualizationDatavizPracticing Data Science

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