With AutoBrewML Framework the time it takes to get production-ready ML models with great ease and efficiency highly accelerates.
Project description
Traditional machine learning model development is resource-intensive, requiring significant domain/statistical knowledge and time to produce and compare dozens of models. With automated machine learning, the time it takes to get production-ready ML models with great ease and efficiency highly accelerates. However, the Automated Machine Learning does not yet provide much in terms of data preparation and feature engineering. The AutoBrewML tries to solve this problem at scale as well as simplifies the overall process for the user. It leverages the Automated ML coupled with components like Data Profiler, Data Sampler, Data Cleanser, Anomaly Detector which ensures quality data as a critical pre-step for building the ML model. With AutoBrewML the time it takes to get production-ready ML models with great ease and efficiency highly accelerates
Modules
Acquisition_DataTypeConversion : Data Acquisition & Transformation
BrewDataProfiler : Exploratory Data Analysis
BrewDataSampling : Data Sampling
Random Sampling
Stratified Sampling
Systematic Sampling
Cluster OverSampling (with SMOTE)
BrewDataCleanser : Data Cleansing
BrewAnomalyDetection : Anomaly Detection
BrewTrainTestSplit : Train-Test Split the data in a given ratio
BrewFeatureSelection : Selection of Most Important Features before Modelling
BrewAutoML_Classifier : Auto trigger and choose ML Model for Classification
BrewAutoML_Regressor : Auto trigger and choose ML Model for Regression
BrewAutoML_TimeSeries : Auto trigger and choose ML Model for Time series Forecasting
Responsible AI Guidelines
BrewFairnessEvaluator : Evaluate the Fairness of a Model with respect to metrics across various cohorts of a sensitive feature
BrewDisparityMitigation : Mitigate the Bias observed in above Evaluator
Prerequisites: - Python version >=3.8.0
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