The various embodiments herein provide a method and system for automated model building, validation and selection of best performing models. The method comprises of selecting a dataset available for modeling from one or more external data sources, dividing the dataset into at least three parts, selecting one or more modeling methods along with associated parameters ranges based on the model to be built, identifying one or more fitness functions against which the models need to be evaluated, generating a plurality of model building experiment variation that can be run utilizing a first part of the dataset, obtaining values of the fitness function for the different modeling method experiments on the first part of the dataset, obtaining a second fitness value by re-evaluating the generated models from the different experiments on a second part of the dataset to evaluate the model performance on unseen data during training, selecting, one or more best performing models by comparing the first fitness value and the second fitness value, generating fitness values by an algorithm processing module using selected one or more best performing models on the remaining datasets, and selecting the best model from the conducted evaluation.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An automated method of generating and selecting models, the method comprising steps of: selecting, by a data extraction module, a dataset available for modeling from one or more external data sources; preparing, by the data preparation module data for modeling by processing the raw data obtained from one or more external data sources as per the requirements of the scientist/modeler; dividing, by a data management module, the prepared dataset into at least three parts; selecting, by an algorithm management module, one or more modeling methods along with associated parameters ranges based on the model to be built; identifying, by a parameter design module, one or more fitness functions against which the models need to be evaluated; generating, by the algorithm management module, a plurality of model building experiment variation that can be run utilizing a first part of the dataset; obtaining, by an algorithm processing module values of the fitness function for the different modeling method experiments on the first part of the dataset; obtaining, by the algorithm processing module, the value of the same or different second fitness function by running the generated models from the different experiments on a second part of the dataset to evaluate the model performance on unseen data during training; selecting, by a model validation and selection module, one or more best performing models by comparing the first fitness value and the second fitness value; generating, by the algorithm processing module, values of the same or different fitness function using selected one or more best performing models on the remaining datasets; and comparing, by a model validation and selection module, the various fitness functions to select the best model. 2. The method of claim 1, wherein the dataset is obtained by merging data from one or more external data sources using a database connector module. 3. The method of claim 1, wherein the first part of the dataset is training data. 4. The method of claim 1, wherein the second part of the dataset is testing data. 5. The method of claim 1, wherein the third and more parts of the dataset comprises of the validation data. 6. The method of claim 1, wherein the model is selected using Pareto front. 7. The method of claim 1, wherein the selection of best performing model is performed iteratively to obtain one or more best performing models. 8. An automated system for generating and selecting models, the system comprises of: a database connector module that creates a dataset by receiving data from one or more external data sources and merging them; a data extraction module that selects the dataset available for modeling from one or more external data sources; a data preparation module that processes the raw data for modeling based on requirements; a data management module that divides the dataset into at least three parts; an algorithm management module adapted for: selecting one or more modeling methods based on the model to be built; generating a plurality of model building experiment variation that can be run utilizing a first part of the dataset; a parameter design module adapted for: designing algorithm parameters, input data parameters, and fitness function design parameters; an algorithm processing module adapted for: obtaining the value of the fitness function by running different modeling method experiments on the first part of the dataset and evaluating their final performance for each of the runs; obtaining by the algorithm processing module the value of the same or different fitness function by running the generated models from the different experiments on the second part of the dataset to evaluate the fitness on unseen data during training; and a model validation and selection module adapted for: selecting one or more best performing models by comparing the first fitness value and the second fitness values; obtaining the same or different fitness function by using the algorithm processing module to run the selected one or more best performing models on the remaining validation dataset; and evaluating the one or more best performing models using the various fitness functions in phases to select the best model. 9. One or more computer-readable media having computer-usable instructions stored thereon for performing a method of the automated selection of models, the method comprising steps of: selecting, by a data extraction module, a dataset available for modeling from one or more external data sources; preparing, by a data preparation module, the modeling dataset by processing the raw data as per requirements of the scientist dividing, by a data management module, the dataset into at least three parts; selecting, by an algorithm management module, one or more modeling methods along with associated parameters ranges based on the model to be built; generating plurality of model building experiment variation that can be run utilizing a first part of the dataset; identifying, by a parameter design module, one or more fitness functions against which the models need to be evaluated; obtaining, by an algorithm processing module, the value of the fitness function by running different modeling method experiments on the first part of the dataset and evaluating their final performance for each of the runs; obtaining, by the algorithm processing module, the value of the same or different fitness function by running the generated models from the different experiments on a second part of the dataset to evaluate the fitness on unseen data during training; selecting, by a model validation and selection module, one or more best performing models by comparing the first fitness value and the second fitness value; obtaining, by the algorithm processing module, value of the same of different fitness function by running one or more best performing models on the remaining validation dataset; and evaluating, by the model validation and selection module, one or more best performing models by comparing the values of the various fitness functions to select the best model.