1. A method for creating one or more statistical classifiers for recommending one or more applications and one or more computing infrastructures for executing the one or more applications, the method comprising: extracting, by one or more processors, a first set of performance parameters corresponding to the one or more applications and the one or more computing infrastructures from a historical data pertaining to the execution of the one or more applications on the one or more computing infrastructures; selecting, by the one or more processors, a set of application-specific parameters corresponding to the one or more applications and a set of infrastructure-specific parameters corresponding to the one or more computing infrastructures, from the first set of performance parameters, based on one or more statistical techniques, wherein the one or more statistical techniques comprises at least one of Bayesian Information Criteria (BIC), Akaike Information Criteria (AIC), Deviance Information Criteria (DIC) or Gibb's Sampling Algorithm; determining, by the one or more processors: a similarity between each pair of applications from the one or more applications based on the set of application-specific parameters, a similarity between each pair of computing infrastructures from the one or more computing infrastructures based on the set of infrastructure-specific parameters, and a similarity between each combination of an application from the one or more applications and a computing infrastructure from the one or more computing infrastructures, based on the set of application-specific parameters and the set of infrastructure-specific parameters; and creating, by the one or more processors, the one or more statistical classifiers, based on the determined similarity.