An approach to Machine Learning and Data Analytics Lifecycle

During this stage a framework of statistics is explored for data collection, data cleansing, data collection, data validation, and data exploration. Descriptive analytic methods built with simple, univariate analysis, data visualizations, data insights, and derived variables. The predictive modeling is finalized by identifying number of methods and techniques, identifying the best fit model, interpretation of the model, and finally the model is deployed to the production during the operationalization phase of the data analytics project by involving all the stakeholders leveraging several machine learning, deep learning algorithms in R language for the corporations. The growing complexity of the big data and the emerging technical landscape of connected data platform bring some complex challenges to the organization to support the executive decision-support systems. Several consulting firms adopt the best practices with their methodology and accelerators for implementing the data science projects leveraging data analytics lifecycle best practices.