The Secondary Step of the Experiment where various Time Series Models have been developed and have been applied to visualize the Results.
Objectives: The objective of the Project is to build a Machine Learning based Web Service that automatically provides the weekly forecasts of the next month for each store and each product for any Ecommerce based Retail Store. Methods/Statistical analysis: The dataset used in the Project has been imported from UCI Repository and Azure Blob Storage. Once the data is available, I will perform data pre-processing, followed by Training of Time Series Models, Feature Engineering, Training of Regression Models, and Evaluation of models and finally Deployment of Different Web-Services for Time-Series Model and Regression Model. This will be followed by a comparative study of the Regression Models and the Time Series Models that I will be using in my Project. Findings: I will be using all the Regression Models and the Time Series Models for the Project Purpose. I intend to find the relationships among the various parameters of the Dataset by analysing the effects of each of the Parameters (weather/pricing) on forecasting the weekly sales for the subsequent months. I intend to make a comparative study of all the Regression Models and find out the best model that suits my data. Additionally, I would like to make an in depth study of each of the Time Series Model and figure out the best suitable model for the Purpose. To improve the results, I would like to create a hybrid model that would incorporate the best Regression and the Time Series Model. And finally I intend to deploy the model as a Web Service that can be used as a template for fast prototyping and deployment of machine learning solutions.