The model which we have presented is a Linear Regression Model. In the results above we see that predictions can be done on the basis of the data available and is approximately accurate. An accurate forecast is very important for the demand planning team. The data used in this project and building the model is using the sales-in data for different stores. The important factor to be considered is the stability of the model and removing the game-playing. A community version of a platform is used to build the model. Linear Regression model is developed in pyspark. After the results are generated, dataframe of results is validated and generated and is sent backto the Azure SQL database to be used in Power BI.In the future work, different techniques will be considered and researched. Time-Series and Machine Learning to be built in one platform and check how the minimization of mse produces the forecast. The predictions can be hyper parameterized to give more accurately tuned results. Also, in the PowerBI report more measures and visualizations can be made on basis of individual’s thought process.
Punit Gupta is an Associate Professor in the Department of Computer and Communiction Engineering, Manipal University Jaipur, Jaipur, Rajisthan, India.Has got M.Tech. Degree in Computer Science and Engineering from Jaypee Institute of Information Technology (Deemed University) in 2012 on "Trust Management in Cloud Computing".
Harshit Ladia,Kabir Kakkar,Kriti Rai
Yogesh Agrawal, Rishika Mamgain
Number of Pages:
LAP LAMBERT Academic Publishing
Machine Learning, prediction, Azure, Cloud