In this machine learning project, you will use the video clip of an IPL match played between CSK and RCB to forecast key performance indicators like the number of appearances of a brand logo, the frames, and the shortest and longest area percentage in the video.
The project will use rasa NLU for the Intent classifier, spacy for entity tagging, and mongo dB as the DB. The project will incorporate slot filling and context management and will be supporting the following intent and entities. Intents : product_info | ask_price|cancel_order Entities : product_name|location|order id The project will demonstrate how to generate data on the fly, annotate using framework and how to process those for different pieces of training as discussed above .
This project will cover the understanding of Apache Spark with main focus on one of its components, Spark SQL. We will understand how Spark and Spark SQL works, its internal functioning, its capabilities and advantages over other data processing tools. We are going to take up one business problem in the area of Supply Chain. Our tech stack will be Databricks and the latest Spark 3.0 for this project. We will use Spark SQL to understand the business data and generate insights from it which must help us frame a solution for our business problem.
In this spark project, we will continue building the data warehouse from the previous project Yelp Data Processing Using Spark And Hive Part 1 and will do further data processing to develop diverse data products.