Estimating churners before they discontinue using a product or service is extremely important. In this ML project, you will develop a churn prediction model in telecom to predict customers who are most likely subject to churn.
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 .
In this Databricks Azure project, you will use Spark & Parquet file formats to analyse the Yelp reviews dataset. As part of this you will deploy Azure data factory, data pipelines and visualise the analysis.
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.