Reference no: EM132220363
Assignment - Project: Big Data Use Case For Telecommunication Fraud Detection
The Objective:
Use data analytics to distinguish the real local calls and the fake locals (interconnect bypass or SIM Box calls)
Use the analytics to determine the revenue loss to the service provider and government
Use the analytics to design solutions to block the SIM Box Calls
Use the analytics to determine locations of primary activity of SIM box equipment.
Determine the behavior of SIM Box call receivers in terms of call drops, complains, completion rates and the related.
Using big data to determine where operators must concentrate their efforts in fighting sim fraud activities.
CAPSTONE PROJECT: FINAL SUBMISSION
Final deployment of the project
List your major learnings from this project
Major tools used
Final number of data points used in the project
Final number of data points used for training.
Final number of data points used for testing / validation.
Mention cross validation techniques used, if any.
Data sourced from.
Methods used to source the data.
Challenges faced to source data.
Describe the efforts spent and challenges overcome during data preparation, and the steps used.
Outline the major steps used to implement the project.
List the major machine learning techniques used / implemented in your project.
Mention the major challenges faced and overcome during model creation.
List the techniques used to explore the data.
List the outcome of data exploration.
List any unsupervised learning technique used during data exploration.
How many features were finally identified to create the model.
Describe your Feature Engineering steps, if any.
List and describe any feature reduction techniques used.
Recommendations for future work, if any.
Attachment:- Assignemnt Files.rar
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