Fraud Detection and Analysis for Insurance Claim using Machine Learning | Python IEEE Project
Fraud Detection and Analysis for Insurance Claim using Machine Learning | Python Final Year IEEE Project 2022 - 2023.
Buy Link: https://bit.ly/3RZInjk
(or)
To buy this project in ONLINE, Contact:
Email: jpinfotechprojects@gmail.com,
Website: https://www.jpinfotech.org
Project Title: Fraud Detection and Analysis for Insurance Claim using Machine Learning.
Implementation: PYTHON.
Algorithm / Model Used: Random Forest Classifier.
Web Framework: Flask.
Cost (In Indian Rupees): Rs.5000/
IEEE Base Paper Abstract:
Insurance Company working as commercial enterprise from last few years has been experiencing fraud cases for all type of claims. Amount claimed by fraudulent is significantly huge that may causes serious problems, hence along with government, different organization also working to detect and reduce such activities. Such frauds occurred in all areas of insurance claim with high severity such as insurance claimed towards auto sector is fraud that widely claimed and prominent type, which can be done by fake accident claim. So, we aim to develop a project that work on insurance claim data set to detect fraud and fake claims amount. The project implements machine learning algorithms to build model to label and classify claim. Also, to study comparative study of all machine learning algorithms used for classification using confusion matrix in term soft accuracy, precision, recall etc. For fraudulent transaction validation, machine learning model is built using PySpark Python Library.
REFERENCE:
Abhijeet Urunkar; Amruta Khot; Rashmi Bhat; Nandinee Mudegol, “Fraud Detection and Analysis for Insurance Claim using Machine Learning”, 2022 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES), IEEE CONFERENCE, 2022.
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Fraud Detection and Analysis for Insurance Claim using Machine Learning | Python Final Year IEEE Project 2022 – 2023.
Buy Link: https://bit.ly/3RZInjk
(or)
To buy this project in ONLINE, Contact:
Email: jpinfotechprojects@gmail.com,
Website: https://www.jpinfotech.org
Project Title: Fraud Detection and Analysis for Insurance Claim using Machine Learning.
Implementation: PYTHON.
Algorithm / Model Used: Random Forest Classifier.
Web Framework: Flask.
Cost (In Indian Rupees): Rs.5000/
IEEE Base Paper Abstract:
Insurance Company working as commercial enterprise from last few years has been experiencing fraud cases for all type of claims. Amount claimed by fraudulent is significantly huge that may causes serious problems, hence along with government, different organization also working to detect and reduce such activities. Such frauds occurred in all areas of insurance claim with high severity such as insurance claimed towards auto sector is fraud that widely claimed and prominent type, which can be done by fake accident claim. So, we aim to develop a project that work on insurance claim data set to detect fraud and fake claims amount. The project implements machine learning algorithms to build model to label and classify claim. Also, to study comparative study of all machine learning algorithms used for classification using confusion matrix in term soft accuracy, precision, recall etc. For fraudulent transaction validation, machine learning model is built using PySpark Python Library.
REFERENCE:
Abhijeet Urunkar; Amruta Khot; Rashmi Bhat; Nandinee Mudegol, “Fraud Detection and Analysis for Insurance Claim using Machine Learning”, 2022 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES), IEEE CONFERENCE, 2022.
#python #pythonprojects #machinelearningproject #pythonprogramming #pythonprojectforbeginners #pythonprojectideas #pythonmachinelearning #machinelearning #machinelearningpython #finalyearproject #ieeeprojects #finalyearprojects #datascience #datascienceproject #artificialintelligenceproject #projects #deeplearning #deeplearningproject #computerscienceproject #deeplearningprojects #majorprojects #academicprojects #majorproject
