A model was built to predict the total insurance claim amount payable by the insurance company using machine learning techniques such as regression in python.

This is a note book of exploratory data analysis on cross selling of health insurance customers on vehicle insurance product and using machine learning to predict whether a customer is interested or not in vehicle insurancen

Developed a machine learning–based fraud detection system to identify potentially fraudulent vehicle insurance claims using historical insurance data. The project applies data preprocessing, exploratory data analysis, and classification models to distinguish between legitimate and fraudulent claims.

AI-Visionary 車両画像判定AI

A company has customer data that contains 8 columns of customer details and another table having name customer_policy data contains the policy details of the customer. The company Intends to offer some discounts in premium for certain customers.

Vehicle Insurance Claim Fraud Detection using decision-trees, random-forest and logistic regression.

To predict whether the Health insurance policy-holders (customers) from past year will also be interested in Vehicle Insurance provided by the company.

AWS‑powered end‑to‑end MLOps pipeline for vehicle insurance data ingestion, training & deployment

A complete Vehicle Insurance Management System built to streamline policy management, claims tracking, and customer information using efficient and scalable methods.

This is a project to demonstrate a robust MLOps pipeline to manage the lifecycle of a "vehicle insurance prediction" machine learning model

Vehicle Insurance Fraud Detection using Machine Learning to classify insurance claims as fraudulent or genuine. The project involves data preprocessing, exploratory data analysis, handling class imbalance, and building classification models like Logistic Regression, Decision Tree, and Random Forest to identify fraud patterns and improve detection.

ClaimScope is a vehicle insurance claims portfolio intelligence platform that identifies warranty concentration, geographic imbalance, and anomalous claims using explainable analytics. Built on DuckDB and IsolationForest, it turns raw claims data into actionable triage signals — no actuarial black boxes, no LLM fabrication.