Machine Learning For Insurance: Predict Claim & Assess Risk

Predict insurance claim amount, build insurance risk assessment model, and detect claim fraud with machine learning

What you'll learn

Learn about machine learning applications in insurance and its technical limitations

Learn how to predict insurance claim amount using XGBoost

Learn how to build insurance risk assessment model using Logistic Regression

Learn how to detect insurance claim fraud using Support Vector Machine

Learn how to predict insurance claim amount using LightGBM

Learn how to build insurance risk assessment model using Random Forest Classifier

Learn how to detect insurance claim fraud using K Nearest Neighbor

Learn how to test machine learning model using synthetic data

Learn how to handle class imbalance using Synthetic Minority Oversampling Technique

Learn how to conduct feature importance analysis using Random Forest Regressor

Learn how to analyze relationship between age, gender, and insurance claim amount

Learn how to find correlation between body mass index and blood pressure with insurance claim amount

Learn how to find correlation between smoking status and insurance claim amount

Learn how insurance risk assessment models work. This section covers data preprocessing, feature selection, train test split, model training, and assessing risk

Learn how to clean dataset by removing missing values and duplicates

Requirements

No previous experience in machine learning is required

Basic knowledge in Python and insurance

Description

Welcome to Machine Learning for Insurance: Predict Claim & Assess Risk course. This is a comprehensive project based course where you will learn how to build insurance risk assessment models, predict insurance claim amounts, and detect insurance claim fraud using models like XGBoost, LightGBM, Random Forest, Logistics Regression, SVM, and KNN. This course is a perfect combination between machine learning and risk assessment, making it an ideal opportunity to level up your data science skills while improving your technical knowledge in insurance business. In the introduction session, you will learn about machine learning applications in insurance and also its technical limitations. Then, in the next section you will learn how insurance risk assessment models work. This section will cover data collection, data preprocessing, feature selection, splitting data into training and testing sets, model selection, model training, assessing risk, and model evaluation. Afterward, you will download insurance datasets from Kaggle, it is a platform that provides many high quality datasets from various industries. Once everything is ready, we will start the project, firstly we will clean the dataset by removing missing values and duplicates, once the data is clean and ready to use, we will start exploratory data analysis, in the first section, we are going to analyze the relationship between age, gender, and insurance claim amount, which will enable us to identify demographic patterns in claim behavior and better understand how different age groups and gender identities influence the likelihood and size of insurance claims. Following that, we are going to find the correlation between body mass index and blood pressure with insurance claim amount, which will allow us to quantify how health indicators relate to the amount claimed, providing valuable insights into health related risk factors. Afterward, we are going to investigate the correlation between smoking status and insurance claim amount, which will help us to evaluate how lifestyle choices such as smoking contribute to higher insurance claim amounts and increased risk profiles.Then after that, we are going to conduct feature importance analysis using a Random Forest model, which will allow us to identify and rank the most influential features affecting insurance claim amounts, enabling more focused and efficient model development. Next, we are going to predict insurance claim amounts using XGBoost and LightGBM regressors, which will enable us to leverage the power of machine learning to make accurate predictions and capture complex interactions between input features and claim amounts. Following that, we are going to build an insurance risk assessment model using Logistic Regression and Random Forest classifiers, which will enable us to classify individuals based on risk levels, allowing insurance companies to improve underwriting strategies and make informed decisions. Then, we are also going to detect insurance claim fraud using Support Vector Machines and K Nearest Neighbors, which will enable us to identify unusual claim patterns, flag suspicious activity, and reduce financial losses due to fraudulent claims. Lastly, at the end of the course, we are going to test our machine learning models using synthetic data generated by ChatGPT, which will allow us to validate model robustness in diverse scenarios by formatting synthetic datasets into CSV files and uploading them to a Gradio user interface.Before getting into the course, we need to ask this question to ourselves, why should we integrate machine learning to insurance? Well, here is my answer, machine learning enables insurance companies to make faster, more accurate decisions, reducing costs and improving operational efficiency. By predicting risks and detecting potential fraud more effectively, insurance businesses can enhance profitability and maintain competitive advantage in a rapidly evolving market.Below are things that you can expect to learn from this course:Learn about machine learning applications in insurance and its technical limitationsLearn how insurance risk assessment models work. This section covers data collection, data preprocessing, feature selection, splitting data into training and testing sets, model selection, model training, assessing risk, and model evaluationLearn how to clean dataset by removing missing values and duplicatesLearn how to analyze relationship between age, gender, and insurance claim amountLearn how to find correlation between body mass index and blood pressure with insurance claim amountLearn how to find correlation between smoking status and insurance claim amountLearn how to conduct feature importance analysis using Random Forest RegressorLearn how to predict insurance claim amount using XGBoostLearn how to predict insurance claim amount using LightGBMLearn how to build insurance risk assessment model using Logistic RegressionLearn how to build insurance risk assessment model using Random Forest ClassifierLearn how to detect insurance claim fraud using Support Vector MachineLearn how to detect insurance claim fraud using K Nearest NeighborLearn how to test machine learning model using synthetic dataLearn how to handle class imbalance using Synthetic Minority Oversampling Technique

Overview

Section 1: Introduction to the Course

Lecture 1 Introduction

Lecture 2 Table of Contents

Lecture 3 Whom This Course is Intended for?

Section 2: Tools, IDE, and Datasets

Lecture 4 Tools, IDE, and Datasets

Section 3: Machine Learning Applications in Insurance

Lecture 5 Machine Learning Applications in Insurance

Section 4: How Insurance Risk Assessment Model Works?

Lecture 6 How Insurance Risk Assessment Model Works?

Section 5: Finding & Downloading Insurance Datasets From Kaggle

Lecture 7 Finding & Downloading Insurance Datasets From Kaggle

Section 6: Uploading Insurance Dataset to Google Colab IDE

Lecture 8 Uploading Insurance Dataset to Google Colab IDE

Section 7: Cleaning Dataset by Removing Missing Values & Duplicates

Lecture 9 Cleaning Dataset by Removing Missing Values & Duplicates

Section 8: Analyzing Relationship Between Age, Gender, and Insurance Claim Amount

Lecture 10 Analyzing Relationship Between Age, Gender, and Insurance Claim Amount

Section 9: Finding Correlation Between BMI and Blood Pressure with Insurance Claim Amount

Lecture 11 Finding Correlation Between BMI and Blood Pressure with Insurance Claim Amount

Section 10: Finding Correlation Between Smoking Status and Insurance Claim Amount

Lecture 12 Finding Correlation Between Smoking Status and Insurance Claim Amount

Section 11: Conducting Feature Importance Analysis with Random Forest Regressor

Lecture 13 Conducting Feature Importance Analysis with Random Forest Regressor

Section 12: Predicting Insurance Claim Amount with XGBoost

Lecture 14 Predicting Insurance Claim Amount with XGBoost

Section 13: Predicting Insurance Claim Amount with LightGBM

Lecture 15 Predicting Insurance Claim Amount with LightGBM

Section 14: Building Insurance Risk Assessment Model with Logistic Regression

Lecture 16 Building Insurance Risk Assessment Model with Logistic Regression

Section 15: Building Insurance Risk Assessment Model with Random Forest Classifier

Lecture 17 Building Insurance Risk Assessment Model with Random Forest Classifier

Section 16: Detecting Insurance Claim Fraud with Support Vector Machine

Lecture 18 Detecting Insurance Claim Fraud with Support Vector Machine

Section 17: Detecting Insurance Claim Fraud with K Nearest Neighbour

Lecture 19 Detecting Insurance Claim Fraud with K Nearest Neighbour

Section 18: Testing Machine Learning Model with Synthetic Data

Lecture 20 Testing Machine Learning Model with Synthetic Data

Section 19: Conclusion & Summary

Lecture 21 Conclusion & Summary

Machine learning engineers who are interested in building insurance risk assessment models and predicting claim amount,Insurance analysts and actuaries who are interested in leveraging machine learning into their workflow