The data contains various features like the meal type given to the student, test preparation level, parental level of education, and students' performance in Math, Reading, and Writing. Feel free to share your thoughts in the comment section and you can also connect with me in Linkedin.Thank You. We use cookies on Kaggle to deliver our services, analyze web traffic, and improve your experience on the site. The dataset that I am using for the task of health insurance premium prediction is collected from Kaggle. A person who has taken a health insurance policy gets health insurance cover by paying a particular premium amount. The values in this column are mentioned as 0 and 1 where 0 means not bought and 1 means bought. Well, older patients with a higher bmi who smoke are charged the most out of anyone in our data set. how is oration performed in ancient times? Hose burnt oil in kitchen right shoulder strain. This dataset is available in Kaggle. The features are anonymized into cat1-cat116 and cont1-cont14, effectively masking interpretation for the dataset and nullifying any industry knowledge advantage. I'm a writer and data scientist on a mission to educate others about the incredible power of data. topic page so that developers can more easily learn about it. http://dyzz9obi78pm5.cloudfront.net/app/image/id/560ec66d32131c9409f2ba54/n/Auto_Insurance_Claims_Sample.csv, https://www.kaggle.com/c/allstate-claims-severity, The data from "Data: A Collection of Problems from Many Fields for the Student and Research Worker" by Andrews and Herzberg, http://www.statsci.org/data/general/motorins.txt, Building a safer community: Announcing our new Code of Conduct, Balancing a PhD program with a startup career (Ep. This data can be downloaded from the following websites for study and research: While trying to perform competitively in the Kaggle was tough. http://www.statsci.org/data/general/motorins.txt. Above, youll noticed I loaded packages such as parsnip and recipes. (Link mentioned at the end of this blog). MACHINE LEARNING IN INSURANCE - Accenture.com. The data set source for this model collected from Insurance collision reports, personal insurance, and car models. This is how you can easily explore every column of this data. Kaggle. To keep things simple, we are not going to use cross-validation to find the optimal k. Instead, we are just going to say k = 10. Chart 1: Feature importance plot top features for the lightGBM model: Chart 2: Feature importance plot top features for the XGBoost model: The winner is a Senior Data Scientist working at PRISM, the biggest insurance risk sharing pool for public entities in California. You signed in with another tab or window. 2017, cloud.google.com/blog/big-data/2017/03/using-machine-learning-for-insurance-pricing-optimization. Claims should be carefully evaluated by the insurer, which may take time. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. The data we chose was released by Kaggle, an open-source data site. Some of the key feature engineering steps performed by the winning solution are summarised below. This project presents a code/kernel used in a Kaggle competition promoted by Data Science Academy in December of 2019.. Career Path countdown for new role as a Claims Adjuster Trainee with Progressive Insurance. To learn more, see our tips on writing great answers. Insurance Fraud-datection in early stages using Machine learning. topic, visit your repo's landing page and select "manage topics.". There are, however, a pattern that appears to be two levels coming off of that baseline. The combined model is therefore stronger than the individual components. However, a graph of the contributions of the quantitative variables above to the first two components depicts three different groups that may be correlated with each other: a group formed by the upper right quadrant, the lower right quadrant, and the lower left quadrant. Also, after the Kaggle test dataset was dummified, we noticed that there were variables that were present in the test set that did not exist in the training set. left vs. right, high vs. low), multiple body parts (e.g. Rationale for sending manned mission to another star? (M)arried, (S)ingle, (U)nknown. By Arta Seyedian Medical Cost Personal Datasets Insurance Forecast by using Linear Regression Link to Kaggle Page Link to GitHub Source Around the end of October 2020, I attended the Open Data Science Conference primarily for the workshops and training sessions that were offered. Students Performance in Exams. The R^2 would suggest that our regression has a fit of ~82%, although a high R^2 doesnt always mean the model has a good fit and a low R^2 doesnt always mean that a model has a poor fit. This article discusses how to write a simple console program for Insurance price prediction using ML.NET. Below is the link to articles published by the winner on Medium: The Juypter Notebook for the winning solution can be viewed on GitHub. The aim of this competition is to build a predictive model that can predict the probability that a particular claim will be approved immediately by or not insurance company based on the resources available at the beginning of the . By having a dataset given to us in a clean format, the process of taking data and churning out predictions was accelerated greatly. The premium amount of a health insurance policy depends on person to person as many factors affect the premium amount of a health insurance policy. The intuition there was to having the very different models cancel out each others errors, while focusing more on the higher scoring models. sign in You must proceed your writing. Moreover, we lost out on attempting to interpret our dataset due to the anonymity of the variables. So here I will train the model by using the random forest regression algorithm: Now lets have a look at the predicted values of the model: So this is how you can train a machine learning model for the task of health insurance premium prediction using Python. Despite its rigor, the winning solution performed several natural language processing techniques to identify the information above. I hope you liked this article on health insurance premium prediction with machine learning using Python. This is a binary classification problem, but instead of predicting classes, I am predicting probabilities. We first split our data into training and testing sets. DateReported Date that accident was reported. It only takes a minute to sign up. Thanks for contributing an answer to Open Data Stack Exchange! df.drop('region',axis=1,inplace=True) newdf= pd.concat([df,df_region],axis=1) # as now we have to normalize the data, so we concatenate the columns on which feature engineering was performed. Data Science Academy Kaggle Competition. The model resulted in an average validation mean absolute error of 1134 and a leadership board score of 1113 that put us in the top 25%. In the section below, I will take you through the task of Insurance Prediction with Machine Learning using Python. Boosted tree models allow us to see how important different features are in making predictions across the dataset. To improve on this, we decreased the number of features down to the square root of the number of features. Transform BMI such that it will have mean zero and variance one. In this case, we are breaking down the original data into k classes, and within each of those classes we will re-using MLP to build a predictive model. We participated in the Allstate Insurance Severity Claims challenge, an open competition that ran from Oct 10 2016 - Dec 12 2016. In this article, I will walk you through the task of Insurance Prediction with machine learning using Python. Using Machine Learning for Insurance Pricing Optimization | Google Cloud Blog. Google, Google Cloud Platform, 19 Mar. In this article, I will take you through the task of health insurance premium prediction with machine learning using Python. If nothing happens, download GitHub Desktop and try again. Historical data is classified into two classes, 0 and 1. This data set is sourced from a book titled Machine Learning with R by Brett Lantz. However, because the types of customers are so diverse and the correlation between the characteristics is not obvious, the use of simple statistics cannot enable insurance companies to make accurate judgments about customers. Working closely with their actuarial and data analytics teams, she develops predictive models to enhance actuarial reserving, ratemaking, and other related business problems. This article provides a comprehensive explanation for stacking. Only 4.08% of the variance in our dataset can be explained from the first five components, which are the highest contributors to the percentage of variance explained. Python Machine Learning: Machine Learning and Deep Learning with Python, Scikit-Learn, and TensorFlow. Inspiration Identify flood damage claims caused by surface water flooding or fluvial flooding with an ArcGIS Python Toolbox. The model gave a CV score of 1721 and a LB score of 1752. could you please share it? Here our task is to train a machine learning model to predict whether an individual will purchase the insurance policy from the company or not. This dataset contains 7 features as shown below: age: age of the policyholdersex: gender of policyholder (female=0, male=1)BMI: Body mass index, providing an understanding of the body, weights that are relatively high or low relative to height, objective index of body weight (kg / m ^ 2) using the ratio of height to weight, ideally 18.5 to 25steps: average walking steps per day of the policyholderchildren: number of children/dependents of the policyholdersmoker: smoking state of policyholder (non-smoke=0;smoker=1)region: the residential area of the policyholder in the US (northeast=0, northwest=1, southeast=2, southwest=3)charges: individual medical costs billed by health insurance. The aim of this competition is to build a predictive model that can predict the probability that a particular claim will be approved immediately by or not insurance company based on the resources available at the beginning of the process, helping the insurance company to accelerate the payment release process and thus provide better service to the client. Using less features forces the model to consider different features per split, which ended up improving the models MAE score to 1188. After graduating, Regan worked at State Street Global Advisors on business intelligence systems, performing Luke is an aspiring data scientist with a background in Applied Mathematics, Mathematical Modeling, and Programming. All rights reserved. We create dummy variables (step_dummy) for all nominal predictors, so smoker becomes smoker_yes and smoker_no is implied through omission (so if a row has smoker_yes == 0) because some models cannot have all dummy variables present as columns. Many types of insurance exist today and there are so many companies that offer insurance services. In particular, how the winner derived features, built and evaluated the model which led to the best performance. View All Professional Development Courses, Designing and Implementing Production MLOps, Natural Language Processing for Production (NLP), An Ultimate Guide to Become a Data Scientist, EDA and machine learning Ames housing price prediction project, Machine learning Uber vs. Lyft price prediction modeling, Meet Your Machine Learning Mentors: Kyle Gallatin, NICU Admissions and CCHD: Predicting Based on Data Analysis. Please Nature Medicine paper. It appears that the good, ol fashioned linear model beat k-Nearest Neighbors both in terms of RMSE but also R^2 across 10 cross-validation folds. That is, this feature was implicitly an indicator for claims inflation, which is a sensible driver for claims costs. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Auto Insurance | Kaggle Only after we applied neural network models as well as the method of ensembling, we were able to get to the top 2%. As you can see, we used our workflow object as our input. I hope you liked this article on the task of Insurance Prediction with Machine Learning using Python. We modeled an eight-level ordinal life insurance-risk response on a pre-cleansed and pre-normalized Prudential data set consisting of 59,381 observations and 128 predictors of which 13 were continuous, 5 discrete, and the remainder categorical. This dataset contains 1338 rows of insured data, where the Insurance charges are given against the following attributes of the insured: Age, Sex, BMI, Number of Children, Smoker and Region. Next we have the number of smokers vs non-smokers. Click here to navigate to Kaggle website. Moreover, we lost out on attempting to interpret our dataset due to the anonymity of the variables. Stacking is a popular technique for squeezing extra model performance needed to win Kaggle competitions. Its best do do transformations on outcomes before creating a recipe. insurance-claims Star Here are 53 public repositories matching this topic. The challenge behind fraud detection in machine learning is that frauds are far less common as compared to legit insurance claims. From plots below (Regression coefficients progression for lasso paths , Mean squared error on each fold), the best alpha value is 5.377 which could help reduce the number of features in the dummy dataset from 1099 to 326. The libraries involved to perform the analysis are - python's pandas, numpy, seaborn, matplotlib, missingno. Add a description, image, and links to the To preprocess the data, we first wanted to remove any highly correlated variables. Training the model on an increasing number of trees improved the predictive power, but took more computational power. It took a while for the team to build this communication and pipeline up, but eventually we were able to share knowledge and get multiple workflows running. DependentsOther The number of dependants excluding children. However, this did allow us to focus on practicing fitting and training models. According to Kaggle, There are 26 data attributes in this model descript. As a brief introduction, tidymodels is, like tidyverse, not a single package but rather a collection of data science packages designed according to tidyverse principles. The book Data: A Collection of Problems from Many Fields for the Student and Research Worker by Andrews and Herzberg has such a data set as: Table 68.1 Third Party Motor Insurance for Sweden, 1977 (112642 We bind the resulting predictions with the actual charges found in the training data to create a two-column table with our predictions and the corresponding real values we attempted to predict. After getting the first impressions of this data, I noticed thesmokercolumn, which indicates whether the person smokes or not. a greedy search), during which it was found that the lightGBM model had better predictive power compared to XGBoost. Technology Enthusiastic Guy. To include all dummy variables, you can use one_hot = TRUE. The RMSE generated by our test data is insignificantly different from the one generated by our cross-validation! A tag already exists with the provided branch name. He is passionate about advancing data analytics in the life insurance industry. GGally is a package that facilitates the process of exploratory data analysis by automatically generating ggplots with the variables present in the input data frame to help you get a better understanding of the relationships that might exist between them. Below are the steps summarizing the whole project : 1. Posted on February 14, 2021 by rstats | LIBD rstats club in R bloggers | 0 Comments, Around the end of October 2020, I attended the Open Data Science Conference primarily for the workshops and training sessions that were offered. He obtained his Bachelors degree from Northeastern University in Computer Science. Healthcare Revenue Cycle Analysis Suite. Of all the industries rife with vast amounts of data, the Insurance market surely has to be one of the greatest treasure troves for both data scientist and insurers alike. I really need a dataset about automobile insurance claims to train and test learning algorithms. Intuitively, the free text description of the claim should provide some insight into ultimate cost. By combining the results from different models, we can average out the errors to improve our score and reduce variance in our error distribution. Second, the optimizing method resulted in a leadership board score of 1105, even lower than the first score. The derived features proved to greatly assist with model performance and explanation. In this blog, Im going to create a few ML models using Scikit-learn library and well compare the accuracy for each of them. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. An interesting observation was that the numeric part of the claim number (e.g. You signed in with another tab or window. Should convert 'k' and 't' sounds to 'g' and 'd' sounds when they follow 's' in a word for pronunciation? This included targeted string searches to identify the position of the injury, and identifying whether a word is a verb or a noun to separate out cause of injury from the body parts. cartierbraceletlove Sim, F. Se der pra fazer as duas coisas, tipo os bichinhos da imagem (que dormem de mos dadas para se equilibrarem e flutuarem melhor), melhor ainda! With our best scoring model with a MAE of 1101, we were placed at the top 2% of the leaderboard by the end of the two weeks. However, despite this bounty, much of the Insurance industry is still built around 17th century . US Health Insurance Dataset | Kaggle I will convert all categorical values to 1 and 0 first because all columns are important for training the insurance prediction model: Now lets split the data and train the model by using the decision tree classification algorithm: The model gives a score of over 80% which is not bad for this kind of problem. First, the simple average resulted in a leadership board score of 1108 already much better than our single best model. In our research, we want to use the k-means algorithm to find an optimal classification group number, that is to say, the classification group number that can make the value of MSE become the smallest. Sato, Kaz. For decades, the task of predicting claims costs, particularly in the general insurance industry, has been dominated by the use of Generalised Linear Models. Splitting Dataset into testing and training, Applying StandardScalar to X_train and y_test. XGBoost and Neural Networks are known to be strong learners, and we expected them to perform best amongst other machine learning models. A guide on setting up Guidewire Software applications fast. Exploratory Data Analysis (EDA) solution to Kaggle caravan insurance Based on the researches on the subject of car insurance, constructed machine learning models to classify customers by characteristics for insurance customers and predicted claim amount. 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Based on the modelling, and as shown in the feature importance charts below for the two models, the following features had the best predictive powers for the ultimate claims costs: Injured body parts extracted from the free-text claims descriptions seemed to inform ultimate claims costs. Looking at our validation predictions against the true values, the largest errors accumulate around the outlier points. Helping to detect fraud in Automobile Insurance Claims using Python, ML and Neo4j Graph Database. Normalization. PDF Modeling Life Insurance Risk - SAS Support ClaimDescription Free text description of the claim. In this competition my best score was 0.4929 and I got the position 38 on the leaderboard. There are a lot of information within these texts, including the injured body part, how it was injured, position of the body part (e.g. This type of problems is known as imbalanced class classification. At Actuaries Digital our purpose is to provide a platform for actuaries to showcase their diverse talent and thought leadership to the profession and to those in the industries served by actuaries. this sector do not notice this. Is it possible to type a single quote/paren/etc. His graduate work specialized in developing and applying new Computational Fluid Dynamic algorithms to astrophysical fluid dynamic problems Regan is an aspiring data scientist who comes from a computer science background. Predict if a driver will file an insurance claim next year. If youll notice, there are about two different blobs projecting from 0,0 to the center of the plot. Thought I'd list them here: Published: Auto Insurance Claims - Automobile Insurance claims including Dataset are available in competition's pages. We can see the regression coefficients progression for lasso path in the graph below , which indicates the changing process of coefficients with alpha value. Looking at a correlation plot of the continuous variables, we saw that variables cont1, cont6, and cont11 were highly correlated with variables cont9, cont10, and cont12 respectively. Learn more about the CLI. So this is how you can train a machine learning model for the task of insurance prediction using Python. Why are mountain bike tires rated for so much lower pressure than road bikes? A couple of new automobile insurance claim data sets have become available since this question was asked. Work fast with our official CLI. Work fast with our official CLI. They use data from their database about everyone they have contacted to promote their insurance services and try to find the most potential people who can buy insurance. For Loop Over Keys and Values in a Python Dictionary, Currency Exchange Rate Forecasting using Python. Below are some examples of the free-text claims descriptions in the raw data: Free-text is one form of unstructured data. Learn more about the CLI. The KNN model is simply defined as follows:`): KNN regression is a non-parametric method that, in an intuitive manner, approximates the association between independent variables and the continuous outcome by averaging the observations in the same neighbourhood. Here we will look at a Data Science challenge within the Insurance space. Another great thing about tidymodels is that it streamlines the process of comparing predictive performance between two different models. Data Science Challenges-Loan Grant | Kaggle And here is a direct link for the data: You signed in with another tab or window. There was a problem preparing your codespace, please try again. Malhotra, Ravi, and Swati Sharma. DateTimeOfAccident Date and time of accident. This is an important feature of this dataset because a person who smokes is more likely to have major health problems compared to a person who does not smoke. We also used Support Vector Regression to fit our data . This allows large number of trees to be produced per model. Risk Classification Assessment for Life Insurance Eligibility Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. I want to note that this data set is pretty clean; you will probably never encounter a data set like this in the wild. Sep 23, 2020 -- Photo by Marek Studzinski on Unsplash G etting new. Preliminary cross validation and parameter tuning on our test set revealed that the algorithm was computationally expensive, taking ~12 hours on our machine. Here our task is to train a machine learning model to predict whether an individual will purchase the insurance policy from the company or not. Mission statement, docs and project management. For the task of Insurance prediction with machine learning, I have collected a dataset from Kaggle about the previous customers of a travel insurance company. @Joe San Pietro is there any data description avaiable for this dataset (Auto Insurance Claims - Automobile Insurance claims including location, policy type and claim amount). For a better understanding when analyzing this data, I will convert 1 and 0 to purchased and not purchased: Now lets start by looking at the age column to see how age affects the purchase of an insurance policy: According to the visualization above, people around 34 are more likely to buy an insurance policy and people around 28 are very less likely to buy an insurance policy. The two factor levels in sex seem to be about the same in quantity. As a result, there is no one particular variable that dominates nor can we reduce our dataset to only a few components. Nice! This helps a company to target the most profitable customers and saves time and money for the Insurance Company. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. WC3485403) was predictive of claims costs. Using the supplied and derived features, the winning solution adopted a model stacking technique combining two base models, a lightGBM and a XGBoost model respectively. In addition, also to allow for skewness, the one outlier claim with $4m cost was scaled down to $1m. The datasets below may include statistics, graphs, maps, microdata, printed reports, and results in other forms. About Dataset Context An insurance group consists of 10 property and casualty insurance, life insurance and insurance brokerage companies. Unit vectors in computing line integrals of a vector field. In simple terms, it allows other models to compensate for poor predictions in an existing model. The task of insurance prediction is something that adds value to every insurance company. cpatrickalves/kaggle-insurance-claim-classification - GitHub Its common to see stacking of five or more models in Kaggle competitions. insurance-claims To normalize the data, we transformed the data by taking the log of the loss. The size of the neighbourhood needs to be set by the analyst or can be chosen using cross-validation (we will see this later) to select the size that minimises the mean-squared error. Although, this was very insightful, this new information did not help our regression model much, so we turned our attention to other methods raise improve our error rates. prathibha13/Insurance-Claim-Prediction - GitHub This is sensible as some body parts are more vulnerable to others (e.g. dataset = pd.read_csv('insurance.csv') Viewing the first 5 of the dataset. This repository contains the code components of work carried out for analyzing the Medical Provider Fraud Detection dataset with the intent to find most important features to crack down the potentially fraud providers. Preliminary tuning revealed an epsilon value of ~ 1.035142 , and a cost of 3.1662, giving us a CV performance of 1570. I am struggling with the diff between 'claim amount' and 'Total Claim Amount' for instance. This article discusses the winning solution for the competition. For the task of Insurance prediction with machine learning, I have collected a dataset from Kaggle about the previous customers of a travel insurance company. rev2023.6.2.43474. Making statements based on opinion; back them up with references or personal experience. Exploratory data analysis using visualizations helped understand the data better and the regression models helped in predictions.