Principal Software Engineer, Afiniti
Data Engineering Lead - Uber
Principal Data Scientist - Cyber Security Risk Management, Verizon
Senior Data Engineer, Slintel-6sense company
Insurance Pricing Forecast Using XGBoost Regressor
In this project, we are going to talk about insurance forecast by using linear and xgboost regression techniques.
Insurance pricing forecast Machine Learning Project Template Outcomes
- Understanding the insurance pricing problem statement
- Exploratory Data Analysis on Categorical and Continuous Data
- Univariate Data Analysis
- Bivariate Data Analysis
- Understand Correlation Analysis
- Categorical Correlation with Chi-squared
- Correlation between Categorical and Target Variables with ANOVA
- Label Encoding for Categorical Variables
- Understanding Linear Regression Assumptions
- Implementing Linear Regression
- Validating Linear Regression Assumptions
- Understanding XGBoost Regressor
- Implementing XGBoost Regressor
- Building pipelines with Sklearn’s Pipeline operator
- Implementing BayesSearchCV for XGBoost Hyperparameter Optimization
- Evaluating Models with Regression Metrics - RMSE
- Presenting Non-Technical Metrics for Stakeholders
Get started today
Request for free demo with us.
Architecture Diagrams
Unlimited 1:1 Live Interactive Sessions
![number-icon]()
60-minute live sessionSchedule 60-minute live interactive 1-to-1 video sessions with experts.
![number-icon]()
No extra chargesUnlimited number of sessions with no extra charges. Yes, unlimited!
![number-icon]()
We match you to the right expertGive us 72 hours prior notice with a problem statement so we can match you to the right expert.
![number-icon]()
Schedule recurring sessionsSchedule recurring sessions, once a week or bi-weekly, or monthly.
![number-icon]()
Pick your favorite expertIf you find a favorite expert, schedule all future sessions with them.
-
![number-icon]()
Use the 1-to-1 sessions to- Troubleshoot your projects
- Customize our templates to your use-case
- Build a project portfolio
- Brainstorm architecture design
- Bring any project, even from outside ProjectPro
- Mock interview practice
- Career guidance
- Resume review
Customers sharing their love on online platforms
Source: 
Source: 
Source: 
Source:
Source:
Source:
Source:
Source:
Source:
Source:
Source:
Source:
Source:
Benefits
250+ end-to-end project solutions
Each project solves a real business problem from start to finish. These projects cover the domains of Data Science, Machine Learning, Data Engineering, Big Data and Cloud.
15 new projects added every month
New projects every month to help you stay updated in the latest tools and tactics.
500,000 lines of code
Each project comes with verified and tested solutions including code, queries, configuration files, and scripts. Download and reuse them.
600+ hours of videos
Each project solves a real business problem from start to finish. These projects cover the domains of Data Science, Machine Learning, Data Engineering, Big Data and Cloud.
Cloud Lab Workspace
New projects every month to help you stay updated in the latest tools and tactics.
Unlimited 1:1 sessions
Each project comes with verified and tested solutions including code, queries, configuration files, and scripts. Download and reuse them.
Technical Support
Chat with our technical experts to solve any issues you face while building your projects.
7 Days risk-free trial
We offer an unconditional 7-day money-back guarantee. Use the product for 7 days and if you don't like it we will make a 100% full refund. No terms or conditions.
Payment Options
0% interest monthly payment schemes available for all countries.
Testimonials
I come from Northwestern University, which is ranked 9th in the US. Although the high-quality academics at school taught me all the basics I needed, obtaining practical experience was a challenge. This is when I was introduced to ProjectPro, and the fact that I am on my second subscription year only goes to prove that the ROI is satisfactory. I managed to switch to analytics companies, only because of the relevant practical experience this product served me with. I now work at a leading healthcare startup as a Senior Analytics Consultant. I am a customer who is not only satisfied with ProjectPro but also mighty impressed by how Dezyre bends over backward to ensure customer satisfaction. I have had a couple of interactions with Binny and each time I was left happy and content. I also had a conversation with their investors, and I was really glad to articulate my appreciation of the product. They not only have enterprise-grade projects, but also set up 1:1 sessions with seasoned experts in case we get stuck, or are having trouble understanding a certain concept. As the cherry on the icing, there are experts to guide you with resume writing and interview preparation as well, to culminate the whole process of making you job-ready. Kudos to ProjectPro!
Abhinav Agarwal
Graduate Student at Northwestern University
Having worked in the field of Data Science, I wanted to explore how I can implement projects in other domains, So I thought of connecting with ProjectPro. A project that helped me absorb this topic was "Credit Risk Modelling". To understand other domains, it is important to wear a thinking cap and that's where ProjectPro helped me. I also got a chance to talk to experts who have worked on these domains - they helped me by walking through the project. Kudos to the ProjectPro team!
Gautam Vermani
Data Consultant at Confidential
ProjectPro is an awesome platform that helps me learn much hands-on industrial experience with a step-by-step walkthrough of projects. There are two primary paths to learn: Data Science and Big Data. In each learning path, there are many customized projects with all the details from the beginner to the expert. As a new data science learner, you can just follow these projects to master the important techniques quickly. It is really helpful for both my research and job searching. Hope you can come and join ProjectPro to win a great future for yourself.
Jingwei Li
Graduate Research assistance at Stony Brook University
I come from a background in Marketing and Analytics and when I developed an interest in Machine Learning algorithms, I did multiple in-class courses from reputed institutions though I got good theoretical knowledge, the practical approach, real word application, and deployment knowledge were missing. ProjectPro helped me bridge that gap. ProjectPro has real-time projects that helped me improve my skills. What I liked most is that I get exposure to so many projects, given the work nature I wouldn't have gotten exposure to such a variety of projects and their approaches. It is helping me apply knowledge to other projects too. I highly recommend ProjectPro to everyone who wants to excel in their DataScience career.
Ameeruddin Mohammed
ETL (Abintio) developer at IBM
Comparison with other platforms
We provide ready-made project templates that solve real business problems, end-to-end and comes with solution code,
explanation videos, cloud lab environment and tech support.
End-to-end implementation
End-to-end implementation
Real industry grade projects
by industry experts
by industry experts
Ready-made solutions to real business problems
Detailed Explanations
Courses/ Tutorials
Our expert panel
Principal Software Engineer, Afiniti
Data Engineering Lead - Uber
Principal Data Scientist - Cyber Security Risk Management, Verizon
Senior Data Engineer, Slintel-6sense company
Director of Data Science & AnalyticsDirector, ZipRecruiter
Dev Advocate, Pinecone and Freelance ML
Data Scientist, Boeing
Senior Data Engineer, Publicis Sapient
Chief Science Officer at DataPrime, Inc.
Big Data Engineer, Beyond Limits
Senior Data Scientist, Mawdoo3 Ltd
Data Engineering Manager, Microsoft Corporation
University of Economics and Technology, Instructor
Data Science Consultant, Fractal Analytics
Data Scientist, Credit Suisse
Big Data & Analytics architect, Amazon
Machine Learning Manager, Adobe
Data Scientist, Inmobi
Director of Business Intelligence , CouponFollow
Senior Applied Scientist, Amazon
Head of Data science, OutFund
Global Data Community Lead | Lead Data Scientist, Thoughtworks
Senior Data Engineer, National Bank of Belgium
Data Engineer, Microsoft
Data Engineer - Capacity Supply Chain and Provisioning, Microsoft India CoE
NLP Engineer, Speechkit
Data Scientist, SwissRe
Senior Data Platform Engineer, GoodRx
Senior Data Engineer, Hogan Assessment Systems
Data and Blockchain Professional
Data Science, Yelp
Chief Scientific Officer, Machine Medicine Technologies
Head of Data Science, Slated
Project Description
Overview
Insurance companies cover expenses the policyholder incurs from damages to health or property policies commonly offered: medical bills, house, motor vehicle, and fire insurance, and financial losses such as a loss of income against a fee or premium paid by the client. Traditional approaches to premium calculation require a lot of time-consuming human labor and are getting more complicated daily to capture the increasingly complex interactions in the data.
Insurance firms should normally collect a higher premium than the amount given to the insured individual if that person files a valid claim to generate a profit. Since profitability is the fundamental factor that helps the insurance firm survive, they need a mechanism for reliably forecasting healthcare expenses.
Hence, our goal is to build a machine learning model that helps establish the rates by predicting the charges or payouts done by the health insurance firm to maintain profitability.
In this project, we will primarily focus on building an XGBoost Regressor to determine healthcare expenses based on features such as age, BMI, smoking, etc. We will also learn about categorical correlation, build a linear regression model as a baseline and compare it with the results of the XGBoost Regressor. We will eventually learn how to communicate technical results to stakeholders who are not technical.
Aim
This data science project aims to build and evaluate linear and xgboost regression models and determine the healthcare charges of each customer. This analysis will help the insurance firm to strategize a premium plan that will help maximize the profits.
Data Description
The insurance price forecast dataset contains historical records for 1338 insured customers. The column definitions are below
-
age: Age of the primary beneficiary.
-
sex: Gender of the primary beneficiary.
-
BMI: Body mass index of primary beneficiary
-
children: Number of children the primary beneficiary has.
-
smoker: Whether the primary beneficiary smokes.
-
region: The primary beneficiary's residential area in the US.
-
charges: Individual medical costs billed by health insurance.
Tech Stack
-
Language: Python
-
Libraries: pandas, numpy, matplotlib, plotly, statsmodels, sklearn, xgboost, skopt
Approach
-
Exploratory Data Analysis (EDA)
-
Distributions
-
Univariate Analysis
-
Bivariate Analysis
-
Correlation
-
Pearson Correlation
-
Chi-squared Tests
-
ANOVA
-
-
-
Build and evaluate a baseline linear model
-
Linear regression assumptions
-
Data preprocessing
-
Model training
-
Model evaluation
-
RMSE
-
-
-
Improve on the baseline linear model
-
Introduction to a non-linear model - XGBoost
-
Data preprocessing
-
Using Sklearn's `Pipeline` to optimize the model training process
-
Model evaluation
-
RMSE
-
-
Comparison to the baseline model
-
-
Presenting the results to non-technical stakeholders
Insurance pricing forecast Machine Learning
Topics Covered
- Business Problem and Approach 07m
- Data Introduction 04m
- Exploratory Data Analysis 03m
- Univariate Analysis 05m
- Bivariate Analysis 06m
- Collinearity between Features 05m
- Correlation with the Target 03m
- Linear Regression Assumptions 03m
- Linear Model Data Preprocessing 06m
- Linear Regression Model Building 02m
- Linear Regression Model Evaluation 06m
- XGBoost Introduction 03m
- XGBoost Regression Part 1 02m
- XGBoost Regression Part 2 03m
- XGBoost Regression Part 3 03m
- XGBoost Regression Part 4 01m
- XGBoost Regression Part 5 03m
- XGBoost Data Preprocessing 01m
- XGBoost Model Training 09m
- XGBoost Model Evaluation 03m
- Model Comparison 02m
- Result Presentation 03m
You might like these too
Recommended
Projects
Latest Blogs
Your A-Z Guide to AWS Data Engineer Certification Roadmap
The ultimate AWS Data Engineer Certification Roadmap - a step-by-step guide for mastering data engineering on Amazon Web Services. | ProjectPro
Adaboost Algorithm Explained in Depth
Exploring the AdaBoost Algorithm Applications, Working and Projects in Python.| ProjectPro
We power Data Science & Data Engineering
projects at
Join more than
115,000+ developers worldwide
Get a free demo
