1-844-696-6465 (US)        +91 77600 44484        help@dezyre.com
all-state-insurance-claims-severity-prediction.jpg

Data Science Project-All State Insurance Claims Severity Prediction

Data science project in R to develop automated methods for predicting the cost and severity of insurance claims.
4.84.8

Users who bought this project also bought

What will you learn

  • Basic exploratory analysis using the claims data
  • Insights from exploratory data analysis
  • Factors to be considered for claims processing and severity prediction
  • Implementation of the model using R
  • Building smarter predictive models including XGBoost

What will you get

  • Access to recording of the complete project
  • Access to all material related to project like data files, solution files etc.

Prerequisites

  • Language used: R

Project Description

When you’ve been devastated by a serious car accident, your focus is on the things that matter the most: family, friends, and other loved ones. Pushing paper with your insurance agent is the last place you want your time or mental energy spent. This is why Allstate, a personal insurer in the United States, is continually seeking fresh ideas to improve their claims service for the over 16 million households they protect.

In this data science project, you will develop automated methods for predicting the cost, and severity, of claims.

Instructors

 
Pradeepta

Curriculum For This Mini Project

 
  Import Data Files
00:01:32
  Problem Statement
00:04:00
  Data Overview
00:02:41
  What Model to use?
00:03:36
  Exploratory Data Analysis
00:12:14
  Distribution Type
00:02:11
  Shapiro Test
00:04:17
  Transformations
00:06:10
  Outliers
00:10:12
  Removing Outliers - Capping Method
00:39:52
  Dependent variable distribution
00:09:42
  Recap
00:01:42
  Modelling Techniques
00:02:43
  Correlation Table
00:03:08
  Zero Variance
00:04:20
  Residuals having Normal Distribution
00:21:14
  Multicolinearity
00:02:01
  Multicolinearity - Stepwise AIC
00:13:30
  Multicolinearity - Compute VIF
00:02:42
  Heteroscedasticity
00:02:23
  XGBoost Model
00:16:06
  Conclusion
00:00:34