Ensemble Machine Learning Project - All State Insurance Claims Severity Prediction

In this ensemble machine learning project, we will predict what kind of claims an insurance company will get. This is implemented in python using ensemble machine learning algorithms.

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Project Template Outcomes

  • Detailed business description and the problem being addressed through analytics
  • Data uploading using popular pandas python package
  • Dataset overview and how to analyze a sample of the dataset
  • Exploratory data analysis to understand the Allstate insurance claim dataset
  • Analyzing 5 point summary and studying data distribution for categorical variables
  • Handling missing values for categorical and continuous variables
  • Outlier treatment with visual techniques (Box-Plots)
  • Difference between Label/One-Hot-Encoder and which technique to use
  • Use of Pickle file format to store and load models
  • Feature selection and elimination using Correlation, Constant Variance and Chi-Square statistical tests
  • Understanding ensemble Machine Learning algorithms
  • Hyper-parameter tuning using Sklearn functions
  • Model selection using RMSE as the model evaluation metric
  • Model deployment creating FlaskAPI

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Project Description

Overview

All State, a personal insurance company in the United States, is interested in leveraging data science to predict the severity and the cost of insurance claims post an unforeseen event.

This ensemble machine learning project will help you understand the best practices followed in approaching a data analytics problem through python language focusing on using data science packages. We will predict how severe insurance claims will be for All State. We accomplish this using ensemble machine learning algorithms.

 

Tech Stack
Language: Python

Libraries:  pandas, NumPy, seaborn, matplotlib, sklearn, shap

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