https://github.com/darshjasani/claims-analysis

This repository contains a comprehensive analysis of claims data, detailing the workflow from data preprocessing to model evaluation. The goal of this analysis is to build predictive models to improve claims prediction and management.
https://github.com/darshjasani/claims-analysis

analysis data linear machine-learning python

Last synced: about 1 month ago
JSON representation

This repository contains a comprehensive analysis of claims data, detailing the workflow from data preprocessing to model evaluation. The goal of this analysis is to build predictive models to improve claims prediction and management.

• Host: GitHub
• URL: https://github.com/darshjasani/claims-analysis
• Owner: darshjasani
• Created: 2024-07-11T02:00:49.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2024-08-04T06:19:37.000Z (5 months ago)
• Last Synced: 2024-08-04T07:31:15.426Z (5 months ago)
• Topics: analysis, data, linear, machine-learning, python
• Language: Jupyter Notebook
• Homepage:
• Size: 614 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Claims Analysis

This repository contains a comprehensive analysis of claims data, detailing the workflow from data preprocessing to model evaluation. The goal of this analysis is to build predictive models to improve claims prediction and management.

## Table of Contents

1. [Introduction](#introduction)

2. [Data Description](DataDescription.txt)

3. [Data Preprocessing](#data-preprocessing)

4. [Exploratory Data Analysis (EDA)](#exploratory-data-analysis-eda)

5. [Feature Engineering](#feature-engineering)

6. [Model Training and Evaluation](#model-training-and-evaluation)

7. [Results and Findings](#results-and-findings)

8. [Conclusion](#conclusion)

9. [Future Work](#future-work)

10. [Usage](#usage)

## Introduction

This notebook provides a detailed analysis of claims data, demonstrating various techniques in data preprocessing, feature engineering, and model evaluation. The primary objective is to develop robust predictive models to forecast claim outcomes accurately.

## Data Preprocessing 

- **Data Cleaning:** Handled missing values and outliers.

- **Encoding:** Converted categorical variables into numerical values.

- **Scaling:** Applied scaling techniques to normalize the data.

## Exploratory Data Analysis (EDA)

- **Descriptive Statistics:** Summarized key statistics of the dataset.

- **Visualizations:** Used plots and charts to understand data distributions and relationships.

- **Correlation Analysis:** Identified significant correlations between variables.

## Feature Engineering

- Created new features to enhance the predictive power of the models.

- Applied techniques like one-hot encoding and normalization.

## Model Training and Evaluation

- Trained various machine learning models including logistic regression, decision trees, and others.

- Evaluated models using performance metrics such as accuracy, precision, recall, F1-score, and ROC curves.

- Conducted cross-validation to ensure the robustness of the models.

## Results and Findings

- **Logistic Regression:** Achieved the highest accuracy and best overall performance with an AUC score of 0.85.

- **Key Features:** Claim amount and policy type were identified as significant predictors of claim outcomes.

## Conclusion

This notebook provides a detailed analysis of claims data, demonstrating the complete workflow from data preprocessing to model evaluation. The logistic regression model outperformed others, highlighting the importance of specific features in predicting claim outcomes. These insights offer valuable guidance for improving claims prediction and management.

## Future Work

- Further feature engineering to discover additional predictive features.

- Fine-tuning models to improve accuracy and robustness.

- Incorporating advanced techniques like ensemble methods and deep learning.