https://github.com/saba-gul/medical-insurance-cost-estimation-using-machine-learning
This repository contains code for predicting medical charges using linear regression and conducting sensitivity analysis on the model's performance.
https://github.com/saba-gul/medical-insurance-cost-estimation-using-machine-learning
exploratory-data-analysis health-care-analytics health-informatics healthcare healthcare-data machine-learning-algorithms medical-application python3
Last synced: 8 months ago
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This repository contains code for predicting medical charges using linear regression and conducting sensitivity analysis on the model's performance.
- Host: GitHub
- URL: https://github.com/saba-gul/medical-insurance-cost-estimation-using-machine-learning
- Owner: Saba-Gul
- License: mit
- Created: 2024-04-27T11:36:57.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-04-27T11:46:56.000Z (over 1 year ago)
- Last Synced: 2025-01-13T16:28:20.697Z (9 months ago)
- Topics: exploratory-data-analysis, health-care-analytics, health-informatics, healthcare, healthcare-data, machine-learning-algorithms, medical-application, python3
- Language: Jupyter Notebook
- Homepage:
- Size: 699 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Medical-Insurance-Cost-Estimation-Using-Machine-Learning
This repository contains code for predicting medical charges using linear regression and conducting sensitivity analysis on the model's performance.
## Introduction
Healthcare costs are a significant concern for individuals and families worldwide. Understanding the factors that influence medical charges can help healthcare providers and policymakers better allocate resources and plan interventions. In this project, we use linear regression to predict medical charges based on demographic and health-related factors.
## Dataset
The dataset comprises several features:
- **age**: Age of the individual.
- **sex**: Gender (male/female).
- **bmi**: Body Mass Index.
- **children**: Number of children/dependents.
- **smoker**: Smoking status (yes/no).
- **region**: Geographical region.
- **charges**: Individual medical costs billed by health insurance.
## Tasks
### Data Preprocessing
- Handle missing or anomalous data.
- Convert categorical variables (sex, smoker, region) into numerical formats using encoding techniques.
- Normalize/standardize numerical features if required.
### Exploratory Data Analysis (EDA)
- Analyze the distribution of key variables (e.g., age, BMI, charges).
- Investigate relationships between features and the target variable (charges).
- Identify potential outliers or influential points.
### Model Development
- Split the dataset into training and testing sets.
- Implement a linear regression model.
- Evaluate model performance using appropriate metrics (e.g., R-squared, Mean Squared Error).
### Advanced Analysis
- Implement regularized linear models (Ridge, Lasso) to see if they yield better results.
- Conduct a sensitivity analysis to understand the robustness of the model.
## Files
- `medical_charges_prediction.ipynb`: Jupyter Notebook containing code for data preprocessing, EDA, model development, and sensitivity analysis.
- `insurance.csv`: Dataset containing medical charges and related features.
## Requirements
- Python 3.x
- Jupyter Notebook
- pandas
- scikit-learn
- matplotlib
## Usage
1. Clone the repository:
```
git clone https://github.com/your_username/medical-charges-prediction.git
```
2. Navigate to the project directory:
```
cd medical-charges-prediction
```
3. Open and run the Jupyter Notebook `medical_charges_prediction.ipynb` to execute the code and analyze the results.
## Results
- The linear regression model achieved an R-squared value of 0.78 and a Mean Squared Error of 0.23.
- Sensitivity analysis revealed high fluctuation in model performance metrics across different train-test splits, indicating sensitivity to variations in the training data.
## Conclusion
Linear regression provides a baseline model for predicting medical charges based on demographic and health-related factors. Further analysis, including regularized linear models and sensitivity analysis, can help improve model robustness and generalization.
## Acknowledgements
- The dataset used in this analysis is sourced from [Kaggle](https://www.kaggle.com/mirichoi0218/insurance).