Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/djdhairya/medical-insurance-cost-prediction-
https://github.com/djdhairya/medical-insurance-cost-prediction-
csv linear-regression machine-learning numpy pandas r-square-values scikit-learn seaborn
Last synced: 7 days ago
JSON representation
- Host: GitHub
- URL: https://github.com/djdhairya/medical-insurance-cost-prediction-
- Owner: djdhairya
- License: mit
- Created: 2024-08-17T07:33:45.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2024-08-17T07:38:14.000Z (about 2 months ago)
- Last Synced: 2024-09-23T06:02:59.131Z (11 days ago)
- Topics: csv, linear-regression, machine-learning, numpy, pandas, r-square-values, scikit-learn, seaborn
- Language: Jupyter Notebook
- Homepage:
- Size: 164 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Medical-Insurance-Cost-Prediction-
In this project, we aim to predict medical insurance costs by employing machine learning techniques, specifically Linear Regression. The process begins with data preprocessing, where we clean and prepare the dataset by handling missing values, encoding categorical variables like sex, smoking status, and region, and normalizing features such as age, BMI, and number of children.
Next, we visualize the data to explore relationships between the features (age, sex, smoking status, children, region, BMI) and the target variable, which is the insurance charge. Visualization helps us understand how each feature influences the insurance cost and whether there are any correlations.
After preprocessing and visualization, we use Linear Regression to model the relationship between the input features and the insurance charges. By training the model on the prepared data, we can predict the insurance cost for new individuals based on their age, sex, smoking status, number of children, region, and BMI. This model helps insurers estimate the cost of providing insurance to different individuals, allowing for more accurate pricing strategies.