You will not be forking this time, please take some time to read this instructions:
Once you are finished creating your linear regression model, make sure to commit your changes, push to your repository and go to 4Geeks.com to upload the repository link.
Predicting the medical insurance cost of a person
This dataset has 7 columns. We will use the 'charges' column as the target variable because we want to create a model that predicts the cost of the insurance based on different factors.
age: age of primary beneficiary.
sex: insurance contractor gender, female or male.
bmi: Body mass index.
children: number of children covered by health insurance / Number of dependents.
region: the beneficiary's residential area in the US, northeast, southeast, southwest, northwest.
charges: Individual medical costs billed by health insurance.
The dataset can be found in this project folder as 'medical_insurance_cost.csv' file. You are welcome to load it directly from the link (https://raw.githubusercontent.com/4GeeksAcademy/linear-regression-project-tutorial/main/medical_insurance_cost.csv), or to download it and add it to your data/raw folder. In that case, don't forget to add the data folder to the .gitignore file.
If you find yourself struggling with this project, you can check out the solution guide: https://github.com/4GeeksAcademy/linear-regression-project-tutorial/blob/main/solution_guide.ipynb
Time to work on it!
Use the explore.ipynb notebook to find patterns and valuable information about relationships between features or between feature and target.
Hint: There are no null values
Don't forget to write your observations.
Now that you have a better knowledge of the data, in your exploratory notebook create a first linear regression model with your data, in order to predict the insurance prima.
Choose a metric to measure your results.
Hypertune your model to improve your results.
Use the app.py to create your final machine learning modeling pipeline.
Save your final model in the 'models' folder.
In your README file write a brief summary.