This project aims to predict health costs using a regression algorithm. The dataset contains information about different individuals, including their healthcare costs. The regression model is implemented using TensorFlow and Keras.
The dataset used for this project is provided in the insurance.csv file. It includes features such as age, sex, BMI, smoker status, region, and charges.
The jupyter notebook contains the link to the dataset along with the starter code.
- Python 3.x
- TensorFlow
- NumPy
- Pandas
- Matplotlib
- scikit-learn
The regression model is a simple neural network with one hidden layer. You can customize the architecture based on your requirements.
The model is evaluated on the Mean Absolute Error (MAE) and Mean Squared Error (MSE) metrics. The training history is visualized to monitor the model's performance over epochs.
- This project is based on a challenge of freecodecamp Machine Learning with Python course to predict healthcare costs using regression.