1- Data Preprocessing and Model Deployment (CatBoost)
2- Interface (Streamlit)
3- API (FastAPI)
4- Automation (Docker)
- Data Preprocessing and Model Development (Catboost)
- We will perform data preprocessing steps to prepare the dataset with customer information such as account and demographics for model training
- For a data set where we have a lot of categorical variables, it would be a good choice to use the CatBoost model, which has shown success in categorical variables
- Interface (Streamlit)
- Develop an interactive web interface to visualize and share model outputs
- Also a small predict facility in this interface
- API (FastAPI)
- Creating an API using FastAPI to enable real-time predictions of the model
- Providing APls for integration with other systems and automated decision-making processes
- Automation (Docker)
- Packaging the entire solution into a container using Docker
- Leveraging Docker to automate deployment processes and enable easy deployment of the solution to different environments