Implementing workout recommendaton model for our SIH Hackathon Problem Statement on developing a Fitness App. Leveraged scikit-learn library to build and train the machine learning model, utilizing algorithms such as decision trees or ensemble methods to predict suitable workout recommendations based on user characteristics.
Python Modules and Libraries Required:
- NumPy: For numerical computations and generating random user data.
- scikit-learn: For building and training the machine learning model, as well as performing data preprocessing and evaluation tasks.
- Pandas: For data manipulation and preprocessing, facilitating efficient handling of structured data.
- Matplotlib or Seaborn: For data visualization and analysis, aiding in the exploration of input data and model evaluation results.
- Flask or Django: For web application development and integrating the machine learning model into the application backend.
- Flask-RESTful or Django REST framework: For building RESTful APIs to communicate between the frontend and backend components of the application.
- Docker: For containerizing the application and ensuring portability and scalability across different environments.
- Git: For version control and collaboration, enabling seamless integration of changes and contributions from team members.