Describe your solution here.e.g. Our solution is a Mental Health Monitor and Virtual Companion for students in the digital classroom. It acts as a virtual friend to the student and keeps her company during online classes. It also reminds her of class schedules, submissions, and other aspects of the classroom. It monitors for mental wellbeing vitals like class participation, connecting with friends, and sends motivational messages. In future versions, we can add connect with parents, teachers, and other stakeholders to address the well-being and academic excellence in a holistic manner.We have used the below techniques for our solution.1. Machine learning to train for the virtual conversations for the virtual companion.2. Data exploration to explore the academic and digital activity data to use in training for predicting behavior patterns
Use the open source, cloud APIs, or public libraries listed below in your application development based on your technology preferences, such as primary language. The below list also provides a view of the components' rating on different dimensions such as community support availability, security vulnerability, and overall quality, helping you make an informed choice for implementation and maintenance of your application. Please review the components carefully, having a no license alert or proprietary license, and use them appropriately in your applications. Please check the component page for the exact license of the component. You can also get information on the component's features, installation steps, top code snippets, and top community discussions on the component details page. The links to package managers are listed for download, where packages are readily available. Otherwise, build from the respective repositories for use in your application. You can also use the source code from the repositories in your applications based on the respective license types.
Use the open source, cloud APIs, or public libraries listed below in your application development based on your technology preferences, such as primary language. The below list also provides a view of the components' rating on different dimensions such as community support availability, security vulnerability, and overall quality, helping you make an informed choice for implementation and maintenance of your application. Please review the components carefully, having a no license alert or proprietary license, and use them appropriately in your applications. Please check the component page for the exact license of the component. You can also get information on the component's features, installation steps, top code snippets, and top community discussions on the component details page. The links to package managers are listed for download, where packages are readily available. Otherwise, build from the respective repositories for use in your application. You can also use the source code from the repositories in your applications based on the respective license types.
Machine Learning
You may be using multiple libraries for different functions in your solution. Please create one group for each function and add the libraries to each group. e.g. I have created a group for Machine Learning which has the libraries used in my solution.The below libraries helps in capturing the embeddings for the text. The embeddings are vectoral representation of text with their semantics.
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scikit-learnby scikit-learn
scikit-learn: machine learning in Python
Python Updated: 0 d ago License: Permissive
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faissby facebookresearch
A library for efficient similarity search and clustering of dense vectors.
C++ Updated: 4 mo ago License: Permissive
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Data Exploration
You may be using multiple libraries for different functions in your solution. Please create one group for each function and add the libraries to each group. e.g. I have created a group for the libraries used for Data Exploration in my solution.The data exploration helps in doing extensive analysis of different data types and in assisting to understand the patterns. Pandas is used in our solution for data manipulation and analysis.
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pandasby pandas-dev
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Python Updated: 3 d ago License: Permissive
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numpyby numpy
The fundamental package for scientific computing with Python.
Python Updated: 7 d ago License: Permissive
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Kit Solution Source
The Kit Solution source group should have library which has the full source code for implementing this solution added here. As described in the guidelines for the hackathon, your hackathon solution built should be added in GitHub as a public project and add it under this group. You can find your library using the FIND feature. You can find your library easily by searching by the full name i.e author/project-name.The below library has been added as a sample representation of the solution source
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faq-virtual-agentby balaji-munusamy
Build your faq virtual agent in 5 minutes
Jupyter Notebook Updated: 4 mo ago License: Permissive
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Kit Deployment Instructions
Please describe specific instructions to easily deploy your hackathon solution.In case of python projects you can specify all your pre-requisite software in a requirements.txt file which can be used to install all dependencies at one go. e.g. pip install -r requirements.txtThis will enable a one click install for your kit and ready to deployYou can also include any quick validation steps to verify if the deployment is working.
