Course Recommendation Kit

by weaver

This Course Recommendation kit provides analytical view of students’ performance in mathematics and recommends if a student can consider math for his/her higher education.The key features of this solution are - Analysis of grades of students - Visualisation of patterns - Prediction of grade in final test - Recommendation of math for higher education

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.

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers.
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notebookby jupyter

Jupyter Interactive Notebook

JavaScript Updated: 3 mo ago License: Proprietary

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vscodeby microsoft

Visual Studio Code

TypeScript Updated: 0 d ago License: Permissive

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Data Mining

Our solution integrates data from various sources, and we have used below libraries for exploring patterns in these data and understanding correlation between the features.
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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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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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Data Visualisation

The patterns and relationships are identified by representing data visually and below libraries are used.
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matplotlibby matplotlib

matplotlib: plotting with Python

Python Updated: 4 d ago License: No License

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seabornby mwaskom

Statistical data visualization in Python

Python Updated: 2 mo ago License: Permissive

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Machine learning

Below libraries and model collections helps to create the machine learning models for the core prediction of use case in our solution.
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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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tensorflowby tensorflow

An Open Source Machine Learning Framework for Everyone

C++ Updated: 0 d ago License: Permissive

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pytorchby pytorch

Tensors and Dynamic neural networks in Python with strong GPU acceleration

C++ Updated: 0 d ago License: Proprietary

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Kit Solution Source

Our complete solution and all dependent assets are available in the below library project. This is a fully working deployable project for demo.
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student-grade-analyticsby balaji-munusamy

Analyse academic and non-academic information of students and predict grades

Jupyter Notebook Updated: 2 mo ago License: Permissive

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Kit Deployment Instructions

The entire solution is available as a package to download and install from the source code repository. Follow below instructions to download and deploy the solution. 1. Navigate to the repository by clicking the component under Kit Solution Source section 2. Clone the repository 3. Locate requirements.txt file to find all the dependencies 4. Run the command 'pip install -r requirements.txt' to install all the dependencies 5. Open the Jupyter Notebook from the package as entire solution is available in the form of Jupyter Notebook 6. Execute all the cells in Jupyter Notebook If there're any challenges while installing dependencies, run the command below to upgrade pip and try again. python -m pip install --upgrade pip
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