Cognitive_Challengers (Challenge 1)

by mvneema10

This project is related to the submission for #BuildWithAI 2021 for Challenge 1. CHALLENGE #1 - Digital Learning & EducationThe Pandemic has impacted education - classes have moved online, students have been isolated on screens and coping with this change. Despite the challenges, the digital school has the potential to transform education. How can we empower students and teachers in this new digital school paradigm?The kit is a collection of all the libraries we used to develop the predictive model to predict the Success Factor based on the student's input such as course module, date, score, activity type, total clicks on materials, disable, imd_band, studied credits, etc. The project can be extended to predictive the Knowledge factor where the students are knowledgeable based on the combination of date assignment, activity like Forum, Quizzes, subpage, clicks, type, etc.

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.

Solution Source, Deployment Instructions are not available for this kit.

Libraries:

These are the basic libraries that are used to build the entire project.
p

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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Data Visualization:

The project uses the Seaborn package to develop key insights as well as analyses.
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seabornby mwaskom

Statistical data visualization in Python

Python Updated: 2 mo ago License: Permissive

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Feature Engineering:

The project uses a label encoder to encode the necessary columns for model fitting and prediction.
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label-encoderby deadshot674gam

Python Updated: 12 mo ago License: No License

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Model Building:

The project uses Logistic Regression to develop the model for predicting Success Factors for the list of inputs. The model predicts based on the inputs provided by the user that the student will succeed or fail in the course module.
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LogisticRegressionby raj_pandi

Logistic Regression is a Machine Learning classification algorithm that is used to predict the probability of a categorical dependent variable. In logistic regression, the dependent variable is a binary variable that contains data coded as 1 or 0

Python Updated: 4 y ago License: No License

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Front End Application:

The project uses Flask, HTML, CSS for the Front End UI application.
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HTMLby Ye-Yint-Naing

HTML Updated: 2 y ago License: No License

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CSSby Hachero

My resets and preferences

CSS Updated: 5 y ago License: Permissive

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flaskby pallets

The Python micro framework for building web applications.

Python Updated: 9 d ago License: Permissive

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