Cognitive_Challengers (Challenge 1)
by mvneema10 Updated: Nov 1, 2021
This project is related to the submission for #BuildWithAI 2021 for Challenge 1. CHALLENGE #1 - Digital Learning & Education The 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.
These are the basic libraries that are used to build the entire project.
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 38499 Version:v2.0.2 License: Permissive (BSD-3-Clause)
The fundamental package for scientific computing with Python.
Python 23587 Version:v1.24.3 License: Permissive (BSD-3-Clause)
The project uses the Seaborn package to develop key insights as well as analyses.
Statistical data visualization in Python
Python 10737 Version:v0.12.2 License: Permissive (BSD-3-Clause)
The project uses a label encoder to encode the necessary columns for model fitting and prediction.
Python 3 Version:Current License: No License
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.
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 0 Version:Current License: No License
Front End Application:
The project uses Flask, HTML, CSS for the Front End UI application.
The Python micro framework for building web applications.
Python 63073 Version:2.2.5 License: Permissive (BSD-3-Clause)