Student grades - Predictive Analyti
by kandikits Updated: Oct 20, 2022
This Predictive Analytics kit provides an analytical view of students’ performance in mathematics and predicts grades to be scored in the final test.
The key features of this solution are:
- Analysis of grades of students
- Visualisation of patterns
- Prediction of grade in the final test
Student grade predictive analytics created using this kit are added in this section. The entire solution is available as a package to download from the source code repository.
- Download, extract and double-click kit installer file to install the kit. Ensure you extract the zip file before running it.
- After successful installation of the kit, press 'Y' to run the kit and execute cells in the notebook.
- To run the kit manually, press 'N' and locate the zip file 'student-grade-analytics.zip'.
- Extract the zip file and navigate to the directory 'student-grade-analytics'.
- Open the command prompt in the extracted directory 'student-grade-analytics' and run the command 'jupyter notebook'.
- Locate and open the 'Predictive Analytics on Student grades.ipynb' notebook from the Jupyter Notebook browser window.
- Execute cells in the notebook.
Click on the button below to download the solution and follow the deployment instructions to begin set-up. This 1-click kit has all the required dependencies and resources you may need to build your Predictive Analysis App.
For a detailed tutorial on installing & executing the solution as well as learning resources including training & certification opportunities, please visit the OpenWeaver Community
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. Jupyter Notebook is used for our development.
Jupyter Interactive Notebook
Jupyter Notebook 9914 Version:v7.0.0a18 License: Permissive (BSD-3-Clause)
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.
The fundamental package for scientific computing with Python.
Python 23036 Version:v1.24.2 License: Permissive (BSD-3-Clause)
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 37439 Version:v2.0.0rc1 License: Permissive (BSD-3-Clause)
The patterns and relationships are identified by representing data visually and below libraries are used for that.
matplotlib: plotting with Python
Python 17111 Version:v3.7.1 License: No License
Statistical data visualization in Python
Python 10513 Version:v0.12.2 License: Permissive (BSD-3-Clause)
The open and composable observability and data visualization platform. Visualize metrics, logs, and traces from multiple sources like Prometheus, Loki, Elasticsearch, InfluxDB, Postgres and many more.
TypeScript 54661 Version:v8.5.22 License: Strong Copyleft (AGPL-3.0)
Below libraries and model collections helps to create the machine learning models for the core prediction of use case in our solution.
An Open Source Machine Learning Framework for Everyone
C++ 172599 Version:v2.12.0 License: Permissive (Apache-2.0)
scikit-learn: machine learning in Python
Python 53572 Version:1.2.2 License: Permissive (BSD-3-Clause)
Kit Solution Source
Analyse academic and non-academic information of students and predict grades
Jupyter Notebook 0 Version:v1.0.0 License: Permissive (Apache-2.0)