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Predict Students Grade Banner This Predictive Analytics kit provides analytical view of students’ performance in mathematics and predicts grades to be scored in final test. The key features of this solution are: - Analysis of grades of students - Visualisation of patterns - Prediction of grade in final test To install this kit, scroll down to refer 'Kit Deployment Instructions' section and follow instructions.

Training and Certification - How to build Student Grades Predictor

Watch this self-guided tutorial on how you can use Libraries to analyze data and build model for prediction; Dataset for training and validation of model; unseen dataset for testing to build your own Student Grades Predictor. Completed the training? Apply for your Participation Certificate and Achievement Certificate now! Tag us on social media with a screenshot or video of your working application for a chance to be featured as an Open Source Champion and get a verified badge.

Kit Deployment Instructions

Download, extract and double-click kit installer file to install the kit. Note: Do ensure to extract the zip file before running it. Follow below instructions to run the solution. 1. After successful installation of the kit, press 'Y' to run the kit and execute cells in the notebook. 2. To run the kit manually, press 'N' and locate the zip file 'student-grade-analytics.zip' 3. Extract the zip file and navigate to the directory 'student-grade-analytics' 4. Open command prompt in the extracted directory 'student-grade-analytics' and run the command 'jupyter notebook' 5. Locate and open the 'Predictive Analytics on Student grades.ipynb' notebook from the Jupyter Notebook browser window. 6. Execute cells in the notebook

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. Jupyter Notebook is used for our development.

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.

Data Visualisation

The patterns and relationships are identified by representing data visually and below libraries are used for that.

Machine learning

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

Kit Solution Source

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.


If you need help to use this kit, you can email us at kandi.support@openweaver.com or direct message us on Twitter Message @OpenWeaverInc.
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