Predict student grades with machine learning. For founders in education.
intermediateโฑ 1-2 hours๐ต Free
186 stars70 forksPythonQuality 8/10Updated 10/31/2025100% free ยท open source
What it is
Makes predictions about student grades using machine learning on CSV files.
What you can make with it
Automations like predicting student grades based on factors like attendance and homework completion.
How it helps
This helps education founders quickly identify which students are at risk of struggling, allowing them to focus support on those who need it.
Real use case example
"A founder building an education platform wants to identify students who are struggling in math. They upload a CSV file with student data to the skill, train the model, and then use it to predict which students are at risk. They can then focus resources on those students, leading to improved outcomes."
If you're new
Novices can pick up this skill as a way to understand the basics of machine learning and its applications in education without needing extensive experience.
If you're senior
Senior engineers can leverage this skill to integrate machine learning into complex education platforms, streamlining processes and improving results.
Common confusion cleared up
Some users might mistakenly think this skill is exclusive to grading, but it can be used to predict any outcome based on relevant factors.
Best inside these AI tools
Claude DesktopCursor
Pairs with
CSV filesmachine learning datasets
Why we list it on WorkflowStacks: This is included because it's an open-source way for education founders to implement machine learning in their workflows.
What it does
Predict student grades using machine learning classifiers based on historical data from a CSV file.
Install / run
Clone the repository using `git clone https://github.com/FaizanZaheerGit/StudentPerformancePrediction-ML.git`
When to use it
โขYou have a large dataset of student performance and want to identify key factors affecting grades.
โขYou need to predict student grades for a new semester based on past performance.
โขYou want to understand how different variables such as attendance, homework, and exam scores impact student grades.
Quick start
1Navigate to the project directory using `cd StudentPerformancePrediction-ML`
2Install required libraries by running `pip install -r requirements.txt`
3Prepare your dataset in a CSV file, ensuring it matches the format expected by the project
4Run the prediction model using `python prediction.py`
5Review the predicted grades and analysis in the output files generated by the script
Ready-to-paste prompt
Run `python prediction.py --dataset student_data.csv --model logistic_regression` to predict grades using logistic regression on your student data CSV file.
Heads up: Ensure your CSV file is in the same directory as the script and named correctly, as the script assumes a specific file name and path, or modify the script to match your dataset location.
Saves to your device
Topics
ai-ml
artificial-intelligence
artificial-intelligence-projects
csv-files
data-science
machine-learning
machine-learning-projects
ml-models
python
python3
What's inside โ free to inspect
No purchase needed
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