ai-agent

Predicting-Student-Performance-Using-Machine-Learning

Name: Predicting-Student-Performance-Using-Machine-Learning
Author: skprasad117

Founders, predict student test performance with machine learning. Uses demographic and academic data.

From the repo · “Using machine learning algorithms, this project aims to predict student performance in standardized tests based on demographic and academic data.”

intermediate⏱ 1-2 hours💵 Free + open-source data used is a prerequisite for the model

52 stars25 forksJupyter NotebookGuide quality 8/10 · reviewed Jul 1Updated 6/4/2023100% free · open source

What it is

Use machine learning to predict student test scores based on their demographic and academic data.

Real use case example

"A founder of a small school district wants to identify the most effective ways to support struggling students. They upload student demographic and academic data to the model, and use 3-5 key variables to predict test scores. By comparing their predictions with actual results, they can target specific areas for improvement, leading to better student outcomes."

Best insideClaude Desktop

When to use it

•When you need to identify students who may require extra support to improve their test scores.
•When analyzing the impact of demographic factors on student performance in standardized tests.
•When developing strategies to improve student outcomes based on predictive analytics.

Ready-to-paste prompt

Run the following code in a cell in the Jupyter Notebook to train a Random Forest Classifier: `rf = RandomForestClassifier(n_estimators=100, random_state=42); rf.fit(X_train, y_train)`

Heads up: Ensure you have the necessary Python libraries installed, including pandas, numpy, and scikit-learn, as the project relies on these libraries for data manipulation and machine learning tasks.

Saves to your device

Use with Claude

New

Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.

✅ Light setup

Installs with a command or two; your AI agent can do it for you.

Try it instantly — no install

Claude Code

mkdir -p ~/.claude/skills/predicting-student-performance-using-machine-learning && curl -fsSL https://workflowstacks.com/api/skills/predicting-student-performance-using-machine-learning/claude-skill -o ~/.claude/skills/predicting-student-performance-using-machine-learning/SKILL.md

Open in another AI app

Opens the app with this repo with the prompt ready to go — no copy-paste needed.

Connect the whole catalog (MCP)

claude mcp add --transport http workflowstacks https://workflowstacks.com/api/mcp

Adds a WorkflowStacks connector to Claude Code: search and load any skill here by chatting.

How Predicting-Student-Performance-Using-Machine-Learning works

Codeflow

Free to inspect

Predicting-Student-Performance-Using-Machine-Learning is a tiny Jupyter Notebook project (~450 lines across 13 code files). Setup is light: install Python, run one command. Last commit 41 months ago, no license file, no tests found.

Size

Tiny codebase

~450 lines · 13 code files · 4 min skim

Setup

Light setup

Install Python, run one command.

Runs on

Python

No API keys detected

Jupyter Notebook 95%Python 4%HTML 1%

Where to start reading

1
README.md
Start here — what it does and how to install it
2
app.py
Where the program starts running
3
requirements.txt
Dependencies and the commands it exposes
4
src/__init__.py
Inside src/ — the main logic begins here

What's in each folder

src/Core code — the actual logic11 files

templates/Templates2 files

artifacts/Folder5 files

catboost_info/Folder4 files

Notebook/Research & notebooks3 files

static/Images & static assets2 files

READMENo tests foundThin docsNo CINo licenseLast update 41 mo ago

Use with Claude

New

Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.

✅ Light setup

Installs with a command or two; your AI agent can do it for you.

Try it instantly — no install

Claude Code

mkdir -p ~/.claude/skills/predicting-student-performance-using-machine-learning && curl -fsSL https://workflowstacks.com/api/skills/predicting-student-performance-using-machine-learning/claude-skill -o ~/.claude/skills/predicting-student-performance-using-machine-learning/SKILL.md

Open in another AI app

Opens the app with this repo with the prompt ready to go — no copy-paste needed.

Connect the whole catalog (MCP)

claude mcp add --transport http workflowstacks https://workflowstacks.com/api/mcp

Adds a WorkflowStacks connector to Claude Code: search and load any skill here by chatting.

Quick Actions

Details

Creator

skprasad117

Language

Jupyter Notebook