Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/theoddysey/student-perfomance-model
A machine learning model for student enrollments
https://github.com/theoddysey/student-perfomance-model
data-science data-visualization jupyter-notebook machine-learning python
Last synced: 14 days ago
JSON representation
A machine learning model for student enrollments
- Host: GitHub
- URL: https://github.com/theoddysey/student-perfomance-model
- Owner: TheODDYSEY
- Created: 2023-11-15T20:51:50.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2023-11-18T18:37:08.000Z (about 1 year ago)
- Last Synced: 2024-12-04T04:18:48.453Z (2 months ago)
- Topics: data-science, data-visualization, jupyter-notebook, machine-learning, python
- Language: Jupyter Notebook
- Homepage:
- Size: 288 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Student Enrollment and Graduation Prediction
## Overview
This project aims to predict student enrollment and graduation success based on historical student enrollment data, academic records, and demographic data..
## Table of Contents
- [Overview](#overview)
- [Setup](#setup)
- [Prerequisites](#prerequisites)
- [Installation](#installation)
- [Usage](#usage)
- [Running the Code](#running-the-code)
- [Code Structure](#code-structure)
- [Data](#data)
- [Model Evaluation](#model-evaluation)
- [Visualizations](#visualizations)
- [Saved Model](#saved-model)
## Setup
### Prerequisites
Ensure you have the following software installed:
- Python (>=3.6)
- Pandas
- Scikit-learn
- Matplotlib
- Seaborn
- Joblib
### Installation
Install the required dependencies using the following command:
```bash
pip install pandas scikit-learn matplotlib seaborn joblib
```
## Usage
### Running the Code
1. Clone the repository:
```bash
git clone https://github.com/TheODDYSEY/Student-Perfomance-Model
cd Student-Performance-Model
```
2. Run the Python script:
```bash
python model_code.py
```
## Code Structure
- `model_code.py`: The main script containing the machine learning model training, evaluation, and visualization code.
- `enrollment_model.pkl`: The serialized RandomForestClassifier model saved for future use.
- `graduation_model.pkl` :The serialized model for future use
## Data
The sample data used for model training is provided in the script as a hardcoded string. For real-world scenarios, replace this data with your dataset in CSV or Excel format.
## Model Evaluation
The script outputs the following:
- Confusion matrix
- Classification report
## Visualizations
The script generates visualizations:
- Confusion matrix heatmap
## Saved Model
The trained model is saved as `enrollment_model.pkl` and `graduation_model.pkl` for future use.