ahmed-alllam / student-grade-predictor

A Machine learning model using RandomForest and Artificial Neural Network to predict a student's final letter grade in a specific course

Student Grade Prediction Model

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About The Project

This project aims to develop a student grade prediction model using machine learning techniques. The project uses TensorFlow for creating an Artificial Neural Network (ANN), scikit-learn for various machine learning algorithms, pandas for data manipulation, and seaborn for data visualization.

Features:

The student grade prediction model offers the following features:

• Utilizes an Artificial Neural Network (ANN) implemented using TensorFlow to predict student grades.
• Leverages scikit-learn's diverse machine learning algorithms to enhance prediction accuracy and model performance.
• Employs pandas for efficient data handling, preprocessing, and transformation.
• Utilizes seaborn for generating insightful visualizations to aid in data exploration and analysis.

Data Source

The dataset used for training and evaluating the student grade prediction model is sourced from the UCI Machine Learning Repository. The dataset, titled "Student Performance," was collected and compiled by Paulo Cortez in 2014. It contains relevant information for predicting student grades. Reference:

@misc{misc_student_performance_320,
  author       = {Cortez,Paulo},
  title        = {{Student Performance}},
  year         = {2014},
  howpublished = {UCI Machine Learning Repository},
  note         = {{DOI}: https://doi.org/10.24432/C5TG7T}
}

Tech Stack

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Contact

Ahmed Allam - LinkedIn - [email protected]