AHBRIJESH / Naive_Bayes_Algorithm

The Student Success Predictor employs Naive Bayes to assess the likelihood of students achieving scores above 90, integrating study hours and personal factors. This model aids educators in identifying key elements influencing academic excellence, facilitating targeted interventions for enhanced student success. Contributions Welcome!!

Student Success Predictor using Naive Bayes

Welcome to the Student Success Predictor project! This implementation utilizes Naive Bayes to estimate the probability of a student scoring above 90 in examinations based on various parameters such as hours of study, wakeup time, handwriting, and language fluency.

Overview

• model.ipynb: This Jupyter Notebook contains the implementation of the Naive Bayes model. The notebook walks through data preprocessing, model training, and prediction using the specified parameters.

Parameters Used

1. Hours of Study: The number of hours a student dedicates to studying.
2. Wakeup Time: The time the student wakes up in the morning.
3. Handwriting: Evaluation of handwriting quality.
4. Language Fluency: Proficiency in language.

How to Use

1.Clone this repository:

git clone https://github.com/AHBRIJESH/Naive_Bayes_Algorithm.git
Naive_Bayes_Algorithm.git

2. Open and run the cells in model.ipynb using Jupyter Notebook or any compatible environment.
3. Input the required parameters: Hours of Study, Wakeup Time, Handwriting, and Language Fluency.
4. The model will predict the probability of the student scoring above 90 in examinations.

Project Structure

• model.ipynb: The main Jupyter Notebook for the Naive Bayes implementation.
• README.md: The project's documentation.

Contribution

Feel free to contribute by opening issues, providing suggestions, or submitting pull requests. Let's collaborate to enhance the accuracy and usability of the Student Success Predictor!

Happy coding! 🚀