The Predictive Learning Analytics project is a machine learning-based web application built with Flask. It predicts student performance based on various input features such as gender, class, attended classes, total classes, and math scores.

The project consists of the following components:

1. predictive-learning.py: This Python script contains the code for data preparation, feature engineering, model building, and evaluation. It uses machine learning models to predict student performance.

2. Flask.py: The Flask-based web application that serves as the user interface for inputting student data and obtaining predictions. It uses the trained machine learning model to make predictions.

3. index.html: The HTML template for the web interface, allowing users to input student data.

4. styles.css: The CSS stylesheet for styling the web interface.

Before running the application, ensure that you have the following prerequisites installed:

• Python 3.x
• Flask
• scikit-learn
• pandas
• joblib (for loading the trained model)
• jQuery (for handling form submissions in the HTML template)

The repository structure is organized as follows:

• predictive-learning.py: Python script containing data preparation, model building, and evaluation.
• Flask.py: Flask web application for serving the machine learning model.
• index.html: HTML template for the web interface.
• styles.css: CSS stylesheet for styling the web interface.

This project uses various open-source libraries and tools, including Flask, scikit-learn, pandas, and jQuery. Special thanks to the open-source community for their contributions.

If needbe contact Cosmus Mutuku here: [email protected]