FINAL GRADE PREDICTION

FACTORS INFLUENCING A STUDENT FINAL GRADE

Table of Contents

• Goal and Aim of the project
• Description of Data
• Process
• Result
• Result
• Deliverables

Goal of the project

The FINAL GRADE PREDICTOR aims for all students to predict their final score, which is out of 20, based on their inputs for the most important features to predict . Students will be able to visualize how their inputs for the features have a positive and negative impact, exposing them to be aware of how their actions impact their academic performance .

Description of the Data

The combined Student dataset contained 33 columns and 1044 rows, including academic, social, demographic and economic features and their resulting Grade 1, Grade 2 and Grade 3

Process

• Data Preprocessing
• Exploratory Data Analysis
• Label Encoding
• Feature Engineering
• Models Training
• Hypertunning models
• Evaluation and Optimization
• Shap display of Feature Importance
• Deployment

Result

After all the processes, we had Random Forest Regressor as the best model for the project, and we deployed it on Streamlit using the local host. Steps to deploy locally if u want :

• Run on the local command line GPA python file with the command (python -m streamlit run GPA.py )

Deliverables

• The link shows the corresponding demo of our project
• https://youtu.be/46BGV0mzT4o