IDARE Enterprise AI predictive analytics platform background

Use Case

Predicting the GPA of Students

Predicting GPA trends using key academic and behavioral factors to enhance student performance.

1Overview & Strategic Importance

Regression Solution Academic Data

Problem Statement

Academic performance, as measured by GPA, is a crucial indicator of student success and future opportunities. Predicting GPA is essential for identifying students at risk of underperformance and tailoring interventions to enhance their academic outcomes. Multiple factors, such as study habits, parental involvement, and participation in extracurricular activities, influence GPA. Accurate prediction helps educators and policymakers make informed decisions to foster student success.

Required Solutions

Developing a regression model to predict GPA based on demographics and habits.
Identifying key influences on academic performance.
Providing data-driven insights for targeted student support.

Solution Objectives

Perform exploratory data analysis to uncover performance patterns.
Develop a regression model for accurate GPA forecasting.
Conduct scenario analysis to identify high-impact variables.
Optimize model for real-world school environments.

Understanding the Problem

GPA is influenced by habits, parental education, and support systems.

Understanding the nuanced dual effects of extracurriculars and the critical impact of absences allows for personalized support and effective policy interventions.

2About the Data

Data Collection

This dataset comprises comprehensive data on 2,392 high school students, including demographics, study time, parental involvement, and extracurriculars.

Dataset Source

Major Parameters Description

Download Training Data

StudentID

A unique identifier for each student (1001 to 3392).

Age

Age of the student (15–18 years).

Gender

Gender of the student (0: Male, 1: Female).

Ethnicity

Ethnic background (0: Caucasian, 1: African American, 2: Asian, 3: Other).

ParentalEducation

Education level of parents (0: None, 1: High School, 2: Some College, 3: Bachelor's, 4: Higher).

StudyTimeWeekly

Weekly study time (0–20 hours).

Absences

Number of absences during the school year (0–30).

Tutoring

Whether the student receives tutoring (0: No, 1: Yes).

ParentalSupport

Level of parental support (0: None to 4: Very High).

3Using iDareAI

Guided Mode Initialization

AUploading Dataset

Click on the **'Upload CSV or Excel Data'** button → Select a source for the dataset → Upload `gpa_prediction_train.csv`. The system automatically analyzes the file, extracts column descriptions, and identifies the top value-adding targets for prediction.

BChoosing Analysis Mode

Choose between autonomous machine learning or manual building. In autonomous mode, simply ask a question like:

What changes can improve student GPA based on the patterns in this data?
Which factors in the data have the biggest impact on student GPA?

Operation Using Autonomous Guided Mode

AQuery Response

Increasing StudyTimeWeekly and providing adequate Tutoring and Parental Support is essential. Reducing absences is also crucial for higher GPA potential.

BAI Application

The dynamic system lets users test different scenarios by adjusting predictor values like study time and tutoring to see how they affect student outcomes.

Model Fine-Tuning/Manual Model Building

ASelecting Prediction Target

'GPA' column was selected as the target.

BSelecting Analysis Type

The analysis target is a numerical column. Hence, the 'Regression' analysis type is selected.

CSelecting Model Group/Item

DSelecting Features

Select features related to study habits, support systems, and participation.

ESelecting Training Level

The "Slow" training level with "High Performance" configuration was selected for thorough academic analysis.

AI Modeling Details

Linear Regression is considered the best ML model for GPA prediction with the highest accuracy and lowest possible error percentages.

Training Analysis Details

APredicted Target

BPredicted Trend

CError Trend

DFeature Importance

Finalize Models

Once satisfied with accuracy, click 'Deploy'. Real-time GPA monitoring will help in immediate academic intervention.

4AI APPLICATION

Manual Model Building

Modify sliders for `StudyTimeWeekly`, `ParentalSupport`, `Tutoring`, and `Absences` to see real-time impact on predicted GPA.

AI Application Demo

High values of StudyTimeWeekly and ParentalSupport, and low values of Absences yield a higher GPA prediction.

Saving the Project

Save your project by clicking the icon at the bottom left corner of the textbox.

Sharing the Project

Share the application for single on-demand predictions once the analysis is saved.

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