Predicting Student Academic Performance

on

Introduction

This project demonstrates how data science techniques can be applied in the education sector to predict student academic performance. Using Python and Jupyter Notebook, the project covers the complete workflow—from data collection and analysis to building and evaluating a predictive model. The goal is to showcase how data-driven insights can support teachers, administrators, and educational institutions in understanding student outcomes.

๐ŸŽฏ Project Objectives

1. Collect and Analyze Student Data

The project begins with gathering student-related information such as attendance, study hours, past grades, and other influencing factors.
Data preprocessing steps like handling missing values, encoding categorical variables, and feature scaling are performed to prepare the dataset for modeling.

2. Explore Relationships Between Factors and Academic Outcomes

Exploratory Data Analysis (EDA) is conducted to identify:

  • Patterns and correlations

  • Factors that significantly impact student performance

  • Insights that can guide feature selection

Charts, graphs, and statistical summaries help visualize these relationships.

3. Train a Prediction Model Using Linear Regression

A Linear Regression model is trained to predict student academic results based on the available features.
The notebook walks through:

  • Splitting the dataset into training and testing sets

  • Fitting the linear regression model

  • Understanding coefficient values and their impact on prediction

4. Evaluate Model Accuracy

Various performance metrics are used to measure the effectiveness of the model, such as:

  • Mean Absolute Error (MAE)

  • Mean Squared Error (MSE)

  • R-squared (R²)

These metrics help determine whether the prediction model is reliable and how it can be improved.

5. Demonstrate Real-World Use of Data Science in Education

This project highlights how machine learning can support:

  • Identifying at-risk students

  • Improving academic planning

  • Making data-backed decisions in schools or training institutions

It serves as a practical example for learners and educators interested in applying data science to real educational challenges.



Comments

Post a Comment