Online Quiz School Data Analytics Final Year Project with Source Code

Project Features in this Final Year Project

• Online Quiz School data analytics
• Student performance analysis
• Kaggle Students Performance in Exams dataset support
• Built-in sample data support
• Exploratory data analysis
• Dataset shape inspection
• Data type inspection
• Missing-value analysis
• Statistical summary generation
• Correlation analysis
• Gender-wise score comparison
• Ethnicity-wise score comparison
• Test preparation analysis
• Score distribution analysis
• Parental education pass-rate analysis
• Pass/Fail prediction
• Random Forest model
• Logistic Regression model
• Six visualization outputs
• Output graph export
• Python script execution
• IDLE execution support
• Suitable for project report and viva explanation

Data Analytics / EDA Features in this Final Year Project

• Loads Students Performance in Exams dataset
• Supports real Kaggle dataset download
• Supports manual dataset placement
• Uses built-in sample data if real dataset is not available
• Displays total rows and columns
• Displays column data types
• Checks missing values
• Prints statistical summaries
• Calculates correlations
• Analyzes gender-based score comparison
• Analyzes race/ethnicity-based score variation
• Analyzes test preparation course distribution
• Analyzes score histogram
• Analyzes parental education and pass rate
• Supports education analytics report writing

Visualization Features in this Final Year Project

Graphs are saved inside:

outputs/

1. Gender Score Comparison
   File: 01_gender_score_comparison.png
   Chart type: Bar chart
   Shows score comparison based on gender.
2. Ethnicity Score Boxplot
   File: 02_ethnicity_score_boxplot.png
   Chart type: Box plot
   Shows score spread across race/ethnicity groups.
3. Correlation Heatmap
   File: 03_correlation_heatmap.png
   Chart type: Heatmap
   Shows correlation between math, reading, and writing scores.
4. Test Preparation Pie Chart
   File: 04_test_prep_pie.png
   Chart type: Pie chart
   Shows distribution of students who completed or did not complete the test preparation course.
5. Score Histogram
   File: 05_score_histogram.png
   Chart type: Histogram
   Shows score distribution.
6. Education Pass-Rate Comparison
   File: 06_education_passrate.png
   Chart type: Pass-rate comparison chart
   Shows how parental education level relates to student pass rate.

Machine Learning Features in this Final Year Project

• Student Pass/Fail prediction
• Education-based classification problem
• Uses Random Forest model
• Uses Logistic Regression model
• Predicts student performance outcome
• Uses exam score and student-related features
• Supports model comparison
• Useful for understanding student performance factors
• Suitable for machine learning project explanation
• Supports academic prediction and education analytics demonstration

Dataset Details in this Final Year Project

Dataset name:
Students Performance in Exams

Dataset source:
Kaggle

Dataset domain:
Online Quiz School / Student Performance / Education Analytics

Dataset columns:

• gender
• race/ethnicity
• parental level of education
• lunch
• test preparation course
• math score
• reading score
• writing score

Target output:
Pass/Fail prediction based on student performance.

Manual dataset file:

StudentsPerformance.csv

Dataset placement:
Place the CSV file in the project folder.

Files / Modules Included in this Final Year Project

• online_quiz_school_analysis.py — main Python analysis and ML script
• download_kaggle_data.py — Kaggle dataset downloader
• requirements.txt — Python dependency list
• README.md — project documentation
• StudentsPerformance.csv — optional real Kaggle dataset
• outputs/ — generated visualization folder

Output Files in this Final Year Project

The project generates graphs inside:

outputs/

Expected output files:

• 01_gender_score_comparison.png
• 02_ethnicity_score_boxplot.png
• 03_correlation_heatmap.png
• 04_test_prep_pie.png
• 05_score_histogram.png
• 06_education_passrate.png

Console output includes:

• Dataset shape
• Data type summary
• Missing-value summary
• Statistical summary
• Correlation details
• Machine learning output
• Pass/Fail prediction results

Option 1: Run using Python script

1. Open terminal in the project folder:

   cd "online-quiz-school-data-analytics-project"

2. Install dependencies:

   pip install -r requirements.txt

3. Optional: download real Kaggle data:

   python download_kaggle_data.py

4. Or manually download the dataset and place it in the project folder:

   StudentsPerformance.csv

5. Run the project:

   python online_quiz_school_analysis.py

6. Check generated outputs:

   outputs/

Option 2: Run using IDLE

1. Open:

   online_quiz_school_analysis.py

2. Press:

   F5

3. Or select:

   Run → Run Module

4. Check generated graph files in:

   outputs/

Kaggle Setup

To use the real Kaggle dataset:

1. Install Kaggle CLI:

   pip install kaggle

2. Create a Kaggle account.

3. Go to:

   Kaggle Account → Create New API Token

4. Place kaggle.json in:

   ~/.kaggle/

5. On Windows, place it in:

   C:\Users\<user>\.kaggle\

6. Run:

   python download_kaggle_data.py

This project has no login credentials because it is not a web application.

Credential note:
No admin panel, user login, password, database authentication, or dashboard login module is included. The project runs locally as a Python data analysis and machine learning script.