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DATA-ANALYTICS Project
Online Quiz School Data Analytics Final Year Project Source Code
Get runnable Online Quiz School Data Analytics Final Year Project Source Code with database files, project setup instructions, live demo options and installation support. This project resource helps students understand the implementation, modules, workflow and technical architecture of a complete DATA-ANALYTICS project.
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Project's Overview
Online Quiz School Data Analytics Project is a Python-based final year project developed for analyzing student exam performance using exploratory data analysis, visualization, and machine learning. This major project uses the Students Performance in Exams dataset from Kaggle and analyzes student scores based on gender, race/ethnicity, parental education level, lunch type, test preparation course, math score, reading score, and writing score. The project performs EDA, checks dataset shape, data types, missing values, statistical summaries, and correlations. It generates six report-ready visualizations and trains machine learning models such as Random Forest and Logistic Regression to predict student Pass/Fail status. This Online Quiz School data analytics source code is suitable for students who need a final year project, major project, minor project, source code, and project report based on Python, education analytics, exam performance analysis, and machine learning.
Login Credentials
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.
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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/
- Gender Score Comparison
File:01_gender_score_comparison.png
Chart type: Bar chart
Shows score comparison based on gender. - Ethnicity Score Boxplot
File:02_ethnicity_score_boxplot.png
Chart type: Box plot
Shows score spread across race/ethnicity groups. - Correlation Heatmap
File:03_correlation_heatmap.png
Chart type: Heatmap
Shows correlation between math, reading, and writing scores. - 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. - Score Histogram
File:05_score_histogram.png
Chart type: Histogram
Shows score distribution. - 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:
genderrace/ethnicityparental level of educationlunchtest preparation coursemath scorereading scorewriting 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 scriptdownload_kaggle_data.py— Kaggle dataset downloaderrequirements.txt— Python dependency listREADME.md— project documentationStudentsPerformance.csv— optional real Kaggle datasetoutputs/— generated visualization folder
Output Files in this Final Year Project
The project generates graphs inside:
outputs/
Expected output files:
01_gender_score_comparison.png02_ethnicity_score_boxplot.png03_correlation_heatmap.png04_test_prep_pie.png05_score_histogram.png06_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
-
Open terminal in the project folder:
cd "online-quiz-school-data-analytics-project" -
Install dependencies:
pip install -r requirements.txt -
Optional: download real Kaggle data:
python download_kaggle_data.py -
Or manually download the dataset and place it in the project folder:
StudentsPerformance.csv -
Run the project:
python online_quiz_school_analysis.py -
Check generated outputs:
outputs/
Option 2: Run using IDLE
-
Open:
online_quiz_school_analysis.py -
Press:
F5 -
Or select:
Run → Run Module -
Check generated graph files in:
outputs/
Kaggle Setup
To use the real Kaggle dataset:
-
Install Kaggle CLI:
pip install kaggle - Create a Kaggle account.
-
Go to:
Kaggle Account → Create New API Token -
Place
kaggle.jsonin:~/.kaggle/ -
On Windows, place it in:
C:\Users\<user>\.kaggle\ -
Run:
python download_kaggle_data.py
README Included
Setup Support Available
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.