Student Performance Prediction System using Python Machine Learning (ML)
“Student Performance Prediction System ” is a web-based application that integrates machine learning with the Django framework. This project using machine learning and data analytics with help of this technique now it is possible to analyze large volumes of educational data and uncover patterns that can be used to forecast student performance more accurately. Predictive systems can assist educators in identifying students at risk of underperforming, enabling timely intervention and support. At the same time, such systems can help students monitor their academic progress, understand their strengths and weaknesses, and take corrective measures to improve outcomes. The system leverages Python libraries like NumPy, pandas, scikit-learn, and matplotlib, integrated with Django for web development, to provide predictions in a user-friendly interface. It allows users to predict their performance in both numeric and graphical formats.
Tech Stack Used
Frontend / Web Interface:
Django (Python Web Framework) — Used to create the web interface for user input, displaying predictions, and managing data
HTML5, CSS3, JavaScript — For rendering and styling web pages
Bootstrap (optional) — For responsive UI components
Django Templates — For dynamic web page rendering
Machine Learning / Backend Logic:
scikit-learn — Machine Learning library used to implement algorithms like Logistic Regression, Decision Tree, Random Forest, KNN
NumPy→ For numerical operations and matrix manipulation
Pandas → For handling and preprocessing datasets
joblib → To save and load the trained machine learning model
django-widget-tweaks →
django-widget-tweaksis a small but super useful Django package that helps you customise form fields in templates without changing the Python form class.
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Database:
SQLite — Lightweight relational database used to store user data and predictions
Django ORM (Object Relational Mapper) — Handles interaction between Django models and the SQLite database
Tools & Environment:
Python 3.x — Core programming language used
PyCharm — IDE for development
Virtualenv / pip — For managing dependencies
Key Features
Student Profile Management
Each student has a dedicated profile containing personal, academic, and contact details.
Stores important indicators such as attendance, study hours, past scores, assignments submitted, and extracurricular activities.
Performance Prediction using Machine Learning
Predicts academic performance (grades or outcomes) based on key study indicators.
Utilizes regression/classification models for accurate forecasting.
Provides probability/confidence scores along with the predicted results.
Interactive Dashboards
Displays predictions in graphical (charts/plots) and tabular formats.
Helps students and administrators visualize performance trends over time.
Provides insights into strengths, weaknesses, and academic progress.
Prediction History Tracking
Maintains a record of all past predictions for each student.
Enables comparison between past forecasts and actual academic outcomes.
Helps in monitoring improvement or decline in performance.
Role-Based Authentication
Students can register, log in, and view/manage their own performance predictions.
Administrators have access to manage courses, student profiles, and overall system usage.
Secure authentication using Django’s built-in user management system.
Admin Panel
Provides administrators with full control to manage students, courses, and prediction results.
Includes tools to monitor activity logs and ensure transparency.
Easy navigation with a user-friendly interface built using Bulma/Bootstrap.
Data Insights and Analytics
Offers detailed reports and visual analytics on student performance.
Identifies at-risk students early so that corrective action can be taken.
Helps educators and institutions make data-driven decisions.
Scalable and Extensible
Built with Django, ensuring modularity and scalability.
Machine learning models can be retrained with new datasets for improved accuracy.
Can be extended to support advanced features such as real-time monitoring, recommendation systems, or integration with LMS platforms.
How to run the Student Performance Prediction System using Python Machine Learning (ML)
1. Download the zip file
2. Extract the file, copy student_performance_django_ml, the folder and paste it on the desktop
3. Open PyCharm and import the project into PyCharm
4. Install five libraries (if not installed)
pip install joblib
pip install numpy
pip install scikit-learn
pip install pandas
pip install django-widget-tweaks
5. Run the Project using the following command
python manage.py runserver
Now, click the URL http://127.0.0.1:800,0 and the Project will run
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