Analyzing student performance using Python and machine learning. This presentation showcases a Streamlit application designed to analyze student grades, predict success, and inform teaching strategies. Objectives: • To build a machine learning model to analyze and predict student performance based on subject-wise marks. • To design an intuitive, user-friendly web application to input and display student data. • To enhance data-driven decision-making in educational institutions by providing insights into student performance.
Features: • Input Collection: Users can enter subject-wise marks for individual students through a simple form. • Automatic Calculation: The system will calculate the average score based on the entered marks. • Pass/Fail Prediction: Based on the average score, the model predicts the pass/fail status of the student. • Performance Visualization: Subject-wise performance will be presented in the form of interactive charts for easy analysis.
Core Functionalities: • Enter Marks: Input marks for each subject to calculate the student's average. • View Results: Display the student's pass/fail status and overall performance summary. • Visualize Data: Show subject-wise performance using graphs such as bar charts or pie charts. • Prediction Model: Machine learning algorithm to predict pass/fail status based on historical data.
HOW THE SYSTEM WORKS
- User Inputs: o Educators begin by entering the student’s name and marks for each core subject, such as Mathematics, Physics, Chemistry, English, and Computer Science. The system ensures that the input is clean and consistent for accurate analysis.
- Model Processing: o The machine learning model processes the input data, calculating the average score based on the marks entered for each subject. Using this average score, the model then determines if the student passes or fails, applying a predefined threshold (e.g., 40%).
- Display Results: o The application dynamically displays the student’s average score, pass/fail status, and visualizes the student’s performance in each subject through charts, providing a clear overview of their strengths and weaknesses.
- Feedback Loop: o The system allows educators to input data for multiple students sequentially, enabling easy analysis and comparison of performance across different students.