By
@Ilina
A simple machine learning web app that predicts student marks based on user inputs. Built using Python and Streamlit with an interactive, easy-to-use interface. This project demonstrates the basic workflow from data and prediction to a deployed web application.
@Ilina
on
Student Marks Prediction
I built this interactive web app using Python, Machine Learning, and Streamlit. The app takes student-related inputs and uses a machine-learning model to predict academic marks.
My project predicts a student’s marks based on the information entered into the app. The user provides the required student-related details, and the machine-learning model analyses those inputs to generate an estimated score.
The project aims to show how data and machine learning can help us understand and predict academic performance in a simple, interactive way.
What I enjoyed most was turning a machine-learning model into something people can actually use. I learned how to work with data, build a prediction model, connect it to a user-friendly interface, and deploy the final app online.
For me, this project was much more than just making a prediction. It helped me understand how to take an idea, work with data, build a model, and turn it into a real web application that others can use.
Try my project here:
https://student-marks-prediction-ilina-k-sri-yoeftkx66b55vgcqhvteea.streamlit.app/
I’m still learning, experimenting, and improving with every project I build, so your support would mean a lot. It would also help me get closer to my goal of getting a 3D printer.
Thank you so much for checking it out!
@Ilina
on
Student Marks Prediction
1h 58m 44s logged
Student Marks Prediction
An interactive Machine Learning web application that predicts student marks based on user-provided information.
The project demonstrates how data science and machine learning can be combined with Streamlit to turn a prediction model into a simple, interactive web application.
About the Project
Student performance can be influenced by several factors. This project explores how data can be used to build a predictive model for estimating a student’s marks.
Instead of keeping the machine-learning model inside a notebook or Python script, I created an interactive Streamlit interface that allows users to enter information and receive a predicted result.
This project helped me understand the complete basic machine-learning workflow:
Data → Analysis → Model → Prediction → Interactive Web App
Features
Uses data to make predictions about student performance
Applies machine-learning concepts
Accepts user input interactively
Generates a predicted student mark
Provides a simple web-based interface
Gives predictions instantly
Deployed online using Streamlit
Technologies Used
Python
Streamlit
Data Science
Machine Learning
Pandas
Scikit-learn
How It Works
The application follows a simple prediction pipeline:
User Input
↓
Data Processing
↓
Machine Learning Model
↓
Prediction
↓
Predicted Student Marks
Users enter the required information through the Streamlit interface. The application processes the values and sends them to the trained prediction model.
The model then calculates an estimated score and displays the prediction to the user.
Try the App
https://student-marks-prediction-ilina-k-sri-yoeftkx66b55vgcqhvteea.streamlit.app/
No installation is required to use the online version.
What I Learned
Through this project, I developed experience with:
preparing data for machine-learning tasks
understanding relationships between variables
building a prediction workflow
using Python for data science
working with machine-learning models
accepting and processing user input
connecting a model to a web interface
creating interactive applications with Streamlit
deploying a data-science project online
One of the most useful lessons from this project was learning that a machine-learning model becomes much more accessible when users can interact with it through a simple interface.
