- Member 1: Rithika Mary shimit - Jain University Kochi
- Member 2: Joshna Joy N - Jain University Kochi
https://joshnajoyn517-bot.github.io/CodeX/
Placement Predictor is a web-based tool that helps students estimate their placement chances based on skills, academic performance, and other criteria. It provides insights and suggestions to improve placement readiness.
Students often don’t know their placement readiness level or what skills they need to improve.
Our platform analyzes user inputs and predicts placement chances while giving recommendations to enhance skills and opportunities.
For Software:
- Languages used: HTML, CSS, JavaScript
- Frameworks used: None
- Libraries used: None
- Tools used: VS Code, GitHub
List the key features of your project: -Student Data Input: Users can enter academic details like CGPA, skills, and experience.
-Placement Prediction: Predicts placement chances using your trained model/logic.
-User-Friendly Interface: Simple web interface to enter data and view results quickly.
-Real-time Results: Instant prediction output with clear status or score.
Home page showing placement prediction form
User entering academic details
Prediction result displayed after submission
System Architecture:
-User enters data
-JavaScript processes input
-Prediction logic calculates result
-Output displayed instantly on webpage
Application Workflow
-User opens website
-Inputs academic details
-Clicks predict button
-System processes data
-Result displayed instantly
https://drive.google.com/file/d/1sUh2uKPu-h2a9F98mobaWwKBDzkZl2Lm/view?usp=sharing
This video demonstrates the complete user flow of the Placement Predictor — entering student details, selecting skills, generating placement prediction results, and showing key features like real‑time analysis and recommendation output.
Live Website:https://joshnajoyn517-bot.github.io/CodeX/
Tool Used: ChatGPT Purpose:
-Help with HTML/CSS layout
-JavaScript logic ideas
-Debugging small errors
-README documentation help
Key Prompts Used:
-Create a simple placement prediction form using HTML CSS JS
-Help me write JavaScript logic for prediction result display
-Fix layout issues in my webpage
Help me write project documentation
Percentage of AI-generated code:
- 10–20%
Human Contributions:
-UI design and layout decisions
-Prediction logic creation
-Testing and debugging
-GitHub deployment
-Documentation editing
Rithika Mary Shimit – UI Design, Documentation, Testing
Joshna Joy N – Frontend Development, Deployment, Prediction Logic
Made with ❤️ at TinkerHub