Student Placement Predictor App

I want to build a straightforward yet academically solid application that predicts a student’s chance of campus placement. The solution must be beginner-friendly so it can double as a B.Tech final-year project, but still follow good data-science practice.

Model & data
• I will supply—or you may suggest—a publicly available student dataset.
• The prediction model should be coded in Python using Pandas, NumPy and scikit-learn, focusing on grades and overall academic performance as the key features.
• Please include clear notebooks or scripts that show data cleaning, exploratory analysis, feature engineering, training, testing and the final accuracy metrics (precision, recall and overall accuracy).

Web interface
A lightweight front end is enough: Flask or Django are both acceptable, so choose whichever lets you move faster. The interface only needs a simple form for entering student details and a results page displaying the predicted placement probability alongside the model’s accuracy.

Deliverables
1. Complete, well-commented source code (Python + HTML/CSS/JS if used).
2. A requirements.txt or environment.yml for easy setup.
3. Step-by-step documentation explaining how to run the project, retrain the model with new data and deploy the web app locally or on Heroku/Render.
4. Screenshots or a short screen-capture video showing the working application and prediction output.

Acceptance criteria
• Model trains without errors and achieves clear accuracy metrics on a held-out test set.
• Web app runs locally with one command and returns predictions instantly.
• Code and docs are understandable for an undergraduate audience.

If this sounds like something you can craft efficiently, let’s talk timelines and milestones so I can integrate it into my final-semester submission.