Student Placement Predictor

I already have the dataset, an initial Logistic Regression model, and a rough Streamlit prototype. I now need a clean, production-ready web application that lets a student enter basic details—academic scores, extracurricular activities, and technical skills—and instantly returns the percentage likelihood that they will be placed. No login or sign-up flow is required; the interface should open straight to the form and result. Here is what is in place so far: • Data: placement_data.csv inside /data • Training scripts: generate_data.py and train_model.py in /src • A pickled baseline model (placement_model.pkl) and a placeholder Streamlit file (app.py) You’ll refine or retrain the model with scikit-learn, tidy the codebase, and polish the Streamlit UI so the prediction feels seamless and engaging. Pandas will remain the core data-handling tool, and the final Logistic Regression model must be saved back to /models as placement_model.pkl for reproducibility. Deliverables 1. Updated, well-commented app.py with an intuitive layout, real-time prediction, and clear success/error messaging. 2. Any revised preprocessing or training code in /src, keeping the current folder structure intact. 3. A concise README that explains how to set up a Python virtual environment, install requirements, retrain the model, and launch the Streamlit app. 4. Model artefacts (placement_model.pkl) reflecting the latest training run. Acceptance criteria • Entering sample student data returns a probability score without crashes or visible stack traces. • The three input categories map cleanly to the trained feature columns. • All required Python libraries are declared in requirements.txt. • Streamlit run app.py starts the interface in one command on a fresh machine. If questions arise about feature engineering or UI flow, let me know early so we can keep the scope tight and deliver a smooth, informative experience for future users.