Risny01/student-scholarship-predictor

Student Scholarship Predictor

A machine learning web application that predicts whether a student is likely to receive a scholarship based on their academic performance and personal attributes.

Dataset

The app uses a student performance dataset (Student_performance_dataset.csv) containing 6,000 student records with the following columns:

• —Student_ID / Student_Name — identifiers (not used in model)
• —Math_Score, Science_Score, English_Score — academic scores (0–100)
• —Attendance_Percentage — class attendance rate (0–100)
• —Age — student age
• —Gender — Male / Female / Other
• —Department — CS / ECE / IT / MECH / CIVIL
• —Scholarship — target label: Yes or No

One row with a missing Scholarship value is dropped before training.

Model

A RandomForestClassifier (scikit-learn) is trained with:

• —80/20 stratified train/test split (random_state=42)
• —n_estimators=200, max_depth=5, min_samples_leaf=10
• —class_weight="balanced" to handle any class imbalance

Gender, Department, and Scholarship columns are encoded with separate LabelEncoder instances. The live test accuracy is displayed in the app header.

Usage

Adjust the sliders and selectors to describe a student, then click Predict to see the scholarship outcome and probability breakdown.