Imjolayemi/ScholarSafe_Nigeria

ScholarSafe Nigeria is an AI-powered student dropout risk prediction for Nigerian schools. Identify at-risk students before it's too late. # ScholarSafe Nigeria 🎓 **Student Dropout Risk Predictor** Developed as part of the **3MTT NextGen Cohort 4 Knowledge Showcase**, this project leverages Machine Learning to proactively identify Nigerian students at risk of dropping out. By analyzing demographic, academic, and socio-economic factors, it provides educators and policymakers with actionable insights to improve student retention. ----- ## 📋 Table of Contents - Project Overview - Key Features - Technology Stack - Project Structure - Installation & Setup - Machine Learning Model - Author ----- ## 📂 Project Overview Nigeria faces significant challenges in student retention across various educational levels. **ScholarSafe Nigeria** is a data-driven tool designed for the Education pillar of the 3MTT program. The app predicts the probability of a student dropping out based on 27 distinct variables, including: * **Academic Performance:** Average test scores, score trends, and grade repetitions. * **Attendance:** Recent consecutive absences and overall attendance rate. * **Socio-Economic Factors:** Household income quintile, fee payment percentage, and distance to school. * **Vulnerability Indicators:** Early marriage risk, involvement in child labor, and household structure. ----- ## ✨ Key Features - **Bulk CSV Prediction:** Upload a batch of student records to generate risk scores for entire schools or districts. - **Single Student Analysis:** Use an interactive form to assess an individual student's risk profile. - **Risk Tier Classification:** Categorizes students into **Low**, **Medium**, or **High Risk** tiers with recommended actions. - **Interactive Visualizations:** Includes risk tier distribution charts and feature importance analysis. - **Custom UI:** A polished, "Nigeria-inspired" interface with theme-consistent styling. ----- ## 🛠 Technology Stack * **Language:** Python 3.14+ * **Web Framework:** Streamlit * **Machine Learning:** Scikit-learn (Random Forest, Logistic Regression) * **Da …