Mainabryan/-DropNet-Predicting-University-Dropout-Risk-in-Kenya

Predicts university dropout risk in Kenya using logistic regression based on household income, HELB support, and financial stress levels. Built with Python and Streamlit. ##OVERVIEW OF STREAMLIT #Predict te overview of the schooldrop_out # -DropNet-Predicting-University-Dropout-Risk-in-Kenya Predicts university dropout risk in Kenya using logistic regression based on household income, HELB support, and financial stress levels. Built with Python and Streamlit. # 🎓 Kenya University Dropout Risk Predictor A data-driven machine learning project that predicts the likelihood of a university student in Kenya dropping out due to financial stress, limited HELB support, or high academic burden. --- ## 📌 Problem Overview Many Kenyan university students face **financial instability**, rising tuition fees, reduced government support (like HELB), and mental health struggles. These factors directly impact student retention, performance, and future opportunities. With recent government changes in student funding, it's become crucial to understand **which students are most at risk of dropping out** — and why. --- ## ✅ Project Goal To build an **interactive machine learning app** that: - Predicts dropout risk based on a student’s background and financial data - Highlights the impact of financial aid, scholarships, and mental health risk - Offers policymakers and citizens a way to simulate funding outcomes ---) ## 📥 1. Loading the Dataset & EDA (Exploratory Data Analysis) We began by importing a custom dataset simulating the financial and academic details of university students in Kenya. ### Why This Step Matters: - Understanding the **structure and types of data** is critical before modeling. - We explored key variables like `household_income`, `program_cost_per_year`, and `financial_stress_level` to identify trends, outliers, and relationships. ### Actions Taken: - Loaded the data using Pandas - Inspected data types, column distribution, and sample rows - Visualized variables using **Seaborn** and **Matplotlib**: - Histograms for income & academic performance - Count plots for dropout risk by stress level - Box plots to check income v …