Welcome to my Student Dropout Prediction App!

In this video, I showcase a machine learningpowered web application built using Streamlit that predicts student outcomes: dropout, enrollment, or graduation. This app provides an interactive interface for exploring student data, performing detailed analyses, and generating predictions based on academic and personal features.

Key Features of the App:
Data Visualization: Univariate, Bivariate, and Multivariate Analysis (PCA) using Matplotlib and Seaborn to detect patterns and relationships.
XGBoost Prediction Model: A robust machine learning model trained on academic data to predict student outcomes.
⚡ Interactive Interface: Built with Streamlit, the app allows users to input features like admission grade, previous qualifications, and more to make predictions in realtime.
PCA Visualization: Principal Component Analysis to reduce data dimensionality and visualize underlying patterns.
Tech Stack:
Python: Data handling with Pandas, machine learning with XGBoost, and Scikitlearn.
Streamlit: Fast and interactive app development.
Matplotlib & Seaborn: Powerful visualizations for insights.
PCA: Used for multivariate analysis to uncover hidden data structure.

Whether you're interested in data science, education analytics, or machine learning models, this video will walk you through how we can predict and visualize student outcomes with realworld data.

Project GitHub: https://github.com/matidesalegn/Stude...]
Try the App: https://studentdropoutpredictionmo...

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