The goal of this project is to develop a predictive model using machine learning classification algorithms to identify students who are likely to drop out.
the aim is to build a robust predictive model that can effectively forecast the likelihood of students dropping out.
In today's educational landscape, student retention and success are of utmost importance for educational institutions. Identifying students who are at risk of dropping out and implementing timely interventions can greatly contribute to improving graduation rates and ensuring academic success.
- Data collected through Kaggle datasets.
- Data processing and descriptive analysis
- Exploratory Data Analysis using Python visualization tools to gain insights into the data and identify any patterns or trends.