Title: Predictive Analysis of Student Performance using Machine Learning in Python
Abstract:
In the realm of education, understanding and predicting student performance is crucial for effective intervention and support. This college project focuses on employing machine learning techniques to conduct predictive analysis of student performance, utilizing the versatile programming language Python. The aim is to develop a model that can forecast student outcomes based on various academic and non-academic factors.
The project begins by collecting a diverse set of data, including academic records, attendance, study habits, and socio-economic background. This data is then preprocessed to ensure accuracy and completeness, followed by feature selection to identify the most relevant variables influencing student performance.
Python’s powerful machine learning libraries, such as scikit-learn and TensorFlow, will be employed to build and train predictive models. The project will explore various algorithms, including regression and classification techniques, to determine the most effective approach for predicting student grades and identifying at-risk students.
The significance of this project lies in its potential to assist educational institutions in early identification of students who may require additional support. By leveraging machine learning, educators can proactively address challenges faced by students, fostering a more personalized and effective learning environment. The outcomes of this research aim to contribute valuable insights to the field of education and enhance strategies for student success.