#machinelearning #datascience #python
In this comprehensive tutorial, we delve into the development of an Education Recommendation System or Studies Recommendation System using Python and machine learning. We start by exploring a dataset containing student information, including academic scores across various subjects, extracurricular activities, and career aspirations. After preprocessing the data, we implement a range of machine learning models and select the best-performing ones. These models are then serialized into pickle files for easy deployment.

Following that, we showcase the development of an end-to-end web application using Flask, where users can input their subject scores, and our recommendation system provides the top five or three future career aspirations based on their academic performance. Throughout the tutorial, we discuss the implementation details, model evaluation, and insights gained from the data.

Whether you're a beginner or an experienced data scientist, this tutorial offers valuable insights into building recommendation systems and deploying machine learning models for educational purposes.
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