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Student-Dropout-Classification

Overview

This Jupyter Notebook project was originally completed three months ago. Recently, I made some modifications to improve its functionality and accuracy. However, please note that this notebook may not be as efficient or up-to-date as some of the other notebooks available on my GitHub (e.g Bank Marketing Campaign). It serves as a valuable learning experience and showcases the progress made over time.

Features

  • Detailed analysis and insights
  • Data cleaning and preprocessing
  • Visualization of key metrics
  • Model optimization and fitting
  • Model evaluation and visualization

Modifications

  • Updated model fitting
  • Enhanced data visualization
  • Improved code efficiency
  • Boosted code clearness and stability
  • Elevated model accuracy and precision

Skills Gained

  • Data analysis and manipulation using Pandas
  • Data visualization with Matplotlib and Seaborn
  • Machine learning model implementation with Scikit-learn
  • Hyperparameter tuning and model evaluation
  • Feature selection and dimensionality reduction

License

This project is licensed under the MIT License.

About

This repo works on a kaggle dataset. it provides a comprehensive view of students enrolled in various undergraduate degrees offered at a higher education institution. It includes demographic data, social-economic factors and academic performance information that can be used to analyze the possible predictors of student dropout and academic success.

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