🎓 Decision Support Platform for Students Using Machine Learning

🚀 Overview

This project aims to develop a decision support platform for students, leveraging machine learning algorithms to provide personalized recommendations. The platform assists students with critical decisions in their academic journey, such as course selection, career orientation, and time management. By using a combination of data collection, machine learning models, and real-time recommendation systems, this platform aims to enhance student decision-making and improve their academic and career outcomes.

📌 Key Highlights

• Machine learning-driven recommendations for course selection, career paths, and time management.
• Survey-based dataset of student needs and behaviors to tailor recommendations.
• Various machine learning algorithms applied, including classification and regression models (Logistic Regression, KNN, SVM).
• Real-time adaptive recommendations based on student choices and behaviors.
• Orientation scenario simulations to visualize potential academic and career impacts.

🔬 Methodology

Data Collection:

• Survey-based data collection to understand student needs.
• Utilization of publicly available educational datasets.

Preprocessing & Analysis:

• Data cleaning, missing value handling, and exploratory data analysis (EDA) to uncover trends and patterns.

Model Development:

• Implementation of machine learning models, including classification algorithms (Logistic Regression, KNN, SVM) and regression algorithms for predicting academic performance.
• Evaluation and comparison of models using accuracy, precision, recall, F1-score, and cross-validation.

Real-time Recommendations:

• A dynamic system that updates recommendations based on the student’s behavior and choices on the platform.

🎯 Impact & Contribution

• Provides personalized, data-driven recommendations to students, improving their academic and career decisions.
• Bridges machine learning with student decision-making to enhance student outcomes.
• Contributes to the broader educational field by offering insights into machine learning's potential in educational decision support systems.

🛠️ Technologies Used

• Python, Scikit-learn, TensorFlow
• Classification Algorithms: Logistic Regression, KNN, SVM
• Regression Models for academic performance prediction
• Cloud-Based Data Repositories for storing and sharing survey results and datasets
• Visualization Tools: Matplotlib, Seaborn, Plotly

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👨‍💻 Contributors

• Timothy Adeyemi 🚀
  • GitHub: @iamadeyemi
  • LinkedIn: iamadeyemi

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📜 License

This project is licensed under the MIT License – feel free to use and improve it!

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Happy coding! 🚀🏡💻