Turkiye Student Evaluation Analysis Using Advanced Machine Learning Techniques for Optimized Educational Interventions and Improved Learning Outcomes

AI @ Freshers.in

Project Abstract:

Background: Assessing student performance and understanding the factors influencing their academic success are essential for designing targeted educational interventions and improving learning outcomes. The Turkiye Student Evaluation dataset provides a valuable resource for analyzing student performance using machine learning (ML) techniques. This project aims to develop a robust and reliable student evaluation analysis model using advanced machine learning techniques to facilitate data-driven decision-making in education and contribute to optimized educational interventions.


  1. To collect, preprocess, and analyze the Turkiye Student Evaluation dataset, which includes student performance data and various features related to their academic experience.
  2. To identify the most relevant features for effective student evaluation analysis using feature selection techniques.
  3. To implement various machine learning algorithms, including classification, regression, ensemble methods, and deep learning, to create a high-performance student evaluation analysis model.
  4. To evaluate the performance of the analysis model using appropriate metrics and validate its effectiveness in predicting student performance and identifying influential factors.
  5. To provide actionable insights and recommendations for educators, administrators, and policymakers based on the student evaluation analysis model’s output.


  1. Data collection and preprocessing: The project will involve the collection and preprocessing of the Turkiye Student Evaluation dataset. Data preprocessing steps, such as data cleaning, normalization, and encoding, will be performed to ensure the data is suitable for ML model training.
  2. Feature selection: Techniques such as Recursive Feature Elimination (RFE), Principal Component Analysis (PCA), and correlation analysis will be used to identify the most relevant features for student evaluation analysis.
  3. Model development: ML algorithms, including Logistic Regression, Decision Trees, Random Forest, XGBoost, and deep learning models like Neural Networks, will be applied to develop the student evaluation analysis model. Hyperparameter tuning and model selection will be conducted through cross-validation and grid search techniques.
  4. Model evaluation: The performance of the ML models will be assessed using metrics such as accuracy, precision, recall, F1-score, and area under the Receiver Operating Characteristic (ROC) curve.
  5. Insights and recommendations: The student evaluation analysis model’s output will be analyzed to derive actionable insights and recommendations for educators, administrators, and policymakers, enabling data-driven decision-making and optimized educational interventions.

Expected Outcomes: The project will result in a comprehensive student evaluation analysis model capable of accurately predicting student performance and identifying influential factors based on the Turkiye Student Evaluation dataset. The implementation of this model in the education sector will enable educators, administrators, and policymakers to make informed decisions, ultimately contributing to optimized educational interventions and improved learning outcomes for students.

Keywords: Turkiye Student Evaluation, machine learning, student performance, feature selection, data preprocessing, model evaluation, educational interventions, learning outcomes, data-driven decision-making.

Author: user

Leave a Reply