Challenge background
Student success in the education system in Turkey can be influenced by various factors. This project aims to develop a model using data science and machine learning techniques to predict student success.
Within the scope of the project, demographic information, socio-economic status, learning environments, school performance, and other relevant data of students will be used. These data will be analyzed to identify factors that affect student success and to create a machine learning model that predicts student performance.
During the development of the model, various machine learning algorithms can be utilized, such as linear regression, decision trees, support vector machines, or artificial neural networks. Additionally, based on data-driven insights, recommendations and strategies can be provided to improve student success.
The problem
In this project, the Omdena Ankara, Turkey Chapter team will be utilizes data analysis and machine learning methods to enhance student success in the education system and promote data-driven education policies. The findings obtained will be utilized by education administrators, teachers, and policymakers to identify more effective strategies for enhancing students' academic achievements.
Goal of the project
In this project, the Omdena Ankara, Turkey Chapter team will be utilizes data analysis and machine learning methods to enhance student success in the education system and promote data-driven education policies. The findings obtained will be utilized by education administrators, teachers, and policymakers to identify more effective strategies for enhancing students' academic achievements.
Project timeline
- 1
Week 1
Dataset Scoping
- 2
Week 2
Dataset Scoping
- 3
Week 3
Dataset Preprocessing
- 4
Week 4
Dataset Preprocessing
- 5
Week 5
Model Selection
- 6
Week 6
Model Selection
- 7
Week 7
Model Training & Evaluation
- 8
Week 8
Model Training & Evaluation
What you'll learn
Machine Learning, Data Visualization, Data Analysis, EDA