This project aims to predict students' academic performance using machine learning techniques. By analyzing various factors that influence academic success, the model provides insights to help educators identify students who may need additional support.
The repository includes:
- Dataset: dataset-raw.csv containing relevant student data.
- Python Code: Students' Academic_PythonCode.ipynb implementing the prediction model.
- Presentation: Students' Academic_Presentation.pdf summarizing the project's findings and methodologies.
The project involves:
- Data Preprocessing: Cleaning and preparing the data for analysis.
- Exploratory Data Analysis (EDA): Visualizing data to identify patterns and correlations.
- Model Training: Implementing machine learning algorithms to predict academic performance.
- Evaluation: Assessing the model's accuracy and refining it for better predictions.
The findings and performance metrics of the prediction model are detailed in the "Students' Academic_Presentation.pdf" . This presentation provides a comprehensive overview of the project's outcomes and insights.