GradeAssist to analyze student data and identify key patterns and trends. Here's how I built it:
1. Data Collection: Used Kaggle to source the CSV data file.
2. Data Import: Imported the CSV into an SQL server.
3. Data Understanding: Analyzed the information to uncover insights.
4. SQL to Python Integration: Connected the database to Python for further analysis.
5. Data Visualization: Leveraged Tkinter to visualize the data effectively.
Technologies powering GradeAssist:
- Python: Core language for data manipulation
- Tkinter: Created an intuitive graphical user interface
- Matplotlib: Advanced mathematical calculations and visualizations
- Pandas: Saved data into an Excel file
- Pypyodbc: Connected the database to Python
- NumPy: Converted lists into arrays for operations
- fpdf: Generated PDF reports
اسم المستقل | صافيناز ا. |
عدد الإعجابات | 0 |
عدد المشاهدات | 2 |
تاريخ الإضافة |