Overview
This project focuses on developing a recommendation and scheduling system for students to optimize their study patterns. By leveraging a combination of a recommendation engine, genetic algorithms, and data analysis, the system provides personalized study timetables to enhance learning efficiency.
Project Components
Recommendation Engine
- File:
Recommendation.py - Description: Implements a recommendation engine that suggests study schedules based on the student's historical data and preferences.
Genetic Algorithm
- File:
TimetableLogic.py - Description: Utilizes a genetic algorithm to optimize the study timetable by considering various constraints and objectives.
Data Analysis
- File:
Data_preprocessing.ipynb - Description: Notebook for preprocessing and analyzing the dataset to extract meaningful patterns and insights.
- File:
Synthetic_DataCreation.ipynb - Description: Notebook for generating synthetic data to augment the training dataset.
Timetable Scheduling
- File:
WeeklyTimetable.py - Description: Logic for creating weekly timetables based on the recommendations and genetic algorithm outputs.
- File:
main.py - Description: Main script that integrates all components to generate and display the final study timetable.
Datasets
- International Students Time Management Data:
International students Time management data.csv - Student Study Hour Data:
Student Study Hour V2.csv - Final Data:
final_data.csv
Technologies
- Programming Language: Python
- Libraries and Frameworks:
- Pandas
- NumPy
- Scikit-learn
- TensorFlow
- Matplotlib
- Seaborn
- Tools:
- Jupyter Notebook
- Git
- GitHub
Repository
You can find the complete code and resources for this project in the GitHub repository.
Created as part of an effort to improve student time management and study efficiency through advanced algorithms and data analysis.