Higher Education Students Performance Evaluation using Supervised Machine Learning Techniques Test phase, srs, design phase and source code final deliverable

Higher Education Students Performance Evaluation using Supervised Machine Learning Techniques Test phase, srs, design phase and source code final deliverable

Project Domain / Category
AI/Machine Learning/Prototype base
Abstract / Introduction
Most of the countries place significant emphasis on education to accelerate their growth. The student’s performance before entering the exams or taking the course is also consistent with the fact that well-educated people benefit their countries more. To improve individual student performance and meet this goal, the quality of education must improve in the current term. Students’ personal information, educational preferences and family history are some of the key indicators. Our objective in this project is to evaluate the higher education student’s performance using supervised Machine Learning algorithms(Decision trees, Artificial Neural Networks, and Support Vector Machine ) on the given dataset.
Pre-Requisites:
This project is easy and interesting but requires in depth study of data mining and machine learning techniques.
Dataset: https://archive.ics.uci.edu/ml/datasets/Higher+Education+Students+Performance+Evaluatio n+Dataset
Functional Requirements:
The following are the functional requirements of the project:
1. System must be set the environment online/offline (If Required)
2. System apply different data processing techniques (Data Normalization, Missing value imputation, Data Transformation, etc…)
3. System must be split the given dataset into testing and training.
4. System must trained the specified model.
5. User must be evaluate mentioned models in the form of Confusion Matrix, Accuracy, Precision, Recall
6. User must be discussed the results of given algorithms ( Decision trees, Artificial Neural Networks, and Support Vector Machine).
7. User must retrained the model if accuracy is not good (less than 60%) by changing different training parameters (If Required) Tools:
● Language: Python (Only python language)
● IDE: JupyterNotebook, Google Colab, Pycharm, Spyder, etc.
Supervisor:
Name: Talha Mahboob Alam