Project Overview

Goal Evaluator as tool for identification of problematic studying behaviour of students and advices on learning optimization

Dataset Description

Categorised based on 3 main datasets :

• Student Behavior Study​
• Courses and Credits​
• Students Information

You could find the “Student Behavior Study​ ” dataset at this link : https://la-api.codeiin.com/students/behaviour

project architecture

Implementation Technologies

This project is based on the following technologies:

• Front-End
  • Dashboard
  • React.js library
• Visualisation
  • D3.js / C3.JS
• Back-End
• Web Server
  • Python
  • Flask
• Machine Learning Pipeline
  • Scikits Learn
• Database
  • mongoDB

Machine Learning Pipeline

Machine Learning with sklearn, pandas.
To run the code you need to import standard libraries for data preparation and analysis.

import numpy as np
import pandas as pd
import requests
import json
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt

Phases

• Outlier Detection (LocalOutlierFactor)
• Feature Selection (Feature Importances)
• Model Selection
• Hyper Parameter Tuning on the RidgeRegression Model including KFold Cross Validation(GridSearchCV)
  • Parameters to be optimize: param = { 'solver':['svd', 'cholesky', 'lsqr', 'sag'],'alpha': [1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 1, 10, 100], 'fit_intercept':[True, False],'normalize':[True, False]}
  • Optimization parameter: 'neg_mean_absolute_error'

Visualisation

All Visualisation chart is built using: C3/D3.js

To deploy the project

you need to install below requirements on our system:

For the server: First Download Python-3.9.16

you need to install the requirements on our system:

pip install -r requirements.txt

or

pip3 install -r requirements.txt

Installation for Macbook M1

• brew install miniforge
• conda create -n sklearn-env -c conda-forge scikit-learn --file requirements.txt
• conda activate sklearn-env

How to Run :

• conda activate sklearn-env
• python ./src/main.py

For the Frontend:

To install the packages run :

• npm install

Then, run the development server:

• npm run dev

or

• yarn dev

Group Members

• Hla Abuhamra
• Heiner Ploog
• Hadil Khbaiz
• Ruidan Liu
• Yifei Yao