Student Admission Prediction (SAP-Project)

Project Idea

This is a student project about predicting the chance of admission. For this project we are using Graduate Admission Dataset from Kaggle. We will use machine learning to analyze the data, find a model to predict the University Ranking and then visualize the result.

Requirments and Preparation

• Required Libraries

  • Dash
  • Numpy
  • Pandas
  • scikit-learn
  • Joblib

• Start Server

  • Create a virtual environment for the project: python -m venv venv
  • Activate the virtual environment for windows: venv\Scripts\activate
    • Activate the virtual environment for Linux/macOS: source venv/bin/activate
  • Install required libraries in the project: pip install dash pandas numpy joblib
  • Start server: python app.py

Implementation approach

Dataset:

https://www.kaggle.com/mohansacharya/graduate-admissions?select=Admission_Predict_Ver1.1.csv

Algorithms:

• Regression
  • DecisionTree
  • Linear Regression
  • RandomForest
  • KNeighbours
  • SVM
  • AdaBoostClassifier
  • GradientBoostingClassifier
  • Ridge
  • BayesianRidge
  • ElasticNet
  • HuberRegressor

Tools:

• DASH/Plotly

Project Arcithecture:

• Machine learning model: regression with RandonForestRegressor
• Backend: Flask which is embedded in Dash
• Frontend: Dash/Plotly

Visualization:

Home

Dataset

Dashboard

Machine Learning

Prediction

SAP-Project on GitHub:

https://github.com/LameesKadhim/SAP-project

SAP-Project on Heroku:

https://predict-student-admission.herokuapp.com/

SAP-Project Video Trailer:

https://youtu.be/rXDHiqIxYuQ

Deployment steps on Heroku

• Step 1. Create a new folder for your project:

  $ mkdir sap-project

  $ cd sap-project

• Step 2. Initialize the folder with git and a virtualenv

  $ git init // initializes an empty git repo

  $ virtualenv venv // creates a virtualenv called "venv"

  $ venv\Scripts\activate // Activate the virtual environment for windows

  -uses the virtualenv for linux and Macos: $ source venv/bin/activate

  You will need to reinstall your app's dependencies with this virtualenv:

  $ pip install dash

  $ pip install plotly

  You will also need a new dependency, gunicorn, for deploying the app:

  $ pip install gunicorn

• Step 3. Initialize the folder with the (app.py), requirements.txt, and a Procfile for deployment

  content of Procfile is --> web: gunicorn app:server

  creation of requirements.txt file:

  requirements.txt describes your Python dependencies. You can fill this file in automatically with:

  $ pip freeze > requirements.txt

• Step 4 Initialize Heroku, add files to Git, and deploy

  $ heroku create my-dash-app # change my-dash-app to a unique name

  $ git add . # add all files to git

  $ git commit -m 'Initial app boilerplate'

  $ git push heroku master # deploy code to heroku

Contributors

• Saif Almaliki

• Lamees Kadhim

• Tamanna

• Kunal

• Sepideh Hosseini Dehkordi