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JOSAA Analysis Portal

The JOSAA Analysis Portal is a web-based application designed to analyze, visualize, and predict seat allocations in Indian Institutes of Technology (IITs) and other participating institutions based on historical data. This project is divided into three phases: Data Scraping and Cleaning, Data Visualization, and Backend Integration with Django.

Table of Contents

  1. Project Overview
  2. Phase 1: Data Scraping and Cleaning
  3. Phase 1½: Data Analysis and Visualization
  4. Phase 2: Web Integration and Backend with Django
  5. Project Setup
  6. Running the Project
  7. Screenshots
  8. Video Demonstration

Project Overview

The project aims to provide insights and predictions on seat allocations for various engineering institutions under the JOSAA framework. It involves:

  • Phase 1: Scraping and cleaning data using Beautiful Soup, NumPy, and Pandas. (The final scraping file is in the directory web_Scrapping_josaa)
  • Phase 1½: Conducting Exploratory Data Analysis (EDA) and creating visualizations.
  • Phase 2: Developing a backend with Django to process queries, manage the database, and display visualizations on a web page.

Phase 1: Data Scraping and Cleaning

Technologies Used

  • HTML & CSS: For basic web page structure and styling.
  • Beautiful Soup: For scraping data from web pages.
  • NumPy: For efficient numerical operations on data.
  • Pandas: For data manipulation and analysis.

Resources

Phase 1½: Data Analysis and Visualization

Technologies Used

  • Exploratory Data Analysis (EDA): To understand and explore the data.
  • Data Visualization: Using Matplotlib and Seaborn for creating plots and graphs.

Resources

Phase 2: Web Integration and Backend with Django

Technologies Used

  • Django: For backend development, managing the database, and handling web requests.
  • SQLite: The database engine used for this project.

Resources

Project Setup

  1. Clone the Repository:

    git clone https://github.com/yourusername/josaa-analysis-portal.git
    cd josaa-analysis-portal
  2. Create and Activate Virtual Environment:

    python -m venv venv
    source venv/bin/activate   # On Windows use `venv\Scripts\activate`
  3. Install Python setuptools:

    pip install setuptools
  4. Install Dependencies:

    pip install -r requirements.txt
  5. Apply Migrations:

    python manage.py makemigrations
    python manage.py migrate

Running the Project

To run the project locally, use the following command:

python manage.py runserver

Navigate to http://127.0.0.1:8000/ in your web browser to access the application.

Screenshots

image image image image

Video Demonstration

For a detailed walkthrough, watch the deployment video here (<--youtube video on deployment):

339185755-808e9bbc-0605-4c0c-b179-7f0768eace3a.mp4

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