Installation Guide

Step 1: Check Requirements

Make sure Python 3.11 and pip are installed on your system. You can confirm this by running the commands below in your terminal.

python --version
pip --version

Step 2: Create a Virtual Environment

Set up an isolated environment so the project dependencies stay separate from your system packages.

python -m venv venv

Activate it on Windows:

venv\Scripts\activate

Activate it on Linux or macOS:

source venv/bin/activate

Step 3: Install Dependencies

Install all the required libraries from the requirements file.

pip install -r requirements.txt

Step 4: Configure the Environment File

Copy the example environment file and keep the project in offline mode. Only add a Groq API key if you want the optional AI career roadmap.

cp .env.example .env

Inside the .env file, keep this value:

USE_GROQ=false

Step 5: Generate Data and Train Models

Open the Jupyter notebooks and run them in this order to create the datasets and save all trained models into the models folder.

1. 00_generate_data.ipynb
2. 01_eda_cleaning.ipynb
3. 02_eligibility_model.ipynb
4. 03_success_probability.ipynb
5. 04_career_model.ipynb

Step 6: Run the Application

Start the Flask server and open the address shown in your terminal in any browser.

python app.py

Step 7: Use the Platform

Fill in the student profile form to view eligibility results, scholarship recommendations, ranking, success probability, career suggestions, and the explainable AI dashboard. The SQLite database file is created automatically on the first run.