🎓 AI Student Dropout Predictor

An intelligent, machine learning-powered web application designed to assess and predict student dropout risks based on academic, lifestyle, and demographic factors. It features a modern, interactive React frontend and a fast API backend.

🌟 System Previews

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🚀 Features

• Live Risk Assessment: Real-time percentage predictions and visually distinct risk levels (Low, Medium, High).
• Dynamic Action Plans: Actionable, AI-recommended interventions tailored to the student's specific risk category.
• Premium UI: Glassmorphism design, Lottie animations, massive beautiful typography, and a multi-step interactive form.
• Machine Learning API: Powered by a pre-trained Logistic Regression model served via FastAPI.

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🛠️ Tech Stack

• Frontend: React 19, Vite, Bootstrap 5, Lottie-Web, Lucide-React
• Backend: Python 3, FastAPI, Uvicorn
• Machine Learning: Scikit-Learn, Pandas, Joblib

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💻 Installation & Setup

1. Backend Setup (Virtual Environment)

The backend requires Python and several Data Science libraries. It is highly recommended to use a virtual environment (venv).

# 1. Open your terminal in the project root directory

# 2. Create a virtual environment named 'venv'
python -m venv venv

# 3. Activate the virtual environment
# On Windows:
venv\Scripts\activate
# On macOS/Linux:
source venv/bin/activate

# 4. Install the required dependencies
pip install -r requirements.txt

2. Running the Backend Server

Once the virtual environment is fully activated and dependencies are installed, start the FastAPI server:

# Navigate to the api directory
cd api

# Run the server
python main.py

# Alternatively, you can run it via uvicorn directly:
# uvicorn main:app --reload --port 8080

The API will be available at http://127.0.0.1:8080.

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3. Frontend Setup

Open a new terminal window (leave the backend server running in the first one).

# 1. Navigate to the frontend directory
cd frontend

# 2. Install the necessary Node.js modules
npm install

# 3. Start the Vite development server
npm run dev

The beautiful frontend will be available at http://localhost:5173.

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📁 Project Structure

• /frontend - React application containing all UI components, custom CSS, and public assets (like the Lottie JSON files).
• /api - FastAPI Python backend hosting the machine learning models (.pkl scaler, encoder, and model) and the main.py routing logic.
• requirements.txt - Categorized list of required Python dependencies.