Job-Interview-Platform-Simulation-using-ANN-and-Classification-and-Regression-Tree-Algorithm-CART-

A web-based simulation platform designed to assess job candidates using Artificial Neural Networks (ANN) and Classification and Regression Tree (CART) algorithms. Built with Flask, this system supports prescreening, scoring, and report generation — all wrapped in a responsive, brand-aligned interface.

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🚀 Features

• Candidate prescreening via interactive forms
• ANN-based scoring with CART fallback logic
• Dynamic report generation and PDF export
• Upload and manage candidate files
• Responsive UI with calming, organic aesthetics
• Modular architecture for maintainability

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🧰 Tech Stack

Layer	Tools & Libraries
Backend	Python, Flask
ML Models	TensorFlow, scikit-learn
Frontend	HTML, CSS, JavaScript
Data Handling	Pandas, NumPy
PDF Export	pdfkit
Deployment	GitHub, local server (XAMPP)

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🛠 Setup Instructions

1. Clone the repository

   git clone https://github.com/dnsdcz/Job-Interview-Platform-Simulation-using-ANN-and-Classification-and-Regression-Tree-Algorithm-CART-.git
   cd phyt  
   

2. Create a virtual environment python -m venv tf-env

3. Activate the environment tf-env\Scripts\activate

4. Install dependencies pip install -r requirements.txt

5. Run the app python app.py

Folder Structure

phyt/
├── app.py
├── routes.py
├── templates/
├── static/
├── uploads/
├── summary_reports/
├── prescreen-job_interview_ANN/
├── Job-Interview-Platform-Simulation-using-ANN-and-CART/
├── model.h5
├── model_fallback.pkl
├── requirements.txt
└── README.md