PROJECT-4-WEB-WIZARDS

🌐 AI-Powered Machine Learning Web Application

A full-stack web application that integrates multiple Machine Learning prediction models and an AI chatbot into a single platform.
This project demonstrates how trained ML models can be deployed and accessed through a user-friendly web interface.

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🚀 Project Overview

This web application allows users to interact with various machine learning models for real-world prediction tasks.
All models are deployed on a live website using Flask, enabling real-time predictions without requiring technical expertise.

🔍 Features

• Flight Price Prediction
• Diabetes Prediction
• Customer Churn Prediction
• Movie Review Sentiment Analysis
• Spam Message Detection
• AI Chatbot (Google Gemini)

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🧠 Machine Learning Models Used

Model	Description
Flight Price Prediction	Predicts flight ticket prices based on travel features
Diabetes Prediction	Predicts diabetes risk using medical parameters
Churn Prediction	Identifies customers likely to leave a service
Movie Review Analysis	Classifies reviews as positive or negative
Spam Detection	Detects spam messages
AI Chatbot	Provides conversational assistance using Gemini API

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🛠️ Tech Stack

Machine Learning

• Python
• Pandas
• NumPy
• Scikit-learn
• Joblib / Pickle

Web Development

• Flask
• HTML5
• CSS3
• JavaScript
• Jinja2

Deployment & Tools

• Render (or similar cloud platform)
• Git & GitHub
• Environment Variables (.env)

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🤖 Google Technologies Used

• Google Gemini API – AI chatbot integration
• REST API integration standards
• Secure environment variable management

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🎯 Problem Statement

Machine learning models are often limited to research and notebooks.
This project solves the problem of making ML models accessible to real users by deploying them on a web platform where predictions can be obtained instantly.

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📂 Project Structure

├── static/
│ ├── css/
│ └── js/
├── templates/
│ ├── index.html
│ └── ...
├── models/
│ ├── flight_model.pkl
│ ├── diabetes_model.pkl
│ └── ...
├── app.py
├── requirements.txt
├── .env.example
└── README.md

Link of the Website : https://project-4-web-wizards-3.onrender.com/

On deployment platforms, add these keys in the Environment Variables section.
create a .env file locally and
write GEMINI_API_KEY1='your own generated API key'

▶️ How to Run Locally

1. Clone the repository:

git clone https://github.com/Mr-Magic1/PROJECT-4-WEB-WIZARDS.git

2. Install dependencies:

pip install -r requirements.txt

3. Run the application

python app.py

4. Open in browser

http://127.0.0.1:5000

📜 License

This project is for educational and learning purposes.

🙌 Author

1. Kailash Vishwakarma (Team Leader) 
Managed - Backend and Machine Learning
Machine Learning & Web Development Enthusiast

2. Khushboo Yadav 
Managed - Frontend Design of the website
The Designer

3. Hartik Verma
Managed - Machine learning and team support