💧 Water Price Management System

Project Overview

Water Price Management is a web application developed for the Indore Municipal Corporation to optimize water supply and pricing. The project aims to reduce expenses by simulating and analyzing key parameters such as pipeline cost, distance, and temperature, which directly and indirectly affect water distribution costs.

Key Features

• Price Optimization: Simulates parameters to help set optimized water prices within budget constraints.
• Heatmap Visualization: Displays complaint density for each area, aiding in targeted management.
• User Interface: Allows administrators to visualize data and adjust parameters in real-time.

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🎯 Key Features

• 📊 Predictive Analytics - ML-powered water pricing and supply forecasting
• 📝 Complaint Management - Multi-channel user feedback system
• 🗺️ Regional Analytics - Distribution pattern analysis and insights
• ⚡ Resource Optimization - Data-driven allocation recommendations

🛠️ Technology Stack

Layer	Technology
🧠 Machine Learning	XGBoost + scikit-learn
⚙️ Backend	Node.js + Express + MongoDB
💻 Web Frontend	React + Vite + Tailwind CSS
📱 Mobile	Flutter (iOS & Android)
🔧 Model Serving	Flask / FastAPI

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🏗️ Architecture

water-price-management/
│
├── 🧠 XGBoost/              # ML training pipeline
├── ⚙️ server/               # Express REST API
├── 💻 client/               # React web application
└── 📱 water_project/        # Flutter mobile app

🔄 System Flow

┌─────────────┐
│   Mobile    │──┐
│     App     │  │
└─────────────┘  │
                 ├──► ┌─────────────┐      ┌──────────────┐
┌─────────────┐  │    │   Express   │      │              │
│     Web     │──┼───►│     API     │─────►│   MongoDB    │
│   Frontend  │  │    │   Server    │      │              │
└─────────────┘  │    └─────────────┘      └──────────────┘
                 │           │
┌─────────────┐  │           │
│   Admin     │──┘           ▼
│   Portal    │         ┌──────────────┐
└─────────────┘         │  ML Model    │
                        │   Service    │
                        └──────────────┘

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🚀 Getting Started

📋 Prerequisites

• 🐍 Python 3.8 or higher
• 📦 Node.js 16 or higher
• 🎯 Flutter 3.0 or higher
• 🗃️ MongoDB (Local or Atlas)

⚡ Quick Setup

1️⃣ Clone Repository

git clone <repository-url>
cd water-price-management

2️⃣ Python Environment

python -m venv .venv
source .venv/bin/activate  # Windows: .\.venv\Scripts\Activate.ps1

3️⃣ Install Dependencies

pip install -r XGBoost/requirements.txt
cd server && npm install
cd ../client && npm install

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🤖 Machine Learning Pipeline

📂 Directory Structure

XGBoost/
├── 📄 app.py                    # Training script
├── 📊 Training_Data_1.xlsx      # Sample dataset
├── 📋 requirements.txt          # Dependencies
└── 🎯 xgboost_model.pkl        # Trained model (generated)

🎓 Training Process

The ML pipeline uses XGBoost with automated preprocessing for optimal predictions.

📦 Required Dependencies

• pandas (≥1.5.0)
• scikit-learn (≥1.2.0)
• xgboost (≥1.7.0)
• joblib (≥1.2.0)
• numpy (≥1.23.0)
• openpyxl (≥3.0.0)

🔧 Training Steps

1. 📥 Prepare your dataset in Excel format
2. ✏️ Update dataset path in app.py
3. ▶️ Run training
4. ✅ Model artifact generated

📤 Output

• Trained Model: xgboost_model.pkl
• Console Reports: Sample predictions & feature importance

🌐 Model Serving

Deploy as a microservice for production-ready inference.

🎯 Deployment Options

• Flask - Lightweight and simple
• FastAPI - Modern, fast, with auto documentation
• Docker - Containerized deployment

🚦 Starting Inference Server

python XGBoost/inference_api.py

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🔌 Backend API

📍 Location

server/

🎯 Core Capabilities

Express.js REST API providing authentication, complaint management, and analytics.

🛣️ API Endpoints

Method	Endpoint	Description	🔐 Auth
📝 POST	/api/auth/signup	User registration	❌
🔑 POST	/api/auth/login	User authentication	❌
📮 POST	/api/complaints/submit	Submit complaint + image	✅
📊 GET	/api/complaints/regions	Regional statistics	✅
🗺️ GET	/parameters/:subRegion	Regional parameters	✅

⚙️ Configuration

Environment Variables (.env in server/)

PORT=5000
MONGODB_URI=mongodb://localhost:27017/water_management
JWT_SECRET=your_secret_key_here
ML_API_URL=http://localhost:5001

▶️ Starting Server

cd server
npm install
npm start

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🌐 Web Frontend

📍 Location

client/

Modern React application with beautiful UI and responsive design.

✨ Key Features

• 🔐 Authentication - Secure user login/signup
• 👤 Profile Management - User dashboard
• 🗺️ Regional Visualization - Interactive water supply maps
• 📝 Complaint System - Easy submission with image upload
• 📊 Real-time Dashboards - Live data insights
• 📱 Responsive Design - Works on all devices

⚙️ Configuration

Environment Variables (.env in client/)

VITE_API_BASE_URL=http://localhost:5000/api

🚀 Development

cd client
npm install
npm run dev

📦 Production Build

npm run build

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📱 Mobile Application

📍 Location

water_project/

Cross-platform mobile solution for iOS and Android.

🏗️ Building

cd water_project
flutter pub get
flutter run

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🤝 Contributing

We welcome contributions! Here's how to get started:

🔄 Workflow

1. 🍴 Fork the repository
2. 🌿 Create feature branch (feature/improvement)
3. ✏️ Commit your changes
4. 📤 Push to your branch
5. 🎯 Open a Pull Request

📏 Code Standards

• 🐍 Python: Follow PEP 8
• 📜 JavaScript: Use ESLint config
• 🎯 Dart/Flutter: Follow official style guide
• ✅ Testing: Write unit tests for features
• 📚 Documentation: Update docs for changes

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🌟 Project Status

Last Updated: October 2025

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Made with 💙 for better water management