Water Consumption Prediction System

Table of Contents

• Overview
• Features
• Project Structure
• Installation
• Usage
• Contributors
• License

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Overview

The Water Consumption Prediction System is a deep learning-powered tool designed to monitor and forecast water consumption patterns in rural areas. Using an LSTM model implemented with TensorFlow, the project aids in sustainable resource management by providing accurate predictions based on historical data.

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Features

• LSTM Model: Predicts water consumption patterns using TensorFlow.
• Customizable Input: Accepts various water consumption datasets for tailored predictions.
• Scalable Design: Can be adapted for different regions with diverse water usage patterns.

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Project Structure

Water-Consumption-Prediction/
├── models/
│   └── water_consumption/      # LSTM model for water consumption prediction
└── README.md

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Installation

Step 1: Clone the repository

git clone https://github.com/your-username/Water-Consumption-Prediction.git
cd Water-Consumption-Prediction

Step 2: Install dependencies

Install TensorFlow for the water consumption model:

pip install tensorflow

Install other project dependencies:

pip install -r requirements.txt

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Usage

1. Run the script to process the dataset and generate predictions.
2. Input your water consumption dataset as a CSV file to the model.
3. View the output predictions directly in the terminal or export them to a file.

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Contributors

• Auhona: Developed the water consumption model (LSTM with TensorFlow).

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License

This project is licensed under the MIT License.