This project utilizes machine learning techniques to predict water quality parameters based on environmental factors. It aims to provide accurate forecasts for parameters such as pH level, dissolved oxygen, turbidity, etc., using datasets sourced from reputable sources like the U.S. Environmental Protection Agency (EPA) or local government agencies.
- Predicts water quality parameters based on environmental variables.
- Utilizes various machine learning algorithms for accurate predictions.
- Provides an interactive web application for real-time predictions.
- The project uses publicly available water quality datasets from sources like EPA or WHO.
- Includes features such as temperature, precipitation, pH, dissolved oxygen, conductivity, turbidity, etc.
- Exploratory Data Analysis (EDA) to understand data distribution and correlations.
- Feature Engineering to extract relevant features and create new ones if necessary.
- Model Selection and Training using various machine learning algorithms.
- Model Evaluation to assess the performance using appropriate metrics.
- Deployment of the trained model using Flask or Django for real-time predictions.
- Clone the repository:
git clone https://github.com/aksaini8414/water-quality-prediction.git
- Install the required dependencies: pip install -r requirements.txt
- Preprocess your data and ensure it follows the required format.
- Train the model using the provided scripts or notebooks.
- Deploy the model using Flask or Django for real-time predictions.
- Access the web application and input environmental variables to get water quality predictions.
- Fork the repository.
- Create a new branch (
git checkout -b feature/new-feature). - Make your changes and commit them (
git commit -am 'Add new feature'). - Push to the branch (
git push origin feature/new-feature). - Create a pull request.
- Mention any contributors or sources of inspiration.