This Data Science project aims to predict the potability of water using machine learning. Our goal is to assess water quality from various sources and develop models that can determine its safety for consumption. Through thorough data analysis and visualization, we seek to identify the key factors affecting water quality.
- Evaluate water potability from diverse sources.
- Perform Exploratory Data Analysis (EDA) for dataset insights.
- Develop predictive models for water potability.
- Contribute to ensuring safe and clean water for communities.
- Data Exploration: Examine water quality features, such as chemical composition and physical properties.
- Data Visualization: Use visualization techniques to highlight patterns and relationships in the data.
- Statistical Analysis: Apply statistical methods to uncover correlations and dependencies.
- Model Development: Build machine learning models based on EDA insights.
- Web Page: Create a webpage using Streamlit to showcase the project.
- Python
- Data Analysis Libraries (Pandas, NumPy)
- Data Visualization Libraries (Matplotlib, Seaborn)
- Machine Learning Libraries (Scikit-Learn)
The dataset includes information on various water quality parameters and attributes, which are crucial for making accurate predictions.
Clone this repository and follow the Jupyter Notebook or Python scripts to run the project locally. Ensure all necessary dependencies are installed.
Contributions are welcome! Feel free to create issues, suggest improvements, or submit pull requests.
Let's work together to ensure everyone has access to clean and safe water!