Water Potability Prediction

This project aims to predict the potability of water samples using machine learning techniques. The dataset used in this project contains various water quality parameters such as pH, hardness, solids, chloramines, sulfate, conductivity, organic carbon, and trihalomethanes.

Table of Contents

1. Introduction
2. Data Exploration
3. Data Preprocessing
4. Model Selection and Tuning
5. Model Evaluation
6. Conclusion
7. Usage
8. Contributing
9. License

Introduction

Access to clean and safe drinking water is a fundamental human right. However, many regions around the world face challenges in ensuring the potability of their water sources. This project aims to develop a machine learning model that can accurately predict the potability of water samples based on various water quality parameters.

Data Exploration

The Jupyter notebook provided explores the dataset in detail, including:

• Importing necessary libraries
• Loading the water potability dataset
• Performing descriptive statistics and visualizations to understand the data
• Analyzing the relationship between water quality parameters and potability

Data Preprocessing

The notebook also covers the data preprocessing steps, including:

• Handling missing values
• Scaling the numerical features
• Splitting the dataset into training and testing sets

Model Selection and Tuning

The notebook evaluates the performance of various classification models, including:

• Logistic Regression
• Support Vector Classifier (SVC)
• K-Nearest Neighbors (KNN)
• Decision Tree Classifier
• Gaussian Naive Bayes
• and others

The best-performing models are then fine-tuned using a randomized search approach to optimize their hyperparameters.

Model Evaluation

The final model, a Gradient Boosting Classifier, is evaluated using cross-validation. The average F1-score, precision, and accuracy are reported.

Conclusion

The project demonstrates the application of machine learning techniques to predict the potability of water samples. The developed model can be used to assist in water quality monitoring and decision-making processes.

Usage

To use the water potability prediction model, you can follow these steps:

1. Clone the repository: git clone https://github.com/your-username/water-potability-prediction.git
2. Install the required dependencies: pip install -r requirements.txt
3. Run the Jupyter notebook: jupyter notebook water_potability_prediction.ipynb
4. Explore the notebook and use the trained model for your own water potability predictions.

Contributing

Contributions to this project are welcome. If you find any issues or have suggestions for improvements, please feel free to open an issue or submit a pull request.

License

This project is licensed under the MIT License.