Water Quality Prediction

Welcome to the Water Quality Prediction project! This repository contains an end-to-end machine learning to predict the potability of water based on physicochemical properties. This project showcases advanced techniques in data preprocessing, exploratory data analysis (EDA), feature engineering, and machine learning, achieving an impressive accuracy of 81% and an F1 score of 80%, outperforming other attempts on the same dataset.

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Table of Contents

1. Project Overview
2. Key Features
3. Methodology
4. Results
5. Future Work
6. Acknowledgments

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Project Overview

Water quality is a critical issue worldwide, impacting human health and ecosystems. This project leverages machine learning techniques to predict whether water is safe for consumption based on its physicochemical attributes. The dataset used includes various features such as pH, hardness, and dissolved solids, and the target variable is binary:

• 1: Potable (safe to drink)
• 0: Non-potable (unsafe to drink)

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Key Features

• Powerful Visualization: Interactive and insightful plots that reveal relationships, trends, and distributions in the dataset.
• Advanced Missing Values Handling: Leveraged imputation techniques to address missing data effectively without compromising the dataset's integrity.
• Outlier Handling: Used robust methods to identify and mitigate the impact of outliers, ensuring model reliability.
• SMOTE Technique: Applied Synthetic Minority Oversampling Technique (SMOTE) to handle class imbalance, enhancing model performance for the minority class.
• Comprehensive EDA: Explored correlations, feature importance, and statistical summaries to uncover hidden patterns.
• Feature Engineering: Transformed features to improve model performance.
• Multiple Models: Trained and compared several models, including Random Forest, XGBoost, and SVM, to identify the best-performing approach.
• Hyperparameter Tuning: Optimized models through grid search and cross-validation to achieve peak performance.
• State-of-the-Art Accuracy: Achieved an accuracy of 81% and an F1 score of 80%, surpassing previous efforts on this dataset.

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Methodology

1. Data Preprocessing:

   • Addressed missing values using statistical imputation techniques.
   • Detected and treated outliers to enhance data quality.
   • Applied SMOTE to address class imbalance and improve predictions for the minority class.

2. Exploratory Data Analysis (EDA):

   • Visualized feature distributions, correlations, and trends.
   • Identified key factors influencing water potability.

3. Feature Engineering:

   • Standardized and normalized data for better model convergence.

4. Model Training:

   • Evaluated multiple algorithms, including:
     • Logistic Regression
     • KNN
     • Support Vector Machines (SVM)
     • Random Forest
     • XGBoost
   • Fine-tuned hyperparameters for optimal results.

5. Evaluation:

   • Used metrics such as accuracy, precision, recall, and F1 score.
   • Conducted thorough cross-validation to ensure robustness.

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Results

The final model achieved the following metrics:

• Accuracy: 81%
• F1 Score: 80%

These results are a significant improvement over other models trained on the same dataset.

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Future Work

To further enhance this project, the following improvements can be explored:

1. Expand Dataset: Incorporate additional water quality parameters or more diverse geographical data to improve model robustness.

2. Advanced Models: Experiment with deep learning models like neural networks or other techniques to further enhance accuracy.

3. Real-Time Deployment: Develop a web or mobile application for real-time water potability predictions using the trained model.

4. Feature Engineering: Explore more advanced feature engineering techniques to uncover hidden patterns in the data.

5. Hyperparameter Tuning: Perform extensive hyperparameter optimization for XGBoost and other models to achieve even better performance.

6. Explainability: Use tools like SHAP or LIME to explain model predictions and improve interpretability.

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Acknowledgments

Special thanks to the contributors of the dataset and the open-source tools used in this project, including NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, and XGBoost.