💧 Water Potability Analysis System

This project provides a comprehensive system for analyzing and predicting water potability using machine learning. It features data loading, preprocessing, statistical analysis, various machine learning models, interactive visualizations, and a user-friendly Streamlit interface for real-time predictions.

✨ Features

• Data Loading & Preprocessing: Handles data loading from a remote URL, missing value imputation, and duplicate removal.
• Statistical Analysis: Provides detailed descriptive statistics, correlation analysis, and data quality assessment.
• Machine Learning Models: Implements and compares multiple classification models (Random Forest, Logistic Regression, SVM, Gradient Boosting) to predict water potability.
• Interactive Visualizations: Generates various plots (pie charts, bar charts, histograms, scatter plots, heatmaps) to explore data distributions and relationships.
• Real-time Prediction: Allows users to input water parameters and get instant predictions on water potability with confidence scores.
• Prediction History: Keeps a log of past predictions and provides summary statistics.
• Modular & OOP Design: Built with a robust Object-Oriented Programming (OOP) architecture for maintainability and extensibility.

🚀 Installation

To set up and run this project locally, follow these steps:

1. Clone the repository: ```bash git clone https://github.com/your-username/water-potability-analysis.git cd water-potability-analysis ```

2. Create a virtual environment (recommended): ```bash python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate ```

3. Install dependencies: ```bash pip install pandas numpy scikit-learn streamlit plotly ``` (Note: A requirements.txt file can be generated from these dependencies for easier management.)

🏃 Usage

To run the Streamlit application, execute the following command from the project's root directory:

```bash streamlit run app.py ```

This will open the application in your web browser, where you can navigate through different sections for data overview, analysis, visualizations, model training, and predictions.

📊 Data Source

The dataset used in this project is sourced from a Vercel Blob URL: ``` https://hebbkx1anhila5yf.public.blob.vercel-storage.com/water_potability_preprocessed-aP2VS7drsoWULn1qmITGHQDpRcDEhe.csv

Live Link

``` https://water-potability-analysis.streamlit.app