💧 Water Quality Monitoring Project

This is a beginner-level Data Engineering project where you will build a simple pipeline using Python to process water sensor data and determine the safety of the water quality based on predefined thresholds.

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🚀 Objectives

• Learn to structure a Python project.
• Use Git for version control and push to GitHub.
• Work with CSV files using pandas.
• Practice data cleaning and transformation.
• Implement logic using functions and classes.
• Understand how code modularity works.
• Collaborate via branches using Git.

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🛠️ Getting Started

1. Clone the repo

git clone https://github.com/your-username/Water_Quality_Monitoring.git
cd Water_Quality_Monitoring

2. Create a virtual environment and install dependencies

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt

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🔧 Tasks to Complete

• load_data.py: Write a function to load a CSV file into a DataFrame.
• clean_data.py: Clean the data — handle missing values, fix invalid entries.
• evaluate.py: Write a class that checks if each row meets safe pH and turbidity ranges.
• main.py: Load, clean, evaluate, and print results — run your full pipeline here.

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📊 Sample Data (sensor_data.csv)

sensor_id,location,ph,turbidity,temperature
1,Lake A,7.2,0.9,25
2,Lake B,8.7,1.1,23
3,Lake C,,0.5,22
4,Lake D,7.1,,24
5,Lake E,6.4,0.8,20

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📤 Expected Output

Sensor 1 at Lake A: ✅ Safe
Sensor 2 at Lake B: ❌ Unsafe (pH too high)
Sensor 3 at Lake C: ❌ Unsafe (missing pH)
Sensor 4 at Lake D: ❌ Unsafe (missing turbidity)
Sensor 5 at Lake E: ❌ Unsafe (pH too low)

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💡 Bonus (Optional)

• Save the output to results.csv.
• Accept location name as an input from the terminal.
• Count how many lakes are safe vs unsafe.
• Use classes to model sensor readings.

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🧱 Project Structure

Water_Quality_Monitoring/
├── README.md
├── .gitignore
├── requirements.txt
├── data/
│   └── sensor_data.csv          # Raw sample sensor data
├── notebooks/                   # For exploration (optional)
├── src/
│   ├── load_data.py             # TODO: Write logic to load CSV file
│   ├── clean_data.py            # TODO: Write logic to clean data
│   ├── evaluate.py              # TODO: Write logic to assess water safety
│   └── main.py                  # TODO: Combine everything to run the pipeline

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🤝 Contributing

1. Fork the repo
2. Create a new branch (git checkout -b feature-name)
3. Commit your changes (git commit -am 'Add something')
4. Push to the branch (git push origin feature-name)
5. Open a pull request