Our comprehensive water quality dataset contains 500 samples with 6 key parameters:

Statistical Summary of Water Quality Parameters:
                     pH  Temperature (°C)  Turbidity (NTU)  Dissolved Oxygen (mg/L)  Conductivity (µS/cm)
count        500.000000        500.000000        500.000000               500.000000            500.000000
mean           7.161140         22.054400          4.169400                 8.382200            344.362000
std            0.107531          0.903123          0.397492                 0.822396             13.038672
min            6.830000         20.300000          3.100000                 6.000000            316.000000
25%            7.080000         21.200000          3.800000                 7.800000            333.000000
50%            7.160000         22.200000          4.200000                 8.400000            344.000000
75%            7.250000         22.900000          4.500000                 9.100000            355.000000
max            7.480000         23.600000          5.100000                 9.900000            370.000000

Advanced statistical analysis capabilities including descriptive statistics, correlation analysis, and data quality assessment.

Core Analytics:

• 📈 Statistical Summaries: Complete descriptive statistics for all parameters
• 🔍 Data Quality Assessment: Missing value detection and data type analysis
• 📊 Distribution Analysis: Understanding parameter distributions and outliers
• 🎯 Correlation Studies: Strong correlation discovered (r=0.705) between pH and dissolved oxygen

Professional-grade visualizations for water quality parameter analysis and reporting.

Visualization Suite:

• 📊 Distribution Plots: Histograms with KDE curves for parameter distributions
• 🔗 Correlation Matrices: Heatmaps showing parameter relationships
• 📈 Scatter Plots: Relationship analysis with trend lines
• 🎭 Pair Plots: Comprehensive multi-parameter visualization
• 📉 Regression Plots: Linear relationship visualization with confidence intervals

Machine learning models for water quality parameter prediction and assessment.

ML Capabilities:

• 🎯 Linear Regression: Predict conductivity using multiple parameters
• 📊 Statistical Modeling: OLS regression with detailed statistical summaries
• 🔮 Parameter Prediction: Multi-feature models for water quality forecasting
• 📈 Model Evaluation: Comprehensive model performance assessment

• 🐍 Python-Powered: Built with pandas, scikit-learn, and seaborn
• 📓 Jupyter Integration: Interactive analysis environment
• 📊 Statistical Analysis: Advanced statistical modeling with statsmodels
• 🎨 Professional Plots: Publication-ready visualizations
• 🔍 Exploratory Data Analysis: Comprehensive EDA workflow
• 📈 Trend Analysis: Time-series and parameter trend identification
• 📋 Automated Reports: Structured analysis output
• 🔬 Research-Ready: Suitable for academic and professional research

Python 3.x

Pandas

NumPy

Scikit-learn

Matplotlib

Jupyter

Core Libraries:

• Python 3.x: Main programming language
• Pandas: Data manipulation and analysis
• NumPy: Numerical computing foundation
• Matplotlib: Static plotting and visualization
• Seaborn: Statistical data visualization

Advanced Analytics:

• Scikit-learn: Machine learning algorithms
• Statsmodels: Statistical modeling and testing
• Plotly: Interactive visualizations
• Jupyter: Interactive development environment

Visualization Stack:

• Seaborn: Statistical plotting with beautiful defaults
• Matplotlib: Publication-quality figures
• Plotly: Interactive and dynamic charts

graph TB
    subgraph "Data Input"
        A[Water Quality CSV] --> B[Load Dataset]
        B --> C[Data Inspection]
    end
    
    subgraph "Exploratory Analysis"
        C --> D[Statistical Summary]
        D --> E[Distribution Analysis]
        E --> F[Correlation Analysis]
    end
    
    subgraph "Visualization"
        F --> G[Histograms & KDE]
        G --> H[Scatter Plots]
        H --> I[Pair Plots]
        I --> J[Regression Plots]
    end
    
    subgraph "Modeling"
        J --> K[Linear Regression]
        K --> L[Statistical Models]
        L --> M[Predictions]
        M --> N[Model Evaluation]
    end
    
    subgraph "Results"
        N --> O[Insights & Reports]
        O --> P[Visualized Results]
    end

graph LR
    subgraph "Parameter Relationships"
        A[pH: 7.16 ± 0.11] --> B[Strong Correlation]
        B --> C[Dissolved O₂: 8.38 ± 0.82]
        D[Temperature: 22.1 ± 0.9°C] --> E[Weak Correlation]
        E --> F[Conductivity: 344 ± 13 µS/cm]
        G[Turbidity: 4.17 ± 0.40 NTU] --> H[Moderate Correlation]
        H --> I[Multiple Parameters]
    end

• Python 3.7+ (Download)
• pip package manager
• Jupyter Notebook or JupyterLab
• Git (Download)

1. Clone Repository

git clone https://github.com/ChanMeng666/water-quality-testing-data-analysis.git
cd water-quality-testing-data-analysis

2. Install Dependencies

# Install required packages
pip install pandas numpy matplotlib seaborn scikit-learn statsmodels plotly jupyter

3. Launch Jupyter

# Start Jupyter Notebook
jupyter notebook

# Or use JupyterLab
jupyter lab

4. Open Analysis

Open my_notebook.ipynb and run all cells to reproduce the analysis.

🎉 Success! You can now explore the water quality analysis.

# Create requirements.txt
echo "pandas>=1.3.0
numpy>=1.21.0
matplotlib>=3.4.0
seaborn>=0.11.0
scikit-learn>=1.0.0
statsmodels>=0.12.0
plotly>=5.0.0
jupyter>=1.0.0" > requirements.txt

# Install all dependencies
pip install -r requirements.txt

1. Load and Explore Data:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

# Load dataset
df = pd.read_csv('Water Quality Testing.csv')
print(df.head())
print(df.describe())

2. Statistical Analysis:

# Correlation analysis
correlation = df['pH'].corr(df['Dissolved Oxygen (mg/L)'])
print(f'pH-DO Correlation: {correlation:.4f}')

# Distribution analysis
plt.figure(figsize=(10, 6))
sns.histplot(df['pH'], kde=True, bins=30)
plt.title('Distribution of pH Values')
plt.show()

3. Visualization:

# Scatter plot with trend line
plt.figure(figsize=(10, 6))
sns.regplot(x='pH', y='Dissolved Oxygen (mg/L)', data=df)
plt.title('pH vs Dissolved Oxygen Relationship')
plt.show()

Predictive Modeling:

from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score, mean_squared_error

# Prepare features and target
features = ['pH', 'Temperature (°C)', 'Turbidity (NTU)', 'Dissolved Oxygen (mg/L)']
X = df[features]
y = df['Conductivity (µS/cm)']

# Train model
model = LinearRegression()
model.fit(X, y)

# Make predictions
predictions = model.predict(X)
r2 = r2_score(y, predictions)
print(f'Model R² Score: {r2:.4f}')

Statistical Modeling:

import statsmodels.api as sm

# OLS Regression
X_with_const = sm.add_constant(df['pH'])
model = sm.OLS(df['Temperature (°C)'], X_with_const).fit()
print(model.summary())

Parameter Relationships:

• 🔵 pH ↔ Dissolved Oxygen: Strong positive correlation (r=0.705)
• 🟢 Temperature ↔ Conductivity: Moderate correlation
• 🟡 Turbidity ↔ Other Parameters: Various weak to moderate correlations

Model Performance:

• 📊 Conductivity Prediction: Multi-parameter linear model shows good predictive capability
• 📈 Statistical Significance: Most relationships show statistical significance (p<0.05)

This toolkit is perfect for:

• 🌊 Water Quality Assessment: Comprehensive parameter analysis
• 🏭 Environmental Impact Studies: Industrial discharge monitoring
• 🔬 Research Projects: Academic water quality research
• 📊 Regulatory Compliance: Meeting environmental standards
• 🌱 Ecosystem Health: Aquatic ecosystem monitoring

• 👨‍🎓 Data Science Education: Real-world dataset for learning
• 📚 Environmental Science: Practical water quality analysis
• 🔢 Statistics Teaching: Applied statistical analysis examples
• 🤖 Machine Learning: Environmental prediction modeling

Parameter Correlations (Selected):
pH ↔ Dissolved Oxygen:     0.705 (Strong)
pH ↔ Temperature:          0.151 (Weak)
Temperature ↔ Conductivity: Variable
Turbidity ↔ pH:           Negative correlation

Step-by-Step Execution:

# 1. Navigate to project directory
cd water-quality-testing-data-analysis

# 2. Start Jupyter
jupyter notebook

# 3. Open main notebook
# Click on 'my_notebook.ipynb'

# 4. Run all cells
# Kernel -> Restart & Run All

Analysis Structure:

Analysis Workflow:
├── Data Loading & Inspection
├── Descriptive Statistics
├── Distribution Analysis
├── Correlation Studies
├── Visualization Suite
├── Machine Learning Models
├── Statistical Modeling
└── Results & Insights

Extend the Analysis:

# Add new visualization
def create_custom_plot(df, param1, param2):
    plt.figure(figsize=(10, 6))
    sns.scatterplot(data=df, x=param1, y=param2, alpha=0.6)
    plt.title(f'{param1} vs {param2} Analysis')
    return plt.gca()

# Add new statistical test
from scipy import stats

def parameter_significance_test(df, param1, param2):
    correlation, p_value = stats.pearsonr(df[param1], df[param2])
    return {
        'correlation': correlation,
        'p_value': p_value,
        'significant': p_value < 0.05
    }

We welcome contributions to improve water quality analysis capabilities!

1. Fork & Clone:

git clone https://github.com/ChanMeng666/water-quality-testing-data-analysis.git
cd water-quality-testing-data-analysis

2. Create Feature Branch:

git checkout -b feature/new-analysis-method

3. Make Improvements:

• Add new analysis techniques
• Improve visualizations
• Enhance documentation
• Add new datasets

4. Submit Pull Request:

• Provide clear description
• Include example outputs
• Update documentation

• 🔍 New Analysis Methods: Time series analysis, anomaly detection
• 📊 Enhanced Visualizations: Interactive plots, dashboard creation
• 🤖 Advanced Models: Neural networks, ensemble methods
• 📚 Documentation: Tutorials, use case examples
• 🌊 New Datasets: Additional water quality parameters

This project is licensed under the MIT License - see the LICENSE file for details.

Open Source Benefits:

• ✅ Commercial use allowed
• ✅ Modification allowed
• ✅ Distribution allowed
• ✅ Private use allowed

Chan Meng

• LinkedIn: chanmeng666
• GitHub: ChanMeng666
• Email: [email protected]
• Website: chanmeng.live

# Install missing packages
pip install --upgrade pandas numpy matplotlib seaborn scikit-learn

# Reinstall Jupyter
pip install --upgrade jupyter
jupyter --version

# Reset matplotlib backend
import matplotlib
matplotlib.use('inline')  # For Jupyter
# or
matplotlib.use('TkAgg')   # For standalone

🌊 Advancing Water Quality Research Through Data Science 🔬
Empowering environmental scientists and researchers worldwide

⭐ Star us on GitHub • 📖 Read the Documentation • 🐛 Report Issues • 💡 Request Features • 🤝 Contribute

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