I can assist in building a machine learning model for predicting water potability based on your public datasets.
Approach:
Data Analysis & Preprocessing:
Load and explore the CSV datasets to identify patterns, anomalies, and missing values.
Perform data cleaning and feature engineering, including scaling and encoding if needed.
Conduct exploratory data analysis (EDA) for insights into correlations and trends.
Model Development:
Select suitable algorithms for classification (e.g., Random Forest, Gradient Boosting, Neural Networks).
Train and validate multiple models using cross-validation to ensure robust performance.
Evaluate model accuracy, precision, recall, and F1-score to select the best-performing model.
Deployment & Usability:
Package the model into a user-friendly application (e.g., a web interface or API).
Provide detailed documentation for future updates and maintenance.
Tools & Technologies:
Programming Language: Python or R.
Libraries: Scikit-learn, TensorFlow/PyTorch, Pandas, Matplotlib, Seaborn.
Environment: Jupyter Notebook or RStudio for development and visualization.
Deliverables:
Preprocessed dataset ready for training.
A trained, tested, and optimized machine learning model.
Visualizations and a summary report of EDA and model performance.
Codebase with clear documentation.
Let me know if you'd like to proceed!