Tagline: See beyond the surface—predict purity with precision.
TDScope is a smart analytics tool designed to predict Total Dissolved Solids (TDS) in water using environmental parameters like temperature, pH, and more. Built with machine learning, it transforms raw sensor data into actionable insights for water quality monitoring, sustainability, and public health.
- Predict TDS using multiple water quality indicators
- Modular pipeline for data preprocessing, feature engineering, and model training
- Visualizations for exploratory data analysis and prediction results
- Scalable architecture based on the Cookiecutter Data Science template
TDScope/
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
├── src <- Source code for use in this project.
│ ├── **init**.py <- Makes src a Python module
│ │
│ ├── data <- Scripts to download or generate data
│ │ └── make_dataset.py
│ │
│ ├── features <- Scripts to turn raw data into features for modeling
│ │ └── build_features.py
│ │
│ ├── models <- Scripts to train models and then use trained models to make
│ │ │ predictions
│ │ ├── predict_model.py
│ │ └── train_model.py
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ └── visualize.py
│
└── tox.ini <- tox file with settings for running tox; see tox.readthedocs.io
git clone https://github.com/kunal-mallick/TDScope.git
cd TDScope
pip install -e .Run the pipeline using Repro:
dvc reproOr use individual scripts from src/ for custom workflows.
Full documentation is available in the docs/ folder. You can build it using Sphinx:
cd docs
make htmlPull requests are welcome! For major changes, please open an issue first to discuss what you’d like to change.
This project is licensed under the MIT License. Please take a look at the LICENSE file for details.
Kunal Mallick
Machine Learning Enthusiast | Water Quality Advocate