WaterPotability

Demo app

Launch the app

App info

This repository creates a Data Web App about safe water for human consumption by using Streamlit Python Package. More specifically, it allows its users to change the values of the nine predictor variables:

1. pH
2. Hardness
3. Solids
4. Chloramines
5. Sulfate
6. Conductivity
7. Organic_carbon
8. Trihalomethanes
9. Turbidity

trains a Random Forest Classifier model and, finally, observe the prediction of the trained model.

Dataset

The imported csv file contains water quality metrics for 3276 different water bodies.

Reproducing the App

1. First, we create a virtual Python environment called my_venv

  python3 -m venv my_venv

2. Then, we activate the virtual environment

source path_to_your_virtual_environment/bin/activate

3. After getting to the virtual environment's file, install prerequisite packages

wget https://raw.githubusercontent.com/GeorgiosDolias/WaterPotability/main/requirements.txt

and

pip install -r requirements.txt

4. Dowload and unzip contents from Github repo

Dowload and unzip contents from https://github.com/GeorgiosDolias/WaterPotability/archive/main.zip

5. Launch the app

streamlit run WaterPotApp.py

Requirements

Package	Version
streamlit	0.87.0
pandas	1.1.3
sci-kit learn	0.23.2
numpy	1.19.1

Useful Resources

1. Youtube tutorial from Chanin Nantasenamat (Data Professor)
2. More info about the used dataset on Kaggle