Kavi-ya/Water-Potability

Water Potability Dataset

Overview

This dataset contains water quality metrics from 2,556 different water bodies. The primary purpose of this dataset is to serve as a basis for a binary classification problem: predicting whether water is potable (safe for human consumption) based on its chemical and physical properties.

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Features

The dataset consists of 10 columns. The first 9 are features, and the last one is the target variable.

• ph: The pH level of the water, ranging from 0 to 14. This is a crucial measure of how acidic or alkaline water is.
• Hardness: A measure of the concentration of dissolved minerals, primarily calcium and magnesium.
• Solids: The total amount of dissolved solids (TDS) in the water, measured in milligrams per liter (mg/L).
• Chloramines: The concentration of chloramines in the water, measured in mg/L. These are disinfectants used to treat drinking water.
• Sulfate: The concentration of sulfate dissolved in the water, measured in mg/L.
• Conductivity: The electrical conductivity of the water, measured in microSiemens per centimeter (μS/cm). It's an indicator of the amount of dissolved ionic substances.
• Organic_carbon: The amount of organic carbon in the water, measured in mg/L.
• Trihalomethanes: The concentration of Trihalomethanes in the water, measured in micrograms per liter (μg/L). These are byproducts of water disinfection.
• Turbidity: A measure of the cloudiness or haziness of the water caused by suspended particles, measured in Nephelometric Turbidity Units (NTU).

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Target Variable

• Potability: This is the column you want to predict.
  • 1: The water is potable (safe to drink).
  • 0: The water is not potable.

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How to Use

You can easily load and explore this dataset using a library like pandas in Python.

import pandas as pd

# Load the dataset from the CSV file
df = pd.read_csv('water_potability.csv')

# Display the first 5 rows
print(df.head())

# Get a summary of the dataset
print(df.info())

# Check the distribution of the target variable
print(df['Potability'].value_counts())