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--- |
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license: mit |
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tags: |
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- tabular |
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- classification |
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- water-quality |
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pretty_name: "Water Potability" |
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--- |
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# Water Potability Dataset |
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## Overview |
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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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--- |
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## Features |
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The dataset consists of 10 columns. The first 9 are features, and the last one is the target variable. |
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- **ph**: The pH level of the water, ranging from 0 to 14. This is a crucial measure of how acidic or alkaline water is. |
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- **Hardness**: A measure of the concentration of dissolved minerals, primarily calcium and magnesium. |
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- **Solids**: The total amount of dissolved solids (TDS) in the water, measured in milligrams per liter (mg/L). |
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- **Chloramines**: The concentration of chloramines in the water, measured in mg/L. These are disinfectants used to treat drinking water. |
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- **Sulfate**: The concentration of sulfate dissolved in the water, measured in mg/L. |
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- **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. |
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- **Organic_carbon**: The amount of organic carbon in the water, measured in mg/L. |
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- **Trihalomethanes**: The concentration of Trihalomethanes in the water, measured in micrograms per liter (μg/L). These are byproducts of water disinfection. |
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- **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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--- |
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## Target Variable |
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- **Potability**: This is the column you want to predict. |
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- **1**: The water is potable (safe to drink). |
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- **0**: The water is not potable. |
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--- |
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## How to Use |
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You can easily load and explore this dataset using a library like `pandas` in Python. |
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```python |
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import pandas as pd |
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# Load the dataset from the CSV file |
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df = pd.read_csv('water_potability.csv') |
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# Display the first 5 rows |
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print(df.head()) |
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# Get a summary of the dataset |
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print(df.info()) |
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# Check the distribution of the target variable |
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print(df['Potability'].value_counts()) |
