Hydration Pattern Analysis

Tool: Python (Pandas · Matplotlib · Seaborn)
Domain: Health & Wellness
Dataset: (add your dataset source and link here)

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Overview

Adequate hydration is linked to a range of health outcomes, yet individual water intake patterns vary widely across demographics and lifestyle factors. This project uses Python to explore hydration behavior through exploratory data analysis (EDA) — identifying patterns, distributions, and potential correlations in daily water consumption.

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Key Findings

Finding	Insight
Daily intake distribution	(update with actual finding)
Demographic pattern	(update with actual finding)
Lifestyle correlation	(update with actual finding)
Notable outliers	(update with actual finding)

📝 Update this table with your actual findings after analysis

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Analysis Questions

1. What does the distribution of daily water intake look like across the dataset?
2. Are there differences in hydration patterns across demographic groups (age, gender)?
3. Do lifestyle factors (activity level, diet type) correlate with water intake?
4. Are there identifiable outliers or anomalies worth investigating?

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Visualizations

📸 Output charts tersedia di folder /notebooks setelah notebook dijalankan

Charts produced:

• Distribution plot: Daily water intake (histogram + KDE)
• Box plot: Intake by demographic group
• Correlation heatmap: Lifestyle factors vs water intake
• Scatter plot: Activity level vs daily water intake

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Process

1. Data Loading & Inspection

import pandas as pd
df = pd.read_csv('data/hydration_data.csv')
df.info()
df.describe()

2. Data Cleaning

• Handled missing values in intake and demographic columns
• Removed physiologically implausible intake values (outlier filtering)
• Standardized categorical columns (gender, activity level)

3. EDA

• Univariate analysis: distributions, central tendency, spread
• Bivariate analysis: intake vs demographics, intake vs lifestyle factors
• Correlation analysis using Pearson correlation matrix

4. Visualization

• Used Matplotlib and Seaborn for all charts
• Applied consistent color scheme and labeling across plots

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Repo Structure

python-hydration-pattern-analysis/
├── data/
│   └── hydration_data.csv          # Dataset
├── notebooks/
│   └── hydration_eda.ipynb         # Main analysis notebook
└── README.md

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Requirements

pandas
matplotlib
seaborn
jupyter

Install with:

pip install pandas matplotlib seaborn jupyter

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Tools Used

Python Pandas Matplotlib Seaborn Jupyter Notebook

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Notes

• This is a purely exploratory analysis — no predictive model is built
• All findings are descriptive; causal claims require controlled study design
• (Add note about dataset source/limitations here)

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Part of my data portfolio — Rama Yogaswara