DataVisualisation

NFHS-5 District-Level Data Visualization & Exploratory Analysis

This repository contains a collection of data visualization and exploratory data analysis projects, with a primary focus on a district-level analysis of India’s National Family Health Survey (NFHS-5) dataset.
In addition to the NFHS-5 case study, the repository includes smaller analytical and visualization exercises implemented using Python visualization libraries.

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🔗 Interactive Dashboard (Tableau – NFHS-5)

👉 Tableau Public Story:
https://public.tableau.com/views/DataVisCaseStudy/Story1

The Tableau story presents an interactive, narrative-driven analysis of key NFHS-5 indicators, enabling exploration of district-wise and state-wise patterns related to health, literacy, and social inequality.

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📊 Datasets

NFHS-5 (Primary Dataset)

• Source: National Family Health Survey (NFHS-5), Government of India
• Granularity: District-level
• Coverage: Health, nutrition, literacy, maternal and child health, and social indicators

Additional Datasets

• Small and publicly available datasets used for practice, comparison, and visualization experimentation (e.g., sample datasets commonly used for statistical visualization).

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🎯 Objectives

• Analyze regional and district-level disparities in health and social outcomes
• Visualize relationships between female literacy, maternal health, and child health indicators
• Apply geospatial visualization techniques to highlight inequality patterns
• Practice and compare multiple data visualization libraries
• Present insights in a clear, interpretable, and visual-first manner

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🛠️ Tools & Technologies

Visualization Platforms

• Tableau Public – Interactive dashboards and storytelling

Python & Libraries

• Matplotlib – Static plots and exploratory analysis
• Seaborn – Statistical and comparative visualizations
• GeoPandas – District-level and regional geospatial mapping
• Plotly – Interactive and dynamic charts
• Pandas / NumPy – Data cleaning, transformation, and analysis

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📈 Visualizations Included

• District-wise female literacy rates
• Maternal and child health indicators across regions
• Geospatial maps showing inter-district inequality
• Comparative state-wise and district-wise analysis
• Interactive plots highlighting trends and outliers
• Small-scale exploratory visualizations for practice and concept validation

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🔍 Key Insights (NFHS-5 Case Study)

• Significant regional disparities exist in maternal and child health indicators
• Districts with higher female literacy tend to exhibit better health outcomes
• Geospatial analysis reveals clusters of social and health inequality
• Interactive dashboards enhance the interpretability of large public datasets