In 2015, the United Nations unanimously embraced the Sustainable Development Goals (SDGs) as a global initiative to combat poverty, protect the environment, and promote universal peace and prosperity by 2030.
Progress toward these goals is annually tracked through The Sustainable Development Report (SDR), which forms the basis for the SDG Index, a comprehensive measure of each country's performance in achieving the 17 Goals.
SDGs and the SDG Index represent progress scores (ranging from 0 to 100), that signify the rate at which each country is advancing toward the desired annual pace required to attain the Goals by 2030 deadline.
The analysis spans SDG data from 2000 to 2022, excluding 2023 due to substantial changes in the data collection methodology.
The primary objective of this analysis is to delve into the Sustainable Development Goals, aiming to provide the audience with insights that facilitate a comprehensive understanding of the current global landscape.
Tableau Story here
The analysis was done using 2 data sets:
- Sustainable Development Goals (source: Kaggle)
- World GDP (source: World Bank)
- Python
- Tableau
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Data Collection and Open Data Sourcing: Acquired and sourced open data relevant to Sustainable Development Goals, ensuring data credibility and applicability for analysis.
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Data Cleaning: Improved data quality by eliminating duplicates, rectifying missing values, and ensuring data types were consistent.
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Merging Datasets: Prepared and curated datasets for effective merging.
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Exploring Relationships and Patterns: Conducted in-depth analysis to unveil correlations, utilizing correlation matrices, pair plots, and other techniques to identify and understand complex relationships within the data.
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Geospatial Visualization with Python: Utilized libraries such as Geopandas and Folium to create geographical visualizations, including maps and spatial representations, to showcase regional disparities or associations related to Sustainable Development Goals.
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Supervised Machine Learning - Regression: Employed linear regression models to predict the SDG Index based on Sustainable Development Goals.
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Unsupervised Machine Learning- Clustering Analysis: Applied clustering algorithms to identify patterns or groups within the data, allowing for a deeper understanding of distinct segments or categories related to Sustainable Development Goals.
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Time Series Data Sourcing and Analysis: Analyzed time-dependent data, employing time series analysis methods to detect trends, seasonality, and other temporal patterns in the context of Sustainable Development Goals.
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Creating Dashboards with Tableau: Designed interactive and informative dashboards in Tableau, presenting a comprehensive overview of key insights and trends related to Sustainable Development Goals for a user-friendly and accessible representation.