Find, clean, and analyze data through exploratory data analysis (EDA) investigating the World Happiness Index and determining whether weather affects happiness.
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Updated
Apr 8, 2021 - Jupyter Notebook
Find, clean, and analyze data through exploratory data analysis (EDA) investigating the World Happiness Index and determining whether weather affects happiness.
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This repository represents several projects completed in IE HST's MS in Business Analytics and Big Data program; Data Visualization Course.
A colorful bubble plot visualizing the relationship between salary and happiness scores. This project uses Seaborn’s scatterplot() with size and color mapping to represent multi-variable data in a clear and engaging way.
An interactive dashboard that visualises global happiness data (2020-2024), allowing users to explore key factors affecting happiness by continent and country.
This project aims to do the detailed analysis of World happiness index using Python and create a dashboard summarizing entire analysis using both Tableau and Plotly Dash.
The analysis delves into the global happiness index across various countries. Through a comprehensive approach, utilize a variety of graphs to elucidate the intricate relationships between different features encapsulating the essence of happiness.
In this project, members of the BYU-Idaho Online Data Science Society (BYUIODSS) try to answer the question: How did the Covid-19 pandemic affect the World Happiness Report scores?
Repository for Big Data Processing - Contains Jupyter Notebooks and Datasets for data analysis and processing tasks related to Big Data.
This project is about Walkability vs. Subjective Well-Being.
A Flask web app that predicts a country's happiness category using a CatBoost model, with inputs for GDP, trust, dystopia, country and region, styled with responsive CSS.
Repository for exam project of Spatial analytics course 2021 at Aarhus University. Project name: 'Happiness and green urban spaces: how green is happiness?' The project was done by Ruta Slivkaite and Bianka Szöllősi.
Quantitative analysis of how the pandemic affected healthcare systems and national happiness using machine learning.
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