This project focuses on data analysis and visualization to investigate the relationship between the spread of COVID-19 and happiness levels across different countries. It has three primary objectives:
- Preparing Datasets for Analysis
- Learn how to preprocess and merge datasets for analysis.
- Identifying Key Measures
- Calculate relevant metrics to answer critical questions.
- Visualizing the Analysis Results
- Create meaningful visualizations to effectively convey insights.
The datasets are sourced from a course by Johns Hopkins University on Coursera, instructed by Ahmad Varasteh. One dataset consists of the cumulative daily confirmed COVID-19 cases for each country, while the other includes scores for various life factors from people living in those countries. By merging these datasets, we aim to explore the question:
Is there a correlation between the spread of COVID-19 and the happiness levels of a country’s population?
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Task 1: Introduction
- Understand the project's objectives, datasets, and the key questions to be answered through analysis.
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Task 2: Importing and Preparing the COVID-19 Dataset
- Clean and preprocess the COVID-19 dataset by removing unnecessary columns and aggregating the data.
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Task 3: Defining Key Metrics
- Determine and calculate appropriate metrics for the analysis.
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Task 4: Importing and Preparing the World Happiness Report Dataset
- Import the World Happiness Report dataset, clean it by dropping irrelevant columns, and merge it with the COVID-19 data to explore potential correlations.
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Task 5: Visualizing the Results
- Use Seaborn to visualize the results and identify relationships between the variables.
- COVID-19 cases dataset (Johns Hopkins University)
- World Happiness Report dataset (Coursera)
- Python for data manipulation and analysis
- Pandas for dataset cleaning and merging
- Seaborn for data visualization
Project Type: Self-practiced project inspired by a guided project from Coursera.