The ultimate goal of this project is to explore and visualize temporal patterns of earthquakes globally. By leveraging data visualization techniques, the project aims to uncover trends in earthquake occurrences based on time-related variables such as year, month, day of the week, and hour of the day.
This project performs an in-depth analysis of a global earthquake dataset using Python libraries like pandas, matplotlib, and seaborn. It converts raw earthquake data into meaningful insights by:
- Converting and cleaning the time column to extract datetime features.
- Visualizing earthquake occurrences over the years using bar and line plots.
- Analyzing monthly and daily earthquake frequencies using heatmaps and bar charts.
- Exploring relationships between depth, magnitude, and geographical locations.
- Categorizing earthquakes based on magnitude classification.
- Studying the distribution of earthquakes across weekdays and hours.
The analysis is carried out in Jupyter Notebook, with a strong focus on readable and interpretable visuals to support temporal insights.
- Python 3.x
- Pandas
- Matplotlib
- Seaborn
- Jupyter Notebook
- Year-wise and month-wise frequency analysis
- Heatmaps showing earthquake density
- KDE plots for magnitude distribution
- Scatter plots for depth vs magnitude and geographic spread
- Pie charts and violin plots for categorical analysis
This project was inspired and guided by the excellent work shared on LinkedIn.
🔗 LinkedIn Post Reference