UFO Sightings Dashboard - Power BI Project

Project Overview

This project explores UFO sightings data across the United States using Power BI. The dashboard visualizes various metrics to identify patterns, trends, and anomalies in UFO sightings, including sightings by location, date, and duration.

Key Features

• Total Sightings Overview: Displays the total number of UFO sightings from the dataset.
• Sightings by Location: A map visualizing sightings by state and city, highlighting areas with the highest activity.
• Sightings by Date: A trend analysis showing how the frequency of UFO sightings has changed over time.
• Sightings Duration Analysis: Analyzes the duration of sightings and its correlation with sighting frequency.
• Interactive Filters: Allows users to filter data by various parameters such as date range, location, and sighting duration.

Data Source

The dataset used in this project is sourced from National UFO Reporting Center (NUFORC) and includes detailed records of UFO sightings reported by the public.

Tools and Technologies

• Power BI: Used for data visualization and creating interactive reports.
• Power Query: For data cleaning and transformation.
• DAX: Measures and calculations were performed using DAX to create dynamic visuals.

Insights

• Geographical Trends: Certain states show higher frequencies of UFO sightings, which might be linked to factors like population density or reporting behavior.
• Temporal Trends: UFO sightings have fluctuated over time, with notable peaks in specific years.
• Duration Analysis: Longer sighting durations are often reported in specific regions or during particular periods.

Installation

To explore the dashboard:

1. Download the .pbix file from this repository.
2. Open the file using Power BI Desktop.
3. Interact with the visualizations and filters to explore different aspects of the data.

Future Improvements

• Integrating additional datasets (e.g., weather conditions, astronomical events) to analyze their impact on UFO sightings.
• Enhancing visualizations with more advanced features like predictive analytics or clustering.
• Adding a detailed time analysis to understand the most common times or dates for sightings.

How to Contribute

Contributions are welcome! Feel free to submit issues, feature requests, or fork the repository and make your own improvements.

License

This project is open-source and available under the MIT License.