New Year's Eve Resolutions Dashboard

Project Summary

This analysis aimed to explore user behavior, tweet activity, and the popularity of various resolution categories, as well as engagement levels across states, regions, and genders. By examining the dataset, we gained valuable insights into when and where people were tweeting about their resolutions, what types of resolutions were most popular, and how original tweets compared to retweets. The results were visualized in an interactive dashboard, offering multiple filtering options for deeper exploration.

Key Findings:

Engagement by Category

I calculated the total number of original tweets across various resolution categories (e.g., career, finance, health). This analysis identified the most and least engaging resolution types, shedding light on trends in user preferences.

Timing of Tweets

Peak Day: By analyzing tweet timestamps, I identified the day with the highest tweet volume, providing insights into when users were most active in sharing their resolutions.

Peak Hour: I also determined the specific hour when tweet activity peaked, offering a closer look at the times people were most likely to post about their resolutions.

Tweets vs. Retweets per Category

A comparison of tweets and retweets across different resolution categories revealed which categories generated more original content versus those more likely to be shared (retweets). This provided a clear distinction between user-generated content and engagement.

Example of Findings: Categories like personal growth had higher volumes of original tweets, while financial resolutions had more retweets, indicating stronger resonance with users.

Gender-based Analysis

The dataset was segmented by gender to analyze tweet volume and resolution preferences. While tweets have 50% sharing between male and female, applying specific filters allowed us to determine whether certain categories were more popular among men or women, offering key demographic insights into resolution trends.

State and Regional Trends

I grouped tweets by state and geographic region to uncover regional or state differences in tweet volume. This analysis illustrated how trends varied based on location.

Filterable Dashboard

Dynamic Filtering: The final results were presented in an interactive dashboard with filtering options by category, state, region and gender. This allowed dashboard users to explore the data in detail and tailor insights based on their interests.