Summary

Simulated tweet data using Python, visualized insights with seaborn and matplotlib, and analyzed engagement trends to optimize content strategies and enhance audience interaction. Solution

Key Findings:-

The histogram plot showed that the distribution of Likes is skewed to the right, indicating that most posts receive a low to moderate number of Likes, with few outliers receiving a significantly higher number of Likes.

The boxplot revealed variations in Likes across different categories, with some categories having higher median Likes compared to others. 

The mean Likes across all categories provided an overall understanding of the average engagement level in the dataset.  

Approach

Key Steps in Data Analysis:

Data Generation:

    Simulated tweet data with Python (pandas, numpy, random).

    Created fields: Date, Category, and Likes to replicate social media data.

Data Visualization:

    Used seaborn and matplotlib for visual analysis.

    Plotted a histogram for Like distribution.

    Created boxplots to compare Likes across categories.

Data Analysis:

    Calculated overall mean Likes for average engagement.

    Analyzed mean Likes by category to reveal content trends.