palnitin24 / basic_eda

Interpret created social media data and help your social media company identify which category has most likes and which category is performing in terms of daily likes

Social Media EDA using pandas

Summary

Social media has become a ubiquitous part of modern life, with platforms such as Instagram, Twitter, and Facebook serving as essential communication channels. Social media data sets are vast and complex, making analysis a challenging task for businesses and researchers alike. In this project, we explore a simulated social media, for example Tweets, data set to understand trends in likes across different categories.

Solution

This project is to analyze tweets (or other social media data) and gain insights into user engagement. We will explore the data set using visualization techniques to understand the distribution of likes across different categories. Finally, we will analyze the data to draw conclusions about the most popular categories and the overall engagement on the platform.

Approach

In this project, we need pandas, numpy, matplotlib, seaborn, and random libraries. Pandas is a library used for data manipulation and analysis. Numpy is a library used for numerical computations. Matplotlib is a library used for data visualization. Seaborn is a library used for statistical data visualization. Random is a library used to generate random numbers.

I began by generating the data for my project since it's a good way to enhance your exploratory data analysis and the I did cleaning and preprocessing data using pandas. For showcasing the analysis I used boxplot and lineplot to show the number of likes for each categories and to show highest number of likes for each categories, respectively.

Project Link

Notebook link