Clean and analyze social media usage data with Python

Project scenario

You're a data analyst at a marketing firm that promotes brands on social media. Your team wants you to use Python to extract tweets based on specific categories (health, family, food, etc.), clean and analyze the data, and create visualizations. They will use your analysis to help clients improve their social media performance. This insight will allow the firm to deliver tweets on time and within budget, leading to faster results.

Summary

This project aimed to analyze randomly generated social media data using Python's pandas, numpy, matplotlib, and seaborn libraries, exploring trends in user engagement across different categories.

Solution

To achieve this, I first generated synthetic social media data comprising dates, categories, and likes using pandas date range, random choice, and numpy's random functions. Next, I loaded the data into a pandas DataFrame, conducted exploratory data analysis to understand its structure, and cleaned the data by removing null values and duplicates. Then, I visualized the data distribution using histograms and boxplots and performed statistical analysis to calculate mean likes for each category.

Approach

I generated synthetic social media data and loaded it into a DataFrame. Then, I performed exploratory data analysis, cleaned the data, and visualized trends using descriptive statistics and plots. Overcoming challenges with data integrity and visualization, I derived meaningful insights, setting the stage for future analysis.