Overview This project simulates and analyzes social media data to identify trends in user engagement across various content categories. By generating random data, performing data cleaning, visualization, and statistical analysis, this project uncovers insights that can help optimize content strategies based on user engagement metrics.
-Data Generation: Create a realistic set of random data, simulating social media post likes across various categories. -Data Cleaning: Remove null and duplicate data, and ensure correct data types for accurate analysis. -Data Visualization: Use Seaborn and Matplotlib to visualize the distribution of likes and compare engagement across categories. Statistical Analysis: Calculate average likes per category to understand which content types tend to receive more user engagement.
Pandas: For data generation, cleaning, and analysis. Seaborn: For creating visualizations, including histograms, boxplots, and FacetGrid. Matplotlib: To enhance plot customization and display.
1- Data Generation: Simulated a dataset with categories like Food, Travel, Fashion, etc., and assigned random like counts to each post. 2- Data Cleaning: Used Pandas to drop null values and duplicates, ensuring a clean dataset. Converted date and likes columns to the appropriate data types for accurate analysis. 3- Data Visualization: Plotted a histogram to show the distribution of likes across all posts. Used a boxplot to compare the distribution of likes for each category. 4- Statistical Analysis: Calculated the overall mean of likes to understand general engagement. Computed average likes per category using Pandas' groupby method, revealing which categories tend to attract more likes.
The distribution of likes was uniform, as expected from randomly generated data. Categories like Culture and Food showed higher average likes, suggesting these types of content could drive more engagement.
Enhanced Data Generation: Implement more realistic social media data trends, such as adding metrics like comments or shares. Interactive Dashboard: Create an interactive dashboard with Plotly or Dash for a more dynamic exploration of the data. Time-Based Analysis: Include time-based trends to understand changes in engagement over time.