SEO Alexa

The dataset contains information on the top 50 websites across 450 categories in the Alexa ranking, totalling around 12,200 sites. The dataset includes 27 columns suitable for NLP analysis and 57 numerical columns ideal for data analysis and machine learning algorithms. The data can be used to generate various diagrams and insights into website rankings and categories.

Files:

• Check the Python file on Google Colab SEO Alexa or download below:

• The file is very extensive with 83 columns in total, please check this labels explanation Excel below:

Data Acquisition:

• Kaggle

• Libraries used: pandas, matplotlib, numpy, and seaborn

Data Preparation:

• Adult Content Filtering: a markdown cell notes that a tool or method named "Skiporows" was used to filter out "Adult" content sections deemed unnecessary for the analysis.

• Handling Missing Data: all rows with missing values were dropped, reducing the dataset from 11,882 rows to 8,658 rows while keeping all columns.

• Grouping Data: since each line in the dataset corresponds to a unique website, the dataset was grouped by category to make the analysis more manageable.

Data Analytics:

• Summarizing and identifying patterns or trends in the dataset, likely based on website categories or other available features.

• Keyword Opportunity Analysis:

  • Identified which categories of sites have the most keyword optimization opportunities.

  • Analyze keyword gaps to determine which sites could benefit from targeted SEO efforts.

• Audience Overlap Analysis:

  • Explore audience overlap to see which sites are competing for the same audience. This could be visualized using network graphs.

• Engagement Analysis:

  • Compare engagement metrics like daily time on site across different categories or similar sites.

  • Identify which factors (e.g., keyword optimization, audience overlap) correlate most strongly with higher engagement.

• SEO Performance Prediction:

  • Use machine learning techniques to predict a site's global rank or engagement metrics based on its keyword optimization scores and other features.

    
    

• Data Visualization: Correlation Matrix analysis and an Audience Overlap Network Graph

  
  

• What else could have been done if I had more time?

  
  

  • Feature Engineering: Additional features could have been derived to enhance the analysis, such as creating new columns based on existing data to capture more nuanced information.

  • Advanced Analytics: Techniques like clustering, sentiment analysis, or more sophisticated statistical analysis could have been applied to delve deeper into the data.

  • Modeling: If the goal was to predict outcomes (e.g., website ranking), machine learning models could be built and evaluated.

  • Further Data Cleaning: More refined data cleaning steps could be employed, such as handling outliers or normalizing data for better analysis.

  • Detailed Category Analysis: A deeper dive into specific categories could reveal more granular insights.

  • Trend Analysis: Perform time-series analysis if you have historical data to identify trends in site rankings or keyword performance over time.