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Requirements Clarification & Assessment
Understanding the Dataset:
Data Attributes: Identify what features are available in the dataset, such as IP addresses, timestamps, user IDs, session IDs, page view durations, and any user-agent strings.
Data Volume: Determine the size and scope of the dataset to assess computational needs.
Data Quality: Assess the quality of the data, checking for missing values, inconsistencies, or anomalies.
Defining Bots vs. Human Visitors:
Bot Characteristics: Define what constitutes a bot in this context. Typically, bots have high page views, low time spent on pages, and lack interaction with page elements.
Human Characteristics: Humans tend to have fewer page views, spend more time on each page, and interact with elements like scrolling and clicking.
Key Metrics for Differentiation:
Page View Count: Total number of pages viewed in a session.
Time on Page: Average and total time spent per page.
Interaction Patterns: Scrolling behavior, clicks, and navigation paths.
Technical Constraints:
Tools and Technologies: Identify the tools available for analysis (e.g., SQL, Python, R).
Computational Resources: Understand the computational resources available for processing and analysis.
Outcome Expectations:
Determine the expected outcome of the analysis, such as a binary classification of users as bots or humans or a probability score indicating likelihood.