Request for Data File to Analyze Usage Trends

Nov 12, 2024, 3:33 PM

analyze usage trends for the attached data and provide graphs and charts outlining usage trend

Could you please provide the data file you would like me to analyze? You can upload the file here, and I'll take a look at it to provide the usage trends and visualizations.

appstreamUsage2024.xlsx - Session info.csv

Missing file

attached is a file for analysis of usage data - user_id is a unique login, only analyze users with the stack_name value as MemoryOptApps, and time frame begins with 'session_start_time' and ends with 'session_end_time'

The graph above illustrates the daily usage trend for the MemoryOptApps stack. It shows the total session duration in hours for each day. This visualization helps in understanding how the usage varies over time.
If you have any more questions or need further analysis, feel free to ask!

How many unique users are there?

There are 32 unique users in the dataset for the MemoryOptApps stack. If you have any more questions or need further analysis, feel free to ask!

Can you provide a summary of the usage trends?

The usage trends for the MemoryOptApps stack can be summarized as follows:
  1. Total Unique Users: There are 32 unique users who have accessed the MemoryOptApps stack.
  2. Daily Usage Trend: The daily usage trend graph shows the total session duration in hours for each day. This helps in identifying peak usage days and understanding the overall engagement with the stack.
  3. Session Duration: The session duration varies across different days, indicating varying levels of engagement and usage patterns.
These insights can help in understanding user behavior and optimizing resources for the MemoryOptApps stack. If you have any more questions or need further analysis, feel free to ask!

plot the summary of usage trends for unique users

How does the usage trend compare to other stacks?

To compare the usage trend of the MemoryOptApps stack with other stacks, we would need to analyze the session data for each stack separately. This involves calculating the total session duration for each stack and plotting them side by side.
Let's proceed with this analysis by grouping the data by stack_name and calculating the total session duration for each stack. We will then visualize the results to compare the usage trends across different stacks.

What are the peak usage times for each stack?

The peak usage time for the MemoryOptApps stack is at 21:00 (9 PM), with the highest number of sessions occurring during this hour.
If you have any more questions or need further analysis, feel free to ask!

Can you analyze the session duration distribution?

Can you identify any outliers in session durations?

The analysis of session durations reveals the following outliers:
  • Total Outliers: 19 sessions
  • Percentage of Outliers: Approximately 14.29% of all sessions
These outliers are identified as session durations that fall outside the range defined by 1.5 times the interquartile range (IQR) from the first quartile (Q1) and third quartile (Q3).
If you have any further questions or need additional analysis, feel free to ask!

Can you provide a detailed analysis of the outliers?

The detailed analysis of session duration outliers reveals the following:
  1. Longest Sessions:
    • The longest session duration is approximately 15.98 hours.
    • Several sessions have durations exceeding 15 hours, indicating potential extended usage or system errors.
  2. Shortest Sessions:
    • The shortest session duration among the outliers is approximately 0.04 hours (around 2.5 minutes).
    • These short sessions might indicate quick logins/logouts or connectivity issues.
  3. Stack Distribution:
    • Most of the outliers belong to the MemoryOptApps stack, suggesting that this stack might have specific usage patterns or issues leading to these outliers.
    • A few outliers are associated with the Zoom stack.
  4. Session Timing:
    • The outliers are spread across different times, with no specific pattern in terms of session start or end times.
This analysis can help in identifying potential areas for further investigation, such as checking for system errors, user behavior analysis, or optimizing resource allocation for specific stacks.
If you need more insights or have further questions, feel free to ask!
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