Render Some Data

I chose the data set "Mobile Device Usage and User Behavior Dataset"

What I did was data analysis on the data set through python some things I wanted to find out is data the amount of app usage per day, screen time, data usage, age, and gender. I believe that with these factors we can determine common behavior with different people and began to understand how people interact with their mobile devices. 

This graph shows us that some interesting details regarding usage that out of 700 entries the mean use time is 272 minutes per day and the median use time is 228 minutes per day meaning that people on average use their phones for around 16-19 percent of the entire day with active use in apps

This graph shows us the screen time of phone users in general with the mean being around 5.8 hours and the median being around 4.9 hours per day meaning screen time is around 21-24 percent of people daily routine

This is interesting when we use our phone, we generally don't think or worry about data usage per day or how much we actually use. From the data set generally users use from 824-929 mb of cellular data per day.

For this data set and for phone usage and behavior initially I would think that for the usage for age would be lower and more youth but actually its really even with the mean age being around 39 and the median age around 38

For users the gender is basically even with a 52 and 49 split percentage which could indicate how the study was done possibly they collected exactly the same amount from each side to represent everyone

Source: I used VS code specifically python Jupiter notebook to code all graphs from csv data sets.

Source: Find Open Datasets and Machine Learning Projects | Kaggle

AI adoption in general trend line is going up exponentially. AI adoption will be expected to jump even higher in 2025 with its biggest predicted jump yet.

With the job market it appears that while yes, the jobs eliminated is rising per year the job creation is also rising equally and may even overtake it in the near future.

AI revenue in billions is projected to see a great jump from 30 billion to 120 billion which is around a 300% increase globally. A market increase of this size can cause many opportunities and possible negative an AI bubble which can be harmful later on. Meaning the way AI is used will change from software use to other necessities for it.

Source: I used VS code specifically python Jupiter notebook to code all graphs from csv data sets.

Source: The Rise Of Artificial Intelligence (kaggle.com)

The data set I chose was the daily price of eth over time

All time price of Eth over time

I also created a graph that shows the price of eth but also shows the volume for each year and the trend line which is very important for price volatility

Also, with some stats I learned that the annual return is 41% but also with an annual volatility of 74% meaning if you can hold past the very high volatility there is a high chance with 95% confidence that you gain a near 41% return

Source: I used VS code specifically python Jupiter notebook to code all graphs from csv data sets.

Source: Ethereum Historical Data 2018 - 2024 (kaggle.com)