Introduction
One type of data that’s easier to find on the net is Weather data. Many sites provide historical data on many meteorological parameters such as pressure, temperature, humidity, wind_speed, visibility, etc. I have downloaded one such weather dataset from kaggle
The dataset has hourly temperature recorded for last 10 years starting from 2006-04-01 00:00:00.000 +0200 to 2016-09-09 23:00:00.000 +0200. It corresponds to Finland, a country in the Northern Europe.
You can also download the dataset from this Google drive link
Problem Statement
My goal in this project is to transform the raw data into information and then convert it into knowledge.
And to test the Null Hypothesis H0 is “Has the Apparent temperature and humidity compared monthly across 10 years of the data indicate an increase due to Global warming”
Solution
The H0 means I need to find whether the average Apparent temperature for the month of a month say April starting from 2006 to 2016 and the average humidity for the same period have increased or not. This monthly analysis has to be done for all 12 months over the 10 year period. So I am basically resampling the data from hourly to monthly, then comparing the same month over the 10 year period. And, finally, Support the analysis by appropriate visualizations using matplotlib and seaborn library of python
Conclusion
After transforming the data, I have analysed and concluded the following results -
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From the all insights, it is clear that the apparent temperature increased over 10 years i.e. for 2006-2016 for the Finland due to the Global Warming.
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But the humidity is remains constant over 10 years.
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So the null hypothesis has to be rejected.