This Data Analysis project is part of an Internship by Suven Consultants.
In this blog I will share the results of Data Analysis done on a meteorological Data set from Kaggle.
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”
The H0 means we 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.
The Dataset contains the general weather data of Finland from 2006–04–01 to 2016–09–09
It has 12 columns such as Formatted Date ,Summary ,Precip Type , Temperature , Apparent Temperature , Humidity ,Wind Speed , Wind Bearing , Visibility ,Loud Cover , Pressure , daily Summary.
Out of this I’ve used three columns — Formatted Date , Apparent Temperature , Humidity for my analysis.
The Dataset has no null values for the required columns.
The Formatted Date column is of object type. It has been converted into Datetime object so as to access the month and year properties.
Grouping The Data
The records in the data are on an hourly basis i.e. there is a record for each hour of a day for a decade.
I grouped the dataset into years and then into months as per the requirement.
After grouping the data the mean of Apparent temperature and Humidity for each month is calculated.
The Dataset is now ready to plot graphs.
Average Apparent Temperature
This Graphs for the months of February and March show that there is an increase in the average Apparent Temperature almost every year.
In February the average Apparent Temperature in 2006 is -2.99 C while in 2016 it rises up to 4.15 C due to effects of global warming over the decade
Similarly the average Apparent Temperature for March in 2006 is 1.96 C and in 2016 is 5.90
However this trend is not quite followed in some months .
The above graph is of November where we can see the the month experienced a few unusually cold years .
The above graphs are for the months of October and February .It can be seen that the humidity remains over the years. This observation also fits for the rest of the months.
From the above plot, we can say that humidity remained almost constant in these 10 years. Even the average apparent temperature is almost same (as peaks lie on the same line)
From this analysis we can infer that global warming is deteriorating the climate and is affecting various parameters of the environment. Temperature sees sharp rise and fall over the years while humidity remains constant throughout the decade.
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