India has made it to the headlines when it comes to air pollution. The growing air pollution in the country has been one of the major concerns for both the government and the citizens.
Many actions have been taken by the government to control polution, but still the problem stands.
Air pollution is a major problem and should not be ignored. Most of the population is not even aware of the colossal harm caused by the air they breathe everyday.
Being a data science and analysis enthusiast I decided to analyze the Indian air quality data to find something meaningful and to make people aware by the facts and figures.
I have explained the approach in this article : https://medium.com/@shubhankarrawatsam.1999/india-air-pollution-data-analysis-bd7dbfe93841
For a better understanding of how the code is implemented please refer to the above link.
The dataset contains the following features :
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stn_code : Station code. A code given to each station that made the measurements.
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sampling_date : The date when the data was recorded.
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state : It represents the states whose air quality data is measured.
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location : It represents the city whose air quality data is measured.
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agency : Name of the agency that measured the data.
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type : The type of area where the measurement was made.
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so2 : The amount of Sulphur Dioxide measured.
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no2 : The amount of Nitrogen Dioxide measured.
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rspm : Respirable Suspended Particulate Matter measured.
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spm : Suspended Particulate Matter measured.
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location_monitoring_station : It indicates the location of the monitoring area.
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pm2_5 : It represents the value of particulate matter measured.
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date : It represents the date of recording (It is cleaner version of 'sampling_date' feature)