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 1 | # Air-quality-index-prediction-using-LSTM
 2 | In this project, I predict air quality index of a city in China using a long short term memory neural network 
 3 | (which works the best for time series analysis).
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 5 | To get started, you can refer to my notebook. I perform in-depth exploratory data analysis and visualizations to help understand and prove the insights obtained.
 6 | The dataset contains the data starting from January 1, 2013 to February 28, 2017. 
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 8 | It encompasses the concentrations of different pollutants for each hour of the day along with the data of the environmental conditions.
 9 | Pollutants include PM2.5, PM10, SO2, NO2, CO, O3. Environmental conditions include temperature, pressure, dewpoint, rain, wind directon and wind speed per minute.
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11 | Also, I have just displayed the first 5 entries of all the output arrays and lists. The reason I did so was I didn't want you to scroll through a massive number of entries.
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