An LSTM model to predict the pollution levels in the next hour using the weather conditions and pollution levels in the current hour. I have trained the model using both uni-variate(if we consider only one feature) and multi-variate(when we consider multiple features for prediction).

In this, I have used the Air Quality dataset. This is a dataset that reports on the weather and the level of pollution each hour for five years at the US embassy in Beijing, China. The data includes the date-time, the pollution called PM2.5 concentration, and the weather information including dew point, temperature, pressure, wind direction, wind speed and the cumulative number of hours of snow and rain.

• Data preparation
• Data visualization
• LSTM data preparation
• Fit model along with regularization term
• Evaluate model

• Replace NA values
• Parse date-time into pandas dataframe index
• Specified clear names for each columns

• Used boxplot
• Correlation matrix

• Normalized data
• Transformed dataset into supervised learning problem

• Split data into train and test
• Split into i/p and o/p
• Reshape into 3D
• Define 3 layer LSTM architecture with 50 neuron followed by 1 nueron LSTM
• Add dropout at 20% after every layer

• Make prediction
• Invert scaling
• Plot the line graph between actual vs predicted values
• Calculate RMSE(root mean squared errot) and MAPE(mean absolute percentage error)