AIR POLLUTION FORECASTING USING LSTM

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Steps followed:

1. Data preparation
2. Data visualization
3. LSTM data preparation
4. Fit model along with regularization term
5. Evaluate model

Data preparation:

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

Data visualization

• Used matplotlib to plot

LSTM data preparation

• Normalized data
• Transformed dataset into supervised learning problem

Model Fitting

• Split data into train and test
• Split into i/p and o/p
• Reshape into 3D
• Define a 50 neuron followed by 1 nueron LSTM
• Add dropout at 30%
• Plot history of training and testing loss

Evaluate model

• Make prediction
• Invert scalings
• Calculate RMSE