Implementation of Multivariate Time Series Forecasting for Air Pollution using LSTMs in Keras

Built a multivariate LSTM model in Python to forecast air pollution using time series data. Preprocessed data with lag features, normalized inputs, and reshaped for supervised learning. Trained on multiple timesteps, evaluated with RMSE & MAE, and delivered a scalable forecasting pipeline.