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Long Short Term Memory (LSTM) network for time series analysis using Python  In this document, students can enhance their skills in time series analysis by building a Long Short Term Memory (LSTM) network using Python. LSTM is a specific type of Recurrent Neural Network (RNN) known for its ability to accumulate propagation state throughout the input sequence. This means that the model's output from one timestep becomes the input for the subsequent timestep, allowing the model to make predictions based on both current and prior input, resulting in more reliable prediction outcomes. The persistent accumulation of propagation state is the primary advantage of LSTM models.  The specific tasks include: 1. define a many-to-many type sequence prediction problem, such as power consumption forecast for next 3 days 2. Design and develop an LSTM neural network with multiple input time steps and multiple output time steps 3. Design an evaluation metric 4. Conduct performance analysis of the LS

Coursework tasks and Problem statement 1. Identify and evaluate a number of publicly available dataset s related to air pollution and severity of respiratory disease. These may be from sources such as kaggle.com or data.gov.uk 2. Select appropriate datasets, as informed by their interests 3. Integrate and import these datasets into a suitable data storage and processing system, providing rationale for their choice 4. Perform meaningful analysis of the data to derive some simple useful information, as can be obtained by the dataset selected. 5. Provide visualisation of the analysis through any Hadoop-related technologies which the students deem suitable. "If you need the complete report and codes, please leave a comment on this blog. Few screenshots, steps, and codes are not included assuming that you are able to figure them out. If you are having difficulty getting the output, then please leave a comment and I will help you with that." Sample Coursework for the above t