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Understanding the problem statement
Importing the problem statement
Installing Keras and LSTM
Importing the necessary libraries for applying Neural Networks
Performing basic EDA and checking for the null values
Imputing the null values using appropriate method
Plotting a Time Series plot
Creating a Dataset matrix for applying LSTM
Sequentially initializing a Neural Networks
Defining the error function
Understanding solver used "Adam"
Applying LSTM as training model
Visualizing the loss and accuracy with each epoch
Tuning the final model and using it to make predictions
Saving the predictions made in CSV format
Deep learning is an upcoming field, where we are seeing a lot of implementations in the day to day business operations, including segmentation, clustering, forecasting, prediction or recommendation etc. Deep learning architecture has many branches and one of them is the recurrent neural network (RNN), the method that we are going to analyze in this deep learning project is about Long Short Term Memory Network (LSTM) to perform time series forecasting for univariate time series data.