I designed a univariate LSTM to predict diesel prices in the future.For the sake of ease i splitted the dataset into two files diesel_train and diesel_test into 80% and 20% values respectively.I could have used train_test_split() method but this way reflected more sleekness.
Epoch: A full pass over all of your training data.
Loss: A scalar value that we attempt to minimize during our training of the model. The lower the loss, the closer our predictions are to the true labels.
Prediction.png - A plot of actual diesel price v/s predicted price from the model.
diesel_train.csv - Data for training the data.
diesel_test.csv- Data for testing the data.
DieselPricePred.ipynb-Python notebook showcasing implementation.