<jupyter_start><jupyter_text>______Copyright <NAME>For more information, visit us at www.pieriandata.com RNN Example for Time Series **TASK: IMPORT THE BASIC LIBRARIES YOU THINK YOU WILL USE**<jupyter_code># IMPORTS HERE<jupyter_output><empty_output><jupyter_text>DataInfo about this data set: https://fred.stlouisfed.org/series/TRFVOLUSM227NFWARead in the data set "Miles_Traveled.csv" from the Data folder. Figure out how to set the date to a datetime index columns<jupyter_code># CODE HERE<jupyter_output><empty_output><jupyter_text>**Task: Change the column names to Value**<jupyter_code># CODE HERE<jupyter_output><empty_output><jupyter_text>**TASK: Plot out the time series**<jupyter_code># CODE HERE<jupyter_output><empty_output><jupyter_text>**TASK: Perform a Seasonal Decomposition on the model and plot out the ETS components**<jupyter_code># CODE HERE<jupyter_output><empty_output><jupyter_text>Train Test Split **TASK: Figure out the length of the data set**<jupyter_code># CODE HERE<jupyter_output><empty_output><jupyter_text>**TASK: Split the data into a train/test split where the test set is the last 12 months of data.**<jupyter_code># CODE HERE
len(test)<jupyter_output><empty_output><jupyter_text>Scale Data **TASK: Use a MinMaxScaler to scale the train and test sets into scaled versions.**<jupyter_code># CODE HERE<jupyter_output><empty_output><jupyter_text>Time Series Generator**TASK: Create a TimeSeriesGenerator object based off the scaled_train data. The n_input is up to you, but at a minimum it should be at least 12.**<jupyter_code>#CODE HERE<jupyter_output><empty_output><jupyter_text>Create the Model**TASK: Create a Keras Sequential Model with as many LSTAM units you want and a final Dense Layer.** **TASK: Fit the model to the generator (it should be a lot of epochs, but do as many as you have the patience for! :)**<jupyter_code># CODE HERE<jupyter_output><empty_output><jupyter_text>**TASK: Plot the history of the loss that occured during training.**<jupyter_code># CODE HERE<jupyter_output><empty_output><jupyter_text>Evaluate on Test Data**TASK: Based on your test data and input size, create an appropriate;y sized "first evaluation batch" like we did in the lecture.**<jupyter_code># CODE HERE<jupyter_output><empty_output><jupyter_text>**TASK: Generate predictions into the same time stamps as the test set**<jupyter_code># CODE HERE<jupyter_output><empty_output><jupyter_text>Inverse Transformations and Compare**TASK: Inverse Transform your new forecasted predictions.**<jupyter_code>#CODE HERE<jupyter_output><empty_output><jupyter_text>**TASK: Create a new dataframe that has both the original test values and your predictions for them.**<jupyter_code># CODE HERE<jupyter_output><empty_output><jupyter_text>**TASK: Plot out the test set against your own predicted values.**<jupyter_code># CODE HERE<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.09252173913043478, 'fraction_numerical': 0.002434782608695652, 'mean_word_length': 7.9875, 'pattern_counts': {'":': 0, '<': 66, '<?xml version=': 0, '>': 66, 'https://': 1, 'lorem ipsum': 0, 'www.': 1, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '16', 'dir': 'jupyter-structured-clean-dedup', 'id': '294482', 'license': 'Apache-2.0', 'n_tokens_mistral': 989, 'n_tokens_neox': 935, 'n_words': 299, 'path': 'tsa/jose/UDEMY_TSA_FINAL (1)/07-Deep-Learning-Models/02-RNN-Exercise.ipynb', 'repo_name': 'juspreet51/ml_templates', 'size': '2881'}