Table of Contents

1. Challenge

   Retrieve and Load the Monthly Precipitation Data

   1. View the data to understand how to parse it.
   2. Retrieve the Monthly Precipitation JSON data.
   3. Load the JSON data into the program.
   4. Parse the dictionary into time steps and precipitation values.
2. Challenge

   Normalize, Split, and Window the Dataset

   1. Review the model expectations to understand how to prepare your data.
   2. Normalize all of the data.
   3. Split the data into training, validation, and testing datasets.
   4. Convert the raw sequences into windowed sequences.
3. Challenge

   Build and Train a Model to Predict the Precipitation

   1. Review the model expectations to understand how your model should accept and output data.
   2. Create an appropriate neural network model using Keras.
   3. Compile your model with an appropriate loss function for the problem type and data. Include any metrics you want to use to evaluate your model.
   4. Train your model to reach the desired performance. Remember to capture your history!
   5. Save the model.
4. Challenge

   Evaluate Your Model with the Test Data

   1. Generate model statistics on the test data. Ensure you've met or exceeded the desired performance metric.
   2. Plot the predictions to see how well the model works.
   3. Plot the values from your training history.