• create a new endpoint that accepts a POST request of the features required to run the machine learning model that you trained and saved in last milestone

1. Pick an approach to transfer the dataframe from python to R.

• Parquet file
• Feather file
• Pandas exchange
• Arrow exchange

2. Discuss why you chose this approach over others.

Discuss and finalize a team work contract for DSCI-525.

1. Download the data from figshare to your local computer using the figshare API (you can make use of requests library).
2. Extract the zip file, again programmatically, similar to how we did it in class.

Create a folder called notebooks in the repository and create a notebook for this milestone in that folder.

1. Use one of the following options to combine data CSVs into a single CSV.
   • Pandas
   • DASK
2. When combining the csv files make sure to add extra column called “model” that identifies the model (tip : you can get this column populated from the file name eg: for file name “SAM0-UNICON_daily_rainfall_NSW.csv”, the model name is SAM0-UNICON).
3. Compare run times and memory usages of these options on different machines within your team, and summarize your observations in your milestone notebook.

1. Investigate at least two of the following approaches to reduce memory usage while performing the - - EDA (e.g., value_counts).

• Changing dtype of your data
• Load just columns what we want
• Loading in chunks
• Dask

2. Discuss your observations.

In the textbox provided on Canvas for the Milestone 1 assignment include:

The URL of your public project's repository
The URL of your notebook for this milestone