ananyachadha / stats112 Goto Github PK

Hi! The data I used in the data folders are a free-trial of paid data, from the YesEnergy API. I also used a free trial of meteomatics API and weather APIs in my scripts directly. As a result, this is a private repo, since I don't want to expose paid data.

STRUCTURE:

1. Data Collection
2. Data Cleaning
3. Data Exploration
4. Machine Learning
5. Final Machine Learning (In Final ML, we run the pipeline in a giant for loop, to loop through many nodes. This lets us get accuracy scores for many nodes by running just one file.) The combined file merges collection, cleaning and ML. It omits exploration since we are running a known pipeline.

1-Getting Data.ipynb put data in spreadsheets to be imported into data cleaning file. data from Yesenergy for prices data from sunrise sunset API data from meteomatics API data from synoptic labs API data from airport lat/long API

2-Cleaning Data.ipynb clean it up joins change variable names removes $, commas, nans converts timezones from utc to pst

3-Data Exploration.ipynb graph how price has changed over time graph price throughout the day graph price over the months graph price spread through day price spread through months

4-Machine Learning.ipynb predict price spread given input variables clean "return $/MW" column to just be binary buy or sell if it's positive or negative predict buy or sell given input variables

5- CombinedMLPipeline.ipynb is where the magic happens! Here we combine ALL files 1, 2, and 4 to create a for loop to iterate through many nodes.

Data: in this folder we have price data for each node. I got this from YesEnergy. It was my only non-api data. Intermediate Data: a lot of my data is from APIs and they are saved as csvs through the process. If you want to check in what a CSV looks like, it's probably in the intermediate data folder.