mirror of
https://github.com/Azure/MachineLearningNotebooks.git
synced 2025-12-22 02:25:12 -05:00
32 lines
932 B
Python
32 lines
932 B
Python
# imports
|
|
import pickle
|
|
from datetime import datetime
|
|
from azureml.opendatasets import NoaaIsdWeather
|
|
from sklearn.linear_model import LinearRegression
|
|
|
|
# get weather dataset
|
|
start = datetime(2019, 1, 1)
|
|
end = datetime(2019, 1, 14)
|
|
isd = NoaaIsdWeather(start, end)
|
|
|
|
# convert to pandas dataframe and filter down
|
|
df = isd.to_pandas_dataframe().fillna(0)
|
|
df = df[df['stationName'].str.contains('FLORIDA', regex=True, na=False)]
|
|
|
|
# features for training
|
|
X_features = ['latitude', 'longitude', 'temperature', 'windAngle', 'windSpeed']
|
|
y_features = ['elevation']
|
|
|
|
# write the training dataset to csv
|
|
training_dataset = df[X_features + y_features]
|
|
training_dataset.to_csv('training.csv', index=False)
|
|
|
|
# train the model
|
|
X = training_dataset[X_features]
|
|
y = training_dataset[y_features]
|
|
model = LinearRegression().fit(X, y)
|
|
|
|
# save the model as a .pkl file
|
|
with open('elevation-regression-model.pkl', 'wb') as f:
|
|
pickle.dump(model, f)
|