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weather_forecasting_tutorial

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Shafeek Saleem commited on Jul 25, 2023

Commit

c532c54

1 Parent(s): b14d8eb

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Files changed (1) hide show

pages/3_Training the Model.py CHANGED Viewed

@@ -54,7 +54,7 @@ def create_model_inputs(data, lag, mean_period, target_variable):
     X = df_processed[
         ["Location", "MinTemp", "MaxTemp", "Rainfall", "WindGustDir", "WindGustSpeed", "WindDir9am", "WindDir3pm",
          "WindSpeed9am", "WindSpeed3pm", "Humidity9am", "Humidity3pm", "Pressure9am", "Pressure3pm", "Temp9am",
-         "Temp3pm", "RainToday", target_variable + "_mean"]].dropNa()
     from sklearn.preprocessing import LabelEncoder
     label_encoder = LabelEncoder()
     X['RainToday'] = label_encoder.fit_transform(X['RainToday'])

     X = df_processed[
         ["Location", "MinTemp", "MaxTemp", "Rainfall", "WindGustDir", "WindGustSpeed", "WindDir9am", "WindDir3pm",
          "WindSpeed9am", "WindSpeed3pm", "Humidity9am", "Humidity3pm", "Pressure9am", "Pressure3pm", "Temp9am",
+         "Temp3pm", "RainToday", target_variable + "_mean"]].dropna()
     from sklearn.preprocessing import LabelEncoder
     label_encoder = LabelEncoder()
     X['RainToday'] = label_encoder.fit_transform(X['RainToday'])