![]() ‘Horizontal_Distance_To_Roadways’ and ‘Horizontal_Distance_To_Fire_Points’ - my reasoning is that since the description of the project says that the “forest cover types are mainly a result of ecological processes rather than forest management practices” I wanted to discard any features that suggested any type of man activity.Hillshade values - my understanding is that these values can be calculated based on other features (‘Slope’ and ‘Aspect’) and for that reason they should not provide any additional information.The corresponding samples were removed as well ‘Soil_Type7’, ‘Soil_Type8’, ‘Soil_Type14’, ‘Soil_Type15’, ‘Soil_Type25’, ‘Soil_Type36’, ‘Soil_Type37’ - these soil types have very few samples compared to the others, so I assumed them not to be as relevant. ![]() My results are not too far from yours but here’s my two cents anyway.įirst of all I did not consider all the columns as features for the final model. I would definitely consider having that not just a side note on the Final Project both on this path and also for the Data Science Path! The notion that models can be saved and utilized later on as a program is quite necessary! My bad if there is info on that during the paths I’ll have my presentation done later, but I’d like to point something out! It was the first time I’ve seen info on this Skill Path on how to save and open a model! Quite simple, just Model.fit(x_train, y_train, batch_size=200, epochs=50, validation_split=0.1) pile(optimizer=Adam(learning_rate=0.01), loss=SparseCategoricalCrossentropy(), metrics=) Model.add(Dense(8, activation='softmax')) With the following model, model = Sequential()
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