45 lines
1.2 KiB
Python
45 lines
1.2 KiB
Python
import pandas as pd
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from sklearn.model_selection import train_test_split
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# get data
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sells = pd.read_csv('data/Property Sales of Melbourne City.csv')
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# delete unnecessary columns and drop rows with NaN values
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columns_to_drop = [
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'Lattitude',
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'Longtitude',
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'CouncilArea',
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'Propertycount',
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'Method',
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'SellerG',
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'Date',
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'Postcode',
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'Bedroom2',
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'Bathroom',
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'Car',
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'BuildingArea',
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'Address'
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]
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sells = sells.drop(columns_to_drop, axis=1).dropna()
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# normalize values
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sells["Price"] = sells["Price"] / sells["Price"].max()
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sells["Landsize"] = sells["Landsize"] / sells["Landsize"].max()
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sells["Distance"] = sells["Distance"] / sells["Distance"].max()
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# split to train/dev/test subsets
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X = sells
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Y = sells.pop('Price')
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X_train, X_temp, Y_train, Y_temp = train_test_split(X, Y, test_size=0.3, random_state=1)
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X_val, X_test, Y_val, Y_test = train_test_split(X_temp, Y_temp, test_size=0.5, random_state=1)
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# save subsets to files
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X_train.to_csv('X_train.csv', index=False)
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X_val.to_csv('X_val.csv', index=False)
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X_test.to_csv('X_test.csv', index=False)
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Y_train.to_csv('Y_train.csv', index=False)
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Y_val.to_csv('Y_val.csv', index=False)
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Y_test.to_csv('Y_test.csv', index=False)
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