2024-04-02 22:20:19 +02:00
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import scikit-learn as sklearn
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from scikit-learn.preprocessing import OneHotEncoder
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from scikit-learn.model_selection import train_test_split
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2024-04-02 20:40:42 +02:00
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import pandas as pd
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import subprocess
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2024-04-02 21:50:10 +02:00
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subprocess.run(["kaggle", "datasets", "download", "muhammadbinimran/housing-price-prediction-data", "--unzip"])
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2024-04-02 20:40:42 +02:00
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housing_price_dataset = pd.read_csv('housing_price_dataset.csv')
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hp_train_test, hp_dev = sklearn.model_selection.train_test_split(housing_price_dataset, test_size=0.1)
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hp_train, hp_test = sklearn.model_selection.train_test_split(hp_train_test, test_size=1000)
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hp_train = pd.get_dummies(hp_train, columns=['Neighborhood'])
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hp_dev = pd.get_dummies(hp_dev, columns=['Neighborhood'])
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hp_test = pd.get_dummies(hp_test, columns=['Neighborhood'])
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hp_train.to_csv('hp_train.csv', index=False)
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hp_dev.to_csv('hp_dev.csv', index=False)
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hp_test.to_csv('hp_test.csv', index=False)
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