47 lines
1.6 KiB
Python
47 lines
1.6 KiB
Python
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#Downloading DataSet from kaggle
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from kaggle.api.kaggle_api_extended import KaggleApi
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api = KaggleApi()
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api.authenticate()
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api.dataset_download_file('mssmartypants/water-quality', file_name='waterQuality1.csv')
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#Reading DataSet from csv using pandas library
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import pandas as pd
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water = pd.read_csv('waterQuality1.csv')
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#water.describe(include='all')
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# Clearing DataSet from non digit values in is_safe column
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water = water[water['is_safe'].apply(lambda x: str(x).isdigit())]
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water['is_safe'].value_counts()
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# Splitting DataSet on train, dev, test parts
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from sklearn.model_selection import train_test_split
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water_train, water_test = train_test_split(water, train_size=0.8, random_state=1, stratify=water["is_safe"])
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water_test, water_dev = train_test_split(water_test, train_size=0.66, random_state=1, stratify=water_test["is_safe"])
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water_train["is_safe"].value_counts()
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water_test["is_safe"].value_counts()
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water_dev["is_safe"].value_counts()
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print(f'''
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Statystyki zbioru:
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Wielkość zbioru - {len(water)}
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Wielkość podzbioru treningowego - {len(water_train)}
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Wielkość podzbioru walidującego - {len(water_dev)}
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Wielkość podzbioru testowego - {len(water_test)}
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Rozkład częstości parametru mówiącemu o zdantości picia wody (0 oznacza zdanty do picia):
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''')
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#water["is_safe"].value_counts().plot(kind="bar")
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# Normalizing Dataset to [0.0, 1.0] float values
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from sklearn import preprocessing
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water_min_max = preprocessing.MinMaxScaler()
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water_min_max = water_min_max.fit_transform(water)
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water_min_max = pd.DataFrame(water_min_max, columns=water.columns)
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waterNorm = water_min_max
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waterNorm.to_csv('waterQuality.csv', index=False)
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