create python script

create_dataset.py

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