49 lines
1.7 KiB
Python
49 lines
1.7 KiB
Python
import matplotlib.pyplot as plt
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import pandas as pd
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import kaggle
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import StandardScaler
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def download_file():
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kaggle.api.authenticate()
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kaggle.api.dataset_download_files('nasa/meteorite-landings', path='.', unzip=True)
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def split(data):
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meteorite_train, meteorite_test = train_test_split(data, test_size=0.2, random_state=1)
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meteorite_train, meteorite_val = train_test_split(meteorite_train, test_size=0.25, random_state=1)
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return meteorite_train, meteorite_test, meteorite_val
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def normalization(data):
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scaler = StandardScaler()
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data['mass'] = scaler.fit_transform(data[['mass']])
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return data
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def preprocessing(data):
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data = data.dropna(subset=['reclat'])
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incorrect_years_index = data.loc[(data['year'] > 2016) | (data['year'] < 860)].index
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incorrect_location_index = data.loc[(data['reclat'] == 0) & (data['reclong'] == 0)].index
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data.drop(incorrect_years_index.union(incorrect_location_index), inplace=True)
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data.loc[(data['mass'].isnull()) & (data['name'].str.startswith('Österplana')), 'mass'] = 0
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return data
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download_file()
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data = pd.read_csv("meteorite-landings.csv")
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meteorite_train, meteorite_test, meteorite_val = split(data)
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meteorite_train = normalization(meteorite_train)
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meteorite_test = normalization(meteorite_test)
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meteorite_val = normalization(meteorite_val)
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meteorite_train = normalization(meteorite_train)
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meteorite_test = normalization(meteorite_test)
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meteorite_val = normalization(meteorite_val)
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meteorite_train.to_csv('meteorite_train.csv', encoding='utf-8')
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meteorite_test.to_csv('meteorite_test.csv', encoding='utf-8')
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meteorite_val.to_csv('meteorite_val.csv', encoding='utf-8')
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