51 lines
1.8 KiB
Python
51 lines
1.8 KiB
Python
import kaggle
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import pandas as pd
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import numpy as np
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from sklearn import preprocessing
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# kaggle
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kaggle.api.authenticate()
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kaggle.api.dataset_download_files('timmate/avocado-prices-2020', path='.', unzip=True)
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avocado_with_year = pd.read_csv('avocado-updated-2020.csv')
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new = ['date', 'average_price', 'total_volume', '4046', '4225', '4770', 'total_bags', 'small_bags', 'large_bags', 'xlarge_bags', 'type', 'geography']
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avocado = avocado_with_year[new]
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avocado.to_csv("avocado.csv", index=False)
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avocado = pd.read_csv('avocado.csv')
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avocado_train, avocado_validate, avocado_test = np.split(avocado.sample(frac=1), [int(.6*len(avocado)), int(.8*len(avocado))])
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print("Avocado: ".ljust(20), np.size(avocado))
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print("Avocado (train) : ".ljust(20), np.size(avocado_train))
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print("Avocado (validate): ".ljust(20), np.size(avocado_validate))
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print("Avocado (test) ".ljust(20), np.size(avocado_test))
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avocado.describe(include = 'all')
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avocado_train.describe(include= 'all')
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avocado_validate.describe(include = 'all')
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avocado_test.describe(include = 'all')
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avocado.geography.value_counts()
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avocado_test.geography.value_counts()
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avocado_train.geography.value_counts()
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pd.value_counts(avocado['type']).plot.bar()
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pd.value_counts(avocado_train['type']).plot.bar()
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pd.value_counts(avocado_test['type']).plot.bar()
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avocado['average_price'].hist()
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avocado_train['average_price'].hist()
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avocado_validate['average_price'].hist()
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avocado_test['average_price'].hist()
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num_values = avocado.select_dtypes(include='float64').values
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scaler = preprocessing.MinMaxScaler()
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x_scaled = scaler.fit_transform(num_values)
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num_columns = avocado.select_dtypes(include='float64').columns
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avocado_normalized = pd.DataFrame(x_scaled, columns=num_columns)
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for col in avocado.columns:
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if col in num_columns:
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avocado[col] = avocado_normalized[col]
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avocado.isnull().sum()
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avocado.dropna() |