98 lines
2.1 KiB
Python
98 lines
2.1 KiB
Python
import os
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import zipfile
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import pandas as pd
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import numpy as np
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import MinMaxScaler
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os.system("kaggle datasets download -d bartoszpieniak/poland-cars-for-sale-dataset")
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with zipfile.ZipFile("archive.zip", "r") as zip_ref:
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zip_ref.extractall("car_sales")
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csv_file = None
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for root, dirs, files in os.walk("car_sales"):
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for file in files:
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if file.endswith(".csv"):
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csv_file = os.path.join(root, file)
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break
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if csv_file is None:
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raise FileNotFoundError("CSV file not found in the extracted dataset")
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data = pd.read_csv(csv_file)
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train_data, temp_data = train_test_split(data, test_size=0.4, random_state=42)
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dev_data, test_data = train_test_split(temp_data, test_size=0.5, random_state=42)
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def print_stats(df, name):
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print(f"\nStatystyki dla {name}:")
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print(f"Wielkość zbioru: {len(df)}")
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for col in df.columns:
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if df[col].dtype != "object":
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print(f"\nParametr: {col}")
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print(f"Minimum: {df[col].min()}")
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print(f"Maksimum: {df[col].max()}")
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print(f"Średnia: {df[col].mean()}")
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print(f"Odchylenie standardowe: {df[col].std()}")
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print(f"Mediana: {df[col].median()}")
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print_stats(data, "Cały zbiór")
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print_stats(train_data, "Zbiór treningowy")
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print_stats(dev_data, "Zbiór walidacyjny")
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print_stats(test_data, "Zbiór testowy")
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def normalize_data(df, columns):
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scaler = MinMaxScaler()
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for col in columns:
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if df[col].dtype != "object":
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df[col] = scaler.fit_transform(df[[col]])
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normalize_data(train_data, train_data.columns)
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normalize_data(dev_data, dev_data.columns)
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normalize_data(test_data, test_data.columns)
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def clean_data(df):
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df.dropna(inplace=True)
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df.drop_duplicates(inplace=True)
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clean_data(data)
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clean_data(train_data)
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clean_data(dev_data)
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clean_data(test_data)
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train_data.to_csv("./results/train_data.csv", index=False)
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dev_data.to_csv("./results/dev_data.csv", index=False)
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test_data.to_csv("./results/test_data.csv", index=False) |