ium_464914/IUM_2.py
Alicja Szulecka 96945632d6 new script
2024-04-02 19:05:02 +02:00

49 lines
1.7 KiB
Python

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