# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load




# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session

import os
import torch
import torchvision
import tarfile
import torch.nn as nn
import numpy as np
import pandas as pd
import warnings
from bs4 import BeautifulSoup
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
import nltk
import math
import time
import re
from matplotlib import gridspec
import pandas_profiling as pp
import torch.nn.functional as F
from torchvision.datasets.utils import download_url
from torchvision.datasets import ImageFolder
from torch.utils.data import DataLoader
import torchvision.transforms as tt
from torch.utils.data import DataLoader, TensorDataset, random_split
from torchvision.utils import make_grid
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

project_name='05-02-japan-hostel'

# Hyperparameters
batch_size=64

# Other constants
DATASET_URL = "/kaggle/input/japanhostel/Hostel.csv"
DATA_FILENAME = "Hostel.csv"
TARGET_COLUMN = 'price.from'
input_size=4
output_size=1

# Download the data
dataframe = pd.read_csv(DATASET_URL)
dataframe.head()