#Imports
import torch
import torchvision
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split 
from sklearn.preprocessing import StandardScaler
import torch.nn as nn
import torch.nn.functional as F
from torchvision.transforms import ToTensor
from torchvision.utils import make_grid
from torch.utils.data.dataloader import DataLoader
from torch.utils.data import random_split
from torch.utils.data import TensorDataset
from torch.nn import ReLU
from torch.nn import Softmax
%matplotlib inline

# Use a white background for matplotlib figures
matplotlib.rcParams['figure.facecolor'] = '#ffffff'

wine_dataset = pd.read_csv("/Users/Casella/Documents/DATA SCIENCE/2 ANNO/NEURAL COMPUTING/PROJECT/Exams_wine_wine.csv")

wine_dataset.head()

summary_stats = wine_dataset.describe()
sum_stats = summary_stats.transpose() #for a better visualization
sum_stats = sum_stats.drop(['count'], axis=1) #remove the count column because it is unuseful
sum_stats

# Checking for null values
wine_dataset.isnull().sum()