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Created 3 years ago
#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()
fixed acidity 0
volatile acidity 0
citric acid 0
residual sugar 0
chlorides 0
free sulfur dioxide 0
total sulfur dioxide 0
density 0
pH 0
sulphates 0
alcohol 0
quality 0
dtype: int64