Jovian
Sign In
Learn practical skills, build real-world projects, and advance your career

Wine Quality prediction using linear regression (minimal)

Using the boston housing dataset: https://archive.ics.uci.edu/ml/datasets/wine+quality

# Uncomment and run the commands below if imports fail
!conda install numpy pytorch torchvision cpuonly -c pytorch -y
!pip install matplotlib --upgrade --quiet
!pip install jovian --upgrade --quiet
Collecting package metadata (current_repodata.json): done Solving environment: done ## Package Plan ## environment location: /opt/conda added / updated specs: - cpuonly - numpy - pytorch - torchvision The following packages will be downloaded: package | build ---------------------------|----------------- numpy-1.18.4 | py37h8960a57_0 5.2 MB conda-forge ------------------------------------------------------------ Total: 5.2 MB The following packages will be UPDATED: numpy 1.18.1-py37h8960a57_1 --> 1.18.4-py37h8960a57_0 Downloading and Extracting Packages numpy-1.18.4 | 5.2 MB | ##################################### | 100% Preparing transaction: done Verifying transaction: done Executing transaction: done WARNING: You are using pip version 20.1; however, version 20.1.1 is available. You should consider upgrading via the '/opt/conda/bin/python3.7 -m pip install --upgrade pip' command. WARNING: You are using pip version 20.1; however, version 20.1.1 is available. You should consider upgrading via the '/opt/conda/bin/python3.7 -m pip install --upgrade pip' command. 
# Imports
import torch
import jovian
import torchvision
import torch.nn as nn
import pandas as pd
import matplotlib.pyplot as plt
import torch.nn.functional as F
from torchvision.datasets.utils import download_url
from torch.utils.data import DataLoader, TensorDataset, random_split
# Hyperparameters
batch_size=64
learning_rate=5e-7


# Other constants
DATASET_URL = "https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv"
DATA_FILENAME = "winequality-red.csv"
TARGET_COLUMN = 'quality'
input_size=11
output_size=1

Dataset & Data loaders