Data collection

import torch
import torch.nn as nn
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()

!pip install jovian --upgrade --quiet
import jovian
project_name='Akash_Project_alcoholic_sales_Prediction_time_V3_1'

!pip install opendatasets --upgrade --quiet
import opendatasets as od
od.download('https://www.kaggle.com/bulentsiyah/for-simple-exercises-time-series-forecasting')

Please provide your Kaggle credentials to download this dataset. Learn more: http://bit.ly/kaggle-creds
Your Kaggle username: alvin199
Your Kaggle Key: ········

100%|██████████| 1.84M/1.84M [00:00<00:00, 174MB/s]

Downloading for-simple-exercises-time-series-forecasting.zip to ./for-simple-exercises-time-series-forecasting

#path = '/content/drive/MyDrive/Colab Notebooks/Jovian_Pytorch/Alcohol_Sales.csv'
#df = pd.read_csv(path)
df = pd.read_csv('for-simple-exercises-time-series-forecasting/Alcohol_Sales.csv')  #take part of the url from Kaggle
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 325 entries, 0 to 324
Data columns (total 2 columns):
 #   Column          Non-Null Count  Dtype 
---  ------          --------------  ----- 
 0   DATE            325 non-null    object
 1   S4248SM144NCEN  325 non-null    int64 
dtypes: int64(1), object(1)
memory usage: 5.2+ KB