SANDYA VB- TIME SERIES FORECASTING PROJECT.

Problem: For this particular assignment, the data of different types of wine sales in the 20th century is to be analysed. Both of these data are from the same company but of different wines. As an analyst in the ABC Estate Wines, you are tasked to analyse and forecast Wine Sales in the 20th century. Dataset - R In [1]: import numpy as np import pandas as pd import seaborn as sns from matplotlib import pyplot as plt from import rcParams rcParams['ze'] = 13, 6 1. Read the data as an appropriate Time Series data and plot the data. In [2]: df = _csv("R") In [3]: () Out[3]: YearMonth Rose Create PDF in your applications with the Pdfcrowd HTML to PDF API PDFCROWD In [4]: () In [5]: (); () YearMonth Rose .0 .0 .0 .0 .0 Out[4]: YearMonth Rose .0 .0 .0 .0 .0 Create PDF in your applications with the Pdfcrowd HTML to PDF API PDFCROWD In [6]: date = _range(start='01/01/1980', end='08/01/1995', freq='M') date In [7]: df['Time_Stamp'] = pd.DataFrame(date,columns=['Month']) In [8]: () Out[6]: DatetimeIndex(['', '', '', '', '', '', '', '', '', '', ... '', '', '', '', '', '', '', '', '', ''], dtype='datetime64[ns]', length=187, freq='M') Out[8]: YearMonth Rose Time_Stamp .0 .0 Create PDF in your applications with the Pdfcrowd HTML to PDF API PDFCROWD In [9]: df['Time_Stamp']=_datetime(df['Time_Stamp']) df= _index('Time_Stamp') (['YearMonth'],axis=1, inplace = True) () In [10]: ();