Additive vs Multiplicative model in Time Series Data
I want to know which model between additive and multiplicative best suits the above data.


It is hard to tell just by looking at it. 

A multiplicative decomposition roughly corresponds to an additive decomposition of the logarithms. 
The additive decomposition is the most appropriate if the magnitude of the seasonal fluctuations, or the variation around the trend-cycle, does not vary with the level of the time series. When the variation in the seasonal pattern, or the variation around the trend-cycle, appears to be proportional to the level of the time series, then a multiplicative decomposition is more appropriate. Multiplicative decompositions are common with economic time series.

An alternative to using a multiplicative decomposition is to first transform the data until the variation in the series appears to be stable over time, then use an additive decomposition. So, basically you need to check for heteroskedasticity, eliminate that if it is there by transformations and do an additive decomposition of the transformed series. 

Most common transformations are log or square root of the series and are special cases of Power transform.

Reference:
Forecasting principles and practice