I am not able to understand how and what modelling techniques do we
Every data science workflow has the follwing steps:
- Pre-processing (data cleaning and wrangling)
- Exploratory analytics
- Model selection
- Prediction and testing. (And re-iteration)
- (Optional) Reporting the workflow
Does it depend on the data type?
Yes, the entire workflow is dependent on the type and features of the data.
Does it depend on size of data?
Size of data makes a difference in the tools and sometimes(very rarely) the algorithms used.
I have to predict missing values in a given temperature data set and i
am unaware of anything that i can use
There is a lot of material and algorithms on how to impute missing data, which you can refer to and use them accordingly depending on the type of data and the problem statement.