I am absolutely new to this area of predictive modelling in data science. I am not able to understand how and what modelling techniques do we use? Does it depend on the data type? Does it depend on size of data?To be specific to the title, I have to predict missing values in a given temperature data set and i am unaware of anything that i can use. Could someone guide me through?
Using predictive modelling for temperature data set
data mining;missing data;data imputation
I am not able to understand how and what modelling techniques do we  use?Every data science workflow has the follwing steps:Pre-processing (data cleaning and wrangling)Exploratory analyticsModel selectionPrediction and testing.  (And re-iteration)(Optional) Reporting the workflowDoes 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 useThere 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.