Machine learning feature encoding I'm new to Machine Learning. 
I've just finished the Coursera course. :)
And for my first practical attempt I wanted to "analyse" a local used cars selling website in order to compose a modal that would "predict" an end price.
And I have a problem with "encoding" car features: 
Some of them are "discrete" ( make, model, gearbox encoding : 1 - manual, 2 - automatic, 3 - semi-automatic, fuel encoding: 1 - petrol, 2 - diesel, 3 - electro, etc ), 
some are continuous ( engine volume, engine power, milage, etc ).
The issue is - some of these features might be absent as it is not compulsory to fill them all in.
My main question is: should I use some special value for representing a missing feature? 
I don't feel like using "0" (zero) would do any good as "0 * x = 0" - absolutely any "theta" would do in this partical case. Should I set it to, say, "-1" or something? What is a common approach to this?
And what about feature scaling in that case?
 A: For categorical variables code a new category of "missing"; for continuous variables set missing values to any constant value $a$ & add an indicator variable for missingness. E.g. let the linear predictor be given by
$$\eta=\beta_0 + \beta_1 x_1 + \beta_2 q_1 + \ldots$$
When $x_1$ is not missing set $q_1$ to $0$:
$$\eta=\beta_0 + \beta_1 x_1 + \ldots$$
When $x_1$ is missing set $q_1$ to $1$ & $x_1$ to $a$:
$$\eta=\beta_0 + \beta_1 a + \beta_2 + \ldots$$
Note that whatever value you choose for $a$ affects only the interpretation of $\beta_2$, leaving the model's predictions unchanged.† The new predictor $(x_1, q_1)$ can be used in interactions, & $q_1$ alone; but not $x_1$ alone as it contains the arbitrary $a$.
Unfortunately, even when data are missing completely at random this technique leads to biased estimates when the predictors are correlated (i.e. almost always, with observational data). See Jones (1996), "Indicator & stratification methods for missing explanatory variables in multiple linear regression", JASA, 91, 433. Imputation of missing values is preferable. Little & Rubin (2002), Statistical Analysis with Missing Data, is a good introduction to the problems arising with missing data & techniques for dealing with them.
† Of course you need to be careful when using any techniques that penalizes coefficients according to their magnitude.
A: Suppose you have trained Bayesian classifier using the full data.  When classifying a pattern that contains one (or more) unmeasured variables, you marginalize over that variable, as described in textbooks on machine learning and pattern classification (e.g., Chapter 2 of Pattern classification, 2nd ed.) by Duda, Hart and Stork.
