Dear Zerihun, you can only dismiss them. Considering them as zero is not an appropriate strategy. Nowadays statistical softwares can handle missing data correctly. You can simply analyse them using SPSS or SAS with missing values as dots or other signs depend on the software.

Coding as zero would cause any of your estimates to be heavily biased. That said, Ehsan's suggestion would simply delete these records would be better, but also may induce unexpected biases, particularly if the missing value pattern is correlated with the dependent or collection of independent variables.

For example if values are missing because they are censored on the left, for example then regression coefficients would be biased in predictable ways. (Intercept too high and slope too low)

I would suggest getting up to speed on statistical methods for censored data and multiple imputation methods.

https://www.amstat.org/sections/srms/Proceedings/y2010/Files/400142.pdf

http://www-personal.umich.edu/~teraghu/Raghunathan-Dong.pdf

Simply, you can ignore them and use a software can deal with them like SAS

Agree with Patrick, multiple imputation is a valuable approach. An excellent introduction to various kinds of missing handling is

Craig Enders (2010). Applied Missing Data Analysis, Guildford.

Listwise deletion is in inappropriate if the MCAR assumption does not hold.

Thank you all for your answers.