It is unclear why you want to use (multiple) correspondence analysis, but here are a couple of additional points to consider, to add to Kjetil's answer (+1):

• If you use multiple correspondence analysis (MCA) as a dimension reduction method, and plan to use these dimensions as predictors in another model (e.g. logistic regression), then (obviously) do not include the target variable in the computation of the dimensions, at the risk otherwise of data leakage and overfitting.
• MCA allows projecting supplementary variables on the axes. Supplementary variables are variables not used for computing the dimensions (contrary to active variables), but their projection on the axes may be useful for further interpretation and exploration (e.g. to see how they relate to the MCA dimensions). So you could use your target variable as a supplementary variable while using your predictors as active variables - but that really depends on what you're trying to do ultimately.
• If you want to include your target variable in the computation of the dimensions (so not as a supplementary variable) but also want to give it more "weight" than other variables, you could use multiple factor analysis -with one group including only your target variable, and another group including your predictor variables. Note that you could also group your predictors themselves in multiple different groups. But again, choosing this method depends on the question you're trying to answer ultimately.

If you are using R, there are a several packages offering multiple correspondence analysis, with an option for supplementary variables. Look at FactoMiner and GDATools for instance. Multiple factor analysis is also available for these packages. The form under which your data should be depends on the tool you're using (though the two packages I mentioned ask for the raw data, not for a contingency table or a combination of contingency tables). So your second question is more a question of https://stackoverflow.com, and would depend on the package or software you choose to use.

It is unclear why you want to use (multiple) correspondence analysis, but here are a couple of additional points to consider, to add to Kjetil's answer (+1):

• If you use multiple correspondence analysis (MCA) as a dimension reduction method, and plan to use these dimensions as predictors in another model (e.g. logistic regression), then do not include the target variable in the computation of the dimensions, at the risk otherwise of data leakage and overfitting.
• MCA allows projecting supplementary variables on the axes. Supplementary variables are variables not used for computing the dimensions (contrary to active variables), but their projection on the axes may be useful for further interpretation and exploration (e.g. to see how they relate to the MCA dimensions). So you could use your target variable as a supplementary variable while using your predictors as active variables - but that really depends on what you're trying to do ultimately.
• If you want to include your target variable in the computation of the dimensions (so not as a supplementary variable) but also want to give it more "weight" than other variables, you could use multiple factor analysis -with one group including only your target variable, and another group including your predictor variables. Note that you could also group your predictors themselves in multiple different groups. But again, choosing this method depends on the question you're trying to answer ultimately.

If you are using R, there are a several packages offering multiple correspondence analysis, with an option for supplementary variables. Look at FactoMiner and GDATools for instance. Multiple factor analysis is also available for these packages. The form under which your data should be depends on the tool you're using, though the two packages I mentioned ask for the raw data, not for a contingency table or a combination of contingency tables (these packages internally convert your raw dataset into an indicator matrix or into a Burt table, on which the calculations for the MCA are then performed -which should make your life easier). So your second question is more a question for https://stackoverflow.com, and would depend on the package or software you choose to use.

It is unclear why you want to use (multiple) correspondence analysis, but here are a couple of additional points, to add to Kjetil's answer (+1):

• If you use multiple correspondence analysis (MCA) as a dimension reduction method, and plan to use these dimensions as predictors in another model (e.g. logistic regression), then (obviously) do not include the target variable in the computation of the dimensions, at the risk otherwise of data leakage and overfitting.
• MCA allows projecting supplementary variables on the axes. Supplementary variables are variables not used for computing the dimensions (contrary to active variables), but their projection on the axes may be useful for further interpretation and exploration (e.g. to see how they relate to the MCA dimensions). So you could use your target variable as a supplementary variable while using your predictors as active variables - but that really depends on what you're trying to do ultimately.
• If you want to include your target variable in the computation of the dimensions (so not as a supplementary variable) but also want to give it more "weight" than other variables, you could use multiple factor analysis -with one group including only your target variable, and another group including your predictor variables. Note that you could also group your predictors themselves in multiple different groups. But again, choosing this method depends on the question you're trying to answer ultimately.

If you are using R, there are a several packages offering multiple correspondence analysis, with an option for supplementary variables. Look at FactoMiner and GDATools for instance. Multiple factor analysis is also available for these packages.

It is unclear why you want to use (multiple) correspondence analysis, but here are a couple of additional points to consider, to add to Kjetil's answer (+1):

• If you use multiple correspondence analysis (MCA) as a dimension reduction method, and plan to use these dimensions as predictors in another model (e.g. logistic regression), then (obviously) do not include the target variable in the computation of the dimensions, at the risk otherwise of data leakage and overfitting.
• MCA allows projecting supplementary variables on the axes. Supplementary variables are variables not used for computing the dimensions (contrary to active variables), but their projection on the axes may be useful for further interpretation and exploration (e.g. to see how they relate to the MCA dimensions). So you could use your target variable as a supplementary variable while using your predictors as active variables - but that really depends on what you're trying to do ultimately.
• If you want to include your target variable in the computation of the dimensions (so not as a supplementary variable) but also want to give it more "weight" than other variables, you could use multiple factor analysis -with one group including only your target variable, and another group including your predictor variables. Note that you could also group your predictors themselves in multiple different groups. But again, choosing this method depends on the question you're trying to answer ultimately.

If you are using R, there are a several packages offering multiple correspondence analysis, with an option for supplementary variables. Look at FactoMiner and GDATools for instance. Multiple factor analysis is also available for these packages. The form under which your data should be depends on the tool you're using (though the two packages I mentioned ask for the raw data, not for a contingency table or a combination of contingency tables). So your second question is more a question of https://stackoverflow.com, and would depend on the package or software you choose to use.