I will try. > How do you treat colinearity? Use an LASSO as a feature selector or regularizer. > How will you deal with unbalanced data where the ratio of ne

I will try. > How do you treat colinearity? Use an LASSO as a feature selector or regularizer. > How will you deal with unbalanced data where the ratio of negative and positive is huge? User F1 score instead of accuracy as a performance metric. Try using cascade classifier. > How will you decide whether a customer will buy a product today or not given the income of the customer, location where the customer lives, profession and gender? Define a machine learning algorithm for this. More or less, the same as what you said. > Is it useful to apply PCA to your data before SVM classification? No, since kernel-SVM optimization is dependent on data (similarity) and not features. Also, we will be projecting the data into a basis expanded space anyways, so the dimensional reduction would be redundant. > How do you compare a neural network that has one layer, one input and output to a logistic regression model? An MLP-NN is the same as a logistic regressor, if all connections as 1-1 (no basis expansion), no activation function in the data-1st layer connection and Sigmoid as activation function in the 1st layer-output connection. > From a long sorted list and a short 4 element sorted list, which algorithm will you use to search the long sorted list for 4 elements. I am dreadfully bad at core CS questions...throw some AI questions at me. XD > How will inspect missing data and when are they important for your analysis? You might try to impute the missing data from similar datapoints. Maybe use something like K nearest neighbours on the non-missing components to deduce the missing values. Got to be careful doing this though, since it could massively bias your analysis: consider using a special encoding for "missing data" too. Love your solutions. I might also try using cascade classifiers. > Estimate the probability of a disease in a particular city given that the probability of the disease on a national level is low. tough question. Would probably involve me asking a lot of questions about what data I have to play with. Very interview specific.