We analyze the lung cancer data available from the SEER program with the aim of developing accurate survival prediction models for lung cancer using data mining techniques. Carefully designed preprocessing steps resulted in removal/ modification/splitting of several attributes, and 2 of the 11 derived attributes were found to have significant predictive power. Several data mining classification techniques were used on the preprocessed data along with various data mining optimizations and validations. In our experiments, ensemble voting of five decision tree based classifiers and meta-classifiers was found to result in the best prediction performance in terms of accuracy and area under the ROC curve. Further, we have developed an on-line lung cancer outcome calculator for estimating risk of mortality after 6 months, 9 months, 1 year, 2 year, and 5 years of diagnosis, for which a smaller non-redundant subset of 13 attributes was carefully selected using attribute selection techniques, while trying to retain the predictive power of the original set of attributes. The on-line lung cancer outcome calculator developed as a result of this study is available at http://info.eecs.northwestern.edu:8080/LungCancerOutcome-Calculator/.