The detection of exoplanets has rapidly evolved to one of the most important frontiers of astronomical and astrophysical research. The recent decades have seen the development of various techniques for detecting exoplanets. Of these approaches the transit method has received particular interest and has lead to the largest number of discoveries to date. The Kepler K2 mission is an ongoing observational survey, which has generated light curves for thousands of stars, a large fraction of which have yet to be fully explored. To discover and characterize the transiting planets hosted by the respective stars, extensive transit screens are required. However, implementing a pipeline for transit analyses is not straight forward, considering the light curve properties of different survey, the rapid changes brought by technological advancements, and the apparent lack of a golden standard with respect to the applied methodology. The project has reviewed several aspects of exoplanet detection via the transit method. Particular focus was placed on the identification of a suitable workflow covering the relevant steps to move from raw light curve files to a final prediction and characterization of transiting planetary candidates. Adhering to the identified strategy, the major part of the project then dealt with the implementation of a pipeline that integrates and executes all the different steps in a streamlined fashion. Of note, primary focus was placed on the actual selection and implementation of methods into an operational pipeline, but due to the given time constraints extensive optimizations of each individual processing step was outside the scope of this project. Nevertheless, the pipeline was employed to predict transit candidates for K2 campaigns C7, C8, C10, C11, and C12. A comparsion of the most conservative predictions from campaigns C7 and C10 with previously reported exoplanet candidates demonstrated that the pipeline was highly capable of discovering reliable transit candidates. Since campaigns C11 and C12 have not yet been fully explored, the respective candidates predicted for those campaigns in the current project might thus harbour novel planetary transit candidates that would be suitable for follow-up confirmation runs. In summary, the current project has produced a pipeline for performing transiting exoplanet searches in K2 data, which integrates the steps from raw light curve processing to transit candidate selection and characterization. The pipeline has been demonstrated to predict credible transit candidates, but future work will have to focus on additional optimizations of individual method parameters and on the analysis of transit detection efficiencies.