Musicians often use tools such as loop-pedals and multitrack recorders to assist in improvisation and songwriting. While these devices are useful in creating new compositions from scratch, they do not contribute to the composition directly. In recent years, new musical instruments, interfaces and controllers using machine learning algorithms to create new sounds, generate accompaniment or construct novel compositions, have become available for both professional musicians and novices to enjoy. This thesis describes the design, implementation and evaluation of a system for predictive songwriting and improvisation using concatenative accompaniment which has been given the nickname PSCA. In its most simple form, the PSCA functions as an audio looper for vocal improvisation, but the system also utilises machine learning approaches to predict suitable harmonies to accompany the playback loop. Two machine learning algorithms were compared and implemented into the PSCA to facilitate harmony prediction: the hidden Markov model (HMM) and the Bidirectional Long Short-Term Memory (BLSTM). The HMM and BLSTM algorithms are trained on a dataset of lead sheets in order to learn the relationship between the notes in a melody and the chord which accompanies it as well as learning dependencies between chords to model chord progressions. In quantitative testing, the BLSTM model was found to be able to learn the harmony prediction task more effectively than the HMM model, this was also supported by a qualitative analysis of musicians using the PSCA system. The system proposed in this thesis provides a novel approach in which these two machine learning models are compared with regards to prediction accuracy on the dataset as well as the perceived musicality of each model when used for harmony prediction in the PSCA. This approach results in a system which can contribute to the improvisation and songwriting process by adding harmonies to the audio loop on-the-fly.