Identifying Real Estate Opportunities using Machine Learning

The real estate market is exposed to many fluctuations in prices because of existing correlations with many variables, some of which cannot be controlled or might even be unknown. Housing prices can increase rapidly (or in some cases, also drop very fast), yet the numerous listings available online where houses are sold or rented are not likely to be updated that often. In some cases, individuals interested in selling a house (or apartment) might include it in some online listing, and forget about updating the price. In other cases, some individuals might be interested in deliberately setting a price below the market price in order to sell the home faster, for various reasons. In this paper, we aim at developing a machine learning application that identifies opportunities in the real estate market in real time, i.e., houses that are listed with a price substantially below the market price. This program can be useful for investors interested in the housing market. We have focused in a use case considering real estate assets located in the Salamanca district in Madrid (Spain) and listed in the most relevant Spanish online site for home sales and rentals. The application is formally implemented as a regression problem that tries to estimate the market price of a house given features retrieved from public online listings. For building this application, we have performed a feature engineering stage in order to discover relevant features that allows for attaining a high predictive performance. Several machine learning algorithms have been tested, including regression trees, k-nearest neighbors, support vector machines and neural networks, identifying advantages and handicaps of each of them.Comment: 24 pages, 13 figures, 5 table

Applying machine learning to predict future adherence to physical activity programs.

BackgroundIdentifying individuals who are unlikely to adhere to a physical exercise regime has potential to improve physical activity interventions. The aim of this paper is to develop and test adherence prediction models using objectively measured physical activity data in the Mobile Phone-Based Physical Activity Education program (mPED) trial. To the best of our knowledge, this is the first to apply Machine Learning methods to predict exercise relapse using accelerometer-recorded physical activity data.MethodsWe use logistic regression and support vector machine methods to design two versions of a Discontinuation Prediction Score (DiPS), which uses objectively measured past data (e.g., steps and goal achievement) to provide a numerical quantity indicating the likelihood of exercise relapse in the upcoming week. The respective prediction accuracy of these two versions of DiPS are compared, and then numerical simulation is performed to explore the potential of using DiPS to selectively allocate financial incentives to participants to encourage them to increase physical activity.Resultswe had access to a physical activity trial data that were continuously collected every 60 sec every day for 9 months in 210 participants. By using the first 15 weeks of data as training and test on weeks 16-30, we show that both versions of DiPS have a test AUC of 0.9 with high sensitivity and specificity in predicting the probability of exercise adherence. Simulation results assuming different intervention regimes suggest the potential benefit of using DiPS as a score to allocate resources in physical activity intervention programs in reducing costs over other allocation schemes.ConclusionsDiPS is capable of making accurate and robust predictions for future weeks. The most predictive features are steps and physical activity intensity. Furthermore, the use of DiPS scores can be a promising approach to determine when or if to provide just-in-time messages and step goal adjustments to improve compliance. Further studies on the use of DiPS in the design of physical activity promotion programs are warranted.Trial registrationClinicalTrials.gov NCT01280812 Registered on January 21, 2011

Biomechanical analysis of backstroke to breaststroke turns in age-group swimmers: An intervention study (The interplay between the kinematics, dynamometric, hydrodynamics and electromyography factors)

Compreender a aquisição de experiência em habilidades de viragens na perspetiva de um jovem nadador em desenvolvimento, geralmente requer o desenvolvimento de uma relação e interação entre as características do movimento efetivo e o processo de ensino-aprendizagem. No entanto, poucas análises biomecânicas de viragens em nadadores de grupos de idade foram conduzidas para facilitar o diagnóstico biomecânico e a intervenção científica em técnicas de viragem nado costas para bruços. Os objetivos desta Tese foram: (1) identificar as características biomecânicas determinantes em cada uma das quatro diferentes técnicas de viragens de nado costas para bruços e (ii) investigar o efeito de 16 treinos de interferência contextuais sistemáticos de 40 minutos cada (quatro semanas), seguido de prática bloqueada, em série e aleatória sobre como facilitar e aprender as técnicas de viragem de nado de costas para nado bruços. Uma abordagem multidisciplinar, incluindo um sistema de captura de movimento, uma plataforma de força tri-axial subaquática personalizada, eletromiografia de superfície (EMG) e uma abordagem dinâmica inversa utilizando variáveis hidrodinâmicas, foi usada para atingir esse objetivo. Começamos (no primeiro estudo) identificando as principais características biomecânicas e determinantes das viragens open, somersault, bucket e crossover. O comportamento eletromiográfico (EMG) e as variáveis cinemáticas selecionadas das quatro técnicas de viragem foram comparadas no segundo estudo, com ênfase particular na eficácia de rotação e no empurrada da parede. O terceiro estudo comparou as características hidrodinâmicas e a estratégia de arrancamento relacionadas à eficácia fase de saída da viragem. O quarto estudo empregou os modelos de aprendizado de máquina linear e baseado em árvore para identificar os modelos altamente realistas de desempenho das viragens com base em variáveis temporais, cinemáticas e cinéticas abrangentes (incluindo hidrodinâmicas). Finalmente, vimos como um programa de intervenção de quatro semanas que ofereceu aumentos sistemáticos na interferência contextual permite que nadadores de grupos de idade melhorem as técnicas de viragens de nado de costas para nado bruços. Os resultados apontaram que um programa de intervenção de quatro semanas melhorou as técnicas de giro de nado de costas para peito de nadadores de grupos de idade. De acordo com os modelos lineares e não lineares previstos, o desempenho de torneamento otimizado foi alcançado por um compromisso e continuidade entre as fases de entrada e saída das viragens. A eficácia de virada foi diretamente influenciada pelas contribuições da velocidade de aproximação à parede e habilidades de rotação na melhoria da velocidade de rolamento e força de empurrão. A atividade eletromiográfica integrada de oito músculos foi semelhante em quatro variantes de rotação, o eretor da espinha e o gastrocnémio medial foram os mais ativados, com viragem crossover tendo os maiores valores de Iemg na rotação e empurre. Uma comparação de medidas cinéticas revela que a viragem bucket tem um pico de força mais alto, enquanto um impulso horizontal mais alto leva a uma velocidade de empurre mais alta na viragem crossover. A viragem somersault apresentou um deslizamento ligeiramente mais profundo, enquanto as características hidrodinâmicas e a estratégia de saída, como determinantes da eficácia da saída na viragem, não diferiram significativamente entre as quatro técnicas de viragem. PALAVRAS-CHAVE: NATACAO, FAIXAS ETARIA, BIOMECHANICA, VIRAGENSUnderstanding the acquisition of expertise in turning skills from the perspective of a developing young swimmer generally requires the development of a relationship and interaction between characteristics of effective movement and the teaching-learning process. However, few turning biomechanical analyses on age-group swimmers have been conducted to facilitate biomechanical diagnosis and scientific intervention in backstroke to breaststroke turning techniques. The objective of this Thesis were twofold: (i) to identify the biomechanical features that have the greatest influence in each of the four different backstroke to breaststroke turning techniques and (ii) to investigate the effect of four weeks and 16 systematically contextual interference training sessions of 40 minutes each, followed by blocked, serial, and random practice on facilitating learning of the backstroke to breaststroke turning techniques. A multidisciplinary approach, including a motion capture system, a customized underwater tri-axial force plate, surface electromyography (EMG) and an inverse dynamic approach utilizing hydrodynamic variables, was used to accomplish this goal. We began (in the first study) by identifying the key biomechanical features and determinants of open, somersault, bucket, and crossover turning performance. The electromyographic (EMG) behavior and selected kinematic variables of the four backstroke to breaststroke turning techniques were compared in the second study, with a particular emphasis on rotation and push-off efficacy. The third analysis compared the hydrodynamic characteristics and pull-out strategy related to turn out efficacy. The fourth study employed the linear and tree-based machine learning models to identify the highly realistic models of backstroke to breaststroke turn performance based on comprehensive temporal, kinematic, kinetic (including hydrodynamic) variables. Finally, we looked at how a four-week intervention program that offered systematic increases in contextual interference allows age-group swimmers to improve backstroke to breaststroke turning techniques. Results pointed out that a four-week intervention program improved age-group swimmers' backstroke to breaststroke turning techniques. According to the linear and nonlinear predicted models, optimized turning performance was achieved by a compromise and continuity between the turn-in and turn-out phases. Turn-in efficacy was directly influenced by the contributions of approaching velocity to the wall and rotating abilities in improving rolling velocity and pushing-off force. The integrated electromyographic activity of eight muscles was similar across four turning techniques. The erector spinae and gastrocnemius medialis were the most activated muscles, with the crossover turn having the highest rotation and push-off iEMG values. A comparison of kinetic measures reveals that the bucket turn has a higher peak force, while a higher horizontal impulse leads to higher push-off velocity in the crossover turn. The somersault has a slightly deeper gliding depth, while hydrodynamic characteristics and pull-out strategy, as determinants of turn-out efficacy, did not differ between turning techniques

Personalized Fuzzy Text Search Using Interest Prediction and Word Vectorization

In this paper we study the personalized text search problem. The keyword based search method in conventional algorithms has a low efficiency in understanding users' intention since the semantic meaning, user profile, user interests are not always considered. Firstly, we propose a novel text search algorithm using a inverse filtering mechanism that is very efficient for label based item search. Secondly, we adopt the Bayesian network to implement the user interest prediction for an improved personalized search. According to user input, it searches the related items using keyword information, predicted user interest. Thirdly, the word vectorization is used to discover potential targets according to the semantic meaning. Experimental results show that the proposed search engine has an improved efficiency and accuracy and it can operate on embedded devices with very limited computational resources

Learning from machine learning: prediction of age-related athletic performance decline trajectories

Factors that determine individual age-related decline rates in physical performance are poorly understood and prediction poses a challenge. Linear and quadratic regression models are usually applied, but often show high prediction errors for individual athletes. Machine learning approaches may deliver more accurate predictions and help to identify factors that determine performance decline rates. We hypothesized that it is possible to predict the performance development of a master athlete from a single measurement, that prediction by a machine learning approach is superior to prediction by the average decline curve or an individually shifted decline curve, and that athletes with a higher starting performance show a slower performance decline than those with a lower performance. The machine learning approach was implemented using a multilayer neuronal network. Results showed that performance prediction from a single measurement is possible and that the prediction by a machine learning approach was superior to the other models. The estimated performance decline rate was highest in athletes with a high starting performance and a low starting age, as well as in those with a low starting performance and high starting age, while the lowest decline rate was found for athletes with a high starting performance and a high starting age. Machine learning was superior and predicted trajectories with significantly lower prediction errors compared to conventional approaches. New insights into factors determining decline trajectories were identified by visualization of the model outputs. Machine learning models may be useful in revealing unknown factors that determine the age-related performance decline