Predicting rental listing popularity : 2 Sigma connect Renthop

Renting a perfect apartment can be a hassle. There are plenty of features people care about when it comes to finding the apartment, such as price, hardwood floor, dog park, laundry room, etc. Being able to predict people’s interest level on an apartment will help the rental agency better handle fraud control, identify potential listing quality issues, and allow owners and agents to understand renters’ needs and preferences. RentHop, an apartment search engine, along with 2 Sigma, introduced this multiple classification problem in the Kaggle community. It provides the opportunity to use owners’ data to predict the interest level of their apartments on its website. This report attempts to find a pattern of people’s interest level towards rental listing on the website using the dataset from the Kaggle competition. Multiple features are derived from the original dataset. Several common data mining and machine learning techniques are used to improve the accuracy of the predicting model. The final result is evaluated using Log loss function.Statistic

Regression Trees and Random forest based feature selection for malaria risk exposure prediction

This paper deals with prediction of anopheles number, the main vector of malaria risk, using environmental and climate variables. The variables selection is based on an automatic machine learning method using regression trees, and random forests combined with stratified two levels cross validation. The minimum threshold of variables importance is accessed using the quadratic distance of variables importance while the optimal subset of selected variables is used to perform predictions. Finally the results revealed to be qualitatively better, at the selection, the prediction , and the CPU time point of view than those obtained by GLM-Lasso method

TanDEM-X multiparametric data features in sea ice classification over the Baltic sea

In this study, we assess the potential of X-band Interferometric Synthetic Aperture Radar imagery for automated classification of sea ice over the Baltic Sea. A bistatic SAR scene acquired by the TanDEM-X mission over the Bothnian Bay in March of 2012 was used in the analysis. Backscatter intensity, interferometric coherence magnitude, and interferometric phase have been used as informative features in several classification experiments. Various combinations of classification features were evaluated using Maximum likelihood (ML), Random Forests (RF) and Support Vector Machine (SVM) classifiers to achieve the best possible discrimination between open water and several sea ice types (undeformed ice, ridged ice, moderately deformed ice, brash ice, thick level ice, and new ice). Adding interferometric phase and coherence-magnitude to backscatter-intensity resulted in improved overall classification performance compared to using only backscatter-intensity. The RF algorithm appeared to be slightly superior to SVM and ML due to higher overall accuracies, however, at the expense of somewhat longer processing time. The best overall accuracy (OA) for three methodologies were achieved using combination of all tested features were 71.56, 72.93, and 72.91% for ML, RF and SVM classifiers, respectively. Compared to OAs of 62.28, 66.51, and 63.05% using only backscatter intensity, this indicates strong benefit of SAR interferometry in discriminating different types of sea ice. In contrast to several earlier studies, we were particularly able to successfully discriminate open water and new ice classes.Peer reviewe

Simultaneous Determination of Selegiline, Desmethylselegiline, R/S-methamphetamine, and R/S-amphetamine on Dried Urine Spots by LC/MS/MS: Application to a Pharmacokinetic Study in Urine

Objective: Chiral analysis is a crucial method to differentiate selegiline intake from drug abuse. A dried urine spot (DUS) analytical method based on spotting urine samples (10 μL) onto dried spot collection cards, and followed by air-drying and extraction, was developed and validated for the determination of selegiline, desmethylselegiline, R/S-methamphetamine, and R/S-amphetamine.Methods: Methanol (0.5 mL) was found to be the ideal extraction solvent for target extraction from DUSs under orbital-horizontal stirring on a lateral shaker at 1,450 rpm for 30 min. Determinations were performed by direct electrospray ionization tandem mass spectrometry (ESI-MS/MS) under positive electrospray ionization conditions using multiple reaction monitoring mode. The chromatographic system consisted of a ChirobioticTM V2 column (2.1 × 250 mm, 5 μm) and a mobile phase of methanol containing 0.1% (v/v) glacial acetic acid and 0.02% (v/v) ammonium hydroxide.Results and conclusions: The calibration curves were linear from 50 to 5,000 ng/mL, with r > 0.995 for all analytes, imprecisions ≤ 15% and accuracies between −11.4 and 11.7%. Extraction recoveries ranged from 48.6 to 105.4% with coefficients of variation (CV) ≤ 13.7%, and matrix effects ranged from 45.4 to 104.1% with CV ≤ 10.3%. The lower limit of quantification was 50 ng/mL for each analyte. The present method is simple, rapid (accomplished in 12 min), sensitive, and validated by a pharmacokinetic study in human urine collected after a single oral administration of SG