57 skills found · Page 1 of 2
kavgan / Nlp In PracticeStarter code to solve real world text data problems. Includes: Gensim Word2Vec, phrase embeddings, Text Classification with Logistic Regression, word count with pyspark, simple text preprocessing, pre-trained embeddings and more.
zhangyuqing / ComBat SeqBatch effect adjustment based on negative binomial regression for RNA sequencing count data
val-iisc / Crowd Counting ScnnThis project is an implementation of the crowd counting model proposed in our CVPR 2017 paper - Switching Convolutional Neural Network(SCNN) for Crowd Counting. SCNN is an adaptation of the fully-convolutional neural network and uses an expert CNN that chooses the best crowd density CNN regressor for parts of the scene from a bag of regressors. This helps it tackle intra-scene crowd density variation and obtain SOTA results
statdivlab / CorncobCount Regression for Correlated Observations with the Beta-binomial
xiyang1012 / Local Crowd CountingAdaptive Mixture Regression Network with Local Counting Map for Crowd Counting (ECCV2020)
Geraldine-Winston / Paleoclimate Reconstruction Using ML Regression On Proxy DataThis project uses machine learning regression techniques to reconstruct paleoclimate conditions from proxy data such as isotope ratios, pollen counts, and sediment properties. By predicting past temperatures, it provides insights into historical climate patterns, aiding research in paleoclimatology
GeorgeChenZJ / DeepcountDeep Density-aware Count Regressor: a state-of-the-art method for crowd counting
ManchesterBioinference / GPcountsGaussian process regression package for counts data with negative binomial and zero-inflated negative binomial likelihoods
poppinace / Mtc(PLME'17) TasselNet: counting maize tassels in the wild via local counts regression network
abebual / Predicting ICU Patient Clinical Deterioration ReportFor this project, I used publicly available Electronic Health Records (EHRs) datasets. The MIT Media Lab for Computational Physiology has developed MIMIC-IIIv1.4 dataset based on 46,520 patients who stayed in critical care units of the Beth Israel Deaconess Medical Center of Boston between 2001 and 2012. MIMIC-IIIv1.4 dataset is freely available to researchers across the world. A formal request should be made directly to www.mimic.physionet.org, to gain access to the data. There is a required course on human research ‘Data or Specimens Only Research’ prior to data access request. I have secured one here -www.citiprogram.org/verify/?kb6607b78-5821-4de5-8cad-daf929f7fbbf-33486907. We built flexible and better performing model using the same 17 variables used in the SAPS II severity prediction model. The question ‘Can we improve the prediction performance of widely used severity scores using a more flexible model?’ is the central question of our project. I used the exact 17 variables used to develop the SAPS II severity prediction algorithm. These are 13 physiological variables, three underlying (chronic) disease variables and one admission variable. The physiological variables includes demographic (age), vital (Glasgow Comma Scale, systolic blood pressure, Oxygenation, Renal, White blood cells count, serum bicarbonate level, blood sodium level, blood potassium level, and blood bilirubin level). The three underlying disease variables includes Acquired Immunodeficiency Syndrome (AIDS), metastatic cancer, and hematologic malignancy. Finally, whether admission was scheduled surgical or unscheduled surgical was included in the model. The dataset has 26 relational tables including patient’s hospital admission, callout information when patient was ready for discharge, caregiver information, electronic charted events including vital signs and any additional information relevant to patient care, patient demographic data, list of services the patient was admitted or transferred under, ICU stay types, diagnoses types, laboratory measurments, microbiology tests and sensitivity, prescription data and billing information. Although I have full access to the MIMIC-IIIv1.4 datasets, I can not share any part of the data publicly. If you are interested to learn more about the data, there is a MIMIC III Demo dataset based on 100 patients https://mimic.physionet.org/gettingstarted/demo/. If you are interested to requesting access to the data - https://mimic.physionet.org/gettingstarted/access/. Linked repositories: Exploratory-Data-Analysis-Clinical-Deterioration, Data-Wrangling-MIMICIII-Database, Clinical-Deterioration-Prediction-Model--Inferential-Statistics, Clinical-Deterioration-Prediction-Model--Ensemble-Algorithms-, Clinical-Deterioration-Prediction-Model--Logistic-Regression, Clinical-Deterioration-Prediction-Model---KNN © 2020 GitHub, Inc.
neemiasbsilva / Regression In CNNs Applied To Plant Leaf CountRegression in Convolutional Neural Network applied to Plant Leaf Count
skhiearth / Coursera IBM Machine Learning With Python Final ProjectThe following algorithms are used to build models for the different datasets: k-Nearest Neighbour, Decision Tree, Support Vector Machine, Logistic Regression The results is reported as the accuracy of each classifier, using the following metrics when these are applicable: Jaccard index, F1-score, Log Loss. This project counts towards the final grade of the course.
gjy3035 / SCARThe code for "SCAR: Spatial-/Channel-wise Attention Regression Networks for Crowd Counting"
Mamba413 / BessBest Subset Selection algorithm for Regression, Classification, Count, Survival analysis
jia-wan / ResidualRegression PytorchResidual Regression with Semantic Prior for Crowd Counting
inchara1990 / R Code ClassifiersThe R code compares the performance metrics between logistic regression, SVM, Naive Bayes, Knn and random forest classifers in a 10 fold cross validation loop. The features are iteratively selected in a forward selection manner and everytime a new feature(next best feature selected based on regsubsets) is added the accuracy of the classifers are calculated. Finally a graph is plotted at the end depicting the accuracy at every feature count.
shenghh2015 / Cell CountingDeeply supervised density regression for automatic cell counting in microscopy images
chriseppstein / Lame StatsHistograms, Linear Regression, Normal Distribution Analysis, Counting Occurances. I'm sure someone has made a better stats module than this one.
hoangsonww / Olympic Medal Data Analysis🥇 A project for analyzing Olympic medal data with R, combining TidyTuesday records and World Bank indicators to assess raw medal counts, efficiency metrics, and economic context. It generates diverse visualizations, performs regression and clustering, and reveals patterns in national Olympic performance.
Aniket-Thopte / Demand Forecasting Public Bike Rental Predictive Modeling Developed multiple predictive models with 90% accuracy for forecasting the daily-hourly bike rental count using Python & Machine Learning techniques like Regression, Clustering, Ensemble, Neural Network to achieve maximum accuracy
