61 skills found · Page 1 of 3
pritampanda15 / PandaDockPandaDock: Physics based Molecular Docking with GNN Scoring
WenYanger / Keras MetricsSome Metric Implementation in Keras (Such as Pearsons Correlation Coefficient, Mean Relative Error)
AliAmini93 / Fault Detection In DC MicrogridsUsing DIgSILENT, a smart-grid case study was designed for data collection, followed by feature extraction using FFT and DWT. Post-extraction, feature selection. CNN-based and extensive machine learning techniques were then applied for fault detection.
alfonsojimenez / PearsonPearson correlation coefficient calculator
matthijsz / WeightedcorrWeighted correlation in Python. Pandas based implementation of weighted Pearson and Spearman correlations.
pancr9 / Netflix Recommender SystemITCS 6190 : Cloud Computing for Data Analysis project. Movie Recommendation Engine for Netflix Data with custom functions implementation and library usage.
KwokHing / YandexCatBoost Python DemoDemo on the capability of Yandex CatBoost gradient boosting classifier on a fictitious IBM HR dataset obtained from Kaggle. Data exploration, cleaning, preprocessing and model tuning are performed on the dataset
ikizhvatov / Efficient Columnwise CorrelationEfficient ways to compute Pearson's correlation between columns of two matrices in various scientific computing languages
selfdecode / Rd Imputation AccuracyPhasing and genotype Imputation comparison. Have been evaluated: BEAGLE 5.4, EAGLE 2.4.1, SHAPEIT 4, MINIMAC 4, IMPUTE 5, using accuracy metrics like: IQS(Imputation Quality score), r2 (Pearson correlation), Concordance.
dnase / CorrelationCorrelation.py - calculates Pearson Rho, Spearman Rho, and Kendall Tau for determining correlation.
sohilsshah91 / Spatio Temporal Crime Analysis Time Series NYCThis project gives an overview of crime time analysis in New York City . We have created Python Jupyter notebooks for spatial analysis of different crime types in the city using Pandas, Numpy, Plotly and Leaflet packages. As a second part to this analysis, we worked on ARIMA model on R for predicting the crime counts across various localities in the city based on correlations of various demographics correlation in each locality.
iosonofabio / Lshknnk nearest neighbor (KNN) graphs via Pearson correlation distance and local sensitive hashing (LSH).
LOVISHARYX / HRV And GSR As Viable Physiological Markers For Mental Health RecognitionMental stress has become a standard part of day-to-day life. However, experiencing long-term and high-level stress affects the daily life and wellness of the person. Consequently, an individual's performance and management ability degrade significantly in critical situations. Electrocardiogram (ECG), Galvanic Skin Response (GSR), Electromyogram (EMG), Skin Temperature (ST), and Respiration are essential physiological biomarkers to quantify stress effectively. This paper aims to classify the stress level with improved performance based on GSR and ECG-derived Heart Rate Variability (HRV) features using machine and deep learning algorithms. It uses the Stress Recognition in Automobile Drivers (SRAD) dataset, which contains a collection of multiparameter recordings from 17 healthy participants who drive on a prescribed route under various pressure conditions. The work uses Pearson's Correlation, RFECV, and LightGBM feature selection methods with different classifiers to reduce redundancy between features and enhance performance. The accuracy and F1-score for stress level classifications are computed and compared using machine and deep learning algorithms. For binary classification (stress vs. non-stress), Random Forest achieves the best classification accuracy of 93.96 % which is higher than previous works. It also provides an accuracy of 81.41 % for three-class (baseline vs. medium stress vs. high stress) stress level classification.
asoroosh / XDFxDF estimates variance of Pearson's correlations among highly autocorrelated time series.
remingm / Covid19 Correlations ForecastWeb app with interactive forecasts based on correlations
Sahrawat1 / Semantic Textual SimilarityAbstract Semantic Textual Similarity (STS) measures the meaning similarity of sentences. Applications of this task include machine translation, summarization, text generation, question answering, short answer grading, semantic search, dialogue and conversational systems. We developed Support Vector Regression model with various features including the similarity scores calculated using alignment-based methods and semantic composition based methods. We have also trained sentence semantic representations with BiLSTM and Convolutional Neural Networks (CNN). The correlations between our system output the human ratings were above 0.8 in the test dataset. Introduction The goal of this task is to measure semantic textual similarity between a given pair of sentences (what they mean rather than whether they look similar syntactically). While making such an assessment is trivial for humans, constructing algorithms and computational models that mimic human level performance represents a difficult and deep natural language understanding (NLU) problem. Example 1: English: Birdie is washing itself in the water basin. English Paraphrase: The bird is bathing in the sink. Similarity Score: 5 ( The two sentences are completely equivalent, as they mean the same thing.) Example 2: English: The young lady enjoys listening to the guitar. English Paraphrase: The woman is playing the violin. Similarity Score: 1 ( The two sentences are not equivalent, but are on the same topic. ) Semantic Textual Similarity (STS) measures the degree of equivalence in the underlying semantics of paired snippets of text. STS differs from both textual entailment and paraphrase detection in that it captures gradations of meaning overlap rather than making binary classifications of particular relationships. While semantic relatedness expresses a graded semantic relationship as well, it is non-specific about the nature of the relationship with contradictory material still being a candidate for a high score (e.g., “night” and “day” are highly related but not particularly similar). The task involves producing real-valued similarity scores for sentence pairs. Performance is measured by the Pearson correlation of machine scores with human judgments.
null-none / Correlation PearsonPearson product-moment correlation coefficient
MLWave / Kaggle ConnectomicsPython code for the Pearson Correlation Benchmark with Discretization to estimate brain connectivity from neuron activity
kairess / Movie Recommendation EngineSimple movie recommendation engine using Pearson correlation
singhman / MovieRecommendationEngineAn user based and item based movie rating prediction recommender system based on data provided by MovieLens using memory-based Collaborative filtering technique by utilizing Pearson correlation, Euclidean distances, Cosine distances, and K-nearest neighbors algorithms
