136 skills found · Page 2 of 5
Karim-53 / PdllPairwise Difference Learning (PDL) is a meta-learning framework that leverages pairwise differences to transform multiclass problems into binary tasks. This repository includes the original PDL Classifier implementation, along with extended versions for regression and weighted learning scenarios.
ali-ghorbani-k / Credit Risk ManagementA binary classification model is developed to predict the probability of paying back a loan by an applicant. Customer previous loan journey was used to extract useful features using different strategies such as manual and automated feature engineering, and deep learning (CNN, RNN). Various machine learning algorithms such as Boosted algorithms (XGBoost, LightGBM, CatBoost) and Deep Neural Network are used to develop a binary classifier and their performances were compared.
PKPDAI / PKDocClassifierBinary classifier to identify scientific publications reporting pharmacokinetic parameters estimated in vivo
TatevKaren / Convolutional Neural Network Image Recognition Case StudyComputer Vision Case Study in image recognition to classify an image to a binary class, based on Convolutional Neural Networks (CNN), with TensorFlow and Keras in Python, to identify from an image whether it is an image of a dog or cat. (Includes: Data, Case Study Paper, Code)
abhishekdbihani / Home Credit Default Risk RecognitionThe project provides a complete end-to-end workflow for building a binary classifier in Python to recognize the risk of housing loan default. It includes methods like automated feature engineering for connecting relational databases, comparison of different classifiers on imbalanced data, and hyperparameter tuning using Bayesian optimization.
justinmccoy / Keras Binary ClassifierA sequential CNN binary image classifier written in Keras
pakdaman / CalibrationBinary Classifier Calibration Models
Nati-1995 / Brain Tumor MRI ClassificationThis is a deep learning project for automated brain tumor detection and classification using MRI imaging.Binary Classification: Detecting presence/absence of tumors (88.77% accuracy) Multiclass Classification: Classifying among 4 tumor types (78.73% accuracy)
seyedsaeidmasoumzadeh / Binary Text Classification Doc2vec SVMA Python implementation of a binary text classifier using Doc2Vec and SVM
NhanPhamThanh-IT / Logistic Regression Rock Mine Prediction🪨 Machine learning project using logistic regression to classify sonar signals as either rocks or mines. Uses scikit-learn to train a binary classifier on sonar dataset with 60 numerical features for accurate underwater object detection.
jayrodge / Binary Image Classifier PyTorchRepo for https://hackernoon.com/binary-face-classifier-using-pytorch-2d835ccb7816
pf981 / Face Recognition And Detection AnalysisThis code trains and runs performance tests several face detection and recognition algorithms. The algorithms examined are Haar Cascade Classifiers for face detection and Eigenfaces, Fisherfaces, Local Binary Patters (LBP) and Local Intensity Distribution (LID) for face recognition.
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.
infomindgithub / Machine Learning Engineer Nanodegree Capstone PROJECT ANALYSIS*****PROJECT SPECIFICATION: Machine Learning Capstone Analysis Project***** This capstone project involves machine learning modeling and analysis of clinical, demographic, and brain related derived anatomic measures from human MRI (magnetic resonance imaging) tests (http://www.oasis-brains.org/). The objectives of these measurements are to diagnose the level of Dementia in the individuals and the probability that these individuals may have Alzheimer's Disease (AD). In published studies, Machine Learning has been applied to Alzheimer’s/Dementia identification from MRI scans and related data in the academic papers/theses in References 10 and 11 listed in the References Section below. Recently, a close relative of mine had to undergo a sequence of MRI tests for cognition difficulties.The motivation for choosing this topic for the Capstone project arose from the desire to understand and analyze potential for Dementia and AD from MRI related data. Cognitive testing, clinical assessments and demographic data related to these MRI tests are used in this project. This Capstone project does not use the MRI "imaging" data and does not focus on AD, focusses only on Dementia. *****Conclusions, Justification, and Reflections***** [Student adequately summarizes the end-to-end problem solution and discusses one or two particular aspects of the project they found interesting or difficult.] The formulation of OASIS data (Ref 1 and 2) in terms of a dementia classification problem based on demographic and clinical data only (and without directly using the MRI image data), is a simplification that has major advantages and appeal. This means the trained model can classify whether an individual has dementia or not with about 87% accuracy, without having to wait for radiological interpretation of MRI scans. This can provide an early alert for intervention and initiation of treatment for those with onset of dementia. The assumption that the combined cross-sectional and longitudinal datasets would lead to dementia label classification of acceptable accuracy came out to be true. The method required careful data cleaning and data preparation work, converting it to a binary classification problem, as outlined in this notebook. At the outset it was not clear which algorithm(s) would be more appropriate for the binary and multi-label classification problem. The approach of spot checking the algorithms early for accuracy led to the determination of a smaller set of algorithms with higher accuracy (e.g. Gadient Boosting and Random Forest) for a deeper dive examination, e.g. use of a k-fold cross-validation approach in classifying the CDR label. The neural network benchmark model accuracy of 78% for binary classification was exceeded by the classification accuracy of the main output of this study, the trained Gradient Boosting and Random Forest classification models. This builds confidence in the latter model for further training with new data and further classification use for new patients.
fsrt16 / Introduction To Genomic Data Sciences Breast Cancer Detection# Breast-cancer-risk-prediction > Necessity, who is the mother of invention. – Plato* ## Welcome to my GitHub repository on Using Predictive Analytics model to diagnose breast cancer. --- ### Objective: The repository is a learning exercise to: * Apply the fundamental concepts of machine learning from an available dataset * Evaluate and interpret my results and justify my interpretation based on observed data set * Create notebooks that serve as computational records and document my thought process. The analysis is divided into four sections, saved in juypter notebooks in this repository 1. Identifying the problem and Data Sources 2. Exploratory Data Analysis 3. Pre-Processing the Data 4. Build model to predict whether breast cell tissue is malignant or Benign ### [Notebook 1](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB1_IdentifyProblem%2BDataClean.ipynb): Identifying the problem and Getting data. **Notebook goal:Identify the types of information contained in our data set** In this notebook I used Python modules to import external data sets for the purpose of getting to know/familiarize myself with the data to get a good grasp of the data and think about how to handle the data in different ways. ### [Notebook 2](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB2_ExploratoryDataAnalysis.ipynb) Exploratory Data Analysis **Notebook goal: Explore the variables to assess how they relate to the response variable** In this notebook, I am getting familiar with the data using data exploration and visualization techniques using python libraries (Pandas, matplotlib, seaborn. Familiarity with the data is important which will provide useful knowledge for data pre-processing) ### [Notebook 3](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB3_DataPreprocesing.ipynb) Pre-Processing the data **Notebook goal:Find the most predictive features of the data and filter it so it will enhance the predictive power of the analytics model.** In this notebook I use feature selection to reduce high-dimension data, feature extraction and transformation for dimensionality reduction. This is essential in preparing the data before predictive models are developed. ### [Notebook 4](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB4_PredictiveModelUsingSVM.ipynb) Predictive model using Support Vector Machine (svm) **Notebook goal: Construct predictive models to predict the diagnosis of a breast tumor.** In this notebook, I construct a predictive model using SVM machine learning algorithm to predict the diagnosis of a breast tumor. The diagnosis of a breast tumor is a binary variable (benign or malignant). I also evaluate the model using confusion matrix the receiver operating curves (ROC), which are essential in assessing and interpreting the fitted model. ### [Notebook 5](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB_5%20OptimizingSVMClassifier.ipynb): Optimizing the Support Vector Classifier **Notebook goal: Construct predictive models to predict the diagnosis of a breast tumor.** In this notebook, I aim to tune parameters of the SVM Classification model using scikit-learn.
zhangrong1722 / CheXNet PytorchA binary-classifier(pneumonia vs normal) in xray14
smazzanti / Confusion VizInteractive visualization of the output of any binary classifier.
upes-open / OSC Medical Analysis Application Using MLAn CNN based binary classification model to classify X-Ray scans on whether they are suffering from pneumonia or not.
jfrog / DvoraDvora designed to divinate and classify unknown functions for Static Application Security Testing (SAST) of binary files
LeonOnyx / Onyx PowerBI MLMachine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. Machine learning is an important component of the growing field of data science. Through the use of statistical methods, algorithms are trained to make classifications or predictions, uncovering key insights within data mining projects. These insights subsequently drive decision making within applications and businesses, ideally impacting key growth metrics. As big data continues to expand and grow, the market demand for data scientists will increase, requiring them to assist in the identification of the most relevant business questions and subsequently the data to answer them. In this session we will learn how to use Automated Machine Learning to create and apply a binary prediction model in Power BI using Python, Anaconda and Pycaret to quickly classify data.
