Pinned Repositories
MCMC-SIRD-Model
Parameter Estimation & Case Prediction of a SIRD Model using the Metropolis Hasting MCMC algorithm.
SIR
Implementation of the algorithms: Metropolis-Hastings (MCMC) and Importance Resampling (Particle Filter) for a simple Susceptible, Infectious, or Recovered (SIR) model that simulate a flu
Bayesian-Network-and-Machine-Learning-Covid-19-Patient
Predicting the survival chance of an infected person with the Covid-19 Virus
Breast-Cancer-Survival-Analysis
breast_cancer_prediction_machine_learning
Cancer-Patients-Survival-Analysis
Survival Analysis of Lung Cancer Patients
Cancer-Survival-Analysis
Use sklearn-survival to conduct survival analysis on lung cancer dataset
Covid-19-detection-using-Machine-Learning
This project aims to compare different machine learning algorithms like K-nearest neighbors, Random forest and Naive Bayes with respect to their accuracies and then use the best one among them to develop a system which predicts whether a person has COVID or not using the data provided to the model.
Covid-19-prediction
Covid 19 prediction using logistic regression, decision tree and random forest classifier.
COVID-19-prediction-
COVID-19 prediction using Kalman Filter
FathElrhman123's Repositories
FathElrhman123/TIme-Series-Analysis
Time - Series Analysis // State Space Modelling // Stochastic Volatility Model
FathElrhman123/Breast-Cancer-Survival-Analysis
FathElrhman123/Cancer-Survival-Analysis
Use sklearn-survival to conduct survival analysis on lung cancer dataset
FathElrhman123/breast_cancer_prediction_machine_learning
FathElrhman123/COVID-19-prediction-
COVID-19 prediction using Kalman Filter
FathElrhman123/Lung-Cancer-Detection-with-Different-Machine-Learning-Binary-Classification-Model
Detecting Lung cancer with different binary Classification model such as Naive Bayes, Random Forrest Classifier, Decision Tree and Logical Regression.
FathElrhman123/Cancer-Patients-Survival-Analysis
Survival Analysis of Lung Cancer Patients
FathElrhman123/MCMC-SIRD-Model
Parameter Estimation & Case Prediction of a SIRD Model using the Metropolis Hasting MCMC algorithm.
FathElrhman123/Kalman-Filter-Covid19-Cases-Prediction
FathElrhman123/machine-learning-book
Code Repository for Machine Learning with PyTorch and Scikit-Learn
FathElrhman123/Machine-Learning-Classifiers
In this project, Several models are designed to predict whether a Covid-19 patient will recover or not according to some data features. This project uses several classifiers to build these models. These classifiers are KNN, Logistic regression, Bayes, Decision-Trees, SVM.
FathElrhman123/Decision-Trees-and-Random-Forest-Covid-19-Death-in-British-Columbia-
Decision Trees and Random forest, "COVID 19": Number of Death in British Columbia
FathElrhman123/COVID19-Vaccination-Model
A statistical model of the COVID-19 vaccination campaign. It segments the population into agnostics, pro-, and anti-vaccines. Vaccination is modeled as a Poisson process, and social pressure on the population can change their views on vaccines. The model can faithfully reproduce real-world data.
FathElrhman123/Covid-19-detection-using-Machine-Learning
This project aims to compare different machine learning algorithms like K-nearest neighbors, Random forest and Naive Bayes with respect to their accuracies and then use the best one among them to develop a system which predicts whether a person has COVID or not using the data provided to the model.
FathElrhman123/SIR
Implementation of the algorithms: Metropolis-Hastings (MCMC) and Importance Resampling (Particle Filter) for a simple Susceptible, Infectious, or Recovered (SIR) model that simulate a flu
FathElrhman123/Predicting-the-state-of-COVID19-patients-using-Random-Forest-Classifier
FathElrhman123/Covid-19-prediction
Covid 19 prediction using logistic regression, decision tree and random forest classifier.
FathElrhman123/Bayesian-Network-and-Machine-Learning-Covid-19-Patient
Predicting the survival chance of an infected person with the Covid-19 Virus
FathElrhman123/RandomForestCovidCase
Using Random Forest model to predict the state of a patient: released, isolated, deceased
FathElrhman123/stochastic_seir_model
A stochastic SEIR model in Python to simulate epidemic outbreaks.
FathElrhman123/Pandemic-Forecasting-using-Time-dependent-SIRD-and-Poisson-Regression
The repository consist of my codes and relevant datasets for developing markov chain models to predict the Covid 19 conditions in the world.
FathElrhman123/Data-analysis-and-prediction-of-COVID-19
The virus named 2019 Novel Coronavirus (2019-nCoV) that is recognized as the primary source of an outburst of respiratory sickness originally detected in Wuhan, China. As the outbreak of this respiratory illness (COVID-19) advances, epidemiological information is required to escort the process of intervention strategies and situational awareness. This paper presents the analysis and future prediction of corona cases based on the present positive cases using Machine Learning algorithms such as SVM, Polynomial Regression and Bayesian Ridge approach. The accuracy is measured using Mean Absolute Error (MAE) and Mean Squared Error (MSE) and it is observed that the error with respect to Bayesian Ridge is comparatively less meaning the accuracy is high as compared to other algorithms.
FathElrhman123/simulators
Simulators for Compartmental Models in Epidemiology