Mohammad Ehtasham Billah's repositories
Acute-Lymphoblastic-Leukemia-cell-classification-using-Bayesian-Convolutional-Neural-Networks
In this project, we deploy the Bayesian Convolution Neural Networks (BCNN), proposed by Gal and Ghahramani [2015] to classify microscopic images of blood samples (lymphocyte cells). The data contains 260 microscopic images of cancerous and non-cancerous lymphocyte cells. We experiment with different network structures to obtain the model that return lowest error rate in classifying the images. We estimate the uncertainty for the predictions made by the models which in turn can assist a doctor in better decision making. The Stochastic Regularization Technique (SRT), popularly known as Dropout is utilized in the BCNN structure to obtain the Bayesian interpretation.
Classification-in-Caret
Classification in Caret
Synthetic-Minority-Over-Sampling-Technique-SMOTE-
For a classification problem, when classes in the dependent variable are severely imbalanced (e.g. 90 yes, 10% no), training an efficient machine learning model becomes very difficult. However with SMOTE method, we can transform the data into a balaced form and train the model efficiently.
Variable-Selection-By-Least-Absolute-Shrinkage-and-Selection-Operator-LASSO-
All independent variables do not have the similar impact on dependent variable. Here we will try to find the independent varibles that have most significant impact on dependent variable to make the ML algorithm fast and accurate by utilizing LASSO.
Advanced-artificial-neural-network
Artificial neural network with grid search and cross validation
ANN-in-Caret-with-dropout
ANN model fitting in Caret
ANN-with-gridsearch
Gridsearching learning rate, decay and other parameters in ANN
Bayesian-Machine-Learning-in-Breast-Cancer-Diagnosis
The aim of this project is to apply Bayesian Machine Learning Algorithm to predict the diagnosis condition of the patients.
Customer_Churn_Modeling_by_XGBOOST
Predicting the customers that are going to leave the bank using powerful machine learning method Extreme Gradient Boosting or XGBOOST.
Fitting-model-with-random-grid-search-in-Caret
Model fitting with random grid search
Microeconometrics-Project
Microeconometrics Project
Model-fitting-with-grid-search-in-Caret
Model fitting with grid search in Caret
Official-Statistics-Project-Tables
Statistical Tables
Pandas_python
Basic pandas codes
Simple-Artificial-neural-networks
A simple ANN model
Variable-Selection-By-Recursive-Feature-Elimination-RFE-
All independent variables do not have the similar impact on dependent variable. Here we will try to find the independent varibles that have most significant impact on dependent variable to make the ML algorithm fast and accurate by utilizing RFE.