HIV-1 Envelope Sequence Resistance Predictor to 33 Broadly Neutralizing Antibodies
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Updated
Jun 5, 2024 - R
HIV-1 Envelope Sequence Resistance Predictor to 33 Broadly Neutralizing Antibodies
Detect Credit Card Fraud with Machine Learning in R
🌳 Stacked Gradient Boosting Machines
Project to produce supervised ML algorithm to predict which customers are likely to leave and produce .Rmd report
🌲 broom helpers for decision tree methods (rpart, randomForest, and more!) 🌲
mlim: single and multiple imputation with automated machine learning
In this project, exploratory data analysis was used to identify reasons why employees leave and machine learning methods were used predict employee attrition
Faster, better, smarter ecological niche modeling and species distribution modeling
R-based project to analyze lyrics entropy by genre and decade. A hand-engineered feature "words-per-unique-word" is introduced and deeply studied. Spotify and Genius APIs are used
Single-cell multi-omic profiling of glioblastoma-associated myeloid cells
This is a Masters project completed by My Team and I using the statistical methodology Markov Chain and Geometric Brownian Motion for trend and closing price prediction
Using R and machine learning to build a classifier that can detect credit card fraudulent transactions.
This project about the GBM classification model on spam email data set and model optimisation.
A R script that runs Boosted Regression Trees(BRT) on epochs of land use datasets with random points to model land use changes and predict and determine the main drivers of change
Building binary predictors on a heavily imbalanced dataset - exercise on policy cross-selling [kaggle]
Deep Learning for Automatic Differential Diagnosis of Primary Central Nervous System Lymphoma and Glioblastoma: Multi-parametric MRI based Convolutional Neural Network Model
The objective of the project is to create a machine learning model. We are doing a supervised learning and our aim is to do predictive analysis to predict median housing price.
The project involves deciding on the mode of transport that the employees prefer while commuting to office. For this, multiple models such as KNN, Naive Bayes, Logistic Regression have been created and explored to check their model performance metrics. Bagging and Boosting modelling procedures have also been applied to create the models.
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