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Lihao's Projects

breast-cancer-diagnosis icon breast-cancer-diagnosis

Breast cancer diagnosis using multiple statistical/machine learning techniques: XGBoost, support vector machine, random forest, k-neighbours, and deep learning

breast-cancer-prediction icon breast-cancer-prediction

Predicting the probability that a diagnosed breast cancer case is malignant or benign based on Wisconsin dataset

breastcancerdiagnosis-1 icon breastcancerdiagnosis-1

Breast cancer diagnosis using machine learning via the XGBoost Algorithm after visualizing the data set & exploring it.

looped-predictive-modeling-advance icon looped-predictive-modeling-advance

Using a looped code to run Linear Regression, Lasso Regression, Ridge Regression, Elastic Net, KNN, SVM, XGBoost, Ada Boost, Random Forest, Gradient Boost and Neural Network. and KNN to predict cancer

machine-learning-on-breast-cancer-survival-prediction icon machine-learning-on-breast-cancer-survival-prediction

We used different machine learning approaches to build models for detecting and visualizing important prognostic indicators of breast cancer survival rate. This repository contains R source codes for 5 steps which are, model evaluation, Random Forest further modelling, variable importance, decision tree and survival analysis. These can be a pipeline for researcher who are interested to conduct studies on survival prediction of any type of cancers using multi model data.

ml-mt-webapp icon ml-mt-webapp

This project is all about a Machine Learning based Medical Test web app which makes predictions about various diseases using the concept of machine learning.

xgboost icon xgboost

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow

xgboost-cancer-diagnosis icon xgboost-cancer-diagnosis

This project creates an eXtreme Gradient Boosting (XGBoost) machine learning model to determine which genes best predict whether a patient will develop two cancer symptoms: Perineural Invasion (PNI) and Lymphovascular Invasion (LVI).

xgboost-house-price-prediction icon xgboost-house-price-prediction

The house price was estimated based on the user's choices. Xgboost was used for training the model and Streamlit was used for deployment.

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