# 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. 

The pipeline is as follows:

## 1. Model evaluation using 6 different algorithms in R (_Model Evaluation.md_)
  ### 1.1 Random Forest
  ### 1.2 Decision Tree
  ### 1.3 Support Vector Machine
  ### 1.4 Logistic Regression
  ### 1.5 Neural Networks
  ### 1.6 Extreme Gradient Boost
 
## 2. Random Forest Further modelling in R (_Random Forest.md_)
  ### 2.1 Selection of best ntree
  ### 2.2 Model evaluation for all the clusters
  ### 2.3 Calibration plot using Phyton 3
 
## 3. Variable Importance in R (_Variable importance.md_)
  ### 3.1 Using _VSURF_
  ### 3.2 Using _randomForestExplainer_

## 4. Decision Tree in R (_Decision tree.md_)

## 5. Survival analysis in R (_Survival analysis.md_)