SVM_ML
Objective: The repository is a learning exercise to:
Apply the fundamental concepts of machine learning from an available dataset Evaluate and interpret my results and justify my interpretation based on observed data set
The analysis is divided into four sections, saved in juypter notebooks in this repository
Identifying the problem and Data Sources Exploratory Data Analysis Pre-Processing the Data Build model to predict whether breast cell tissue is malignant or Benign
Construct predictive models to predict the diagnosis of a breast tumor. In this notebook, I construct a predictive model using SVM machine learning algorithm to predict the diagnosis of a breast tumor. The diagnosis of a breast tumor is a binary variable (benign or malignant). I also evaluate the model using confusion matrix the receiver operating curves (ROC), which are essential in assessing and interpreting the fitted model.
IMPROVING THE MODEL
Construct predictive models to predict the diagnosis of a breast tumor. In this notebook, I aim to tune parameters of the SVM Classification model using scikit-learn.