Breast Cancer Analysis and Prediction
In this repository, advanced machine learning methods were used to build and test the performance of a selected algorithm for breast cancer diagnosis.
Dataset
The Breast Cancer Wisconsin (Diagnostic) Dataset - obtained from Kaggle - contains features computed from a digitized image of a fine needle aspirate (FNA) of a breast mass and describe characteristics of the cell nuclei present in the image.
The Analysis
Data Loading and Cleaning
Load data into pandas dataframe and "clean" them from missing and incosistent values.
Data Exploration
Produce descriptive statistics and plots to reveal trends and relationships between the variables.
Machine Learning
Split Data to Train/Test Sets
Split data into train and test data sets and use cross validation.
Scale Features
Standarize predictor variables with feature scaling.
Dimensionality Reduction
Select features with Principal Component Analysis (PCA) or SelectKBest(). Plot graphs to show the effect of feature scaling on PCA.
Tune the Algorith and Evaluate
Optimize the algorithm with GridSearchCV and Pipeline. Evaluate with recall score considering it as the most important metric.