Breast-cancer-detection-classification

Project workflow:

1. Framing the problem:

   • Cancer detection: Classification problem
   • Choose an evaluation metric: recall

2. Getting the data:

   • Use publicly available dataset: breast cancer (Wisconsin) dataset

3. Explore, prep and feature engineering:

   • Missing data (we remove one column with missing data)
   • Target class distribution
   • Features distribution, data types, and charachteristics
   • Feature correlation
   • PCA for dimensonality reduction
   • Feature scaling

4. Creating the model:

   • Logistic regression and gridsearch CV to optimise hyperparameters
   • Compare learning curves and cross validation score
   • Select threshold for the highest recall (100%)

5. Presenting results:

   • Results: Confusion metrix highlighting recall and precision
   • Explaining the next steps and how the model will be used