Breast-Cancer-Classification-ML

We are going to create a model which can predict if a person has breast-cancer.The prediction and target will be in numerical format (0 and 1), 0 means doesn't have breast cancer and 1 means have breast cancer.

Prerequisite

• Python 3.10
• Anaconda / conda

Steps

• These are some steps we are going to follow as we code:

  • Run conda create --prefix ./env numpy pandas scikit-learn matplotlib notebook in Anaconda/conda prompt. (creates a virtual environment with all necessary packages installed)
  • run conda activate path/to/env to activate the virtual environment.
  • run jupyter notebook to open jupyter notebook.
  • Create a python notebook.
  • Import all the packages.
  • Import the Breast Cancer Data from sklearn.datasets.
  • Create a pandas Dataframe using the Breast Cancer Data.
  • Check if there are any datafields empty/null.
  • Split the Data into features and target sub sets (X & Y respectively).
  • Import and initiliaze the classification model from sklearn.ensemble.
  • Split the feature and target datasets into train and test dataset.
  • Fit/Train the model using the train dataset.
  • Evaluate the model.

  Evaluation

• To Evaluate our model we use different methods. Scikit-learn provides us many methods for Evaluation. Some which we are going to use are :

  • Default model score method model.score(). (Returns Coefficient of Determination)
  • Cross Validation.
  • sklearn.metrics.accuracy_score
  • ROC (Receiver operating characteristic) curve
  • Confusion matrix

ROC Curve

Perfect ROC Curve