<filename>Final-Project/clean_split_data.py
from sklearn.model_selection import train_test_split


def clean_data(data):
    '''
    Drops ID column of data as well as Not a Number column at end of dataset.
    Retains all mean measurement columns and drops all
    standard errors and worst measurements.
    Remaps diagnosis column to numbered booleans.
    '''
    data.drop(list(data.filter(regex='_se')), axis=1, inplace=True)
    data.drop(list(data.filter(regex='_worst')), axis=1, inplace=True)
    data.drop(list(data.filter(regex='id')), axis=1, inplace=True)
    data.drop(list(data.filter(regex='Unnamed: 32')), axis=1, inplace=True)
    data['diagnosis'].replace('B', 0, inplace=True)
    data['diagnosis'].replace('M', 1, inplace=True)

    return data


def split_data(data):
    '''
    Takes cleaned dataset and splits into train, test sets.
    '''
    X = data[['radius_mean', 'texture_mean', 'perimeter_mean',
              'area_mean', 'smoothness_mean',
              'compactness_mean', 'concavity_mean',
              'concave points_mean', 'symmetry_mean',
              'fractal_dimension_mean']]
    y = data.diagnosis
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=0.2, random_state=42)

    return X_train, X_test, y_train, y_test

<filename>Final-Project/clean_split_data.py
from sklearn.model_selection import train_test_split


def clean_data(data):
    '''
    Drops ID column of data as well as Not a Number column at end of dataset.
    Retains all mean measurement columns and drops all
    standard errors and worst measurements.
    Remaps diagnosis column to numbered booleans.
    '''
    data.drop(list(data.filter(regex='_se')), axis=1, inplace=True)
    data.drop(list(data.filter(regex='_worst')), axis=1, inplace=True)
    data.drop(list(data.filter(regex='id')), axis=1, inplace=True)
    data.drop(list(data.filter(regex='Unnamed: 32')), axis=1, inplace=True)
    data['diagnosis'].replace('B', 0, inplace=True)
    data['diagnosis'].replace('M', 1, inplace=True)

    return data


def split_data(data):
    '''
    Takes cleaned dataset and splits into train, test sets.
    '''
    X = data[['radius_mean', 'texture_mean', 'perimeter_mean',
              'area_mean', 'smoothness_mean',
              'compactness_mean', 'concavity_mean',
              'concave points_mean', 'symmetry_mean',
              'fractal_dimension_mean']]
    y = data.diagnosis
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=0.2, random_state=42)

    return X_train, X_test, y_train, y_test

Drops ID column of data as well as Not a Number column at end of dataset.     Retains all mean measurement columns and drops all     standard errors and worst measurements.     Remaps diagnosis column to numbered booleans. Takes cleaned dataset and splits into train, test sets.