Breast Cancer Detection

• Checked for the null values if present.
• Checked if the dataset is imbalanced.
• Checked for the datatype of variable.
• Converted our target variable to numeric value.
• Normalized the column names.
• Normalized the outliers in the dataset by applying np.log(1p) so that model can learn in a better way.
• Added the column of log to the dataframe.
• Splitted the train, validation and test dataset.
• Trained the model on LogisticRegression from sklearn.
• Applied KFold algorithm for better results.
• Got roc_auc_score of 0.997
• Refer to breastCancerDetection.ipynb