A dataset cancer was given which has 683 rows and 10 columns(independent variables) : 'Clump Thickness', 'UofCSize', 'UofCShape', 'Marginal Adhesion', 'SECSize', 'Bare Nuclei', 'Bland Chromatin', 'Normal Nucleoli' 'Mitoses', 'Class'.
The dependent variable was the Class of cancer( 2 for benign and 4 for malignant)
An analysis of multi-layer perceptron model of a neural network algorithm was done to develop insights that can be used to create a machine learning model. The following concepts were applied in the exploratory data analysis: key statistics, correlation analysis, Neural network and decision tree analysis and evaluation of the model to determine if it is a good fit to the data.