In this repository, I demonstrate the standard workflow of a data science project which include steps like

• data preprocessing ,
• exploratory data analysis.
• building the machine learning model, and
• improving model accuracy using feature reduction

I used the Breast Cancer Wisconsin (Diagnostic) Data Set and trained models like Linear Discriminant Analysis, Random Forest Classifier and Gradient Boosting Decision Trees. To reduce the features, I used Principal Component Analysis and Feature Importance.