Predictions on the breast cancer Wisconsin data set after feature reduction
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.