Breast Cancer Classification

Classifying Breast Tumors Using Random Forest Alghorithm and Decision Tree

This project is developed as part of Digital Skill Fair (DSF) 35.0 by Dibimbing. I'm using Breast cancer wisconsin (diagnostic) dataset from Scikit-learn. This data have 569 number of instances, 30 numeric, predictive attributes and the class of attributs and have 2 class; WDBC-Malignant & WDBC-Benign.

Project Overview:

Data Set Characteristics og Attribute Information is radius (mean of distances from center to points on the perimeter), texture (standard deviation of gray-scale values), perimeter, area, smoothness (local variation in radius lengths), compactness (perimeter^2 / area - 1.0), concavity (severity of concave portions of the contour), concave points (number of concave portions of the contour), symmetry, and fractal dimension (“coastline approximation” - 1).

Project Goals:

The primary goal of this project is to build machine learning models Using Random Forest Alghorithm and Decision Tree.

Key Insights:

This models can serve as an aid in medical diagnostics and show the decision tree provides deeper insights into the factors influencing breast tumor classification.

Feel free to provide feedback or suggestions for improvement. You can reach me at Email: rofiqoazzahra17@gmail.com and Linkedin: https://www.linkedin.com/in/rofiqo-azzahra.

Links

inspirated by: https://github.com/Muzann11/dibimbing-ds/blob/main/README.md

datasets by: https://scikit-learn.org/1.5/modules/generated/sklearn.datasets.load_breast_cancer.html#sklearn.datasets.load_breast_cancer