This project aims to create a classifier using the K-Nearest Neighbors algorithm to diagnose breast cancer as malignant or benign using the Breast Cancer Wisconsin (Diagnostic) Database. Explore: to visualize the different predicition scores with different test splits Select: to choose the best model or optimal train-test split with best performance
The dataset used is the Breast Cancer Wisconsin (Diagnostic) Database. It includes features computed from a digitized image of a fine needle aspirate (FNA) of a breast mass.
Dataset Characteristics:
:Number of Instances: 569
:Number of Attributes: 30 numeric, predictive attributes and the class
:Attribute Information:
- 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
- fractal dimension ("coastline approximation" - 1)
The mean, standard error, and "worst" or largest (mean of the three
worst/largest values) of these features were computed for each image,
- Understanding and application of the K-Nearest Neighbors algorithm.
- Data preprocessing and feature analysis.
- Model evaluation and performance metrics.
A summary of the model's performance and accuracy in classifying breast cancer. Select: to choose the best model or optimal train-test split with best performance
Detailed instructions on how to run the project are provided in the notebooks and scripts.
Python dependencies required to run this project are listed in requirements.txt.
pip install -r requirements.txt