Breast-Cancer-Prediction-Comparing the accuracy of 4 different Machine Learning Algorithms

About:

• The goal of this project is to find the most optimal model that performs the best in diagnosing breast cancer, determining whether the cancer tumors are benign (non-cancerous) or malignant (cancerous)
• Although, this is a .csv multivariate dataset, the features were computed from a digitized image of a fine needle aspirate (FNA) of breast mass, using varying techniques in Linear Programming

Data:

• The number of patients/instances is 569 with 30 features (predictors)
• State origin of the data is Wisconsin, USA
• Source: UCI Machine Learning Repository

How were 30 features extracted from one image?

1. For each nucleus, ten(10) real-valued features were computed:

   ** radius (mean of distances from the center to points on the perimeter)
   
   ** texture (std of grayscale 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)
   

2. (a) Recorded to 4 decimal places, of each ten features above, the mean, standard error, and 'worst' or "largest" (-mean of the 3 largest values), for each image.

3. (b) For example, field 3 = mean radius, field 13 = radius SE, field 23 = worst radius

4. For the response variable's class distribution: 357 benign (B), 212 malignant (M)

Machine Learning (ML) Models for Comparisons:

• K Nearest Neighbor (KNN)
• Random Forest
• Logistic Regression
• Support Vector Machines