Breast-cancer-classifier

Classification using Machine Learning Techniques on Breast Cancer Wisconsin Data Set (diagnosis) Using Support Vector Machines with the accuracy of 0.96

About Dataset

This Project use Wisconsin Data Set https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic). which consist

Data Set Characteristics: Multivariate

Number of Instances:569

Area:Life

Attribute Characteristics:Real

Number of Attributes:32

Associated Tasks:Classification

Number of Web Hits: 9752918

Attribute Information:

• ID number
• Diagnosis (M = malignant, B = benign)

Ten real-valued features are computed for each cell nucleus:

• 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)

Goal

The objective of this model is to find whether the cancer is malignat or benign

About Project

Tools and method utilized:

• python
• Tableau(Optional)
• google colab/jupyter notebook
• Data manipulation using:
  1. pandas,
  2. plotting using seaborn and matplotlib,
  3. Feature selection
  4. Grid search

Conclusions

Once I employed all these methods, we were able to utilize various machine learning metrics. Each model provided valuable insight. The best result is given by svm with hyperparameter using GridSearchCV method

Parameter uses

• C: 20
• gamma': 0.2
• kernel: 'rbf'

classification report

	precision	recall	f1-score	support
0.0	0.90	1.00	0.95	43
1.0	1.00	0.93	0.96	71
accuracy			0.96	114
macro avg	0.95	0.96	0.95	114
weighted avg	0.96	0.96	0.96	114