With the use of the python and it's libraries i made a project for detecting a breast cancer with the applying Machine Learning to a set of data informations. For this project i have used the database from https://www.kaggle.com/uciml/breast-cancerwisconsin-data.

The distribution of the data set classes is: 357 benign and 212 malignant. For data Visualization i have used the seaborn (countplot, heatmap, distplot, boxplot) and matplotlib.

In the algorithms part of the project, because we have only two types of values, we have a classification problem. In this project i have used the tools from the Scikit-Learn library. Algorithms used in this project:

• Stochastic Gradient Descent classifier
• K Nearest Neighbors Classifier
• Random Forest Classifier
• Decision Tree Classifier
• SVC - Support Vector Machines Classifier
• NuSVC - Support Vector Machines Classifier
• LinearSVC - Support Vector Machines Classifier

Also i have used GridSearchCV() for tunning the parameters of the Random Forest Classifier and Decision Tree Classifier.

From all of the experiments done on this data set, we can surely say that Random Forest Classification gives the most accurate results.

1. Data set: https://www.kaggle.com/uciml/breast-cancer-wisconsin-data
2. Pandas: https://pandas.pydata.org/pandas-docs/stable/
3. NumPy and Scipy: https://docs.scipy.org/doc/
4. Seaborn: https://seaborn.pydata.org/
5. Scikit-learn: https://scikit-learn.org/stable/modules/svm.html
6. Wikipedia – Confusion Matrix: https://en.wikipedia.org/wiki/Confusion_matrix
7. Developers.Google: https://developers.google.com/machine-learning/crashcourse/classification/precision-and-recall