https://github.com/shibajyotidebbarma/Machine-Learning-with-Scikit-Learn-Breast-Cancer-Winconsin-Dataset

In this machine learning project I will work on the Wisconsin Breast Cancer Dataset that comes with scikit-learn. I will train a few algorithms and evaluate their performance. I will use ipython (Jupyter).
https://github.com/shibajyotidebbarma/Machine-Learning-with-Scikit-Learn-Breast-Cancer-Winconsin-Dataset

breast-cancer breast-cancer-prediction breast-cancer-wisconsin datascience ipython jupyter-notebook machine-learning machine-learning-algorithms neural-network neural-networks python3

Last synced: 3 months ago
JSON representation

In this machine learning project I will work on the Wisconsin Breast Cancer Dataset that comes with scikit-learn. I will train a few algorithms and evaluate their performance. I will use ipython (Jupyter).

• Host: GitHub
• URL: https://github.com/shibajyotidebbarma/Machine-Learning-with-Scikit-Learn-Breast-Cancer-Winconsin-Dataset
• Owner: shibajyotidebbarma
• Created: 2018-06-23T19:53:32.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2019-05-25T05:53:04.000Z (over 5 years ago)
• Last Synced: 2024-05-29T23:17:49.000Z (5 months ago)
• Topics: breast-cancer, breast-cancer-prediction, breast-cancer-wisconsin, datascience, ipython, jupyter-notebook, machine-learning, machine-learning-algorithms, neural-network, neural-networks, python3
• Language: Jupyter Notebook
• Homepage:
• Size: 347 KB
• Stars: 4
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-ai-cancer - shibajyotidebbarma/Machine-Learning-with-Scikit-Learn-Breast-Cancer-Winconsin-Dataset - A project comparing KNN, logistic regression, and neural network performance on the Wisconsin dataset (Code / Repositories)

README

        
# Machine-Learning-with-Scikit-Learn-Breast-Cancer-Winconsin-Dataset

## KNN

- Prediction Accuracy of 93%

## Logistic Regression

- For C=100, Accuracy = 96.5%

## Neural Network

- Classification Accuracy = 97.2%