https://github.com/akshaybahadur21/BreastCancer_Classification
Machine learning classifier for cancer tissues 🔬
https://github.com/akshaybahadur21/BreastCancer_Classification
breast-cancer breast-cancer-prediction breast-cancer-wisconsin dataset-uci logistic-regression machine-learning python
Last synced: 4 months ago
JSON representation
Machine learning classifier for cancer tissues 🔬
- Host: GitHub
- URL: https://github.com/akshaybahadur21/BreastCancer_Classification
- Owner: akshaybahadur21
- License: mit
- Created: 2017-10-23T17:00:22.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2021-06-04T09:21:20.000Z (over 4 years ago)
- Last Synced: 2024-08-05T10:08:23.450Z (over 1 year ago)
- Topics: breast-cancer, breast-cancer-prediction, breast-cancer-wisconsin, dataset-uci, logistic-regression, machine-learning, python
- Language: Python
- Homepage:
- Size: 21.2 MB
- Stars: 72
- Watchers: 4
- Forks: 61
- Open Issues: 0
-
Metadata Files:
- Readme: readme.md
- License: LICENSE.txt
Awesome Lists containing this project
- awesome-ai-cancer - akshaybahadur21/BreastCancer_Classification - Machine learning classifier for cancer tissues (Code / Repositories)
README
# Breast Cancer Classifier (Logistic Regression) 🔬
[](https://github.com/akshaybahadur21/BreastCancer_Classification/blob/master/LICENSE.txt) [](https://akshaybahadur.com)
This code helps you classify malignant and benign tumors using Logistic Regression
## Code Requirements 🦄
The example code is in Matlab ([R2016](https://in.mathworks.com/help/matlab/) or higher will work).
You can install Conda for python which resolves all the dependencies for machine learning.
## Description 🧪
Logistic regression is named for the function used at the core of the method, the logistic function.
The logistic function, also called the sigmoid function was developed by statisticians to describe properties of population growth in ecology, rising quickly and maxing out at the carrying capacity of the environment. It’s an S-shaped curve that can take any real-valued number and map it into a value between 0 and 1, but never exactly at those limits.
1 / (1 + e^-value)
For more information, [see](https://en.wikipedia.org/wiki/Logistic_regression)
## Some Notes 🗒️
1) Dataset- UCI-ML
2) I have used only 2 features out of 32 to classify.
## Results 📊
## Execution 🐉
To run the code, type `run breast_cancer.m`
```
run breast_cancer.m
```
## Python Implementation 👨🔬
1) Dataset- UCI-ML
2) I have used 30 features to classify
3) Instead of 0=benign and 1=malignant, I have used 1=benign and 2=malignant
## Results 📊
## Execution 🐉
To run the code, type `python B_Cancer.py`
```
python B_Cancer.py
```