https://github.com/keyurparalkar/breast-cancer-detection

Predict whether the cancer is benign or malignant
https://github.com/keyurparalkar/breast-cancer-detection

gradient-descent logistic-regression machine-learning numpy

Last synced: 11 days ago
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Predict whether the cancer is benign or malignant

• Host: GitHub
• URL: https://github.com/keyurparalkar/breast-cancer-detection
• Owner: keyurparalkar
• Created: 2018-01-22T08:22:15.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2018-01-22T08:32:40.000Z (almost 7 years ago)
• Last Synced: 2024-10-12T02:31:31.902Z (about 1 month ago)
• Topics: gradient-descent, logistic-regression, machine-learning, numpy
• Language: Jupyter Notebook
• Homepage: https://www.kaggle.com/keyurparalkar/breast-cancer-prediction-using-logistic-regression
• Size: 60.5 KB
• Stars: 0
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
## Breast Cancer ( 1 = Malignant(M) vs 0 = Benign(B) ) 

### Breast cancer dataset 

This dataset consists of 10 features whose values are obtained in terms of Mean, Standard error and worst case.

First two columns gives us the information about ID and The output itself (Malignant or Benign).

**Instruction for data-preprocessing**

- First load the data.csv file to dataframe.

- Then divide it to test and train dataset in 80:20.

- Normalize the data i.e. feature normalization.

### Normalization

Find out the mean and standered deviation for each column and substract each element from its mean and divide this by its standered deviation.

### Algorithm for logistic regression

**Steps**

- random initialization of w and b

- Forward propogation

- Backward propogation

- gradient descent

### Sigmoid function

This function is required for calculating the hypothesis i.e. y = a = sigmoid(z)

where z = w^TX + b

### Forward and backward propogation

hypothesis in logistic regression is y = a = sigmoid(z) = sigmoid(w^TX + b)