Breast cancer condition prediction

[amukh18]

Description

Given the characteristics of the cell nuclei present in the image of a fine needle aspirate (FNA) of a breast mass, predict if the breast cancer tumour is malignant or benign.

The dataset contains one id column, 30 real valued feature columns, and one string column. Ten real-valued features are computed for each cell nucleus:

a) radius (mean of distances from center to points on the perimeter) b) texture (standard deviation of gray-scale values) c) perimeter d) area e) smoothness (local variation in radius lengths) f) compactness (perimeter^2 / area - 1.0) g) concavity (severity of concave portions of the contour) h) concave points (number of concave portions of the contour) i) symmetry j) fractal dimension ("coastline approximation" - 1)

The mean, standard error and "worst" or largest (mean of the three largest values) of these features were computed for each image, resulting in 30 features. For instance, field 3 is Mean Radius, field 13 is Radius SE, field 23 is Worst Radius.

You may import the dataset using the following code:

from sklearn.datasets import load_breast_cancer
data=load_breast_cancer()

You may also obtain the dataset from the following link: UCI Machine Learning repository

Output: 'M' for malignant, 'B' for benign.

Details

• Technical Specifications: scikit-learn, jupyter notebook
• Type of issue: Multiple
• Time Limit: 2(Two) days

Issue requirements / progress

• Use a logistic regression model on this dataset
• Use a random forest classifier model on this dataset
• Use a support vector machine model on this dataset.

Resources

scikit-learn logistic regression documentation: scikit-learn random forest classifier documentation: scikit-learn support vector machine documentation:

Directory Structure

Create a directory named breast_cancer_prediction. Inside it, create the following directories: logreg for the logistic regression model random_forest for the random forest model svm for the support vector machine model Put your file for each model in the respective directory.

Note

1. Please claim the issue first by commenting here before starting to work on it.
2. Once you are done with the task and have created a Pull Request, please tag @amukh18 to request a review.

[RohanSahana]

@amukh18 Can I work on this issue? But I have already have put a pull request for #28 issue

[amukh18]

@RohanSahana you may work on this issue. This issue and issue #28 are different issues. This is a classification problem.

[RohanSahana]

As per note, I request @amukh18 to review my pull request. The code has been modified as per your request (https://github.com/WebClub-NITK/Hacktoberfest-2k20/pull/57#issuecomment-706130303) and (https://github.com/WebClub-NITK/Hacktoberfest-2k20/pull/57#issuecomment-705471159).

[amukh18]

@RohanSahana I have reviewed your PR. The changes are correct. Good job on your PR!