Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/kshitijshrivastava1903/classifying-cancer-tumour-data-as-malignant-or-benign-using-pca-and-logistic-regression
Built a 97% accurate logistic regression model on breat cancer dataset by reducing the dimensions of the data using Pricipal Component Analysis and applying logistic regression on the reduced 2 principal components, to accurately classify data. Used numpy, pandas, matplotlib, PCA, and scikit-learn.
https://github.com/kshitijshrivastava1903/classifying-cancer-tumour-data-as-malignant-or-benign-using-pca-and-logistic-regression
logistic-regression machine-learning-algorithms matplotlib-pyplot principal-component-analysis
Last synced: about 2 months ago
JSON representation
Built a 97% accurate logistic regression model on breat cancer dataset by reducing the dimensions of the data using Pricipal Component Analysis and applying logistic regression on the reduced 2 principal components, to accurately classify data. Used numpy, pandas, matplotlib, PCA, and scikit-learn.
- Host: GitHub
- URL: https://github.com/kshitijshrivastava1903/classifying-cancer-tumour-data-as-malignant-or-benign-using-pca-and-logistic-regression
- Owner: kshitijshrivastava1903
- Created: 2020-06-11T06:53:59.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2020-06-11T06:57:41.000Z (over 4 years ago)
- Last Synced: 2023-03-10T09:37:09.037Z (almost 2 years ago)
- Topics: logistic-regression, machine-learning-algorithms, matplotlib-pyplot, principal-component-analysis
- Language: Jupyter Notebook
- Homepage:
- Size: 54.7 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0