https://github.com/ananthakrishnan12/breast-cancer-detection
https://github.com/ananthakrishnan12/breast-cancer-detection
Last synced: 3 months ago
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- Host: GitHub
- URL: https://github.com/ananthakrishnan12/breast-cancer-detection
- Owner: Ananthakrishnan12
- Created: 2023-09-23T16:45:06.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-09-23T17:15:03.000Z (over 1 year ago)
- Last Synced: 2023-09-23T20:20:34.886Z (over 1 year ago)
- Language: Python
- Size: 36.3 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Breast Cancer detection and Classification using ML Techniques:
Abstract: The most frequently occurring cancer among Indian women is breast cancer. There is a chance of fifty percent for fatality in a case as one of two women diagnosed with breast cancer die in the cases of Indian women [1]. This paper aims to present comparison of the largely popular machine learning algorithms and techniques commonly used for breast cancer prediction, namely Random Forest, kNN (k-Nearest-Neighbor) and Naïve Bayes. The Wisconsin Diagnosis Breast Cancer data set was used as a training set to compare the performance of the various machine learning techniques in terms of key parameters such as accuracy, and precision. The results obtained are very competitive and can be used for detection and treatment.
Installation:
Anaconda 2021 Required Libaries: numpy==1.20.3 Pandas==1.3.5 matplotlib==3.4.3 scikit-learn==0.24.1
Data Collection: https://www.kaggle.com/datasets/yasserh/breast-cancer-dataset
Steps Involved in this Classifications Models:
1.Data Preprocessing.
2.EDA
3.Feature Engineering
4.Model selection
5.Model Training (Classifier Models)
6.Model Testing
7.Performance metrics
8.Model Deployment (Streamlit)