https://github.com/nirmalyabag20/breast-cancer-prediction-using-machine-learning

This project leverages machine learning to classify breast cancer as malignant or benign based on tumor characteristics. By applying and evaluating multiple algorithms, the model achieves high accuracy, demonstrating the practical application of data-driven solutions in medical diagnostics.
https://github.com/nirmalyabag20/breast-cancer-prediction-using-machine-learning

logistic-regression matplotlib numpy pandas python scikit-learn seaborn

Last synced: 6 days ago
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This project leverages machine learning to classify breast cancer as malignant or benign based on tumor characteristics. By applying and evaluating multiple algorithms, the model achieves high accuracy, demonstrating the practical application of data-driven solutions in medical diagnostics.

• Host: GitHub
• URL: https://github.com/nirmalyabag20/breast-cancer-prediction-using-machine-learning
• Owner: nirmalyabag20
• Created: 2024-09-11T15:30:49.000Z (about 2 months ago)
• Default Branch: main
• Last Pushed: 2024-09-11T15:54:57.000Z (about 2 months ago)
• Last Synced: 2024-10-31T13:04:26.688Z (6 days ago)
• Topics: logistic-regression, matplotlib, numpy, pandas, python, scikit-learn, seaborn
• Language: Jupyter Notebook
• Homepage:
• Size: 202 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
Breast Cancer Prediction using Machine Learning

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Overview~

This project aims to develop a machine learning model to predict breast cancer diagnosis. By analyzing patient data, the model classifies whether a tumor is malignant or benign based on various medical features.

Dataset~

The dataset contains numerical measurements of tumor characteristics for 569 patients, along with a diagnosis label indicating whether the tumor is malignant or benign. The features describe various aspects of the tumors, such as their size, shape, and texture, across different measurement types (mean, standard error, and worst case).

Project Workflow~

1.	Exploratory Data Analysis (EDA)

  o	Analyzed the data distribution and identified patterns between tumor characteristics and diagnoses.

  

  o	Visualized key statistical relationships to understand the underlying structure of the data.

  

2.	Data Preprocessing~

  o	Addressed missing or inconsistent data.

  

  o	Scaled the feature values to ensure better model performance.

  

  o	Transformed categorical values into a format suitable for machine learning algorithms.

  

3.	Model Building~

  o	Tested a variety of machine learning algorithms, including:

  

  .	Logistic Regression

  

  o	Evaluated model performance using accuracy, precision, recall, and F1-score metrics.

  

4.	Model Evaluation~

  o	Implemented cross-validation to enhance model reliability.

  

  o	Assessed models using confusion matrix, classification reports, and ROC-AUC curves to evaluate performance comprehensively.

5.	Results~

   

  o	Achieved an accuracy of 97% with the Logistic Regression model.