A predictive model to determine malignant or benign breast cancer types using gene abnormalities.
Table of Contents 📖
The Breast Cancer Prediction project aims to leverage data from hospitals and apply AI algorithms to predict whether breast cancer is malignant or benign. By analyzing gene abnormalities, this model provides a valuable tool for early detection and diagnosis, potentially saving lives.
- Logistic Regression
- Decision Tree Classifier
- Random Forest Classifier
- Support Vector Classifier
The following accuracies were achieved using the respective algorithms:
- Logistic Regression Method: 97.08%
- Decision Tree Classifier Method: 90.64%
- Random Forest Classifier Method: 93.57%
- Support Vector Classifier Method: 95.91%
To get a local copy of the project up and running, follow these steps:
- Python 3.9 and above
- Jupyter Notebook
- Libraries:
pandas
,numpy
,scikit-learn
,matplotlib
,seaborn
- Clone the repo
git clone https://github.com/amoghasbhardwaj/Breast-Cancer-Prediction.git
- Navigate to the project directory
cd Breast-Cancer-Prediction
- Start Jupyter Notebook
jupyter notebook
- Open the AI-ML Breast Cancer Predictor.ipynb file and run the cells to execute the code.
Usage ∵
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- Open Jupyter Notebook.
-
- Load the dataset and run the data cleaning process.
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- Train the models using the specified algorithms.
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- Evaluate the performance metrics to check the accuracy.
Dataset
For more information regarding the dataset, refer to: Breast Cancer Wisconsin Dataset