This project aims to detect breast cancer using machine learning techniques. We have utilized the logistic regression algorithm as our model to train the data.
The dataset used for training and testing the model has been sourced from Kaggle, a well-known website that provides real-world datasets for various domains. Specifically, the dataset used in this project is breastcancer.csv.
The model employed in this project is a classification model that predicts whether a person has a malignant or benign state of breast cancer. Logistic regression, a widely used classification algorithm, has been implemented to achieve this goal. Logistic regression analyzes the relationship between input features and the probability of a specific outcome, in this case, the classification of breast cancer.
To use this project, follow the steps below:
- Clone this repository to your local machine.
- Ensure that you have the required dependencies installed (list the dependencies if applicable).
- Execute the
breastcancer.ipynbnotebook or the corresponding script to train the logistic regression model on the breast cancer dataset. - Once the model is trained, use the
breastcancer.csvfile to input new data and classify whether a person has a malignant or benign state of breast cancer.
Contributions to this project are welcome. If you have any suggestions, bug fixes, or improvements, please feel free to open an issue or submit a pull request.
This project is licensed under the Apache License 2.0.