Breast Cancer Prediction

This project aims to predict the tumor is malignant or benign from the data of patients having breast cancer using a Logistic Regression Model.

Overview

Breast cancer is one of the most common cancers affecting women worldwide. Early prediction and diagnosis can significantly improve treatment outcomes. This project employs a Logistic Regression Model to assist in predicting breast cancer risk based on various input features.

Technologies Used

• Python
• Pandas
• NumPy
• Scikit-Learn
• Logistic Regression

Dataset

The dataset used in this project is sourced from sklearn datasets. It contains various attributes related to breast cancer that will be used for prediction.

Installation

To run this project, you will need to have Python installed. You can install the required libraries using pip: cmd > pip install pandas numpy scikit-learn

Usage

1. Clone this repository to your local machine: bash $ git clone https://github.com/jagadishdas21/breast-cancer-prediction.git
2. Navigate to the project directory: bash $ cd breast-cancer-prediction
3. Open the Jupyter notebook or Python script and run the code to see the predictions.

Results

The model's performance and predictions are displayed in the results section of the notebook. You can analyze the accuracy and efficiency of the model based on the dataset used.

** Contributions are welcome! If you have suggestions or improvements, please feel free to open an issue or submit a pull request.