This project aims to predict tumor diagnosis (Malignant or Benign) using machine learning techniques, specifically Logistic Regression, on the Breast Cancer Wisconsin (Diagnostic) Data.
The dataset used for this project is the Breast Cancer Wisconsin Data from Kaggle. It contains features computed from digitized images of breast mass that describe characteristics of cell nuclei present in the image.
- notebooks/: Contains Jupyter notebooks for data exploration, preprocessing, model training, and evaluation.
Breast_Cancer_Logistic_Regression.ipynb: Main notebook for the analysis using Logistic Regression.
- data/: Store the dataset file (
breast_cancer_data.csv) or download it from the Kaggle link. - visualizations/: Stores visualizations such as confusion matrices and feature importance plots.
- Clone this repository to your local machine:
git clone https://github.com/your-username/breast-cancer-diagnosis-prediction.git
Install required dependencies using 'pip':
pip install -r requirements.txt
Open and run the Breast_Cancer_Logistic_Regression.ipynb notebook in Google Colab or Jupyter Notebook to follow the analysis and model building process.
Usage 💡 Follow the notebook's cells sequentially to perform data exploration, preprocessing, model training (Logistic Regression), and evaluation. Visualize model performance using confusion matrices and other relevant metrics. Modify parameters or try different machine learning algorithms for experimentation. Share your insights or results with the community or stakeholders.
Note 📝 This project is for educational and demonstration purposes and should not replace professional medical advice or diagnosis.