Breast cancer is one of the most common forms of cancer worldwide. Access to public data related to this disease can significantly improve our understanding and assist doctors in providing better diagnoses for their patients. In this project, I aim to create a machine learning application capable of differentiating between benign and malignant tumors using breast cancer tabular data from the Kaggle repository.
The primary goal of this project is to develop a machine learning model that can accurately classify breast tumors as benign or malignant based on the available data. The model's accuracy is a critical metric.
After training and evaluation, our machine learning model achieved an accuracy rate of 97%. This high accuracy demonstrates the model's ability to effectively distinguish between benign and malignant breast tumors, which can aid healthcare professionals in making informed decisions regarding patient care.
To build and train this machine learning model was utilized the following libraries:
Pandas: For data manipulation and preprocessing.
Scikit-learn: For building and evaluating machine learning models.
NumPy: For numerical operations and array handling.
MLflow: For tracking experiments and model management.
Matplotlib: For data visualization and model performance analysis.
To deploy this machine learning model as an API, was used the following libraries:
FastAPI: A modern web framework for building APIs quickly and efficiently.
Uvicorn: ASGI server that interfaces with FastAPI to serve the model via HTTP endpoints.
This project combines the power of data science and machine learning to contribute to the early detection and accurate diagnosis of breast cancer. By making this model available as an API, we aim to provide a valuable tool for healthcare professionals and researchers in the field of oncology.