Developed a machine learning model to predict the risk of cervical cancer based on patient data. The project involved extensive data preprocessing, exploratory data analysis, and the implementation of multiple machine learning algorithms. The XGBoost classifier achieved the highest accuracy of 97.6%, significantly aiding early detection and preventive healthcare measures.
- Programming Languages: Python
- Libraries: Pandas, NumPy, Seaborn, Matplotlib, Scikit-learn, XGBoost, Plotly
- Tools: Jupyter Notebook
The dataset used for this project can be found in the files of this project, named as 'cervical_cancer.csv'
To run this project, you need to have Python installed along with the following libraries:
- pandas
- numpy
- seaborn
- matplotlib
- scikit-learn
- xgboost
- plotly
- jupyterthemes
You can install these libraries using pip:
pip install pandas numpy seaborn matplotlib scikit-learn xgboost plotly jupyterthemes