I have already trained and deployed a Logistic Regression model in Streamlit that classifies breast-tumour samples as malignant or benign. What I need now is a polished data-visualization layer so users can quickly grasp how each feature influences the prediction. My immediate focus is on bar-chart visualisations. I want clear, well-labelled bars that compare malignant vs. benign distributions, show feature importances, and surface any other insight you think adds value. The work should plug straight into my current Streamlit app and read from the same Pandas DataFrame I am already passing to the model. Although the main task is visualisation, I am also experimenting with feature selection, so if your code can be structured in a way that makes it easy to toggle feature subsets, that will be a plus. Deliverables • Re-usable Python module (or Streamlit component) that produces the requested bar charts • Seamless integration with the existing Streamlit interface—no regressions to current functionality • Clean, readable code using Matplotlib or Seaborn, with comments and docstrings • Brief README explaining how to invoke the charts and adjust feature lists You will be working in a familiar stack—Python, Pandas, NumPy, Scikit-learn, Seaborn/Matplotlib, Streamlit—so please highlight previous projects where you delivered similar visual insight for a machine-learning app.