Breast Cancer Web App

Overview

This project involves analyzing the Breast Cancer dataset using Python libraries like Matplotlib, Seaborn, scikit-learn, Streamlit, and Tableau. It includes the creation of a Tableau dashboard for exploratory data analysis (EDA) and feature distribution comparison between diagnosis classes.

Key Features

• Tableau Dashboard: Created a dashboard to visualize and explore the dataset, comparing feature distributions for different diagnosis classes.
• Streamlit Web App: Individually created and hosted a Streamlit app on GitHub, allowing users to input values and predict breast cancer sample malignancy. The Support Vector Classifier model used achieved an accuracy score of 98%.

Repository

• Link to Repository with Analysis Notebook

Tableau Dashboard Walkthrough

• Walkthrough