Streamlit Cancer Prediction App

Overview

This project is a Streamlit application designed for cancer prediction based on cell nuclei details. It utilizes machine learning models to provide predictions and visualizations, helping users understand the data and the model's output.

Features

• User Input: Users can input cell nuclei details through a sidebar interface.
• Data Visualization: The app includes radar charts to visualize the input data.
• Model Prediction: The application predicts cancer presence based on the input data using a pre-trained logistic regression model.

Technologies Used

• Python: The primary programming language.
• Streamlit: For building the web application.
• Pandas: For data manipulation and analysis.
• NumPy: For numerical operations.
• Plotly: For creating interactive visualizations.
• Scikit-learn: For machine learning functionalities.

Installation

To run this project, you need to install the required packages. Use the following command:

pip install -r requirements.txt

Usage

1. Clone the repository:

   git clone <repository-url>
   cd Streamlit_Cancer_App

2. Run the application:

   streamlit run app/main.py

3. Open your web browser and navigate to the provided local URL.

Data

The application uses a dataset located in the Data folder. Ensure that the data.csv file is present in this directory.

Model

The logistic regression model is stored in the Model directory as logistic_model.pkl, and the scaler is stored as scaler.pkl.

Images

Here are some images related to the project:

Benign Prediction

Malignant Prediction

Technical Details

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