🧠 Frontend-Streamlit-CancerPrediction

A simple and effective breast cancer prediction app powered by a Logistic Regression model and built with Streamlit. This project leverages the Breast Cancer Wisconsin (Diagnostic) Dataset to classify tumors as benign or malignant based on features derived from digitized images of fine needle aspirates (FNA) of breast masses.

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P.S. - If the app site is down, kindly ping me on joeducer.official@gmail.com

📊 Project Overview

This project is divided into two main components:

1. Model Building (Backend)
2. Frontend Deployment using Streamlit

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✅ Features

• Clean preprocessing of breast cancer data.
• Logistic Regression model trained on labeled data.
• Real-time prediction using a saved model and scaler via Pickle.
• Minimal and responsive frontend using Streamlit.
• Input sliders to adjust sample parameters dynamically.
• Final prediction with visual feedback for users.

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🔍 Dataset Used

• Name: Breast Cancer Wisconsin (Diagnostic) Data Set
• Source: UCI Machine Learning Repository
• Target Labels:
  • M: Malignant
  • B: Benign

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🛠 Tech Stack

Component	Technology
Model	Logistic Regression (Sklearn)
Data Handling	Pandas, NumPy
Deployment	Streamlit
Serialization	Pickle
Visualization	Plotly

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🧪 How It Works

📁 Backend:

• Preprocesses and cleans the dataset.
• Trains a LogisticRegression classifier.
• Applies StandardScaler for normalization.
• Saves both model and scaler using pickle.

🌐 Frontend:

• Streamlit app reads the saved model and scaler.
• Takes user input from sidebar sliders.
• Scales input and predicts the tumor class.
• Displays results and a confidence score visually.

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🚀 Streamlit App (Live Demo)

👉 Try the live app here:
🔗 Cancer Prediction App on Streamlit

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🖼 Final Output