Project Detail
Cancer Predict Risk
2024
Website (Streamlit)
Technology :
Python, Pandas, NumPy, Sckit-Learn, Mathplotlib, Streamlit
Core Features :
🧠 ML-Based Cancer Risk Prediction: Uses a trained machine learning model (e.g., Logistic Regression, Random Forest) to predict the likelihood of cancer based on user input data.
📋 User-Friendly Input Form: Collects essential health information such as age, gender, lifestyle habits, and symptoms through an intuitive form interface.
📊 Real-Time Prediction Output: Instantly displays prediction results with a clear probability score and interpretation (Low, Medium, High Risk).
📈 Model Evaluation Dashboard: Visualize model performance using confusion matrix, ROC curve, classification report, and accuracy metrics.
💾 CSV Upload Support: Allows batch prediction by uploading a CSV file with multiple user data entries.
📥 Downloadable Prediction Results: Export prediction outcomes to a CSV file for record-keeping or further analysis.
🔐 Privacy-Focused Design: No personal identifiers are stored; all data is processed locally with a focus on user confidentiality.
💻 Responsive Streamlit Interface: Built using Streamlit for an interactive, clean, and responsive user experience across devices.
Outline
Have you ever worried about the possibility of cancer but found it difficult to access early risk screening tools without visiting a doctor? You’re not alone. Many people hesitate to take action due to limited access, time constraints, or fear of unclear results. Traditional screenings are often costly, time-consuming, or simply unavailable in certain regions.
Introducing Cancer Prediction Risk — a simple, smart, and accessible tool powered by machine learning. This application allows users to input basic health and lifestyle information and receive an instant prediction of their potential cancer risk.
Built using Scikit-learn, Pandas, and deployed with Streamlit, this app offers a clean interface where users can enter data or upload CSV files for batch analysis. The ML model processes the input in real time and returns a risk level (low, medium, or high) along with visual insights such as a ROC curve, confusion matrix, and accuracy score.
To ensure usability and accessibility, the platform also allows users to download their results, visualize key indicators, and better understand their own health status—all while keeping data private and secure.
Whether for personal use, health awareness, or early screening support, Cancer Prediction Risk empowers individuals with technology that informs and educates—right from their browser.
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