This repository contains a deep learning-based cancer type prediction system using a trained convolutional neural network (CNN). The model is deployed using Streamlit, allowing users to upload medical images and receive predictions with a probability distribution displayed in a pie chart.

This project is a Streamlit-based web application designed to predict the likelihood of various diseases based on user-provided health data. By leveraging machine learning models, the app offers predictions for conditions such as diabetes, heart disease, Parkinson's disease, lung cancer, and hypothyroidism.

A simple Cancer Risk Prediction App built with Streamlit and Machine Learning. This app predicts the risk of cancer based on various health factors. 🚑🎗️

This project analyzes breast cancer data to predict tumor malignancy using machine learning models, including regression and classification techniques. It features data visualization, preprocessing, and an interactive Streamlit app for exploration and prediction.

Multiple Disease Prediction System: An ML-based tool for early disease detection (Diabetes, Heart, Parkinson’s, Liver, Hepatitis, Lung Cancer, Kidney, Breast Cancer). Uses a Streamlit interface with trained models (.sav, .json) for risk prediction. Includes a Healthcare Chatbot for assistance.

A machine learning pipeline for lung cancer risk classification with Logistic Regression & Random Forest, deployed in a Streamlit dashboard featuring prediction, visualization, and case study.

Prediction of breast cancer using Random Forest Classification on the Wisconsin Breast Cancer Dataset. Implemented with Streamlit.

ML and DS Projects, this repository contain datasets that used in projects

End-to-end cancer severity prediction using KNN. Features an interactive Streamlit dashboard with data cleaning, EDA, feature selection, and model evaluation.

A Streamlit web app using a Decision Tree model to predict breast cancer survival. Input clinical data (e.g., age, tumor size) via a simple interface to receive an instant prediction ('Alive' or 'Deceased') with confidence probabilities. This tool provides real-time classification using a pre-trained scikit-learn model for immediate insights.

A Breast Cancer Prediction App built using Streamlit and Python

Thyroid Cancer Recurrence Prediction using Machine Learning Models , and user application using Streamlit

ung Cancer Prediction ML web app using Streamlit & Scikit-learn. Gradient Boosting model analyzes symptoms & risk factors. Proof-of-concept with synthetic data - NOT for medical use.

Multi-Disease Detection Web App project integrates AI models to detect Malaria, Pneumonia, Brain Tumor, Alzheimer’s, & Skin Cancer from medical images. Built with Streamlit,provides a user-friendly interface for uploading scans, running predictions,& displaying results instantly, accurate, & accessible disease detection for healthcare application.

This project presents an AI-powered medical diagnosis system implemented as a Streamlit-based web application for disease prediction. The system utilizes machine learning models trained on medical datasets to predict multiple diseases, including diabetes, kidney disease, Parkinson's syndrome, ADHD syndrome, and cancer.

This project is a Streamlit-based web application designed to predict the likelihood of various diseases based on user-provided health data. By leveraging machine learning models, the app offers predictions for conditions such as diabetes, heart disease, Parkinson's disease, lung cancer, and hypothyroidism.

Multiple Disease Prediction System is a supervised Machine Learning Model in Python. I have also deployed this machine learning web app using Streamlit. This web app can predict the diseases of a human such as Diabetes, heart disease, Parkinson's and Breast Cancer using Machine Learning model.

This project predicts if someone has Breast Cancer or not by analyzing the tumor present as Benign or Malignant by taking into account various diagnosis features like radius mean, texture mean , perimeter mean , area mean , smoothness mean , compactness mean , concavity mean , concave points mean , symmetry mean , fractal dimension mean , radius se , texture se , perimeter se , area se , smoothness se , compactness se , concavity se , concave points se , symmetry se , fractal dimension se , radius worst , texture worst , perimeter worst , area worst , smoothness worst , compactness worst , concavity worst , concave points worst , symmetry worst and fractal dimension worst . This project uses Data Science and Machine Learning using Python. We use Linear Regression model for the prediction and Streamlit Library for the deployment.

Breast Cancer Prediction: Machine Learning-based Diagnosis with Streamlit

A Streamlit web application for breast cancer prediction using logistic regression. Users can input key tumor features to receive real-time prediction and confidence scores based on the Breast Cancer Wisconsin Diagnostic Dataset.

Streamlit Cancer Prediction is a user-friendly web app predicting cancer types (benign or malignant). It features an intuitive interface for medical data input, using ML models for accurate predictions. Visualizations aid interpretation, serving healthcare pros, researchers, and patients with clear insights.

"CancerPredictorApp is a Streamlit-based tool that uses machine learning to predict cancer likelihood from input data. Powered by a Kaggle dataset, it provides real-time predictions, showcasing AI's potential in early detection and serving as an educational resource for healthcare applications."

Deploying Machine Learning web app using streamlit on Render

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Classification project for predicting breast cancer diagnosis using Random Forest. Includes EDA, feature scaling, accuracy-based evaluation, and deployment as a Streamlit web app. The trained model is saved using Joblib for reuse.

An end-to-end Breast Cancer Prediction project deployed on Streamlit.

Created and Hosted a Live Lung Cancer Prediction Model using StreamLit.

End‑to‑end breast cancer prediction workflow with reproducible notebooks and Streamlit dashboard.

Using machine Learning for cancer Prediction and Streamlit to deploy the model in web app