🏥 AI-powered breast cancer classification using Logistic Regression with 95% accuracy. Features interactive Gradio web interface for real-time predictions on 30 diagnostic parameters from Wisconsin dataset. Includes comprehensive Jupyter notebooks for model training, evaluation metrics, and deployment-ready architecture for healthcare application.

This personal project incorporates a machine learning model to detect breast cancer using a dataset by scikit-learn. By using Logistic Regression the model is trained to classify tumors to either a malignant (cancerous) class or a benign (non-cancerous) class, offering reliable predictions for simple binary medical classification tasks.

Predicting whether cancer is benign or malignant using Logistic Regression

Developed a breast cancer prediction model using Logistic Regression with data preprocessing, visualization, and evaluation to classify diagnosis effectively.

Breast Cancer Prediction using 8 classification algorithm : Logistic Regression,Support Vector Machine(linear kernel),Support Vector Machine(polynomial kernel),Ensemble Learning Method of Decision Tree,Random Forest,Adaboost Classifier, and lastly voting algorithm based on Logistic Regression,Support Vector Machine(polynomial kernel) and Decision tree. Finally project presented with Python Graphical User Interface using the 2 algorithms having the maximum accuracy : Support Vector Machine(polynomial kernel) and Logistic Regression

Cancer prediction on Wisconsin Breast Cancer Dataset using supervised learning - Logistic Regression. The model have achieved 92% of accuracy

Prediction of Breast Cancer using Logistic Regression/Decision Trees/Boosted Decision Trees

Cancer prediction on Wisconsin Breast Cancer Dataset using supervised learning - Logistic Regression. The model have achieved 94% of accuracy

A from-scratch implementation of Logistic Regression for diagnosing breast cancer using the Wisconsin Diagnostic dataset. Includes full data analysis, visualization, model training, evaluation, and prediction interface.

Breast Cancer Severity Prediction Using Logistic Regression

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Breast cancer prediction🎗️using logistic regression, random forest and artificial neural network

Cancer prediction on Wisconsin Breast Cancer Dataset using supervised learning - Logistic Regression. The model have achieved 94 % of accuracy

Breast Cancer Prediction project uses the logistic regression algorithm to predict whether a breast cancer tumor is benign or malignant based on various features.

A reproducible machine learning pipeline for breast cancer risk prediction using logistic regression, featuring stratified train-test splitting, standardization, and evaluation via confusion matrix and classification metrics.

A from-scratch implementation of Logistic Regression for diagnosing breast cancer using the Wisconsin Diagnostic dataset. Includes full data analysis, visualization, model training, evaluation, and prediction interface.

This study compares ML models—Random Forest, KNN, SVM, and Logistic Regression—using features from the Wisconsin breast cancer dataset. After baseline testing with all 30 features, optimized subsets are selected using PSO, ACO, GA, DE, and WOA. Results show these optimized features greatly improve tumor malignancy prediction accuracy.

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.

breast cancer prediction using logistic regression

Breast Cancer Prediction using Logistic Regression

In the breast cancer dataset, each datapoint has measurements from an image of a breast mass and whether or not it’s cancerous. The goal will be to use these measurements to predict if the mass is cancerous. This dataset is built right into scikit-learn so no need to read in a csv. The accuracy score is 96%

Cancer prediction on Wisconsin Breast Cancer Dataset using supervised learning - Logistic Regression. The model have achieved 94% of accuracy.

Breast Cancer Prediction using Logistic Regression with an accuracy of 96.70 %

This project is Breast Cancer Prediction using Machine Learning. I have make it for helping peoples, specially in rural areas, to predict cancer early and easily. I used main tools like Google Colab, Gradio, and Kaggle for making this project.

Cancer prediction on Wisconsin Breast Cancer Dataset Using Supervised Learning - Logistic Regression. The model have achieved 94% of accuracy

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Created a Breast Cancer Severity Prediction Using Logistic Regression