🏥 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.

A comprehensive machine learning application that predicts breast cancer malignancy using cytology measurements. Features an interactive Streamlit web interface with real-time visualizations including radar charts for cell nuclei analysis. Implements logistic regression with data preprocessing pipelines for accurate benign/malignant classification.

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.

This is a project using the Wisconsin Breast Cancer (Diagnostic) dataset from the UCI Machine Learning Repository. link: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic) I will compare different machine learning models (Logistic Regression, Support Vector Machines) to see what would provide the best classification results in differentiating malignant tumors from benign tumors.

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

This project focuses on breast cancer classification using a Logistic Regression model built from scratch to predict whether the cells are malignant or benign.

Breast Cancer Classification with Logistic Regression

logistic regression from scratch using python to solve binary classification problem using breast cancer dataset from scikit-learn. A complete breakdown of logistic regression algorithm.

Machine Learning Breast Cancer Classification involves developing predictive models to classify breast cancer as benign or malignant based on clinical data, such as tumor size and cell features. Using algorithms like logistic regression, SVM, or neural networks, aiding early detection and improving patient outcomes.

Beginner-friendly Machine Learning classification project that predicts breast cancer as benign or malignant using Logistic Regression. Built with Python and scikit-learn, the model achieves ~96% accuracy and is evaluated using accuracy score and confusion matrix.

Breast Cancer classification with Logistic Regression

Logistic Regression for Breast Cancer Classification using PyTorch

This basic classification of breast cancer project implies concepts of Logistic Regression

This project demonstrates binary classification of breast cancer tumors using both logistic regression and a simple neural network built with PyTorch. The dataset used is the Breast Cancer Wisconsin Diagnostic dataset.

Principal Component Analysis ,"Logistic Regression for Binary Classification: Evaluating Model Performance on the Breast Cancer Dataset"

BreastCancerDetection project includes logistic regression as one of the machine learning algorithms used to detect breast cancer. Logistic regression is a binary classification algorithm that predicts the probability of an event occurring based on input features.

Official repository for "Interpretable Machine Learning Approach for Breast Cancer Classification" - exploring Logistic Regression, Decision Trees, Random Forest, and CatBoost with LIME for interpretable AI in healthcare.

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.

This repository demonstrates the use of Logistic Regression, Random Forest, and XGBoost for breast cancer classification. It covers data preprocessing, hyperparameter tuning, and model evaluation with ROC-AUC and SHAP values, showcasing key skills in healthcare data analytics.

This project is like a tradition for upcoming data scientists. It's about Cancer of the breast diagnosis by checking for the correlations in Breast measurements like Area, Perimeter, radius, concave point etc. It's a classification problem,so I used models like Logistic regression, SVM, Random forest and K nearest neighbor. I used sklearn library for this...For the EDA part, I used pandas,which shows 50-60% of diagnosis is benign. Also the diagnosis have strong correlation with radius,area, perimeter, concavity and concave point.

Description of the Project: + The "Breast Cancer Dataset" is used in this project. It has df.shape=(569, 31) which means 569 rows and 32 columns. + The link of the datset used in this project is -https://www.kaggle.com/uciml/breast-cancer-wisconsin-data + I am importing the important python packages- skelarn, pandas, numpy, seaborn and matplotlib to complete the project. + The machine learning models such as Logistic Regression, Decision Tree, Random Forest, XGBoost, AdaBoost and Gradient Boosting classifier have been used. + The performance of the machine learnig models have been tested on the basis of accuracy score, confusion matrix, classification report, f1 score and roc auc score. + I had tuned hyperparameters to improve the perforamnce for XGBoost model + The good visualization is also important along with accuracy score in model building. The performance of the model have been visualized in this project. Problem statement: The full form of XGBoost is eXtreme Gradient Boosting, also called winner for several kaggle competetion machine learning model. Most of the literatues of Machine Learning found in google has described this model as having best accuracy, efficient and feasibility. It is a decision-tree-based ensemble ML algorithm based on gradient boosting framework. It is considered that XGBoost provides a convenient way of cross-validation. Cross-validation technique is applied to test the model's overfitting during the training phase. If the model gives good accuracy in training dataset but the model works very poor in testing unseen dataset then it is called overfitting or a model of low bias and high variance. I have to calculate the model training and testing errors with different learning rates.As we know that the best technique to choose the learning rate value is between 0 and 1. I will be going to start the test by putting the learning rate as 0.01. It would easy to see the results through good visualization. I am also going to visualize the training and testing errors and accuracies by making a graph. Finally, I will tune the hyperparameters which helps us predict the testing datasets i.e. x_test.

Breast cancer classification using logistic regression.

Breast Cancer Classification using Logistic Regression

Implement logistic regression for breast cancer classification

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logistic-regression-based breast cancer classification, achieving 97% accuracy on the dataset.

This classification models can predict breast cancer probability. One of them is created by sklearn and the other one created from zero.

End-to-end implementation of Logistic Regression for breast cancer tumor classification with evaluation metrics.

Using Sigmoid function to predict Breast Cancer

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