It is a Python based machine learning model which classifies breast cancer tumors as malignant or benign using the UltraSound Images, featuring data preprocessing, model training, and evaluation.

Breast cancer classification project using Machine Learning (Logistic Regression, Random Forest, KNN, SVM)

Implementing XGBoost algorithm on Breast Cancer patients dataset

ML_BreastCancer_Classification

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A machine learning project applying Decision Tree, Naïve Bayes, and K-Nearest Neighbors (KNN) classifiers to a breast cancer healthcare dataset for binary classification. Includes data preprocessing, model training, and performance evaluation using accuracy, precision, recall, and F1-score metrics.

A machine learning project implementing Decision Tree, Logistic Regression, and MLP classifiers to predict breast cancer recurrence. Features data preprocessing, oversampling for class balance, and model comparison using cross-validation, achieving 98.5% accuracy with Decision Trees.

Implementation and comparison of Artificial Neural Network (MLP) and Random Forest models on the Breast Cancer dataset. Includes preprocessing, stratified splits, SMOTE balancing, evaluation with confusion matrix, classification report, cross-validation, and feature importance visualization.

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Multi-algorithm ML classification for breast cancer diagnosis with GridSearchCV hyperparameter tuning

A logistic regression model to classify breast cancer tumors as malignant or benign, featuring data preprocessing, model evaluation, and feature selection with 10-fold cross-validation for robust performance.

Proyek ini bertujuan untuk menganalisis klasifikasi tumor kanker payudara malignant (ganas) dan benign (jinak) dengan menggunakan algoritma Machine Learning, yaitu K-Nearest Neighbors (KNN).

Applied ML Basic project: Breast Cancer (WDBC) with Logistic Regression

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In this project, we develop an advanced machine learning model to enhance the accuracy of breast cancer diagnosis, our approach utilizes a set of sophisticated techniques using Support Vector Machine (SVM) to distinguish between benign and malignant breast tissue images.

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This Machine Learning web application predicts whether a tumor is benign or malignant using the Breast Cancer Wisconsin Diagnostic Dataset. The app allows users to upload a CSV file and select from six different classification models. After selecting a model, the app displays key evaluation metrics .

Breast Cancer Classification on Wisconsin Dataset using Machine Learning

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Evaluating Multiple ML Models on Classification of Breast Cancer Dataset

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A repository containing codes from Advanced Machine Learning (CII3L3) Task 1: Automated ML.

To solve two ML problems where one was about the classification of Breastcancer status to mailgnent or benign and second one was to predict scores given by people on the trip advisor app based on their experiences as features and detecting the best features among them.