Comparison of various machine learning algorithms - KNN, Naive Bayes and SVM for prediction of Breast Cancer

DL projects done in Python

Implementation of Adaboost classifier using Python on breast cancer dataset

Machine Learning Course Term Project

This project is based on the dataset published by UCI MACHINE LEARNING available in Kaggle. The hottest project based on this dataset is developed by BUDDHINI W which has achieved an excellent acccuracy about 94.4% which looks perfect. However, in this repository, you can gain the accuracy of 99.1% on test data split.

K Means implementation for breast cancer data

Wisconsin Breast Cancer Prediction Model Comparison

Determination of whether a tumor is malignant or benign. Accuracy is 97.37%

This repository contains the source code for the classification of breast cancer using Logistic Regression and Decision Tree.

Implementing Decision Tree Classifer from scrath and then train it on breast cancer dataset from the University of Wisconsin Hospitals

Supervised learning models, an assignment from the Data Science and Advanced Analytics course from the Big Data & Analytics Masters @ EAE class of 2021

Several measurements are computed from a digitized image of a fine needle aspirate of a breast mass. The goal of this project is to classify the samples as malignant or benign. The data set is provided by UCI Machine learning repository. The overall balanced accuracy of the proposed Ensemble method on the final validation set is 98.84%.

Visualize the data in 2-D scatter plot and write the inferences, Make a boxplot for each feature and highlight the outlier, if any, then remove the outlier, make again box plot to show the outlier effect and write the inferences.

Classify breast cancer using machine learning supervised

A project dedicated to classifying breast cancer given measured parameters of the cancer itself.

Analysis of mammogram data to draw insights and classify tumors as malignant or benign

Breast Cancer Report - Predicting The Malignancy of Breast Tumors

Welcome to my second DS project also my first notebook on kaggle. In this notebook, I explore the Breast Cancer dataset and develop an RF model to try classifying suspected cells as Benign or Malignant. after using K-fold cross-validation with logistic regression, RF, SVM, and KNN to check the best model for my dataset.