Comparison of various machine learning algorithms - KNN, Naive Bayes and SVM for prediction of Breast Cancer
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Updated
Nov 6, 2020 - Python
Comparison of various machine learning algorithms - KNN, Naive Bayes and SVM for prediction of Breast Cancer
Implementation of Adaboost classifier using Python on breast cancer dataset
K Means implementation for breast cancer data
Determination of whether a tumor is malignant or benign. Accuracy is 97.37%
A project dedicated to classifying breast cancer given measured parameters of the cancer itself.
Breast cancer classification using the random forest model and deployment with docker
Code for an ISEF 2018 Project
📊 Experiments with three clustering techniques.
Propensity to Develop Breast Cancer
📏 Experiments with three dimensionality reduction techniques.
Predict Breast Cancer Wisconsin (Diagnostic) using Naive Bayes
SVM Classifier has been implemented on breast cancer dataset from machine learning repository using Python
Small project to accurately predict nature of a tumour (benign/malignant) using the UCI Wisconsin breast cancer dataset (https://www.kaggle.com/uciml/breast-cancer-wisconsin-data)
This project aims to predict if a cancer diagnosis is benign or malignant using Support Vector Machine (SVM) model. The model utilizes several features related to cancer cells to make predictions.
K-nearest-neighbors algorithm implementation
Data Visualization of the Breast Cancer Wisconsin diagnostic dataset
Objective: To find if a given cancer specimen is malignant or benign using supervised machine learning algorithm- SVM (support vector machine)
This repository consists of all different algorithms I applied on the various Datasets. This repository consists of simple python code for working on common datasets.
Neural Network from scratch without any machine learning libraries
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