Machine learning classifier for cancer tissues 🔬
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Updated
Jun 4, 2021 - Python
Machine learning classifier for cancer tissues 🔬
Machine learning is widely used in bioinformatics and particularly in breast cancer diagnosis. In this project, certain classification methods such as K-nearest neighbors (K-NN) and Support Vector Machine (SVM) which is a supervised learning method to detect breast cancer are used.
Using the Knn algorithm, it detects whether the tumor is benign or malignant in people diagnosed with breast cancer.
Python feed-forward neural network to predict breast cancer. Trained using stochastic gradient descent in combination with backpropagation.
Prediction of breast cancer using Random Forest Classification on the Wisconsin Breast Cancer Dataset. Implemented with Streamlit.
K means clustering for breast-cancer-wisconsin.data from scratch
The aim of the project, to determine whether the breast cancer cell is malignant or benign.I got the dataset from Kaggle.
This repository is for the work I did in machine learning using Python.
Single layer neural network machine learning project for classifying data according to whether it is benign or malignant.
CSE 575 Statistical Machine Learning
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
Classifying breast cancer using supervised machine learning techniques (logistic regression and k-nearest neighbours)
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)
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%
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