KMeans Clustering on Cancer Data Set
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Updated
Dec 6, 2019 - Jupyter Notebook
KMeans Clustering on Cancer Data Set
SVC model predictions on cancer dataset of sklearn and also used GridSearchCV to find the best parameters for the SVC model
This repository is for an IBM course: Exploratory Data Analysis for Machine Learning
This project is my first step into the world of Data Science, thanks to my professor Dr.Helen and my teammates for helping me do this project. The project deals with the analysis of cancer data from www.cancer.org for the years 2011-2014.
Recursive Multi-view Integration for Subtypes Identification
Used Pandas and Python plotting libraries - Matplotlib & Bokeh to analyze and visualize data of a clinical trial to test four potential treatment drugs and their possible effects on tumor volume, metastasis and survival rate | UT Data Analysis and Visualization Nov 2019 - May 2020
A project for my Advanced Artificial Intelligence class to apply AI methods to a real-world problem.
The goal is to use Machine Learning to classify the Cancer as Benign or Malignant. I got a 96.4% accuracy by using the KNearest Neighbors strategy.
Final-year project: System for aggregation and structured querying of cancer pathway data
The goal of this project is to classify cancerous images (IDC : invasive ductal carcinoma) vs non-IDC images.
Cancer deaths are on an alarming rise. Let us explore the global cancer mortality data for 29 different types of cancers from 1990-2019.
Researcher may manipulate their own cancer data via this open source platform.
Diagnostic Cancer Solution - Machine Learning APP with Wisconsin Breast Cancer Database.
A compendium of public data and mimic data used to illustrate marketing tactics
A magyar Rákregiszter adatait feldolgozó, azokat kényelmesen használhatóvá tevő, vizualizáló program.
Used Pandas and Python plotting libraries - Matplotlib & Bokeh to analyze and visualize data of a clinical trial to test four potential treatment drugs and their possible effects on tumor volume, metastasis and survival rate | UT Data Analysis and Visualization Nov 2019 - May 2020
Supervised Learning Experiments on Wisconsin Breast Cancer Dataset
The goal of iCTC is to detect whether peripheral blood cells have CTCs (circulating tumor cell) or not.
In this project I will look at a dataset of patient data relating to breast cancer, and develop a machine learning model that will aim to predict Malignant tumors with the highest accuracy.
Dissertation on Cancer Detection [Prostate Cancer] Research and Study
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