To predict whether the patient has the breast cancer or not.

https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29

Breast cancer became the major source of mortality between women. The accessibility of healthcare datasets and data analysis promote the researchers to apply study in extracting unknown pattern from healthcare datasets. The intention of this study is to design a prediction system that can predict the incidence of the breast cancer at early stage by analyzing smallest set of attributes that has been selected from the clinical dataset. Wisconsin breast cancer dataset (WBCD) have been used to conduct the proposed experiment. The potential of the proposed method is obtained using classification accuracy which was obtained by comparing actual to predicted values.

#k- Nearest Neighbour (k-NN) classification technique: k-NN is a non- parametric method used for classification. In this classification, the output is a class membership. An object is classified by a majority vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbor. It is the simplest algorithm among all the machine learning algorithms.

The repository is a learning exercise to:

• Apply the fundamental concepts of machine learning from an available dataset
• to predict and diagnosis breast cancer.
• Evaluate and interpret my results and justify my interpretation based on observed data set.

• The Project goal is to predict whether the patient has breast cancer or not
• Logistic Regression Provided accuracy of 96%

https://breastcancerprediction101.herokuapp.com/

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