What will you learn

  • Data Summarizing
  • Data Normalisation

What will you get

  • Predict Diagnosis and compare it with Real Diagnosis

Prerequisites

  • Good in Data Classification
  • Data Visualization

Project Description

Build Analytics Dashboards and filtering data on an Analytics Dashboard Item. Track indicators that indicate diagnosis need for intervention. Monitor patient age group, patient ethnicity sub-groups and Patient gender. Set up Item Indicators that define what data you will access, and what thresholds that data will be broken into up. This helps to offer proactive customer service. You produce an accurate forecast for progress of disease to reflect the condition of the patient.

Project Outline:

Early detection of Breast cancer brings benefit to entire health system as the screening need not be dependent on human component rather Machine learning can improve efficiency of detection and guarantees more time treating the disease than detecting it in first place.

 

For this project, we have picked up “Wisconsin Breast Cancer Diagnostic” dataset from the “UCI Machine Learning Repository”. The data has 569 records of Cancer biopsies; each has 32 attributes.

Analysis requirement include:

Use k-NN to classify unlabelled samples by assigning them to class of similar labelled samples

 

For k-NN to work efficiently, we measure distance between unlabelled sample with class of labelled sample. Also, feature with larger range shall dominate distance measurement even in presence of feature with smaller range.