EBEON0322584060-EXL-DATA-ANLAYST-FINAL-PROJECT-EDUBRIGE

  NAME : N.JAYACHITRA
  BATACH:7229-EXL DATA ANALYASIS
  Enrollment Number :EBEON0322584060

PROJECT ABSTRACT:

Breast cancer is the most common cancer amongst women in the world. It accounts for 25% of all cancer cases, and affected over 2.1 Million people in 2015 alone. It starts when cells in the breast begin to grow out of control. These cells usually form tumors that can be seen via X-ray or felt as lumps in the breast area.

The key challenges against it’s detection is how to classify tumors into malignant (cancerous) or benign(non cancerous). We ask you to complete the analysis of classifying these tumors using machine learning (logesitic alogorithm) and the Breast Cancer Wisconsin (Diagnostic) Dataset. It contains expression values for ~12.000 proteins for each sample, with missing values present when a given protein could not be quantified in a given sample.

DATASET:

It is using in kaggle data set https://www.kaggle.com/code/junkal/breast-cancer-prediction-using-machine-learning/data it is using the link

PROCESS:

Data preprocessing

Data Analysis

Data Visulalization

Model Selection

Model Training

Model Deployment

Notes:

This is an extremely experimental project, it is using medium website kaggle notebooks refer.

CONCULATION

The best model to be used for diagnosing breast cancer as found in this analysis is the using logiestic alogorithm s, points_mean','area_mean','radius_mean','perimeter_mean','concavity_mean'. It gives a prediction accuracy of ~97.9 the test data set.