BREAST-CANCER-DETECTION

Table of Contents

• Overview
• Dataset
• Prerequisites
• Installations
• Results

Overview

This analysis aims to observe which features are most helpful in predicting malignant or benign and to see general trends that may aid us in model selection and hyper parameter selection. The goal is to classify whether the breast cancer is benign or malignant. To achieve this i have used machine learning classification methods to fit a function that can predict the discrete class of new input.

Dataset

The dataset is collected from kaggle which has following Attribute Information:

• ID number
• Diagnosis (M = malignant, B = benign) 3–32)
  Ten real-valued features are computed for each cell nucleus:
• radius (mean of distances from center to points on the perimeter)
• texture (standard deviation of gray-scale values) -perimeter -area -smoothness (local variation in radius lengths) -compactness (perimeter² / area — 1.0)
• concavity (severity of concave portions of the contour)
• concave points (number of concave portions of the contour) -symmetry
• fractal dimension (“coastline approximation” — 1) In our dataset we have the outcome variable or Dependent variable i.e Y having only two set of values, either M (Malign) or B(Benign). So we will use Classification algorithm of supervised learning.

Prerequisites

jupyter notebook, python libraries: Numpy, Pandas, seaborn, Label encoder, KNN, Logistic Regression.

Installations

import pandas as pd 

import numpy as np

import matplotlib.pyplot as plt 

import seaborn as sns

from sklearn.preprocessing import LabelEncoder

from sklearn.preprocessing import StandardScaler

from sklearn.model_selection import train_test_split

from sklearn.linear_model import LogisticRegression

Results

In this project in python, I've build a breast cancer tumour predictor on the dataset and created graphs and results for the same. It has been observed that a good dataset provides better accuracy. Selection of appropriate algorithms with good home dataset will lead to the development of prediction systems. These systems can assist in proper treatment methods for a patient diagnosed with breast cancer. I've used logistic regression model and KNN model and out of both KNN performs better.