Breast Cancer Prediction using SVM

This project predicts whether a tumor is benign or malignant using a Support Vector Machine (SVM) model.

It uses the Breast Cancer Wisconsin dataset and demonstrates a complete workflow for a machine learning project.

Tools & Technologies

Python

Pandas

NumPy

Scikit-learn

Matplotlib / Seaborn

Dataset

The dataset contains features computed from digitized images of breast mass.

Columns include: radius_mean, texture_mean, perimeter_mean, area_mean, smoothness_mean, etc.

Target: diagnosis (B = Benign, M = Malignant)

Model

Algorithm: Support Vector Machine (SVM)

Purpose: Classify tumors as benign or malignant based on input features

Project Workflow

Data loading and preprocessing

Feature selection

Splitting data into training and testing sets

Training the SVM model

Evaluating the model performance (accuracy, confusion matrix, etc.)

Visualization of results

Goal

The goal of this project is to build a machine learning model that accurately predicts breast cancer diagnosis, and to practice SVM implementation and evaluation in Python.