Breast-Cancer-Prediction-using-different-Machine-Learning-Algorithms

This project aims to predict breast cancer using various machine learning algorithms. The goal is to improve the accuracy of early detection by selecting and evaluating different models on a dataset of breast cancer cases.

Table of Contents

Project Overview

Dataset

Installation

Usage

Modeling

Evaluation

Results

Project Overview

This project involves using machine learning techniques to classify breast cancer as either malignant or benign. The analysis includes data preprocessing, model selection, and performance evaluation to identify the best-performing model.

Dataset

The dataset used in this project contains various features related to breast cancer cell nuclei, extracted from digitized images. The target variable is the diagnosis, which can be either malignant (M) or benign (B).

Installation

To run this project, you need to have Python installed, along with the following libraries: Copy code pip install numpy pandas matplotlib seaborn scikit-learn

Usage

Clone the repository:

Copy code git clone https://github.com/ArpitKadam/Breast-Cancer-Prediction-using-different-Machine-Learning-Algorithms

Modeling

The project explores several machine learning algorithms, including:

Logistic Regression

K-Nearest Neighbors (KNN)

Support Vector Machine (SVM)

Decision Trees

Random Forests

Gradient Boost Classifier

XGB

Each model is trained and tested on the dataset, with hyperparameter tuning where applicable.

Evaluation

Model performance is evaluated using the following metrics:

Accuracy

Confusion Matrix

Classification Report (Precision, Recall, F1-Score)

Results

The results indicate the effectiveness of different models in predicting breast cancer. The model with the highest accuracy is selected as the best predictor for this dataset.