arnabsaha7 / cancer-cell-classification-using-scikit_learn

Cancer Cell Classification using scikit-learn

This project demonstrates how to classify cancer cells using scikit-learn, a popular machine learning library for Python. The project utilizes the Wisconsin Diagnostic Breast Cancer dataset, which contains information about the features of breast cancer cells and their corresponding diagnoses.

Project Overview

Data Preprocessing:

♦ Load the Wisconsin Diagnostic Breast Cancer dataset

♦ Handle missing values

♦ Encode categorical features

♦ Split the data into training and testing sets

Feature Selection:

♦ Identify relevant features using various feature selection techniques

♦ Evaluate the impact of feature selection on classification performance

Model Training:

♦ Train various classification models using scikit-learn

♦ Evaluate the performance of each model using metrics such as accuracy, precision, recall, and F1-score

Model Comparison:

♦ Compare the performance of different classification models

♦ Select the best-performing model for cancer cell classification

Model Prediction:

♦ Use the selected model to predict cancer cell labels for new data

Prerequisites

♦ Python 3.7 or higher

♦ Jupyter notebook

♦ scikit-learn