# Breast-cancer-risk-prediction > Necessity, who is the mother of invention. – Plato* ## Welcome to my GitHub repository on Using Predictive Analytics model to diagnose breast cancer. --- ### Objective: The repository is a learning exercise to: * Apply the fundamental concepts of machine learning from an available dataset * Evaluate and interpret my results and justify my interpretation based on observed data set * Create notebooks that serve as computational records and document my thought process. The analysis is divided into four sections, saved in juypter notebooks in this repository 1. Identifying the problem and Data Sources 2. Exploratory Data Analysis 3. Pre-Processing the Data 4. Build model to predict whether breast cell tissue is malignant or Benign ### [Notebook 1](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB1_IdentifyProblem%2BDataClean.ipynb): Identifying the problem and Getting data. **Notebook goal:Identify the types of information contained in our data set** In this notebook I used Python modules to import external data sets for the purpose of getting to know/familiarize myself with the data to get a good grasp of the data and think about how to handle the data in different ways.  ### [Notebook 2](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB2_ExploratoryDataAnalysis.ipynb) Exploratory Data Analysis **Notebook goal:  Explore the variables to assess how they relate to the response variable** In this notebook, I am getting familiar with the data using data exploration and visualization techniques using python libraries (Pandas, matplotlib, seaborn. Familiarity with the data is important which will provide useful knowledge for data pre-processing) ### [Notebook 3](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB3_DataPreprocesing.ipynb) Pre-Processing the data **Notebook goal:Find the most predictive features of the data and filter it so it will enhance the predictive power of the analytics model.** In this notebook I use feature selection to reduce high-dimension data, feature extraction and transformation for dimensionality reduction. This is essential in preparing the data before predictive models are developed. ### [Notebook 4](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB4_PredictiveModelUsingSVM.ipynb) Predictive model using Support Vector Machine (svm) **Notebook goal: Construct predictive models to predict the diagnosis of a breast tumor.** In this notebook, I construct a predictive model using SVM machine learning algorithm to predict the diagnosis of a breast tumor. The diagnosis of a breast tumor is a binary variable (benign or malignant). I also evaluate the model using confusion matrix the receiver operating curves (ROC), which are essential in assessing and interpreting the fitted model. ### [Notebook 5](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB_5%20OptimizingSVMClassifier.ipynb): Optimizing the Support Vector Classifier **Notebook goal: Construct predictive models to predict the diagnosis of a breast tumor.** In this notebook, I aim to tune parameters of the SVM Classification model using scikit-learn.

基于Python机器学习的乳腺癌预测模型

Breast Cancer Prediction using 8 classification algorithm : Logistic Regression,Support Vector Machine(linear kernel),Support Vector Machine(polynomial kernel),Ensemble Learning Method of Decision Tree,Random Forest,Adaboost Classifier, and lastly voting algorithm based on Logistic Regression,Support Vector Machine(polynomial kernel) and Decision tree. Finally project presented with Python Graphical User Interface using the 2 algorithms having the maximum accuracy : Support Vector Machine(polynomial kernel) and Logistic Regression

This project is a simple machine learning pipeline using Python and scikit-learn to predict breast cancer patient survival based on clinical data. The model uses the K-Nearest Neighbors (KNN) algorithm to make predictions.

The Breast Cancer Prediction Model is a machine learning application designed to predict whether a breast tumor is cancerous or non-cancerous based on a set of measurements. It utilizes a trained machine learning model built using Python and the Flask web framework.

Breast Cancer Detection App using Streamlit A machine learning web application that predicts breast cancer based on input features. Built with Python, Streamlit, and scikit-learn. Users can input patient data and get predictions instantly.

This project implements a machine learning model (Logistic Regression) trained on the Breast Cancer dataset to predict if a tumor is benign or malignant. It includes a Python script for training the model, a terminal-based prediction tool, and a web application built with Streamlit for interactive predictions.

This project implements a breast cancer prediction model using Python, Streamlit, and various machine learning libraries. The model aims to predict the likelihood of breast cancer based on the provided dataset.

"Breast Cancer Classification: A machine learning model designed to classify breast cancer as benign or malignant based on diagnostic data. Developed using Python and powerful data analysis libraries for accurate and efficient predictions."

Breast Cancer Prediction using Machine Learning: Developed a model to predict breast cancer diagnosis based on clinical and demographic features. Implemented in Python using scikit-learn and pandas. Achieved 94% accuracy on the test dataset.

Breast Cancer Prediction using Machine Learning (Random Forest Classifier). This project uses the Breast Cancer Wisconsin dataset to classify tumors as Malignant or Benign based on medical features. Developed in Python using scikit-learn and Jupyter Notebook.

Built a Machine Learning-based Breast Cancer Prediction system using Python and Scikit-learn. Trained a RandomForest model on key features from the Breast Cancer Wisconsin dataset. Developed a web interface using Flask/FastAPI with HTML and CSS for real-time prediction.

Built a Machine Learning-based Breast Cancer Prediction system using Python and Scikit-learn. Trained a RandomForest model on key features from the Breast Cancer Wisconsin dataset. Developed a web interface using Flask/FastAPI with HTML and CSS for real-time prediction.

OncoVision is a Machine Learning project for early detection of breast cancer. It uses Python and scikit-learn to train predictive models that can classify breast cancer cases based on input features. The project demonstrates how AI can assist in healthcare by providing accurate predictions and helping raise awareness about early detection.

This repository contains a machine learning project for Breast Cancer Prediction using Python. It applies various ML models to classify tumors as benign or malignant based on given features. The project includes data preprocessing, model training, evaluation, and visualizations.

Breast Cancer Prediction using 8 classification algorithm : Logistic Regression,Support Vector Machine(linear kernel),Support Vector Machine(polynomial kernel),Ensemble Learning Method of Decision Tree,Random Forest,Adaboost Classifier, and lastly voting algorithm based on Logistic Regression,Support Vector Machine(polynomial kernel) and Decision tree. Finally project presented with Python Graphical User Interface using the 2 algorithms having the maximum accuracy : Support Vector Machine(polynomial kernel) and Logistic Regression.