Use this kandi 1-Click Solution kit to build your own AI-based Breast Cancer Detection Engine in minutes. ✅ Using this application you can do early stage detection for breast cancer and help in identifying it as malignant(cancerous) or benign(non-cancerous). ✅ You can build predictive analytic based applications with this ready to deploy template application. ✅ Fully modifiable source code is provided to enable you to modify for your requirements. Download the installer and follow instructions from Kit Deployment Instructions section to deploy this in minutes.

Tutorial and certification - how to build AI Powered Breast Cancer Detection Engine

Watch this self-guided tutorial on how you can use Dataset to train the model, Exploratory Data Analysis, and Vector Classification to build your own AI Powered Breast Cancer Detection Engine. Training Certification: Watch the above 10 min tutorial and take this quiz to receive your Participation Certificate and Achievement Certificate. Tag us on social media with a screenshot or video of your working application for a chance to be featured as an Open Source champion and get a verified badge.

Kit Deployment Instructions

⬇️ Download, extract and double-click kit_installer file to install the kit. Note: Do ensure to extract the zip file before running it. Follow below instructions to run the solution. 1. After successful installation of the kit, press 'Y' to run the kit and execute cells in the notebook. 2. To run the kit manually, press 'N' and locate the zip file '' 3. Extract the zip file and navigate to the directory 'breast-cancer-prediction' 4. Open command prompt in the extracted directory 'breast-cancer-prediction' and run the command 'jupyter notebook' 5. Locate and open the 'Virtual Agent for Breast Cancer Prediction Using SVM.ipynb' notebook from the Jupyter Notebook browser window. 6. Execute cells in the notebook

Kit Solution Source

Breast Cancer Prediction created using this kit are added in this section. The entire solution is available as a package to download from the source code repository

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers. Jupyter Notebook is used for our development.

Machine Learning

Simple and efficient tools for predictive data analysis. Scikit-learn is a free software machine learning library which features various classification, regression and clustering algorithms including support-vector machines,etc. Similar libraries for ML support in Java, Scala and R programming language