Build AI Powered Breast Cancer Detection Engine Kit
by kandikits 
Updated: Oct 10, 2022
Solution Kit
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.
Click on the button below to download the solution and follow the deployment instructions to begin set-up. This 1-click kit has all the required dependencies and resources you may need to build your own Breast Cancer Predictive Analysis App.
Deployment Information
1. Download, extract and double-click kit_installer file to install the kit. Do ensure to extract the zip file before running it. 2. After successful installation of the kit, press 'Y' to run the kit and execute cells in the notebook. 3. To run the kit manually, press 'N' and locate the zip file 'breast-cancer-prediction.zip'. 4. Extract the zip file and navigate to the directory 'breast-cancer-prediction'. 5. Open the command prompt in the extracted directory 'breast-cancer-prediction' and run the command 'jupyter notebook'. 6. Locate and open the 'Virtual Agent for Breast Cancer Prediction Using SVM.ipynb' notebook from the Jupyter Notebook browser window. 7. Execute cells in the notebook
Training and Certification - Breast Cancer Prediction
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. Completed the training? Apply for your Participation Certificate and Achievement Certificate now! 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.
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.
jupyterby jupyter
14404 Version:CurrentJupyter metapackage for installation, docs and chat
jupyterby jupyter
Python 14404 Version:Current License: Permissive (BSD-3-Clause)
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
scikit-learnby scikit-learn
54584 Version:1.2.2scikit-learn: machine learning in Python
scikit-learnby scikit-learn
Python 54584 Version:1.2.2 License: Permissive (BSD-3-Clause)
smileby haifengl
5751 Version:v3.0.2Statistical Machine Intelligence & Learning Engine
smileby haifengl
Java 5751 Version:v3.0.2 License: Others (Non-SPDX)
h2o-3by h2oai
6315 Version:CurrentH2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
h2o-3by h2oai
Jupyter Notebook 6315 Version:Current License: Permissive (Apache-2.0)
Kit Solution Source
Breast-Cancer-Predictionby kandi1clickkits
0 Version:CurrentBreast-Cancer-Predictionby kandi1clickkits
Jupyter Notebook 0 Version:Current License: Permissive (Apache-2.0)
Support
If you need help using this kit, you may reach us at the OpenWeaver Community.