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Build AI Powered Breast Cancer Detection Engine Kit

by kandikits Updated: Oct 10, 2022

AI powered Breast Cancer Prediction model 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

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

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

Python star image 13986 Version:Current

License: Permissive (BSD-3-Clause)

Jupyter metapackage for installation, docs and chat

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jupyterby jupyter

Python star image 13986 Version:Current License: Permissive (BSD-3-Clause)

Jupyter metapackage for installation, docs and chat
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vscodeby microsoft

TypeScript star image 141808 Version:1.74.3

License: Permissive (MIT)

Visual Studio Code

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vscodeby microsoft

TypeScript star image 141808 Version:1.74.3 License: Permissive (MIT)

Visual Studio Code
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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

Python star image 52698 Version:1.2.0

License: Permissive (BSD-3-Clause)

scikit-learn: machine learning in Python

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scikit-learnby scikit-learn

Python star image 52698 Version:1.2.0 License: Permissive (BSD-3-Clause)

scikit-learn: machine learning in Python
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smileby haifengl

Java star image 5667 Version:v3.0.0

License: Others (Non-SPDX)

Statistical Machine Intelligence & Learning Engine

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smileby haifengl

Java star image 5667 Version:v3.0.0 License: Others (Non-SPDX)

Statistical Machine Intelligence & Learning Engine
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h2o-3by h2oai

Jupyter Notebook star image 6121 Version:jenkins-master-6089

License: Permissive (Apache-2.0)

H2O 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.

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h2o-3by h2oai

Jupyter Notebook star image 6121 Version:jenkins-master-6089 License: Permissive (Apache-2.0)

H2O 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.
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Kit Solution Source

Breast-Cancer-Predictionby kandi1clickkits

Jupyter Notebook star image 0 Version:Current

License: Permissive (Apache-2.0)

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Breast-Cancer-Predictionby kandi1clickkits

Jupyter Notebook star image 0 Version:Current License: Permissive (Apache-2.0)

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Support

If you need help using this kit, you may reach us at the OpenWeaver Community.

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