Updated: Aug 22, 2023
1-Click Kit 
You can build predictive analytic based applications with this ready to deploy template application. Fully modifiable source code modifies your needs.
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.
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
For Windows OS,
- Download, extract the zip file and run. Do ensure to extract the zip file before running it.
- After successful installation of the kit, press 'Y' to run the kit and execute cells in the notebook.
- To run the kit manually, press 'N' and follow the below steps. To run the solution anytime manually after installation, follow the below steps:
- Navigate to the 'breast-cancer-prediction'.folder located in C:\kandikits
- Open command prompt inside the extracted directory 'breast-cancer-prediction'
- Run this command - "breast-cancer-prediction-env\Scripts\activate.bat" to activate the virtual environment
- Run the command - "cd breast-cancer-prediction"
- Run the command 'jupyter notebook' which would start a Jupyter notebook instance.
- Locate and open the 'Breast Cancer Prediction Using SVM.ipynb' notebook from the Jupyter Notebook browser window.
- Execute cells in the notebook.
For Linux distros and macOS,
- download & install Python3.9 & pip for your respective Linux distros or mac OS.
- download the repository.
- Extract the zip file and navigate to the directory breast-cancer-prediction.zip
- Open a terminal in the extracted directory 'breast-cancer-prediction'
- Create and activate virtual environment using this command: 'virtualenv venv & source ./venv/bin/activate'
- Install dependencies using the command 'pip3.9 install -r requirements.txt'
- Once the dependencies are installed, run the command 'jupyter notebook' to start jupyter notebook (Pls use --allow-root if you're running as root)
- Locate and open the 'Breast Cancer Prediction Using SVM.ipynb' notebook from the Jupyter Notebook browser window.
- Execute cells in the notebook.
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.
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)
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.