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Disease predictor is a way to recognize patient’s health by applying data mining and machine learning techniques on patient treatment history. Symptoms, Diagnosis for Personalized Healthcare Services for a Predictive Analytic Perspective.
Pandas library is used in this kandi kit to predict the probability of disease. The kit has used pandas to load datasets and visualize the data, NumPy to implement our algorithm, and sklearn-pandas to build our model.
In this project we will be using Pandas and Scikit-Learn to create a model that predicts whether or not a patient has a disease based on their demographics and lab results. We will also be using Jupyter Notebook to write code interactively so that we can see how our model performs when we change various parameters such as the number of features, amount of training data, etc.
kandi kit provides you with a fully deployable Disease Predictor. Source code included so that you can customize it for your requirement.
Deployment Information
By using the below libraries you can create the Disease Predictor. The entire solution is available as a package to download from the source code repository.
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 deploy and run the solution.
- Download the 'kit_installer.zip' then unzip the folder.
- Then Double-tap the 'kit_installer' and then command prompt opens, when you're prompted during the installation of the kit, press Y to launch the app automatically and run notebook cell by cell, by clicking on a cell and click Run button below the Menu bar.
- To run the app manually, press N when you're prompted and locate the zip file Disease-Detector.zip.
- Extract the zip file and navigate to the directory Disease-Detector.
- Open command prompt in the extracted directory Disease-Detector and run the command 'jupyter notebook' and run notebook cell by cell, by clicking on a cell and click Run button below the Menu bar.
- Running on local URL: http://127.0.0.1:7860/.
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.
Exploratory Data Analysis
For extensive analysis and exploration of data, and to deal with arrays, these libraries are used. They are also used for performing scientific computation and data manipulation.
pandasby pandas-dev
Python 33259 Version:v1.4.1
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
sklearn-pandasby scikit-learn-contrib
Python 2485 Version:v2.2.0
Pandas integration with sklearn
sklearn-pandasby scikit-learn-contrib
Python 2485 Version:v2.2.0 License: Others (Non-SPDX)
Data Visualization
The patterns and relationships are identified by representing data visually and below libraries are used for generating visual plots of the data.
matplotlibby matplotlib
Python 15355 Version:v3.5.1
matplotlib: plotting with Python
matplotlibby matplotlib
Python 15355 Version:v3.5.1 License: No License
Kit Solution Source
Disease_Detectorby Subazzz
Jupyter Notebook 0 Version:v1.0
Disease_Detectorby Subazzz
Jupyter Notebook 0 Version:v1.0 License: Permissive (MIT)
Support
If you need help to use this kit, you can email us at kandi.support@openweaver.com or direct message us on Twitter Message @OpenWeaverInc .