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.
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.
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.
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.
The patterns and relationships are identified by representing data visually and below libraries are used for generating visual plots of the data.