Human Mining Activity from Smart home for Health Care.

Project Objective

• The main objective of this project is to provide digital health care, which helps all ages of people to monitor their health habits to avoid any sudden behavioral changes for health.
• This project concentrates on a user, power, and time analysis of appliance used by humans.
• Using data analysis and data mining techniques through visualization helps us to find hidden patterns that will help predict future usage.

Methodology

• The main objective of this project is to find out human health issues appliance in a smart home.
• Using FPgrowth after doing user, time, and power analysis by the box, scatter, and line plots.
• The data set is divided into 3 clusters using the Elbow curve method and I later visualized using the k-means algorithm.
• To improve processing speed I do normalized sample data and population data to prove similarity.
• Finally, I used a heatmap for a decision tree regressor after finding regression among the appliances used in the smart home.

Project Environment

• This project is based on smart health care which uses data mining and data analysis techniques to find and predict patterns.
• The IPython notebooks can be viewed by using the following Link in the case of page loading problems just copy and paste the web url like https://github.com/monisha-anila/Smart-health-care/blob/master/k-mean.ipynb.
• The sheet1.csv is the dataset.
• The Analysis.ipynb is used to do user,time and power analysis.
• The FP.ipynb is used to find patterns.
• The k-mean.ipynb is used for clustering the dataset.