You are given sensor data of an office such as light, temperature, humidity, and CO2 measurements.Weka, Rapid Miner & XLMiner

Project Title: Occupancy Classification Data

Background:

You are given sensor data of an office such as light, temperature, humidity, and CO2 measurements.  There is also an attribute to indicate whether the room is occupied. In this way, we could analyze the data for patterns and apply a linear regression model to predict whether the room is occupied.

Objectives:

The objectives of this project are:

  1. To pre-process the data to ensure data is clean and ready for next stage of analysis.
  2. To perform data transformation so as to gain insights into the data.
  3. To perform data visualization so as to discover patterns, trends and etc.
  4. To fit the data into a Linear regression model for classification.

Data Set Information:

Each record in the data set consists of 8 attributes: 

Attribute

Attribute description

index

record index

date

record date time year-month-day hour:minute:second

Temperature

In Celsius

Humidity

In %

Light

In Lux

CO2

In ppm

Humidity Ratio

Derived quantity from temperature and relative humidity, in kg water-vapor/kg-air

Occupancy

, 0 or 1, 0 for not occupied, 1 for occupied status

Detail: https://github.com/LuisM78/Occupancy-detection-data

Attachments:

Instructions Files

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