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Machine Learning or Predictive Models in IoT - Energy Prediction Use Case

In this machine learning and IoT project, we are going to test out the experimental data using various predictive models and train the models and break the energy usage.
4.94.9

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What will you learn

  • Multiple linear regression,
  • Support vector machine with radial kernel,
  • Random forest and
  • Gradient boosting machines (GBM).
  • Use of statistical models with repeated cross validation and evaluated in a testing set

What will you get

  • Access to recording of the complete project
  • Access to all material related to project like data files, solution files etc.

Project Description

This IoT project presents and discusses data-driven predictive models for the energy use of appliances. Data used include measurements of temperature and humidity sensors from a wireless network, whether from a nearby airport station and recorded energy use of lighting fixtures. The machine learning project discusses data filtering to remove non-predictive parameters and feature ranking. The data set is at 10 min for about 4.5 months. The house temperature and humidity conditions were monitored with a ZigBee wireless sensor network. Each wireless node transmitted the temperature and humidity conditions around 3.3 min. Then, the wireless data was averaged for 10 minutes periods. The energy data was logged every 10 minutes with m-bus energy meters. Weather from the nearest airport weather station (Chievres Airport, Belgium) was downloaded from a public data set from Reliable Prognosis (rp5.ru) and merged together with the experimental data sets using the date and time column. Two random variables have been included in the data set for testing the regression models and to filter out non-predictive attributes (parameters). 

Instructors

 
Pradeepta

Curriculum For This Mini Project

 
  Introduction to Problem Statement
00:05:37
  Dataset Overview
00:08:05
  Data PreProcessing
00:00:55
  Import Libraries
00:02:44
  Format Date
00:14:14
  Identify Missing Values
00:02:29
  Plotting univariate features
00:09:28
  Plotting bivariate features
00:06:08
  Identify Outliers
00:12:40
  Visualize energy usage
00:16:29
  Summary statistics using DPLYR
00:10:25
  Heat map for usage pattern
00:33:47
  Recap
00:02:14
  Model Data Preparation
00:03:20
  Correlations Table
00:07:09
  Feature Selection using Boruta
00:09:55
  Adding Dummy Variables to model
00:01:46
  Create Model using RFE control
00:08:15
  List chosen Features for prediction
00:02:14
  Training the Model
00:04:31
  Training the SVM and RF Model
00:03:48
  Conclusion
00:01:30