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Analyzing IoT Data in Python

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更新日 2024/06

Learn how to import, clean and manipulate IoT data in Python to make it ready for machine learning.

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PythonData Manipulation4時間16 ビデオ53 演習4,250 XP6,774達成証明書

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コース説明

Have you ever heard about Internet of Things devices? Of course, you have. Maybe you also have a Raspberry PI in your house monitoring the temperature and humidity. IoT devices are everywhere around us, collecting data about our environment. You will be analyzing Environmental data, Traffic data as well as energy counter data. Following the course, you will learn how to collect and store data from a data stream. You will prepare IoT data for analysis, analyze and visualize IoT data, before implementing a simple machine learning model to take action when certain events occur and deploy this model to a real-time data stream.

前提条件

Data Manipulation with pandas

1

Accessing IoT Data

In this chapter, you will first understand what IoT data is. Then, you learn how to aquire IoT data through a REST API and using an MQTT data stream to collect data in real time.

Introduction to IoT data

IoT devices

Data acquisition

Acquire data with pandas

Understand the data

Read data from file

Understanding the data

Introduction to Data streams

What is MQTT

MQTT single message

Save Datastream

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2

Processing IoT Data

In the second chapter, you will look at the data you gathered during the first chapter. You will visualize the data and learn the importance of timestamps when dealing with data streams. You will also implement caching to an MQTT data stream.

Perform EDA

Histogram Plot

Dealing with missing data

Missing data

Missing data II

Gather minimalistic incremental data

Which timestamp?

Cache Datastream

Date and Time

Prepare and visualize incremental data

Reformat data

Analyzing Energy counter data

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3

Analyzing IoT Data

In this chapter, you will combine multiple datasoures with different time intervals. You will then analyze the data to detect correlations, outliers and trends.

Combining datasources for further analysis

Concatenate DataFrames

Combine and resample

Correlation

Standard deviation

Autocorrelation

Seasonality and trends

Seasonal decomposition

Seasonal decomposition II

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4

Machine Learning for IoT

In this final chapter, you will use the data you analyzed during the previous chapters to build a machine learning pipeline. You will then learn how to implement this pipeline into a data stream to make realtime predictions.

Prepare data for machine learning

Train/Test split

Logistic Regression

Scaling data for machine learning

Model performance

Develop machine learning pipeline

Creating Pipelines

Store Pipeline

Apply a machine learning model

Model predictions

Apply model to data stream

Wrapping up

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Analyzing IoT Data in Python

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