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Step-by-Step Guide to Reading Different Files in Python

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 In the world of data science, automation, and general programming, working with files is unavoidable. Whether you’re dealing with CSV reports, JSON APIs, Excel sheets, or text logs, Python provides rich and easy-to-use libraries for reading different file formats. In this guide, we’ll explore how to read different files in Python , with code examples and best practices. 1. Reading Text Files ( .txt ) Text files are the simplest form of files. Python’s built-in open() function handles them effortlessly. Example: # Open and read a text file with open ( "sample.txt" , "r" ) as file: content = file.read() print (content) Explanation: "r" mode means read . with open() automatically closes the file when done. Best Practice: Always use with to handle files to avoid memory leaks. 2. Reading CSV Files ( .csv ) CSV files are widely used for storing tabular data. Python has a built-in csv module and a powerful pandas library. Using cs...
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How to Use ML in IoT Projects

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Why you need machine learning skills? Let us start with Big data. Big data relates to extremely large and complex data. So, the availability of huge data makes machine learning is popular to use in future prediction.

6 ideas how to use ML in IoT

  1. Machine Learning comprises algorithms that learn from data, make predictions based on their learning, and have the ability to improve their outcomes with experience. Due to the enormity of data involved with Machine Learning, various technologies and frameworks have been developed to address the same. Hadoop is an open-source framework targeted for commodity hardware to address big data scale.
  2. The distributed design of the Hadoop framework makes it an excellent fit to crunch data and draw insights from it by unleashing Machine Learning algorithms on it. 
  3. So, the true value of IoT comes from ubiquitous sensors’ relaying of data in real-time, getting that data over to Hadoop clusters in a central processing unit, absorbing the same, and performing Machine Learning on data to draw insights; all at petabyte scale or more.
  4. In reviewing the use cases and challenges from preceding sections, one thing is very clear. That is to do with the quickness with which certain analytics must be performed. Imagine sending a critical alert late because computing could not be done any faster. Two key gaps here include absorbing incoming data at such a high rate reliably and in observing that Hadoop was not created for real-time streaming data.
  5. It was originally envisaged as a framework for batch processing. Innovators have responded to those challenges well. Let us review some of those technologies now.
  6. SAP HANA with the internet of things came into the picture with real-time processing of data compared to Hadoop which is only batch processing. 
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