Text processing software also called editor (by coders) are very important part of programmers or a writers day to day activity. Optimizing your workflow can help you to be more productive. Don’t confuse productivity with getting more work done, it actually quite the opposite. It’s about getting the same amount of work done in less time and effort and in the best case getting more work done with even less effort. So by this definition of productivity, our editor should help us to produce the same amount of work with less keystrokes. There are lots of editors out that which are trying to help you be more productive but nothing comes close to Emacs. Emacs is an editor you can program and customize the way you want. The purpose of this post is to motivate you to get started and evangelize you to make Emacs as your primary editor.

Data cleaning is a very important part of any data science project as data scientist spend 80% of their time is this step of the project. But not very much attentions is given to the cleaning process and not much research efforts are put to create any sort of framework recently I came across an amazing paper titled as Tidy data by Hadley Wickham in Journal of Statistical Software in which he talks about common problems one might encounter in data cleaning and what a Tidy data looks like I couldn’t agree more to him, he has also created a R package reshape and reshape2 for data cleaning, but the problem was the paper had very little to no code I also found the code version of the paper but it was in R, while most of my data cleaning work is done in pandas, I had to translate all those R solutions to pandas equivalent, so in this post the I will summarize all the main idea of the paper that the author suggests in the paper and also how we can solve it in pandas.

As a data scientist, you may very frequently encounter categorical variable in your dataset like location, car model, gender, etc. You cannot directly use them in our machine learning algorithm as these algorithms only understand numbers. There are various techniques to convert these categorical features to numerical features but that is not the focus of this post, this post is about how to implement these techniques in python. I will talk a little bit about these techniques but won’t go into too much depth, I will emphasise more on various ways how you can implement this technique in python.

Due to the flourish of internet and accessibility of technology incredible platforms like social media, forums, etc have been created for knowledge sharing. Exchanging ideas is not confined to a geographical area. Due to this volume and variety of content is generated in the form of images, video, text, etc. The amount of information is so much that it’s unmanageable to perceive it in bounded time, in such times area of text analytics has got the attention of people in the field of linguistics, business, etc. The goal of the post is to summarize few of the visual text analytical techniques that could help you in your initial phase of text mining or help you create a new feature for creating machine learning model. I will describe few online and offline tools that you could use to help you get started. By offline tools, I mean using python based software packages to created visualization and text pre-processing. Online tools will be web-browser based applications to which just have to paste the text or upload the text file to visualise the results.

K-Nearest Neighbour is the simplest of machine learning algorithms which can be very effective in some cases. The objective of the post it to implement it from scratch in python, you need to know a fair bit of python for and a little bit of numpy for the faster version of the algorithm. Once we have implemented the algorithm we will also see how to improve the performance of the algorithm. As there is no single invincible algorithm, we will look into advantage/disadvantage of the algorithm, this will help us to decide on when to use the algorithm. Alright, then let’s get straight into it.

If you have worked with IOT projects that integrate with other web apps or any other types of system you probably must be dealing with 10 different languages and frameworks, it’s painful. It also gets chaotic to maintain the code base in different language and repository. In such areas of troubles, Nodejs/javascript gives out some ray of hope. In this post, we will create a chat bot that will be able to communicate with Arduino board and its connected sensor and this entire project will be in Nodejs just one language javascript. Nodejs is chosen because it excels at handles I/O operations.

In the previous article we connected Arduino UNO with ESP8266-01 and tried various AT commands to create an Access Point, making a connection to a Wifi Network, listening to the TCP port and passing message back and forth with AT commands. That was all basic tasting water, in the post we will step up the game and we will read sensor data from Arduino and send it to a central server and then will display that sensor data on the mobile client.

For the purpose of demonstration, we will use DHT11 temperature and humidity sensor and will send temperature and humidity data to the central server. For the server and mobile client, we will use Blynk which is has a server which collects sensor data from various devices and there is also Blynk mobile client which you can create a dashboard with drag and drop interface and play around with visualization of the sensor data.

If you ever heard of internet of a thing and have been fascinated by its possibilities and its promises then I am sure you must have heard of Arduino development boards. Arduino is a great piece of hardware to do some quick hardware prototyping and when you are confident with the prototype you go a step further and build more of these devices and make them work for you to do some sort of automation. Adding Wireless capability to Arduino projects will even make it more easy to install/use. In this post, I will connect the ESP8266 chip to Arduino Uno. We will see how we can create Access Point and connect to wireless networks. ESP8266 is a low-cost alternative to Arduino WiFi shield.

Browsing through Arduino projects on the internet recently I came across ACS712 current Hall sensor which can be used to measure current, it can measure both AC and DC current.I found it pretty cool so I ordered one, this post is about how we can measure AC/DC current consumed by the load connected to the power supply. So of the other applications of the sensor that comes to my mind are sensing if a device is on/off, measure instability in power supply, motor control, over current fault protection, etc.

There are lots of sensors that we can play around with Arduino but usually, they operate on 3.3v or 5v but what if you want to control high voltage circuits that don’t operate on such low voltage. Well for these types of devices we use relay circuits. There are different types of Relay circuits with based on parameters like how much current/voltage it can handle, what is the voltage required for controlling their relay etc. The relay circuit we are going to use for this post is operating on 5V and can handle device having a voltage rating of 10A-125V to 10A-230V AC and 10A-28V to 10A-30V DC.