My final semester internship project involved fetching code quality metrics from SonarQube. This blog post is what I learnt about these metrics and SonarQube itself.

What is Code Quality?

Just like any other “quality”, high code quality means good code, low code quality means bad code. But what goes into calling code “good” or “bad”?

What you consider as good code or bad code can differ based on the use case. In a certain type of application, Readability might be the most important factor. …


Location data can be leveraged for many purposes.

So, you’re working on a project or assignment and you need to get spatial data from social media posts.

For many projects, location data is a great way to divide up the data and narrow down interests. For projects that work with the text associated with a social media post, location data can provide very useful insights.

I was assigned a project to build a dashboard for Twitter Sentiment Analysis. I was assigned this task interning at U.S.T Software India. They were looking for students at Bennett University for this task. …


I’m sure many people have wondered when looking at any distributed app code or project, how do I run this?

If you get lucky, you have instructions on what to do included, you follow them and its all good..or is it? Do you know what any of those tools you just setup actually do and how they tie into the function of the app?

We’ll be looking at a few of such tools and how they help in running or developing distributed applications.

Assuming beginner knowledge in developing distributed apps, we will not be going through what a distributed app…


Our primary aim in this article would be to classify forests based on their attributes into 4 classes, Sugi forest, Hinoki forest, Mixed deciduous forest and Other non-forest land. These values are denoted by letters ‘s’,’h’,’d’ and ‘o’ respectively in our dataset.

The dataset can be obtained from the following link at the UCI Machine Learning Repository:

http://archive.ics.uci.edu/ml/datasets/Forest+type+mapping

This data set contains training and testing data from a remote sensing study which mapped different forest types based on their spectral characteristics at visible-to-near infrared wavelengths, using ASTER satellite imagery. The output (forest type map) can be used to identify and/or…

MAANAS DWIVEDI

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