Greetings! I'm Aishwarya Narayanan

Hailing from a diverse background, with roots in Electrical and Electronics Engineering! Forget circuits, I'm captivated by code! While the world of electrical engineering ignited my love for problem-solving, it's the dance of algorithms and the magic of innovation in software that truly makes my circuits hum. Today, as I stand on the cusp of graduating with my B.Tech. in Electrical and Electronics Engineering from SASTRA Deemed University, I'm eager to trade voltage meters for IDEs and dive headfirst into the whirlwind of software development.

👋 Hi, I’m Aishwarya Narayanan
⚙️ I’m pursuing B.Tech Electrical and Electronics Engineering From SASTRA University, Thanjavur
📚 I’m currently learning Data structures and algorithms.
📫 Reach me out: [email protected]

💬 Connect with me !

Aishwarya Narayanan's Projects

age_calculator

Tkinter was used to develop a simple age calculator in this Python project with a graphical user interface (GUI). To see the calculated age, input the user's birthdate in the supplied text entry area and click the "Calculate Age" button.

aishwarya-narayanan03

Config files for my GitHub profile.

c4gt-2023

Code for GovTech(C4GT) is an annual summer coding program that aims to cultivate a community capable of creating and contributing to global Digital Public Goods. To connect with the community, a detailed proposal was submitted. The desire to learn and improve oneself compelled me to contribute to this open-source project.

forage-jpmc-swe-task-1

Starter repo for task 1 of the JPMC software engineering program

forage-jpmc-swe-task-2

Starter repo for task 2 of JPMC's forage program

forage-jpmc-swe-task-3

Starter repo for task 3 of JPMC's Forage program

git

Git Source Code Mirror - This is a publish-only repository but pull requests can be turned into patches to the mailing list via GitGitGadget (https://gitgitgadget.github.io/). Please follow Documentation/SubmittingPatches procedure for any of your improvements.

kubernetes_cluster

To spun up a Kubernetes cluster and use kubectl to explore the cluster set up by default

ml-biology

Machine learning has proven to be an extremely useful tool in the analysis of complex biological data. Machine learning approaches can help extract significant patterns and insights from large-scale biological datasets, such as genomics, proteomics, and medical imaging, leading to new discoveries and breakthroughs in the area of biology.

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