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Hi there 🎉🎈🎉🎈

Here you'll find a small sample of the projects I've hacked on over the years. You can also browse all the projects I've found interesting by looking at what I've starred. You can read about my projects and find more context for many of the repositories hosted here by searching my personal blog.

Personal


🌱 I’m currently learning

Apache Kafka, MongoDB and Deep learning

📫 How to reach me:

[email protected]


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Dhruval's Projects

chatbot icon chatbot

🤖 Build your own chatbot base on IBM Watson

correlated-words icon correlated-words

This project was submitted as a part of Software Group Project. Made in python this is actually an algorithm developed by us to find correlated words in a document. This algorithm doesn’t give you synonyms but rather gives you the words which are correlated to each other based on their occurrences in the document.

design-patterns icon design-patterns

:briefcase: Design patterns written in different programming languages

dish-close icon dish-close

Have you ever wondered what special ingredient makes your favorite dish tick? If you are a foodie who loves to cook and explore his/her culinary skills then DishClose is just for you. Using this system, you can find the key ingredients associated with a dish and also get suggestions for making a different dish using similar ingredients. With the help of machine learning, this system will be trained with a variety of pictures of dishes across the world and will constantly get better at suggestions when users upload their own recipes.

doorbell icon doorbell

Cloud-based photo doorbell with companion app

home-server icon home-server

A media home server to stream video from your laptop to any device on the same Wi-Fi!

ijcss_research_paper icon ijcss_research_paper

In the era of technology and internet, people use online social media services like Twitter, Instagram, Facebook, Reddit, etc. to express their emotions. The idea behind this paper is to understand people’s emotion on Twitter and their opinion towards Presidential Election 2020. We collected 1.2 million tweets in total with keyword like “RealDonaldTrump”, “JoeBiden”, “Election2020” and other election related keywords using Twitter API and then processed them with natural language processing toolkit. A Bidirectional Long Short-Term Memory (BiLSTM) model has been trained and we have achieved 93.45% accuracy on our test dataset. We then used our trained model to perform sentiment analysis on the rest of our dataset. With the sentiment analysis results and comparison with 2016 Presidential Election, we have made predictions on who could win the US Presidential Election in 2020 with pre-election twitter data. We have also analyzed the impact of COVID-19 on people’s sentiment about the election.

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