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  • 👋 Hi, I’m @nagajayanth123
  • 👀 I’m interested in coding
  • 🌱 I’m an ECE Graduate(2019-2023)
  • 💞️ I’m looking to collaborate on projects that are going to be helpful to society
  • 📫 How to reach me E-mail [email protected]

N S K K K NAGA JAYANTH's Projects

-real-time-face-detection-engine- icon -real-time-face-detection-engine-

This detection engine can perform face detection from image files and from live video capture. Here the user must select the option whether to do the face detection on image file or on the live video. For developing this engine, we use OpenCV library and Python as programming language.

acm_hacktoberfest_java icon acm_hacktoberfest_java

This is the official repository for hacktoberfest 2023, organised by ACM.BMU for java files only.

hackfest23 icon hackfest23

This is a beginner friendly repository made specifically for Hacktoberfest 2023 that helps you get your first PR.

home-surveillance-system icon home-surveillance-system

This system is developed in Python by using OpenCV library for computer vision purpose. It helps in detection of secret movements of the burglar and when it detects and notifies the owner with an alarm sound.

java-hack icon java-hack

Fork this repository to contribute your java programs. Happy hacking!!

prediction-using-supervised-ml icon prediction-using-supervised-ml

This code helps us to predict the percentage score of a student based on the no . of hours he/she studies using Supervised ML algorithm. It uses simple linear regression for prediction using python.

sentiment-analysis-ml-project-suven-consultants-and-technology icon sentiment-analysis-ml-project-suven-consultants-and-technology

The main objective in this Internship Project is to predict the sentiment for a number of movie reviews obtained from the Internet Movie Database (IMDb). This dataset contains 50,000 movie reviews that have been pre-labeled with “positive” and “negative” sentiment class labels based on the review content. Besides this, there are additional movie reviews that are unlabeled. The dataset can be obtained from http://ai.stanford.edu/~amaas/data/sentiment/ , courtesy of Stanford University and Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. They have datasets in the form of raw text as well as already processed bag of words formats. We will only be using the raw labeled movie reviews for our analyses. Hence our task will be to predict the sentiment of 15,000 labeled movie reviews and use the remaining 35,000 reviews for training our supervised models.

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