iyashjayesh/OCE-React

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3
Rank 3,963,521 (Top 79 %)
Language
JavaScript
Created about 3 years ago
Updated about 3 years ago

iyashjayesh/OCE-React

iyashjayesh

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monigo

MoniGo is a performance monitoring library for Go apps, offering real-time insights into service-level and function-level metrics. With an intuitive UI, it enables developers to track and optimize performance. Get your Go app's dashboard up in just 10 seconds!

VR-for-Education-

Tinder-clone-Frontend

STUDENT-ATTENDENCE-USING-FACE-RECOGNITION

Face recognition, as one of the most successful applications of image analysis, has recently gained significant attention, especially during the past several years. Our idea proposes an automatic face recognition attendance system for students using raspberry pi and mini cameras based on deep learning and narrowband Internet of things. The system automatically detects and identifies faces and mark present/absent of students with the help of face detection and it points out the technical challenges of building a face recognition system. The system will automatically update the student’s presence in the class to the student’s database and update all the data to the cloud as well as sends It will message to the guardians of absentees and also to the Head of the department. Face recognition processing, including major components such as face detection, tracking, alignment, and feature extraction, and it points out the technical challenges of building a face recognition system. We focus on the importance of the most successful solutions available so far. The final part of the chapter describes chosen face recognition methods and applications and their potential use in areas not related to face recognition.

NASA-Earth-Observatory-Natural-Event-Tracker

TwitterUI-CodeChella

django-web-app

StayCall

Discover your ideal home away from home. Create unforgettable memories with StayCall.

Bank-Marketing-Campaign-Predictive-Analysis

Problem Statement All bank marketing campaigns are dependent on customers’ huge electronic data. The size of these data sources is impossible for a human analyst to come up with interesting information that will help in the decision-making process. Data mining models are completely helping in the performance of these campaigns. The purpose is increasing the campaign effectiveness by identifying the main characteristics that affect a success (the deposit subscribed by the client) based on a handful of algorithms that we will test (e.g. Logistic Regression, Random Forests, Decision Trees and others). With the experimental results we will demonstrate the performance of the models by statistical metrics like accuracy, sensitivity, precision, recall, etc. We the higher scoring of these metrics, we will be able to judge the success of these models in predicting the best campaign contact with the clients for subscribing deposit. The aim of the marketing campaign was to get customers to subscribe to a bank term deposit product. Whether they did this or not is variable ‘y’ in the data set. The bank in question is considering how to optimize this campaign in future. What would your recommendations to the marketing manager be?

Jupyter Notebook

Go-Lang-Resources

Hey Folks 🖐🏻, I'm maintaining this repository to keep track of all the resources I've used to learn Go Lang

Tinder-clone-Backend-withoutPass

Network-Analysis-of-Game-of-Thrones

DataScience Hackathon - " A Network Analysis of Game of Thrones" Team Details: Yash Chauhan and Vidhi Bhavsar Contact.no: 9925877996 Email: [email protected] Our Tasks (1) First load the dataset (2) Time for some Network of Thrones (3) Populate the network with the DataFrame (4) The most important character in Game of Thrones (5) The evolution of character importance (6) What's up with Stannis Baratheon? (7) What does Google PageRank tell us about GoT? (8) Correlation between different measures (9) Conclusion