Crowd sell social media shopping booster-Crowdsell

от dinesh4nitb
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Portfolio
Portfolio
Portfolio
Portfolio
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Crowd sell (Crowell) is a simple but powerful sales promoter by turning your product into the catalog and spread it into your social accounts like Instagram and Twitter by managing tools to find out nearby followers and presenting them the product. Application finds out users who do not follow you back on. You can not only track non-followers but also Follow/ No-follow a single user or multiple users at a time. This is the best growth manager tool for Instagram growth and a must have for all Instagram and Twitter users. Shop the Online Megastore with the free Crowd sell Android app. Choose from the massive selection of original products in Fashion, Electronics, Books, Mobiles and other categories. Get timely alerts on great deals with substantial discounts, every day! This app is great tools for digital marketers and freelance users which can generate lots of free leads generating channels in a matter of seconds. Keep on track and see our updates soon.

image of username dinesh4nitb Flag of India Guna, India

Обо мне

I'm Full Stack Developer with 7+ years of experience, I have built on 15+ Android Apps, 5+ iOS Apps and 5+ websites using PHP, MySQL, AngularJS. I worked on Map/GPS Apps like Uber, Video Photosharing App, Chatting App, Dating App, Fitness App, E-commerce App, Event Sharing App, School App, Job portal App, and many apps. My key skills are: Android iOS Java PHP CodeIgniter Laravel MySQL React Native Mongo WordPress Angular IONIC Firebase Google API Google MAP Any Third Party API Integration Payment Gateway Integration JavaScript HTML5 CSS Architect Design Web Designing Logo Designing Graphics Designing Photoshop I have worked as Research Scholar with the machine learning over 2 years in National Institute of Technology ( NIT ) Bhopal, Madhya Pradesh, India where I developed 3+ Advanced Computing Machine Learning Algorithms that analyze the big data and gives the future prediction for classification and ensemble pruning using variants of voting based extreme machine learning.

$12 USD/ч.

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