srinath1882 Goto Github PK
Name: Srinath1882
Type: User
Location: Bangalore, India
Name: Srinath1882
Type: User
Location: Bangalore, India
This repository contains dataset for creating a model which can write python code based on English description.
Speech to Text either from audio clip or video clip or from Youtube Link
Over the past decade, bicycle-sharing systems have been growing in number and popularity in cities across the world. Bicycle-sharing systems allow users to rent bicycles for short trips, typically 30 minutes or less. Thanks to the rise in information technologies, it is easy for a user of the system to access a dock within the system to unlock or return bicycles. These technologies also provide a wealth of data that can be used to explore how these bike-sharing systems are used. In this project, you will perform an exploratory analysis on data provided by Motivate, a bike-share system provider for many major cities in the United States. You will compare the system usage between three large cities: New York City, Chicago, and Washington, DC. You will also see if there are any differences within each system for those users that are registered, regular users and those users that are short-term, casual users.
This repo contains two modelsAttention model for English to German translation.
Katacoda Scenarios
Simple Linear regression model to predict the Price of Iron in USD
Sentiment Analysis of IMDB Dataset using Pytorch
Seq2Seq Model and Seq2Seq with Attention Model on various Question Answer Datasets
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Business Problem Overview In the telecom industry, customers are able to choose from multiple service providers and actively switch from one operator to another. In this highly competitive market, the telecommunications industry experiences an average of 15-25% annual churn rate. Given the fact that it costs 5-10 times more to acquire a new customer than to retain an existing one, customer retention has now become even more important than customer acquisition. For many incumbent operators, retaining high profitable customers is the number one business goal. To reduce customer churn, telecom companies need to predict which customers are at high risk of churn.
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