mvresh Goto Github PK
Name: Varun
Type: User
Location: Patna,India
Name: Varun
Type: User
Location: Patna,India
Loan-level analysis of Fannie Mae and Freddie Mac data
refactoring ashish code
Data from an audio book app has been collected,from public sources. Logically, it relates to the audio versions of books ONLY. Each customer in the database has made a purchase at least once, that's why he/she is in the database. Idea is to create a machine learning algorithm based on the available data that can predict if a customer will buy again from the Audiobook company. The main idea is that if a customer has a low probability of coming back, there is no reason to spend any money on advertising to him/her. If efforts are focused SOLELY on customers that are likely to convert again, great savings can be made. Moreover, the objective is to identify the most important metrics for a customer to come back again. Identifying new customers creates value and growth opportunities. From .csv file, data can be summarised. There are several variables: Customer ID, ), Book length overall (sum of the minute length of all purchases), Book length avg (average length in minutes of all purchases), Price paid_overall (sum of all purchases) , Price Paid avg (average of all purchases), Review (a Boolean variable whether the customer left a review), Review out of 10 (if the customer left a review, his/her review out of 10, Total minutes listened, Completion (from 0 to 1), Support requests (number of support requests; everything from forgotten password to assistance for using the App), and Last visited minus purchase date (in days). These are the inputs (excluding customer ID, as it is completely arbitrary. It's more like a name, than a number). The targets are a Boolean variable (0 or 1). Data is available for a period of 2 years based on which predictions will be done. So,aim is to find if: based on the last 2 years of activity and engagement, a customer will convert in the next 6 months. If they don't convert after 6 months, chances are they've gone to a competitor or didn't like the Audiobook way of digesting information. The task is : create a machine learning algorithm, which is able to predict if a customer will buy again. This is a classification problem with two classes: won't buy and will buy, represented by 0s and 1s.
Determines the keywords of an input document
Montreal Cyclist Data Analysis
Training openAI Gym in Bitcoin trading environment.
Pure Python from-scratch zero-dependency implementation of Bitcoin for educational purposes
Creating a list from scratch
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.