loan-default-prediction | Loan Default Prediction at Kaggle | Machine Learning library

by songgc Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | loan-default-prediction Summary

loan-default-prediction is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. loan-default-prediction has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However loan-default-prediction build file is not available. You can download it from GitHub.

The code was written for [Loan Default Prediction Competition at Kaggle] and got the prize.

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loan-default-prediction has a low active ecosystem.

It has 22 star(s) with 34 fork(s). There are 7 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. On average issues are closed in 1350 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of loan-default-prediction is current.

Quality

loan-default-prediction has 0 bugs and 0 code smells.

Security

loan-default-prediction has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

loan-default-prediction code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

loan-default-prediction is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

loan-default-prediction releases are not available. You will need to build from source code and install.

loan-default-prediction has no build file. You will be need to create the build yourself to build the component from source.

loan-default-prediction saves you 74 person hours of effort in developing the same functionality from scratch.

It has 192 lines of code, 18 functions and 3 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed loan-default-prediction and discovered the below as its top functions. This is intended to give you an instant insight into loan-default-prediction implemented functionality, and help decide if they suit your requirements.

Fit the model
Transform the trial data
Reduce a Pandas DataFrame
Add a new feature
Convert features to float

Get all kandi verified functions for this library.

loan-default-prediction Key Features

No Key Features are available at this moment for loan-default-prediction.

loan-default-prediction Examples and Code Snippets

No Code Snippets are available at this moment for loan-default-prediction.

Community Discussions

Trending Discussions on loan-default-prediction

Subtracting time and having output in years, rounded to two decimals

QUESTION

Subtracting time and having output in years, rounded to two decimals

Asked 2019-Nov-03 at 00:05

I've got a data set with customer birthdays and I'm looking to convert that variable to age in years, rounded to two or three decimal places. I figured out how to covert the entire column into a timestamp.

One wrinkle is that I don't know how old the data are, but it was posted to a website on April 4, 2019, so I'm using that day as "today" for the purposes of calculating the time delta.

When I try to subtract the two dates, the difference is in days.

Here's what I have and TIA for any help:

The data start with the DOB in day-month-year format, i.e.: 30-12-1993

...

ANSWER

Answered 2019-Nov-03 at 00:05

Consider the following dataframe:

Source https://stackoverflow.com/questions/58676015

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install loan-default-prediction

You can download it from GitHub.
You can use loan-default-prediction like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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