kddcup2018-of-fresh-air | # 30 at KDD CUP 2018 https

by ryancheunggit Python Version: Current License: No License

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

kandi X-RAY | kddcup2018-of-fresh-air Summary

kddcup2018-of-fresh-air is a Python library. kddcup2018-of-fresh-air has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

Hooray~! fellow KDD competitors. I entered this competition on day 1 and very quickly established a reasonable baseline. Due to some personal side of things, I practically stopped improving my solutions since the beginning of May. Even though my methods did not work really well compared to many top players in phase 2, but I think my solution may worth sharing due to it is relative simplicity. I did not touch the meo data at all, and one of my models is just calculating medians.

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Quality

Security

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Support

kddcup2018-of-fresh-air has a low active ecosystem.

It has 37 star(s) with 23 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 2 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of kddcup2018-of-fresh-air is current.

Quality

kddcup2018-of-fresh-air has 0 bugs and 0 code smells.

Security

kddcup2018-of-fresh-air has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

kddcup2018-of-fresh-air code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

kddcup2018-of-fresh-air does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

kddcup2018-of-fresh-air releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

kddcup2018-of-fresh-air saves you 373 person hours of effort in developing the same functionality from scratch.

It has 890 lines of code, 41 functions and 9 files.

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

Top functions reviewed by kandi - BETA

kandi has reviewed kddcup2018-of-fresh-air and discovered the below as its top functions. This is intended to give you an instant insight into kddcup2018-of-fresh-air implemented functionality, and help decide if they suit your requirements.

Download data from Canada API
Normalize datetime columns
Creates a wide wide wide workspace based on the data
Extract the median features for each station
Split widefw x y data
Impute data by city
Compute LGBM regression
Calculate the mean and std
Get city data
Download data from Canada
Replace NaT values with NaN
Calculate the score of a truth test
Compute the mean squared error
R Standardize station variance
Impute a dataframe based on fwbw_method
Impute a Pandas dataframe
Forward backward import
Impute the mean station of data
Extract the median median values from a time series
Convert a pandas dataframe to a wide dataframe
Compute the mean and standard deviation

Get all kandi verified functions for this library.

kddcup2018-of-fresh-air Key Features

No Key Features are available at this moment for kddcup2018-of-fresh-air.

kddcup2018-of-fresh-air Examples and Code Snippets

No Code Snippets are available at this moment for kddcup2018-of-fresh-air.

Community Discussions

No Community Discussions are available at this moment for kddcup2018-of-fresh-air.Refer to stack overflow page for discussions.

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

Vulnerabilities

No vulnerabilities reported

Install kddcup2018-of-fresh-air

You can download it from GitHub.
You can use kddcup2018-of-fresh-air 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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