House-price-Prediction | dataset got from kaggle to try and create a linear model

by bendidi Python Version: Current License: No License

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kandi X-RAY | House-price-Prediction Summary

House-price-Prediction is a Python library. House-price-Prediction has no bugs, it has no vulnerabilities and it has low support. However House-price-Prediction build file is not available. You can download it from GitHub.

The dataset can be downloaded from the Kaggle website in house price prediction compettion. Used Theano and numpy to create a linear regression model with regularization to prevent overfitting. Used feature engeneering to add new variables that correlate strongly with the sale price. Language used :Python Librairies: Theano , Scikit-learn-preprocessing.

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House-price-Prediction has a low active ecosystem.

It has 0 star(s) with 0 fork(s). There are no watchers for this library.

It had no major release in the last 6 months.

House-price-Prediction has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of House-price-Prediction is current.

Quality

House-price-Prediction has 0 bugs and 0 code smells.

Security

House-price-Prediction has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

House-price-Prediction code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

House-price-Prediction does not have a standard license declared.

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

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House-price-Prediction releases are not available. You will need to build from source code and install.

House-price-Prediction has no build file. You will be need to create the build yourself to build the component from source.

It has 91 lines of code, 2 functions and 2 files.

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

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House-price-Prediction Key Features

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Community Discussions

Trending Discussions on House-price-Prediction

Parsing a website with is not working with "fromestring" in python

How to get latitude and latitude for an address column in a dataframe using geopy?

QUESTION

Parsing a website with is not working with "fromestring" in python

Asked 2021-Jul-28 at 14:39

I'm trying to retrieve data from a xml website through "fromstring" of xml.etree.ElementTree. The python code works fine for the test link: https://www.w3schools.com/xml/cd_catalog.xml, but it doesn't work for the link that we'll be using: https://griddata.elia.be/eliabecontrols.prod/interface/oneminuteimbalance/SystemImbalancePrice

I've double checked if the url is correct and it is. The content of both website seems to be read differently, would someone have some advice?

...

ANSWER

Answered 2021-Jul-28 at 14:39

The code below return a list of dicts. Not XML

Source https://stackoverflow.com/questions/68562106

QUESTION

How to get latitude and latitude for an address column in a dataframe using geopy?

Asked 2021-May-08 at 16:19

I am currently working on a kaggle dataset House price prediction

It has errors in the latitude and latitude column, so I decided to use geopy to get right values for those two columns.

And it works fine if I use it on one single address but returns None if applied on entire column.

...

ANSWER

Answered 2021-May-08 at 16:19

The problem is in the address of the dataset. If you use the below try catch code, you can see there are many wrong address in the dataset.

Source https://stackoverflow.com/questions/67448887

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

Vulnerabilities

No vulnerabilities reported

Install House-price-Prediction

You can download it from GitHub.
You can use House-price-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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