housing-library

Name	housing-library JSON
Version	0.1 JSON
	download
home_page
Summary	Sample code for coding practice
upload_time	2023-11-15 14:14:57
maintainer
docs_url	None
author
requires_python	>=3.11
license
keywords	housing data training
VCS
bugtrack_url
requirements	No requirements were recorded.
Travis-CI	No Travis.
coveralls test coverage	No coveralls.

            # Median housing value prediction

The housing data can be downloaded from https://raw.githubusercontent.com/ageron/handson-ml/master/. The script has codes to download the data. We have modelled the median house value on given housing data. 

The following techniques have been used: 

 - Linear regression
 - Decision Tree
 - Random Forest

## Steps performed
 - We prepare and clean the data. We check and impute for missing values.
 - Features are generated and the variables are checked for correlation.
 - Multiple sampling techinuqies are evaluated. The data set is split into train and test.
 - All the above said modelling techniques are tried and evaluated. The final metric used to evaluate is mean squared error.

## To excute the script
python nonstandardcode.py
# To create an environment 
conda env --name mle-dev biopython
conda activate mle-dev
#To export the environment to env.yml
conda env export mle-dev > env.yml
# mle-training

            

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    "description": "# Median housing value prediction\n\nThe housing data can be downloaded from https://raw.githubusercontent.com/ageron/handson-ml/master/. The script has codes to download the data. We have modelled the median house value on given housing data. \n\nThe following techniques have been used: \n\n - Linear regression\n - Decision Tree\n - Random Forest\n\n## Steps performed\n - We prepare and clean the data. We check and impute for missing values.\n - Features are generated and the variables are checked for correlation.\n - Multiple sampling techinuqies are evaluated. The data set is split into train and test.\n - All the above said modelling techniques are tried and evaluated. The final metric used to evaluate is mean squared error.\n\n## To excute the script\npython nonstandardcode.py\n# To create an environment \nconda env --name mle-dev biopython\nconda activate mle-dev\n#To export the environment to env.yml\nconda env export mle-dev > env.yml\n# mle-training\n",
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