housing-5513


Namehousing-5513 JSON
Version 0.1 PyPI version JSON
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home_page
SummarySample code for coding practice
upload_time2023-10-27 09:39:03
maintainer
docs_urlNone
author
requires_python>=3.11
license
keywords housing data training
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requirements No requirements were recorded.
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            # 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
python3 nonstandardcode.py

            

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    "author_email": "ananya kanagarla <ananya.kanagarla@tigeranalytics.com>",
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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\npython3 nonstandardcode.py\n",
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