housing-library-5497


Namehousing-library-5497 JSON
Version 0.1 PyPI version JSON
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SummarySample code for coding practice
upload_time2023-10-30 06:07:04
maintainer
docs_urlNone
author
requires_python>=3.11
license
keywords housing data training
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            # mle-training

# 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
### conda create --name <env-name> biopython
  - script for creating a new environment

### conda activate <env-name>
 - activating the new environment

 - installing necessary packages

### python < scriptname.py >
 - command to run the script

### conda env export <env-name> > <filename.yml>
 - exporting environment

### conda activate
  - changing the environment to default base

            

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    "description": "# mle-training\n\n# 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\n### conda create --name <env-name> biopython\n  - script for creating a new environment\n\n### conda activate <env-name>\n - activating the new environment\n\n - installing necessary packages\n\n### python < scriptname.py >\n - command to run the script\n\n### conda env export <env-name> > <filename.yml>\n - exporting environment\n\n### conda activate\n  - changing the environment to default base\n",
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