housing-library-5495

Name	housing-library-5495 JSON
Version	0.1 JSON
	download
home_page
Summary	Sample code for coding practice
upload_time	2023-10-30 06:05:16
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.
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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 Create a conda environment from new_env.yml file
```env_from_yml
conda env create -f new_env.yml
```

## To activate the created environment
```
conda activate mle-dev
```
## To excute the script
```shell
python nonstandardcode.py
```


            

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