housing-library-5496

Name	housing-library-5496 JSON
Version	0.1 JSON
	download
home_page
Summary	Sample code for coding practice
upload_time	2023-10-31 07:16:27
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.

            # 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.


## Command to create Virtual Enviornemnt:
conda --version
conda create --name mle-dev biopython
conda activate mle-dev

## install the necessary librabries like numpy,pandas , matplotlib and scikit learn
conda install numpy
conda install pandas
conda install matplotlib

## To excute the script
python < scriptname.py >
python nonstandardcode.py

## Exporting the enviorment
conda export --name MLE-training >env.yml

            

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