housing-library-5515 0.2

Sample code for coding practice

Median housing value prediction

The housing data can be downloaded from https://github.com/ageron/handson-ml/blob/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. -performed the tests

##sklearn pypi acount recreation -added the functional tests and unit tests -performed test_training and tets_installation tests in functional testings -performed test_data_ingestion test in unit testing

To excute the script

python src/score.py

##To Excute the distribution file unzip dist/housing_library_5515-0.1-py3-none-any.whl -d wheel_contents cd wheel_contents python3 house_price_prediction/score.py

##To activate the environment conda activate mle-dev ##To export the environment conda env export > environment.yml ##To import the environment conda env create -f environment.yml ##LICENSE This project is licensed under the MIT license - see the (LICENSE) file for details