Skip to main content

Sample code for coding practice

Project description

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

python < scriptname.py >

sklearn pypi account registered and authenticated

-done adding the functional and unit tests. -test installation and test training cases are added to functional tests and executed. -test data ingestion from unit tests is performed.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

housing_library_5503-0.2.tar.gz (6.2 kB view details)

Uploaded Source

Built Distribution

housing_library_5503-0.2-py3-none-any.whl (8.6 kB view details)

Uploaded Python 3

File details

Details for the file housing_library_5503-0.2.tar.gz.

File metadata

  • Download URL: housing_library_5503-0.2.tar.gz
  • Upload date:
  • Size: 6.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.5

File hashes

Hashes for housing_library_5503-0.2.tar.gz
Algorithm Hash digest
SHA256 e03748faaca90f492312a4a43578274772fa97fd23aae83a9c473787aff5a8d4
MD5 bdbbef5260936b326da803e3cb297554
BLAKE2b-256 89840429f3e8ae9a7297b6aeb7705123b7c63f71916d5f72c6a4a6ce81cafbcb

See more details on using hashes here.

File details

Details for the file housing_library_5503-0.2-py3-none-any.whl.

File metadata

File hashes

Hashes for housing_library_5503-0.2-py3-none-any.whl
Algorithm Hash digest
SHA256 e2f3ca1cc96729ee72cace890f0590f1919eb4623a6790a0daa3c10f12e71652
MD5 0985c24bce785d421012fc53e8547084
BLAKE2b-256 06b5c86146759b1427e2f073bc99b8ea7f4ff2841e6048c494a5863b400d3e1c

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page