Skip to main content

Sample code for coding practice

Project description

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 biopython

  • script for creating a new environment

conda activate

  • activating the new environment

  • installing necessary packages

python < scriptname.py >

  • command to run the script

conda env export > <filename.yml>

  • exporting environment

conda activate

  • changing the environment to default base

Project details


Release history Release notifications | RSS feed

This version

0.1

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_5497-0.1.tar.gz (5.7 kB view details)

Uploaded Source

Built Distribution

housing_library_5497-0.1-py3-none-any.whl (6.6 kB view details)

Uploaded Python 3

File details

Details for the file housing_library_5497-0.1.tar.gz.

File metadata

  • Download URL: housing_library_5497-0.1.tar.gz
  • Upload date:
  • Size: 5.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.5

File hashes

Hashes for housing_library_5497-0.1.tar.gz
Algorithm Hash digest
SHA256 3763ad576e633776b706428587dfee47a354566b8925dc659e6d39c3cc631c8c
MD5 aaf17cce4bb1656ef8363c4b3e3f178c
BLAKE2b-256 b2c6a78fbea76deef8e9d06df6ae4dc04823538e7e76cda02758603b4795b0c1

See more details on using hashes here.

File details

Details for the file housing_library_5497-0.1-py3-none-any.whl.

File metadata

File hashes

Hashes for housing_library_5497-0.1-py3-none-any.whl
Algorithm Hash digest
SHA256 795a36451f724c4bde560e73301925184ef34ae7bfb117aab77104dfccb86224
MD5 a5fcdbc1fb93d7ea10ec5e7c4165b15c
BLAKE2b-256 9d2eb14dd199e38cb46c0427ac2e203ea6d3944c98958737a755446a6da76cda

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