Name | housing-library-5497 JSON |
Version |
0.1
JSON |
| download |
home_page | |
Summary | Sample code for coding practice |
upload_time | 2023-10-30 06:07:04 |
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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coveralls test coverage |
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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 excute the script
### conda create --name <env-name> biopython
- script for creating a new environment
### conda activate <env-name>
- activating the new environment
- installing necessary packages
### python < scriptname.py >
- command to run the script
### conda env export <env-name> > <filename.yml>
- exporting environment
### conda activate
- changing the environment to default base
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"description": "# mle-training\n\n# Median housing value prediction\n\nThe 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. \n\nThe following techniques have been used: \n\n - Linear regression\n - Decision Tree\n - Random Forest\n\n## Steps performed\n - We prepare and clean the data. We check and impute for missing values.\n - Features are generated and the variables are checked for correlation.\n - Multiple sampling techinuqies are evaluated. The data set is split into train and test.\n - All the above said modelling techniques are tried and evaluated. The final metric used to evaluate is mean squared error.\n\n## To excute the script\n### conda create --name <env-name> biopython\n - script for creating a new environment\n\n### conda activate <env-name>\n - activating the new environment\n\n - installing necessary packages\n\n### python < scriptname.py >\n - command to run the script\n\n### conda env export <env-name> > <filename.yml>\n - exporting environment\n\n### conda activate\n - changing the environment to default base\n",
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