| Name | housingLibrary-5508 JSON |
| Version |
0.1
JSON |
| download |
| home_page | |
| Summary | Sample code for coding practice |
| upload_time | 2024-01-11 18:02:41 |
| 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.
|
#This change is for the Pull request
# 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.
##Packaging python code and test it using score.py
>> python -m build (Create a .tgz and .whl files of our project for the packing)
>> twine upload dist.* (Upload our packaging files to the pypi to make it globally accesable to all users)
-> Inorder to do upload the files to pypi we need to create an account in the pypi and generate and token.
## How to install the package and import the methods
1. Install the package name = housing-library-5512==0.1
>> pip install housing-library-5512==0.1
2. In code file use these statements to import the required methods.
>> from src import fetch_housing_data, load_housing_data
## Testing with sample code for the package
-> We tested it by running score.py file to test the package
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"description": "#This change is for the Pull request\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 ##Packaging python code and test it using score.py\n >> python -m build (Create a .tgz and .whl files of our project for the packing)\n >> twine upload dist.* (Upload our packaging files to the pypi to make it globally accesable to all users)\n -> Inorder to do upload the files to pypi we need to create an account in the pypi and generate and token.\n\n## How to install the package and import the methods\n1. Install the package name = housing-library-5512==0.1 \n >> pip install housing-library-5512==0.1\n2. In code file use these statements to import the required methods.\n >> from src import fetch_housing_data, load_housing_data\n\n## Testing with sample code for the package\n-> We tested it by running score.py file to test the package\n",
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