| Name | housing-library-5494 JSON |
| Version |
0.1
JSON |
| download |
| home_page | |
| Summary | Sample code for coding practice |
| upload_time | 2023-10-31 11:38:48 |
| 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.
|
# README.md
## 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 execute the script
```python nonstandardcode.py```
## To activate conda environment
```conda activate mle-dev```
## To create environment from yml file
```conda env create --name mle-dev --file=env.yml```
## Command to install isort in conda environment
```conda install isort```
## Command to install black in conda environment
```conda install black```
## Command to install flake8 in conda environment
```conda install flake8```
## Command to refactor python code with isort
``` isort nonstandardcode.py```
## Command to refactor python code with black
``` black nonstandardcode.py```
## Command to refactor python code with flake8
``` flake8 nonstandardcode.py```
Raw data
{
"_id": null,
"home_page": "",
"name": "housing-library-5494",
"maintainer": "",
"docs_url": null,
"requires_python": ">=3.11",
"maintainer_email": "",
"keywords": "housing,data training",
"author": "",
"author_email": "shannu2205 <shanmukha.nallur@tigeranalytics.com>",
"download_url": "https://files.pythonhosted.org/packages/be/7b/025faaaec5ff7ba60b128f2f5a86f3b3ce05310a70355dbd19953291d2ae/housing_library_5494-0.1.tar.gz",
"platform": null,
"description": "\n# README.md\n\n## Median housing value prediction\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 execute the script\n```python nonstandardcode.py```\n\n## To activate conda environment\n```conda activate mle-dev```\n\n## To create environment from yml file\n```conda env create --name mle-dev --file=env.yml```\n\n## Command to install isort in conda environment\n```conda install isort```\n\n## Command to install black in conda environment\n```conda install black```\n\n## Command to install flake8 in conda environment\n```conda install flake8```\n\n## Command to refactor python code with isort \n``` isort nonstandardcode.py```\n\n## Command to refactor python code with black\n``` black nonstandardcode.py```\n\n## Command to refactor python code with flake8\n``` flake8 nonstandardcode.py```\n\n",
"bugtrack_url": null,
"license": "",
"summary": "Sample code for coding practice",
"version": "0.1",
"project_urls": {
"Homepage": "https://github.com/shannu2205/mle-training"
},
"split_keywords": [
"housing",
"data training"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "063c04711fe5684404832f263606c2b4615db14cd88ebe7bb74c75c96eeaccd6",
"md5": "b0e4f3d281a9c643f03781fec69c2130",
"sha256": "c77422559c6881ba76b1410173324842e9f1636127551f7b00562458f7088b3a"
},
"downloads": -1,
"filename": "housing_library_5494-0.1-py3-none-any.whl",
"has_sig": false,
"md5_digest": "b0e4f3d281a9c643f03781fec69c2130",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.11",
"size": 6455,
"upload_time": "2023-10-31T11:38:46",
"upload_time_iso_8601": "2023-10-31T11:38:46.312946Z",
"url": "https://files.pythonhosted.org/packages/06/3c/04711fe5684404832f263606c2b4615db14cd88ebe7bb74c75c96eeaccd6/housing_library_5494-0.1-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "be7b025faaaec5ff7ba60b128f2f5a86f3b3ce05310a70355dbd19953291d2ae",
"md5": "ff8f5adaa90acff7581a6151589e8923",
"sha256": "dfb42d7da6b35c1fdd48002e9a64434935b892dddba0fd21fe73793489788548"
},
"downloads": -1,
"filename": "housing_library_5494-0.1.tar.gz",
"has_sig": false,
"md5_digest": "ff8f5adaa90acff7581a6151589e8923",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.11",
"size": 5522,
"upload_time": "2023-10-31T11:38:48",
"upload_time_iso_8601": "2023-10-31T11:38:48.044538Z",
"url": "https://files.pythonhosted.org/packages/be/7b/025faaaec5ff7ba60b128f2f5a86f3b3ce05310a70355dbd19953291d2ae/housing_library_5494-0.1.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2023-10-31 11:38:48",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "shannu2205",
"github_project": "mle-training",
"github_not_found": true,
"lcname": "housing-library-5494"
}
