housing-library-5503


Namehousing-library-5503 JSON
Version 0.1 PyPI version JSON
download
home_page
SummarySample code for coding practice
upload_time2023-10-30 09:28:21
maintainer
docs_urlNone
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.
            # 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 >

            

Raw data

            {
    "_id": null,
    "home_page": "",
    "name": "housing-library-5503",
    "maintainer": "",
    "docs_url": null,
    "requires_python": ">=3.11",
    "maintainer_email": "",
    "keywords": "housing,data training",
    "author": "",
    "author_email": "VSriHarshitha5503 <harshitha.vadapa@tigeranalytics.com>",
    "download_url": "https://files.pythonhosted.org/packages/35/c9/83ee4ec5584aa55f8196914b8a268445b3fc5473fd9de4419c1a5b47381e/housing_library_5503-0.1.tar.gz",
    "platform": null,
    "description": "# 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\npython < scriptname.py >\n",
    "bugtrack_url": null,
    "license": "",
    "summary": "Sample code for coding practice",
    "version": "0.1",
    "project_urls": {
        "Homepage": "https://github.com/VSriHarshitha5503/mle-training"
    },
    "split_keywords": [
        "housing",
        "data training"
    ],
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "6d29366f7d39b7670758a224a52f30ff27ba64090b311edd96f247aaf6a84851",
                "md5": "80fe0f10359527159fdfa4311a2ec245",
                "sha256": "8aba67ef7797a73ed7e621981974866717dc9a861f52b1da8bcf004fd812264c"
            },
            "downloads": -1,
            "filename": "housing_library_5503-0.1-py3-none-any.whl",
            "has_sig": false,
            "md5_digest": "80fe0f10359527159fdfa4311a2ec245",
            "packagetype": "bdist_wheel",
            "python_version": "py3",
            "requires_python": ">=3.11",
            "size": 8596,
            "upload_time": "2023-10-30T09:28:18",
            "upload_time_iso_8601": "2023-10-30T09:28:18.736465Z",
            "url": "https://files.pythonhosted.org/packages/6d/29/366f7d39b7670758a224a52f30ff27ba64090b311edd96f247aaf6a84851/housing_library_5503-0.1-py3-none-any.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "35c983ee4ec5584aa55f8196914b8a268445b3fc5473fd9de4419c1a5b47381e",
                "md5": "c3a695de3f7d1b06a1d35f688d9676f9",
                "sha256": "9939f3b16957d38f61304e55c4ad3d6710b8ebb1dcf671179b9140d99a80d892"
            },
            "downloads": -1,
            "filename": "housing_library_5503-0.1.tar.gz",
            "has_sig": false,
            "md5_digest": "c3a695de3f7d1b06a1d35f688d9676f9",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": ">=3.11",
            "size": 6236,
            "upload_time": "2023-10-30T09:28:21",
            "upload_time_iso_8601": "2023-10-30T09:28:21.188822Z",
            "url": "https://files.pythonhosted.org/packages/35/c9/83ee4ec5584aa55f8196914b8a268445b3fc5473fd9de4419c1a5b47381e/housing_library_5503-0.1.tar.gz",
            "yanked": false,
            "yanked_reason": null
        }
    ],
    "upload_time": "2023-10-30 09:28:21",
    "github": true,
    "gitlab": false,
    "bitbucket": false,
    "codeberg": false,
    "github_user": "VSriHarshitha5503",
    "github_project": "mle-training",
    "github_not_found": true,
    "lcname": "housing-library-5503"
}
        
Elapsed time: 2.55520s