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        "author": "Yash Lamba",
        "author_email": "yashlamba2000@gmail.com",
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            "Development Status :: 3 - Alpha",
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            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3 :: Only",
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        "description": "# DFFML Models For scikit / sklearn\n\n## About\n\nModels created using scikit.\n\n## Install\n\n```console\n$ python3 -m pip install --user dffml-model-scikit\n```\n\n## Usage\n\n1. Linear Regression Model\n\nFor implementing linear regression to a dataset, let us take a simple example:\n\n| Years of Experience  |  Expertise | Trust Factor | Salary |\n| -------------------- | ---------- | ------------ | ------ |\n|          0           |     01     |      0.2     |   10   |\n|          1           |     03     |      0.4     |   20   |\n|          2           |     05     |      0.6     |   30   |\n|          3           |     07     |      0.8     |   40   |\n|          4           |     09     |      1.0     |   50   |\n|          5           |     11     |      1.2     |   60   |\n\n```console\n$ cat > train.csv << EOF\nYears,Expertise,Trust,Salary\n0,1,0.2,10\n1,3,0.4,20\n2,5,0.6,30\n3,7,0.8,40\nEOF\n$ cat > test.csv << EOF\nYears,Expertise,Trust,Salary\n4,9,1.0,50\n5,11,1.2,60\nEOF\n$ dffml train \\\n    -model scikitlr \\\n    -model-features Years:int:1 Expertise:int:1 Trust:float:1 \\\n    -model-predict Salary \\\n    -model-directory tempdir \\\n    -sources f=csv \\\n    -source-filename train.csv \\\n    -source-readonly \\\n    -log debug\n$ dffml accuracy \\\n    -model scikitlr \\\n    -model-features Years:int:1 Expertise:int:1 Trust:float:1 \\\n    -model-predict Salary \\\n    -model-directory tempdir \\\n    -sources f=csv \\\n    -source-filename test.csv \\\n    -source-readonly \\\n    -log debug\n$ echo -e 'Years,Expertise,Trust\\n6,13,1.4\\n' | \\\n  dffml predict all \\\n    -model scikitlr \\\n    -model-features Years:int:1 Expertise:int:1 Trust:float:1 \\\n    -model-predict Salary \\\n    -model-directory tempdir \\\n    -sources f=csv \\\n    -source-filename /dev/stdin \\\n    -source-readonly \\\n    -log debug\n```\n\n## License\n\nScikit Models are distributed under the terms of the\n[MIT License](LICENSE).\n\n\n",
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