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        "author": "Yash Lamba",
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            "Natural Language :: English",
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        "description": "# DFFML Scratch Models\n\n## About\n\nModels created without a machine learning framework.\n\n## Install\n\n```console\n$ python3 -m pip install --user dffml-model-scratch\n```\n\n## Usage\n\nIf we have a dataset of years of experience in a job and the Salary (in\nthousands) at that job we can use the Simple Linear Regression model to predict\na salary given the years of experience (or the other way around).\n\nFirst we create the file containing the dataset. Then we train the model, get\nits accuracy. And using `echo` pipe a new csv file of data to predict into the\nmodel, and it will give us it prediction of the Salary.\n\n```console\n$ cat > dataset.csv << EOF\nYears,Salary\n1,40\n2,50\n3,60\n4,70\n5,80\nEOF\n$ dffml train -model scratchslr -model-features Years:int:1 -model-predict Salary -model-directory tempdir -sources f=csv -source-filename dataset.csv -source-readonly -log debug\n$ dffml accuracy -model scratchslr -model-features Years:int:1 -model-predict Salary -model-directory tempdir -sources f=csv -source-filename dataset.csv -source-readonly -log debug\n1.0\n$ echo -e 'Years,Salary\\n6,0\\n' | dffml predict all -model scratchslr -model-features Years:int:1 -model-predict Salary -model-directory tempdir -sources f=csv -source-filename /dev/stdin -source-readonly -log debug\n[\n    {\n        \"extra\": {},\n        \"features\": {\n            \"Salary\": 0,\n            \"Years\": 6\n        },\n        \"last_updated\": \"2019-07-19T09:46:45Z\",\n        \"prediction\": {\n            \"confidence\": 1.0,\n            \"value\": 90.0\n        },\n        \"key\": \"0\"\n    }\n]\n```\n\n## License\n\nScratch Models are distributed under the terms of the [MIT License](LICENSE).\n\n\n",
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