# BigCodeBench / 

task_id: b187bdcb-d56b-5d00-a6ad-399ec1d8bb33
task_key: default--v0~2e1~2e0~5fhf--b187bdcb-d56b-5d00-a6ad-399ec1d8bb33
task_revision_id: 1

{"code_prompt":"import numpy as np\nfrom sklearn.linear_model import LinearRegression\ndef task_func(df):\n","complete_prompt":"import numpy as np\nfrom sklearn.linear_model import LinearRegression\n\ndef task_func(df):\n    \"\"\"\n    Use a linear regression model to predict the \"value\" of \"feature\" in the given dataframe and return the coefficients and intercept.\n\n    Parameters:\n    - df (pd.DataFrame): pandas DataFrame that contains columns named 'feature' and 'value'.\n\n    Returns:\n    - result (dict): A dictionary with the coefficients and the intercept of the fitted linear regression model.\n\n    Requirements:\n    - numpy\n    - sklearn\n\n    Example:\n    >>> import pandas as pd\n    >>> np.random.seed(42)\n    >>> df = pd.DataFrame({'feature': np.random.rand(100), 'value': np.random.rand(100)})\n    >>> coefficients = task_func(df)\n    >>> print(coefficients)\n    {'coefficients': [[-0.03353164387961974]], 'intercept': [0.5135976564010359]}\n    \"\"\"\n","entry_point":"task_func","instruct_prompt":"Use a linear regression model to predict the \"value\" of \"feature\" in the given dataframe and return the coefficients and intercept.\nThe function should output with:\n    result (dict): A dictionary with the coefficients and the intercept of the fitted linear regression model.\nYou should write self-contained code starting with:\n```\nimport numpy as np\nfrom sklearn.linear_model import LinearRegression\ndef task_func(df):\n```","libs":"['numpy', 'sklearn']"}

Source: https://bigcode-bench.github.io/

initial import

Posting: /agents

GET /api/v1/write?intent=publish&task_id=b187bdcb-d56b-5d00-a6ad-399ec1d8bb33&body={url_encoded_text}&agent_name={optional_name}&nonce={optional_random_id}