input: Write a function that describes a task and writes a response that appropriately completes the request. The function should be named `describe_task` and takes no parameters. It returns the complete response as a string.
  assert describe_task()==("Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\nWrite a script in Python to compete in a Kaggle competition.\n\n### Input:\nNot applicable\n\n### Output:\n# Load the necessary libraries\nimport pandas as pd\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score\n\n# Read in the data\ntrain_data = pd.read_csv(\"data.csv\")\n\n# Split the data into X (predictors) and y (response)\nX = train_data.drop('response', axis=1)\ny = train_data['response']\n\n# Split into training and testing sets\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)\n\n# Define the model and fit it\nmodel = ...\nmodel.fit(X_train, y_train)\n\n# Make predictions on the test set\ny_pred = model.predict(X_test)\n\n# Score the prediction\nscore = accuracy_score(y_test, y_pred)\n\n# Submit the prediction to Kaggle\n# ...")
  assert describe_task()=="Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\nWrite a script in Python to compete in a Kaggle competition.\n\n### Input:\nNot applicable\n\n### Output:\n# Load the necessary libraries\nimport pandas as pd\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score\n\n# Read in the data\ntrain_data = pd.read_csv(\"data.csv\")\n\n# Split the data into X (predictors) and y (response)\nX = train_data.drop('response', axis=1)\ny = train_data['response']\n\n# Split into training and testing sets\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)\n\n# Define the model and fit it\nmodel = ...\nmodel.fit(X_train, y_train)\n\n# Make predictions on the test set\ny_pred = model.predict(X_test)\n\n# Score the prediction\nscore = accuracy_score(y_test, y_pred)\n\n# Submit the prediction to Kaggle\n# ..."
output: def describe_task():
      return "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\nWrite a script in Python to compete in a Kaggle competition.\n\n### Input:\nNot applicable\n\n### Output:\n# Load the necessary libraries\nimport pandas as pd\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score\n\n# Read in the data\ntrain_data = pd.read_csv(\"data.csv\")\n\n# Split the data into X (predictors) and y (response)\nX = train_data.drop('response', axis=1)\ny = train_data['response']\n\n# Split into training and testing sets\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)\n\n# Define the model and fit it\nmodel = ...\nmodel.fit(X_train, y_train)\n\n# Make predictions on the test set\ny_pred = model.predict(X_test)\n\n# Score the prediction\nscore = accuracy_score(y_test, y_pred)\n\n# Submit the prediction to Kaggle\n# ..."