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  "source_url": "https://www.kaggle.com/competitions/cs506-lab-defective-cookie-detection/data",
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      "name": "data-description",
      "content": "The dataset is derived from cookies I made for a birthday party and my friends who assisted in measuring sensory data.\n\n## Files\n\n*   **cookie_train.csv** - the training set\n*   **cookie_test.csv** - the test set\n\n## Data Format\n\nBelow are the features in the dataset and their measurement techniques:\n\n* **Texture Hardness**: This feature is a measure of how hard or soft the cookie is. This is measured using a penetrometer, and the possible values are in Newtons (N).\n\n* **Texture Chewiness**: This feature is a measure of how much force is required to chew the cookie. This is measured using a texture analyzer, and the possible values are in N.\n\n* **Texture Crispiness**: This feature is a measure of how crispy or crunchy the cookie is. This is measured using an acoustic measurement system, and the possible values are in arbitrary units.\n\n* **Color L***: This feature is a measure of the lightness of the cookie. This is measured using a colorimeter, and the possible values are between 0 (black) and 100 (white).\n\n* **Color a***: This feature is a measure of the redness or greenness of the cookie. This is measured using a colorimeter, and the possible values are between -128 (green) and 127 (red).\n\n* **Color b***: This feature is a measure of the yellowness or blueness of the cookie. This is measured using a colorimeter, and the possible values are between -128 (blue) and 127 (yellow).\n\n* **Taste Sweetness**: This feature is a measure of how sweet the cookie tastes. This is measured using a sensory evaluation method, and the possible values are on a scale from 0 (not sweet) to 10 (extremely sweet).\n\n* **Taste Saltiness**: This feature is a measure of how salty the cookie tastes. This is measured using a sensory evaluation method, and the possible values are on a scale from 0 (not salty) to 10 (extremely salty).\n\n* **Taste Bitterness**: This feature is a measure of how bitter the cookie tastes. This is measured using a sensory evaluation method, and the possible values are on a scale from 0 (not bitter) to 10 (extremely bitter).\n\n* **Shape Diameter**: This feature is a measure of the diameter of the cookie. This is measured using a caliper, and the possible values are in millimeters (mm).\n\n* **Shape Thickness**: This feature is a measure of the thickness of the cookie. This is measured using a caliper, and the possible values are in mm.\n\n* **Smell Intensity**: This feature is a measure of how strong the cookie smells. This is measured using a sensory evaluation method, and the possible values are on a scale from 0 (no smell) to 10 (extremely strong smell).\n\n* **Smell Complexity**: This feature is a measure of how complex the aroma of the cookie is. This is measured using a sensory evaluation method, and the possible values are on a scale from 0 (no complexity) to 10 (extremely complex aroma).\n\n* **Smell Specific Compound**: This feature is a measure of the presence and intensity of a specific aroma compound in the cookie. This is measured using gas chromatography-mass spectrometry (GC-MS), and the possible values are in arbitrary units.\n\n* **Detected Chemical**: This feature is a measure of what prevalent chemical was detected in the cookie, measured using chromatography. The possible values are just the chemical name.\n\n* **Defective**: This is the target variable that indicates whether the cookie is defective or not. The possible values are 0 (not defective) and 1 (defective).",
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      "name": "Description",
      "content": "In this competition, you will assist Rico Bisquito's cookie company from Homework 0 to help the workers determine if a cookie is defective or not. Here, I have defined defective as a cookie that has a quality issue which thus makes them unsuitable for consumption or sale.\n\n\nIn the class' github repository, you will see in this lab there is a `guide.ipynb` to help guide through you making a submission to kaggle and to see the procedure to generate a prediction. ",
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      "name": "Evaluation",
      "content": "The competition's leaderboard will be ranked by simple classification accuracy.",
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      "name": "rules",
      "content": "Have fun with it and **no neural networks can be used**! As a pull request to your lab, just submit your notebook file made in the lab that you edited to make a prediction. Also this is individual --- no teams!",
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