{
  "fetched_at": "2026-10-19T09:35:12Z",
  "source_url": "https://www.kaggle.com/competitions/cmpe188image-classification/data",
  "pages": [
    {
      "name": "data-description",
      "content": "This is image data of Natural Scenes around the world.\nThis Data contains images of size 150x150 distributed under 6 categories.\n{'buildings' -> 0,\n'forest' -> 1,\n'glacier' -> 2,\n'mountain' -> 3,\n'sea' -> 4,\n'street' -> 5 }",
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    },
    {
      "name": "Description",
      "content": "The goal is to create a Classification Model for Images. The task would be the same as a typical Data Science Project, Data Preprocessing, Feature Engineering, Building a Model, Tuning a Model, and predicting the Image classes on the Test cases.\n\nTask:\n- Build [KNN](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html) and [SVM](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html) models and compare their results.\n- Choose one of the tuned models for the final submission.\n- Tuning of Parameters: \nUse [Grid Search](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html) for SVM. \nSome List of [Proximity measures](https://www.kdnuggets.com/2020/11/most-popular-distance-metrics-knn.html) for consideration for KNN\n- Show the Plots for parameter tuning on training or validation sets.\n\nSuggested Workflow:\n1. Read the data, Python Libraries to consider: Matplotlib, PIL, cv2 or any\n2. Preprocess the data (Sklearn library)\n    * [Data Split](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html)\n    * Flatten Files\n    * [[Dimensionality Reduction]](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html)\n    * Image Augmentation?, not necessary!\n[Article](https://towardsdatascience.com/data-augmentation-techniques-in-python-f216ef5eed69) describing the different augmentation techniques.\n    * any other\n3. Model initiation and training.\n4. Model tuning.\n5. Comparisons.",
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      "postTitle": "",
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    },
    {
      "name": "Evaluation",
      "content": "The evaluation pages describe how submissions will be scored and how students should format their submissions. You don't need a sub-title at the top; the page title appears above. For an example of a high quality  evaluation page, see the 2013 KDD Cup page, which we have included below:\n\n---\n\nThe evaluation metric for this competition is accuracy: The number of right classified images divided by the total number of images.\n\n## Submission Format\n\n\n\n\nThe file should be a csv file and have the following format:\n```\nId, Category\n```",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "rules",
      "content": "This is a page where you can include rules that participants must accept before joining. You may wish to include rules like:\n - Don't cheat!\n - Apply yourself!\n - Have fun!",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
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  ]
}