{
  "fetched_at": "2026-10-19T13:40:06Z",
  "source_url": "https://www.kaggle.com/competitions/classification-with-non-deep-classifiers/data",
  "pages": [
    {
      "name": "data-description",
      "content": "<p>The data files train.csv and test.csv contain feature vectors for 32K and 8K of instances, respectively. Each instance is represented by a 784 dimension vector in total. </p>\n\n<h2>  Training </h2>\n\nThe training data set, (train.csv), has 785 columns. The fist one called \"label\", is the label of each instance. The rest of the columns contain the values for each of the dimensions.\n\n<h2> Testing </h2>\n\nThe test data set, (test.csv), is the same as the training set, except that it does not contain the \"label\" column.\n\n\n\n\n\n\n",
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    {
      "name": "Description",
      "content": "<h2> Competition Description</h2>\n<p>In this competition, your goal is to correctly identify categories using the training dataset.  You are free to use everything you have learned in the course but the deep networks. We encourage you to leverage dimensionality reduction techniques and experiment with different algorithms to learn first-hand what works well and how techniques compare.</p>\n\n<h2>Practice Skills</h2>\n\nClassification methods such as SVM and K-nearest neighbors\n",
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    {
      "name": "Evaluation",
      "content": "<h2> Submission </h2>\nYour submission file should be in the following format: For each of the 8K instances in the test set, output a single line containing the \"**id**\" and the category you predict. For example, if you predict that the first instance label is 3, the second instance is 7, and the third instance is 8, then your submission file would look like this:\n\n![Alt text](https://uupload.ir/files/lmkd_p1.png)\n\n<p> You can use the following code to create a sample submission: </p>\n<pre>import pandas as pd\n\nsample_submission = pd.DataFrame(list(zip(test_img_names,y_pred)),columns=['id','label'])\n\nsample_submission.to_csv('sample_submission.csv', index=False)\n</pre>\n\n<p> <b>For more information regarding how to open the dataset and how to correctly save it please go through the code tab and take a look at \"starterkit.ipynb\" notebook</b></p>\n\n\n<h2> Evaluation</h2>\n\n<p> The evaluation metric for this contest is the categorization accuracy or the proportion of test images that are correctly classified. For example, a categorization accuracy of 0.97 indicates that you have correctly classified all but 3% of the instances. </p>",
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    {
      "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\n - Don't cheat!\n - Apply yourself!\n - Have fun!",
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