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  "fetched_at": "2026-10-19T22:35:16Z",
  "source_url": "https://www.kaggle.com/competitions/final-project-adult-census-income-classification/data",
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      "name": "data-description",
      "content": "This page shows up underneath the data files. The data comes from https://www.kaggle.com/uciml/adult-census-income, and was cleaned to remove missing entries. \n\n## Files\n\n*   **training.csv** - the training set\n*   **testing.csv** - the test set\n*   **zero_solution_0_1.csv** - a sample submission file in the correct format. You should submit your solution to this platform in the same format, i.e., The values in \"income\" column should be \"0\" or \"1\", by converting \"<=50K\" to \"0\" and \">50K\" to \"1\".   \n*   **tree_solution_0_1.csv** - a sample submission which is predicted by a decision tree model.  \n*   **overfit_solution_0_1.csv** - a sample submission which is predicted by a overfit decision tree model.  \n\n",
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      "name": "Description",
      "content": "Objective: To understand the basic concepts of classification such as general process of a classification task, training/test data, model validation, overfitting, model selection, evaluation measures, etc.. By designing experiments and comparing the performance of different classifiers, to understand each advanced classification technique and its advantages.  \n\n# Instructions\n\n### 1. Select several classifiers that you think appropriate on this classification task. You are suggested to try at least one from each category: \nCategory 1: KNN, Decision Trees, Naive Bayes\nCategory 2: SVM, AdaBoost, RandomForest, Neural Network\n\nDon´t program the algorithms. Use Python, R, Matlab, etc..\n\n### 2. Train the classifiers using the training data provided in this page. Predict labels for the testing set, and upload your predictions for the classifiers by April 15, 2024.\n\nYou will need to upload each classifier's predictions in a separate submission. Please title your submissions to reflect which algorithm was used, and specify the algorithm in the submission description.\n\n\n### 3. Write project report (no less than 5 pages). \nYou are suggested to use the following template (not obligated, but at least include all the following elements): \n\n\n****\nI.\tIntroduction \nThis section includes introduction of classification, the algorithms you used in the project, evaluation metrics.  Remember mention the objective of the project. \nII.\tData sets description\nIn this section, characteristics of the data set such as size, number and type of attributes, classes should be clearly described to present the problem you are solving. \nIII.\tClassifiers\nIntroduce the models/algorithms you used to solve the classification problem. Additionally, explain the reason why you chose them.  \nIV.\tExperiments and Results \nIntroduce the platform or toolbox you used. \nExperiment design, which includes parameter values, number of runs, evaluation measures, overfit problem addressing, etc.\nShow experiment results, comparisons, analysis and discussions. It is necessary to illustrate the results and comparisons in different ways such as table, figure, screenshots, etc. Remember make explanation on the figures and tables, don´t just leave them there without any comments in the context. \nV.\tConclusion\nConclude the project, discuss the limitations and possible improving methods.\nVI.\tReferences\nPut the references you took in the project design and report writing, including books, papers, websites of data source, software documents, etc. \n****\n  \n### 4. Deliver the project report by April 15, 2024. \n\n\n### 5. Prepare a 20-minutes presentation and present your project in the class on Wednesday April 17, 2024. ",
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      "name": "Evaluation",
      "content": "### Metric\nThe evaluation metric for this competition is Mean F1-Score. The F1 score, commonly used in information retrieval, measures accuracy using the statistics precision p and recall r. Precision is the ratio of true positives *tp* to all predicted positives *tp+fp*. Recall is the ratio of true positives *tp* to all actual positives *tp+fn*. The F1 score is given by:\n\n\\\\( F1 = 2\\frac{p \\cdot r}{p+r}\\ \\ \\mathrm{where}\\ \\ p = \\frac{tp}{tp+fp},\\ \\ r = \\frac{tp}{tp+fn} \\\\)\n\nThe F1 metric weights recall and precision equally, and a good retrieval algorithm will maximize both precision and recall simultaneously. Thus, moderately good performance on both will be favored over extremely good performance on one and poor performance on the other.\n\nMean F1-Score calculates F1 metrics for each label, and finds their average, weighted by support, i.e. the number of true instances for each label. Calculated with http://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html using average='weighted'.\nIn your report you are encouraged to show your results in various metrics such as precision,  accuracy, recall, etc.,  and explain the relations among them.  \n\n### Submission File\n\nFor each id in the test set, you must predict the target variable \"income\". The file should be in csv format and contain a header. See the sample submission \"zero_solution.csv\" under the Data tab for a complete example.",
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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!\n\nJajajajaja………………",
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    {
      "name": "abstract",
      "content": "Classification",
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