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  "fetched_at": "2026-10-20T05:30:56Z",
  "source_url": "https://www.kaggle.com/competitions/SSNAS/data",
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      "content": "#Overview\n\nThe data is related with direct marketing campaigns of a banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be (or not) subscribed.\n\nThe data has been split into two groups:\n\n- training set (train.csv)\n- test set (test.csv)\n\nThe training set should be used to build your machine learning models. For the training set, we provide the outcome (also known as the “ground truth”) for each client. \n\nThe test set should be used to see how well your model performs on unseen data. For the test set, we do not provide the ground truth for each client. It is your job to predict these outcomes. For each client in the test set, use the model you trained to predict whether or not they subscribed to the term deposit.\n\nWe also include sample_submission.csv, as an example of what a submission file should look like.\n\n## Files\n\n*   **bank_train_as.csv** - the training set\n*   **bank_test_as.csv** - the test set\n*   **sample_submission.csv** - a sample submission file in the correct format\n\n## Columns\n\n*   `id` - ID\n*   `age` - Age of the clients\n*   `type_job` - The job type of the clients\n*   `married_status` - Marital status of clients\n*   `level_education` - Education level of clients\n*   `credit_def` - has credit in default?\n*   `bal` - average yearly balance, in euros\n*   `loan_housing` - has housing loan?\n*   `loan_personal` - has personal loan?\n*   `type_contact` - contact communication type\n*   `last_contact_day` - last contact day of the month\n*   `month` - last contact month of year\n*   `duration` - last contact duration, in seconds\n*   `campaign_now` - number of contacts performed during this campaign and for this client\n*   `campaign_prev` - number of contacts performed before this campaign and for this client \n*   `term_deposit` - has the client subscribed a term deposit?",
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      "content": "# Welcome to Round 2!\n\nCongrats on finishing the first round successfully! Now buckle up and get ready for a classification task.\n\n## Task:   \n  \nThe marketing campaigns of a banking institution were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be (or not) subscribed.\n\nOur objective is to predict if the client will subscribe (yes/no) to a term deposit, by building clclassification model.",
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      "name": "Evaluation",
      "content": "##Goal\nThe goal is to predict if the client will subscribe (yes/no) to a term deposit.\n\n##Metric\nYour score is the percentage of clients you correctly predict. This is known as accuracy.\n\n##Submission File Format\nYou should submit a csv file with exactly 1116 entries plus a header row. Your submission will show an error if you have extra columns (id and term_deposit) or rows.\n\nThe file should have exactly 2 columns:\n\nid (sorted in any order)\nterm_deposit (contains your binary predictions: yes, no)",
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      "name": "rules",
      "content": "### Rules:  \n  \n1. Ensure you have read the details in [Data](https://www.kaggle.com/c/SSNAS/data), [Description](https://www.kaggle.com/c/SSNAS/overview/description) and [Evaluation](https://www.kaggle.com/c/SSNAS/overview/evaluation).  \n2. Submit your solution by clicking on the black button [**Submit Predictions**.](https://www.kaggle.com/c/SSNAS/submit)  \n3. After submitting the solution share your code publicly under [Code](https://www.kaggle.com/c/SSNAS/code) tab.   \n4. Your time limit is 1hr.  ",
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