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  "fetched_at": "2026-10-20T07:09:42Z",
  "source_url": "https://www.kaggle.com/competitions/ai-and-ml-level-1-kaggle-competition/data",
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      "name": "data-description",
      "content": "This page appears alongside the data files. It describes what files have been provided and the format of each. There is no single format for this page that is appropriate for all competitions, but you should strive to describe as much as you can here. A little time spent describing the data here can save a lot of time answering questions later.\n\nParticipants should be able to answer these types of questions after reading the data description:\n\nWhat files do I need?  \nWhat should I expect the data format to be?  \nWhat am I predicting?  \nWhat acronyms will I encounter?\n\n## Files\n\n*   **train.csv** - the training set\n*   **test.csv** - the test set\n*   **sample_submission.csv** - a sample submission file in the correct format\n*   **metaData.csv** - supplemental information about the data\n\n## Columns\n\n*   `example_id` - definition of example_id\n*   `feature_1` - definition of feature_1\n*   etc.\n\nAbout Dataset\nUncover the factors that lead to employee attrition and explore important questions such as ‘show me a breakdown of distance from home by job role and attrition’ or ‘compare average monthly income by education and attrition’. This is a fictional data set created by IBM data scientists.\n\nEducation\n1 'Below College'\n2 'College'\n3 'Bachelor'\n4 'Master'\n5 'Doctor'\n\nEnvironmentSatisfaction\n1 'Low'\n2 'Medium'\n3 'High'\n4 'Very High'\n\nJobInvolvement\n1 'Low'\n2 'Medium'\n3 'High'\n4 'Very High'\n\nJobSatisfaction\n1 'Low'\n2 'Medium'\n3 'High'\n4 'Very High'\n\nPerformanceRating\n1 'Low'\n2 'Good'\n3 'Excellent'\n4 'Outstanding'\n\nRelationshipSatisfaction\n1 'Low'\n2 'Medium'\n3 'High'\n4 'Very High'\n\nWorkLifeBalance\n1 'Bad'\n2 'Good'\n3 'Better'\n4 'Best'",
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      "content": "About Dataset\nUncover the factors that lead to employee attrition and explore important questions such as ‘show me a breakdown of distance from home by job role and attrition’ or ‘compare average monthly income by education and attrition’. This is a fictional data set created by IBM data scientists.\n\nEducation\n1 'Below College'\n2 'College'\n3 'Bachelor'\n4 'Master'\n5 'Doctor'\n\nEnvironmentSatisfaction\n1 'Low'\n2 'Medium'\n3 'High'\n4 'Very High'\n\nJobInvolvement\n1 'Low'\n2 'Medium'\n3 'High'\n4 'Very High'\n\nJobSatisfaction\n1 'Low'\n2 'Medium'\n3 'High'\n4 'Very High'\n\nPerformanceRating\n1 'Low'\n2 'Good'\n3 'Excellent'\n4 'Outstanding'\n\nRelationshipSatisfaction\n1 'Low'\n2 'Medium'\n3 'High'\n4 'Very High'\n\nWorkLifeBalance\n1 'Bad'\n2 'Good'\n3 'Better'\n4 'Best'",
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      "name": "Evaluation",
      "content": "Accuracy score is a commonly used metric in machine learning to measure the performance of a classification model. It calculates the ratio of correctly predicted instances to the total number of instances in the dataset. In other words, accuracy measures how often the model's predictions are correct.\n\nThe formula for accuracy score is:\n\n```\nAccuracy = (Number of Correct Predictions) / (Total Number of Predictions)\n```\n\nHere's a breakdown of the components:\n\n- **Number of Correct Predictions:** This is the count of instances for which the model's predictions match the actual labels in the dataset.\n\n- **Total Number of Predictions:** This is the total number of instances in the dataset for which the model makes predictions.\n\nAccuracy is often expressed as a percentage, ranging from 0% (completely inaccurate) to 100% (perfectly accurate). A higher accuracy score indicates that the model is making more correct predictions.\n\nIt's important to note that accuracy is a useful metric for evaluating classification models when the classes in the dataset are balanced. In other words, when each class occurs with roughly the same frequency. However, in situations where class imbalance exists, accuracy can be misleading. For imbalanced datasets, other metrics like precision, recall, F1-score, and area under the ROC curve (AUC-ROC) may provide a more comprehensive view of model performance.\n\nIn summary, accuracy score is a straightforward metric for evaluating the overall performance of a classification model, but it should be used in conjunction with other metrics, especially when dealing with imbalanced datasets or when certain types of errors are more costly or critical than others.",
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      "name": "rules",
      "content": "\nOne account per participant\nYou cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.\n\nNo private sharing outside teams\nPrivately sharing code or data outside of teams is not permitted. It's okay to share code if made available to all participants on the forums.\n\nTeam Mergers\nTeam mergers are allowed and can be performed by the team leader. In order to merge, the combined team must have a total submission count less than or equal to the maximum allowed as of the Team Merger Deadline. The maximum allowed is the number of submissions per day multiplied by the number of days the competition has been running.",
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      "name": "abstract",
      "content": "Creating an overview for a Kaggle competition topic like \"IBM HR Analytics Employee Attrition & Performance\" is an essential step to provide participants with a clear understanding of the problem, the dataset, and the goals of the competition. Below is an example of an overview for this competition:\n\n---\n\n## IBM HR Analytics Employee Attrition & Performance\n\n### Competition Overview\n\n**Introduction:**\nWelcome to the IBM HR Analytics Employee Attrition & Performance competition! In today's dynamic business landscape, understanding and managing employee attrition and performance is crucial for organizations to thrive. This competition presents you with a real-world scenario faced by HR professionals and data scientists alike – predicting and understanding employee attrition while optimizing performance.\n\n**Objective:**\nThe primary objective of this competition is to develop predictive models that can help HR departments identify factors contributing to employee attrition and make informed decisions to retain valuable talent. Additionally, participants are encouraged to explore strategies for enhancing employee performance and engagement.\n\n**Dataset:**\nThe dataset provided for this competition is based on HR data collected by IBM, containing a rich set of features related to employees, including demographics, job role, work environment, and performance indicators. Participants will have access to historical data on employee attrition, which can be used to build and validate predictive models.\n\n**Challenges:**\nParticipants are faced with several challenges in this competition, including but not limited to:\n\n1. **Class Imbalance:** Dealing with imbalanced classes is a common issue in employee attrition prediction. You'll need to develop techniques to handle this imbalance effectively.\n\n2. **Feature Engineering:** Extracting meaningful insights from the dataset and engineering relevant features can significantly impact model performance.\n\n3. **Interpretability:** Understanding which factors influence attrition and performance is crucial for actionable insights. Participants are encouraged to provide model interpretations.\n\n**Evaluation:**\nSubmissions will be evaluated based on their predictive accuracy using a relevant evaluation metric. Detailed information about the evaluation metric can be found in the competition guidelines.\n\n**Why Participate:**\nBy participating in this competition, you'll have the opportunity to:\n\n- Gain practical experience in HR analytics and employee attrition prediction.\n- Contribute to solutions that can benefit organizations by reducing attrition rates and improving employee performance.\n- Showcase your data science skills to a global community of data enthusiasts and potential employers.\n\n**Get Started:**\nTo get started, familiarize yourself with the dataset, explore the problem statement, and start building your predictive models. Don't forget to engage with the Kaggle community and share your insights and approaches.\n\nWe wish you the best of luck in the IBM HR Analytics Employee Attrition & Performance competition. Let's work together to make workplaces better and more productive for employees and organizations!\n\n---\n\nFeel free to customize and expand upon this overview to provide additional details specific to your competition. It's essential to provide clear information to participants so they can effectively tackle the challenges presented in the competition.",
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