{ "fetched_at": "2026-10-24T17:51:01Z", "source_url": "https://www.kaggle.com/competitions/amit-ramadan-ai-challenge-1/data", "pages": [ { "name": "data-description", "content": "![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F26097785%2F47b21230ddc7973b2e4c17b58b6d97f9%2Finbox_26097785_772194a4c4b116e3249952af4cfc228a_Stats%20(4).jpg?generation=1772059847080517&alt=media)\n# 📂 Dataset Description\n\n## Bank Customer Churn Dataset\n\nThis dataset contains structured customer-level information collected from a retail banking institution. It is designed to simulate a real-world churn prediction scenario where financial behavior, demographic attributes, and account characteristics influence customer retention.\n\nThe objective is to predict whether a customer will leave the bank (churn) based on historical records.\n\n---\n\n## 📊 Dataset Files\n\n| File | Description |\n| -------------------------------- | ------------------------------------------------------------------ |\n| `Bank_Churn.csv` | Clean dataset used for modeling |\n| `Bank_Churn_Data_Dictionary.csv` | Detailed explanation of each feature |\n| `Bank_Churn_Messy.xlsx` | Raw / uncleaned version of the dataset (not required for modeling) |\n\nFor this competition, participants should use **Bank_Churn.csv** as the primary modeling dataset.\n\n---\n\n## 📈 Dataset Structure\n\nThe dataset consists of customer-level observations including:\n\n* Unique customer identifiers\n* Financial metrics\n* Demographic attributes\n* Account activity indicators\n* Target label indicating churn status\n\n### Target Variable\n\n| Column | Meaning |\n| --------------------- | ---------------------------- |\n| `churn` (or `Exited`) | 1 → Customer left the bank |\n| | 0 → Customer remained active |\n\nThis is a binary classification problem where the goal is to predict the probability of churn.\n\n---\n\n## 🔎 Feature Categories\n\nThe dataset includes a mix of:\n\n### 📌 Numerical Features\n\n* Credit score\n* Age\n* Account balance\n* Estimated salary\n* Tenure duration\n\n### 📌 Categorical Features\n\n* Geography\n* Gender\n\n### 📌 Binary Indicators\n\n* Has credit card\n* Is active member\n\n---\n\n## ⚠ Modeling Considerations\n\nParticipants should consider:\n\n* Proper encoding of categorical variables\n* Handling potential class imbalance\n* Removing non-informative identifiers (e.g., Customer ID, Row Number)\n* Preventing data leakage\n* Validating models using cross-validation\n\nThe dataset is suitable for:\n\n* Logistic Regression\n* Random Forest\n* Gradient Boosting Models (XGBoost, LightGBM, CatBoost)\n* Neural Networks\n\n---\n\n## 🧠 Real-World Context\n\nCustomer churn prediction in banking is a high-impact problem. Financial institutions rely on predictive models to:\n\n* Reduce customer attrition\n* Improve retention strategies\n* Optimize targeted marketing campaigns\n* Increase customer lifetime value\n\nThis dataset provides a realistic yet structured environment for building and evaluating machine learning models in a business context.\n\n---\n", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "rules", "content": "![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F26097785%2F6ac6e312be78fe24eab0ac5c8d750275%2FStats%20(13).jpg?generation=1772060042621299&alt=media)\n\n\n# 🏛 Official Competition Legal Framework\n\n## Customer Churn Prediction Competition\n\nOrganized by AMIT\n\n---\n\n## 1. Acceptance of Rules\n\nParticipation in this competition constitutes full and unconditional acceptance of these Official Competition Rules.\n\nBy registering and submitting an entry, participants agree to comply with:\n\n* All competition guidelines\n* Submission requirements\n* Evaluation criteria\n* Technical and ethical standards\n\nThese Rules form a binding agreement between the Participant and the Competition Sponsor.\n\nFailure to comply may result in disqualification.\n\n---\n\n## 2. Competition Sponsor\n\nThis competition is organized, managed, and administered by:\n\n**AMIT**\nEgypt\n\nAll decisions made by the Sponsor regarding interpretation of rules, eligibility, scoring disputes, or final rankings are final and binding.\n\n---\n\n## 3. Eligibility\n\n* Participation is open to individuals who register through the official competition platform.\n* Employees, contractors, and affiliates of the Competition Sponsor may participate but are not eligible to win prizes unless explicitly stated otherwise.\n* Participants may not register or compete using multiple accounts.\n\nViolation of account policies results in immediate disqualification.\n\n---\n\n## 4. Submission Requirements\n\nParticipants must:\n\n* Submit a valid `submission.csv`\n* Generate predictions programmatically\n* Avoid manual file editing\n* Ensure reproducibility through a Kaggle Notebook\n* Comply with all preprocessing, validation, and modeling rules\n\nHardcoded outputs, data leakage, reverse engineering, or manipulation of the evaluation system are strictly prohibited.\n\nParticipants are limited to **five (5) submissions per day**.\n\n---\n\n## 5. Evaluation & Ranking\n\nSubmissions are evaluated using:\n\n**ROC AUC (Area Under the ROC Curve)**\n\nFinal rankings are determined exclusively by the **Private Leaderboard score**.\n\nIn the event of identical Private Leaderboard scores, the Sponsor reserves the right to determine the winner based on:\n\n* Validation strategy robustness\n* Code organization\n* Feature engineering quality\n* Absence of data leakage\n* Reproducibility standards\n\nThe Sponsor’s decision is final.\n\n---\n\n## 6. Data Usage & Restrictions\n\nParticipants may use only the data provided within the competition unless explicitly permitted.\n\nThe following are strictly prohibited:\n\n* External dataset usage\n* Reverse engineering the test set\n* Sharing competition data with non-participants\n* Unauthorized redistribution\n\nParticipants must implement reasonable security measures to protect the competition data.\n\n---\n\n## 7. Intellectual Property & Winner Obligations\n\nBy participating, participants retain ownership of their submissions.\n\nHowever, the winner agrees to:\n\n* Provide complete source code used to generate the final solution\n* Provide reproducibility instructions\n* Grant the Sponsor a non-exclusive right to showcase the winning solution for educational and promotional purposes\n\nIf external libraries or tools are used, the participant must ensure appropriate licensing.\n\n---\n\n## 8. Prize Administration\n\nThe competition awards **one (1) winner only**.\n\nThe winner will receive:\n\n* 1,000 EGP cash reward\n* 30% scholarship on any AMIT diploma\n* Official Certificate of Achievement\n* Recognition across AMIT platforms\n\nAll prizes are managed and delivered directly by the Competition Sponsor.\n\nPrizes are non-transferable and non-exchangeable.\n\n---\n\n## 9. Disqualification\n\nThe Sponsor reserves the right to disqualify any participant who:\n\n* Violates competition rules\n* Engages in unethical conduct\n* Attempts to manipulate the leaderboard\n* Submits non-reproducible solutions\n\n---\n\n## 10. Governing Law\n\nThis competition is governed by the applicable laws of the Arab Republic of Egypt.\n\nAny disputes arising from participation shall be resolved under Egyptian jurisdiction.\n\n\n", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "abstract", "content": "![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F26097785%2F148a71203b4dd7d7c6bc3b13a558dfd8%2FStats%20(2).jpg?generation=1772057729335250&alt=media)\n\n# 🚀 Customer Churn Prediction\n\nDevelop a **production-ready machine learning solution** to predict customer churn probability using structured customer data.\n\nThis competition is intentionally designed to simulate **real-world ML workflows**, with strong emphasis on:\n\n* Validation rigor\n* Engineering discipline\n* Reproducibility\n* Model reliability in deployment scenarios\n\n---\n\n## 🎯 Competition Philosophy\n\nThis is **not** just a leaderboard challenge.\n\nParticipants are expected to approach the task as **machine learning practitioners**, focusing on:\n\n* Proper data preprocessing pipelines\n* Robust cross-validation strategies\n* Careful feature engineering\n* Leakage prevention\n* Clear experiment tracking\n* Reproducible model training\n\n---\n\n## 🏗 Expected Professional Standards\n\nYour solution should reflect production-level thinking, including:\n\n* Structured and modular code\n* Deterministic training (fixed random seeds where applicable)\n* Clear separation between training and inference logic\n* Proper probability calibration when necessary\n* Transparent documentation of assumptions\n\n---\n\n## 📈 Core Objective\n\nPredict **continuous churn probabilities**, enabling:\n\n* Customer risk ranking\n* Business-driven prioritization strategies\n* Data-driven retention decision-making\n\n---\n\n\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F26097785%2F736484afe300474c75b53f49fdb72be2%2FStats%20(1).jpg?generation=1772057528838756&alt=media)\n\n## 📊 Business Context\n\nCustomer churn prediction is a critical use case in subscription-based business models.\nAccurate churn modeling enables organizations to:\n\n* Proactively mitigate revenue loss\n* Improve customer retention strategies\n* Optimize marketing and retention resource allocation\n* Prioritize high-risk customer segments\n\nA well-calibrated churn model directly supports data-driven decision-making and long-term customer value optimization.\n\n---\n\n## 🎯 Competition Scope\n\nIn this competition, participants will:\n\n#### 🔹 Analyze structured customer data\n\nPerform exploratory data analysis and understand behavioral and demographic drivers of churn.\n\n#### 🔹 Build a supervised learning model\n\nDevelop a robust binary classification model using historical labeled data.\n\n#### 🔹 Predict churn probabilities (0 → 1)\n\nGenerate continuous probability scores representing churn risk for each customer.\n\n---\n\n## 🏗 Evaluation Philosophy\n\nThis challenge emphasizes more than predictive accuracy. It prioritizes:\n\n* Reliable validation methodology\n* Proper separation between training and inference\n* Clean, modular engineering practices\n* Reproducibility of results\n* Ranking-based predictive performance\n\nParticipants are expected to deliver solutions that reflect real-world machine learning standards rather than purely leaderboard-driven optimization.\n\n---\n", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "foundational-rules", "content": "The following Kaggle Competition Foundational Rules (“ Foundational Rules ”) apply to every competition regardless of whether the Sponsor creates competition-specific rules. Any competition-specific rules provided by the Sponsor are in addition to these rules, and in the case of any conflict or inconsistency, these Foundational Rules control and nullify contrary competition-specific rules.\n###GENERAL COMPETITION RULES - BINDING AGREEMENT\n####1. ELIGIBILITY\na. To be eligible to enter the Competition, you must be:\n1. a registered account holder at Kaggle.com; \n2. the older of 18 years old or the age of majority in your jurisdiction of residence (unless otherwise agreed to by Competition Sponsor and appropriate parental/guardian consents have been obtained by Competition Sponsor); \n3. not a resident of Crimea, so-called Donetsk People's Republic (DNR) or Luhansk People's Republic (LNR), Cuba, Iran, or North Korea; and\n4. not a person or representative of an entity under U.S. export controls or sanctions (see: [https://www.treasury.gov/resourcecenter/sanctions/Programs/Pages/Programs.aspx][1]).\n\nb. Competitions are open to residents of the United States and worldwide, except that if you are a resident of Crimea, so-called Donetsk People's Republic (DNR) or Luhansk People's Republic (LNR), Cuba, Iran, North Korea, or are subject to U.S. export controls or sanctions, you may not enter the Competition. Other local rules and regulations may apply to you, so please check your local laws to ensure that you are eligible to participate in skills-based competitions. The Competition Host reserves the right to forego or award alternative Prizes where needed to comply with local laws. If a winner is located in a country where prizes cannot be awarded, then they are not eligible to receive a prize.\n\nc. If you are entering as a representative of a company, educational institution or other legal entity, or on behalf of your employer, these rules are binding on you, individually, and the entity you represent or where you are an employee. If you are acting within the scope of your employment, or as an agent of another party, you warrant that such party or your employer has full knowledge of your actions and has consented thereto, including your potential receipt of a Prize. You further warrant that your actions do not violate your employer's or entity's policies and procedures. \n\nd. The Competition Sponsor reserves the right to verify eligibility and to adjudicate on any dispute at any time. If you provide any false information relating to the Competition concerning your identity, residency, mailing address, telephone number, email address, ownership of right, or information required for entering the Competition, you may be immediately disqualified from the Competition.\n\n####2. SPONSOR AND HOSTING PLATFORM\n\na. The Competition is sponsored by Competition Sponsor named above. The Competition is hosted on behalf of Competition Sponsor by Kaggle Inc. (\"Kaggle\"). Kaggle is an independent contractor of Competition Sponsor, and is not a party to this or any agreement between you and Competition Sponsor. You understand that Kaggle has no responsibility with respect to selecting the potential Competition winner(s) or awarding any Prizes. Kaggle will perform certain administrative functions relating to hosting the Competition, and you agree to abide by the provisions relating to Kaggle under these Rules. As a Kaggle.com account holder and user of the Kaggle competition platform, remember you have accepted and are subject to the Kaggle Terms of Service at [www.kaggle.com/terms][2] in addition to these Rules.\n\n####3. COMPETITION PERIOD\na. For the purposes of Prizes, the Competition will run from the Start Date and time to the Final Submission Deadline (such duration the “Competition Period”). The Competition Timeline is subject to change, and Competition Sponsor may introduce additional hurdle deadlines during the Competition Period. Any updated or additional deadlines will be publicized on the Competition Website. It is your responsibility to check the Competition Website regularly to stay informed of any deadline changes. YOU ARE RESPONSIBLE FOR DETERMINING THE CORRESPONDING TIME ZONE IN YOUR LOCATION.\n\n####4. COMPETITION ENTRY\na. NO PURCHASE NECESSARY TO ENTER OR WIN. To enter the Competition, you must register on the Competition Website prior to the Entry Deadline, and follow the instructions for developing and entering your Submission through the Competition Website. Your Submissions must be made in the manner and format, and in compliance with all other requirements, stated on the Competition Website (the \"Requirements\"). Submissions must be received before any Submission deadlines stated on the Competition Website. Submissions not received by the stated deadlines will not be eligible to receive a Prize.\nb. Submissions may not use or incorporate information from hand labeling or human prediction of the validation dataset or test data records.\nc. If the Competition is a multi-stage competition with temporally separate training and/or test data, one or more valid Submissions may be required during each Competition stage in the manner described on the Competition Website in order for the Submissions to be Prize eligible.\nd. Submissions are void if they are in whole or part illegible, incomplete, damaged, altered, counterfeit, obtained through fraud, or late. Competition Sponsor reserves the right to disqualify any entrant who does not follow these Rules, including making a Submission that does not meet the Requirements. \n\n####5. INDIVIDUALS AND TEAMS\na. Individual Account. You may make Submissions only under one, unique Kaggle.com account. You will be disqualified if you make Submissions through more than one Kaggle account, or attempt to falsify an account to act as your proxy. You may submit up to the maximum number of Submissions per day as specified on the Competition Website. \nb. Teams. If permitted under the Competition Website guidelines, multiple individuals may collaborate as a Team; however, you may join or form only one Team. Each Team member must be a single individual with a separate Kaggle account. You must register individually for the Competition before joining a Team. You must confirm your Team membership to make it official by responding to the Team notification message sent to your Kaggle account. Team membership may not exceed the Maximum Team Size stated on the Competition Website.\nc. Team Merger. Teams may request to merge via the Competition Website. Team mergers may be allowed provided that: (i) the combined Team does not exceed the Maximum Team Size; (ii) the number of Submissions made by the merging Teams does not exceed the number of Submissions permissible for one Team at the date of the merger request; (iii) the merger is completed before the earlier of: any merger deadline or the Competition deadline; and (iv) the proposed combined Team otherwise meets all the requirements of these Rules. \nd. Private Sharing. No private sharing outside of Teams. Privately 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\n####6. SUBMISSION CODE REQUIREMENTS\na. Private Code Sharing. Unless otherwise specifically permitted under the Competition Website or Competition Specific Rules above, during the Competition Period, you are not allowed to privately share source or executable code developed in connection with or based upon the Competition Data or other source or executable code relevant to the Competition (“Competition Code”). This prohibition includes sharing Competition Code between separate Teams, unless a Team merger occurs. Any such sharing of Competition Code is a breach of these Competition Rules and may result in disqualification.\nb. Public Code Sharing. You are permitted to publicly share Competition Code, provided that such public sharing does not violate the intellectual property rights of any third party. If you do choose to share Competition Code or other such code, you are required to share it on Kaggle.com on the discussion forum or notebooks associated specifically with the Competition for the benefit of all competitors. By so sharing, you are deemed to have licensed the shared code under an Open Source Initiative-approved license (see [www.opensource.org][3]) that in no event limits commercial use of such Competition Code or model containing or depending on such Competition Code.\nc. Use of Open Source. Unless otherwise stated in the Specific Competition Rules above, if open source code is used in the model to generate the Submission, then you must only use open source code licensed under an Open Source Initiative-approved license (see [www.opensource.org][4]) that in no event limits commercial use of such code or model containing or depending on such code.\n\n####7. DETERMINING WINNERS\na. Each Submission will be scored and ranked by the evaluation metric stated on the Competition Website. During the Competition Period, the current ranking will be visible on the Competition Website's Public Leaderboard. The potential winner(s) are determined solely by the leaderboard ranking on the Private Leaderboard, subject to compliance with these Rules. The Public Leaderboard will be based on the public test set and the Private Leaderboard will be based on the private test set.\nb. In the event of a tie, the Submission that was entered first to the Competition will be the winner. In the event a potential winner is disqualified for any reason, the Submission that received the next highest score rank will be chosen as the potential winner.\n\n####8. NOTIFICATION OF WINNERS & DISQUALIFICATION\na. The potential winner(s) will be notified by email. \nb. If a potential winner (i) does not respond to the notification attempt within one (1) week from the first notification attempt or (ii) notifies Kaggle within one week after the Final Submission Deadline that the potential winner does not want to be nominated as a winner or does not want to receive a Prize, then, in each case (i) and (ii) such potential winner will not receive any Prize, and an alternate potential winner will be selected from among all eligible entries received based on the Competition’s judging criteria.\nc. In case (i) and (ii) above Kaggle may disqualify the Participant. However, in case (ii) above, if requested by Kaggle, such potential winner may provide code and documentation to verify the Participant’s compliance with these Rules. If the potential winner provides code and documentation to the satisfaction of Kaggle, the Participant will not be disqualified pursuant to this paragraph.\nd. Competition Sponsor reserves the right to disqualify any Participant from the Competition if the Competition Sponsor reasonably believes that the Participant has attempted to undermine the legitimate operation of the Competition by cheating, deception, or other unfair playing practices or abuses, threatens or harasses any other Participants, Competition Sponsor or Kaggle.\ne. A disqualified Participant may be removed from the Competition leaderboard, at Kaggle's sole discretion. If a Participant is removed from the Competition Leaderboard, additional winning features associated with the Kaggle competition platform, for example Kaggle points or medals, may also not be awarded.\nf. The final leaderboard list will be publicly displayed at Kaggle.com. Determinations of Competition Sponsor are final and binding.\n\n####9. PRIZES\na. Prize(s) are as described on the Competition Website and are only available for winning during the time period described on the Competition Website. The odds of winning any Prize depends on the number of eligible Submissions received during the Competition Period and the skill of the Participants. \nb. All Prizes are subject to Competition Sponsor's review and verification of the Participant’s eligibility and compliance with these Rules, and the compliance of the winning Submissions with the Submissions Requirements. In the event that the Submission demonstrates non-compliance with these Competition Rules, Competition Sponsor may at its discretion take either of the following actions: (i) disqualify the Submission(s); or (ii) require the potential winner to remediate within one week after notice all issues identified in the Submission(s) (including, without limitation, the resolution of license conflicts, the fulfillment of all obligations required by software licenses, and the removal of any software that violates the software restrictions).\nc. A potential winner may decline to be nominated as a Competition winner in accordance with Section 3.8.\nd. Potential winners must return all required Prize acceptance documents within two (2) weeks following notification of such required documents, or such potential winner will be deemed to have forfeited the prize and another potential winner will be selected. Prize(s) will be awarded within approximately thirty (30) days after receipt by Competition Sponsor or Kaggle of the required Prize acceptance documents. Transfer or assignment of a Prize is not allowed. \ne. You are not eligible to receive any Prize if you do not meet the Eligibility requirements in Section 2.7 and Section 3.1 above.\nf. If a Team wins a monetary Prize, the Prize money will be allocated in even shares between the eligible Team members, unless the Team unanimously opts for a different Prize split and notifies Kaggle before Prizes are issued.\n\n####10. TAXES\na. ALL TAXES IMPOSED ON PRIZES ARE THE SOLE RESPONSIBILITY OF THE WINNERS. Payments to potential winners are subject to the express requirement that they submit all documentation requested by Competition Sponsor or Kaggle for compliance with applicable state, federal, local and foreign (including provincial) tax reporting and withholding requirements. Prizes will be net of any taxes that Competition Sponsor is required by law to withhold. If a potential winner fails to provide any required documentation or comply with applicable laws, the Prize may be forfeited and Competition Sponsor may select an alternative potential winner. Any winners who are U.S. residents will receive an IRS Form-1099 in the amount of their Prize.\n\n####11. GENERAL CONDITIONS\na. All federal, state, provincial and local laws and regulations apply.\n\n####12. PUBLICITY\na. You agree that Competition Sponsor, Kaggle and its affiliates may use your name and likeness for advertising and promotional purposes without additional compensation, unless prohibited by law.\n\n####13. PRIVACY\na. You acknowledge and agree that Competition Sponsor and Kaggle may collect, store, share and otherwise use personally identifiable information provided by you during the Kaggle account registration process and the Competition, including but not limited to, name, mailing address, phone number, and email address (“Personal Information”). Kaggle acts as an independent controller with regard to its collection, storage, sharing, and other use of this Personal Information, and will use this Personal Information in accordance with its Privacy Policy <[www.kaggle.com/privacy][6]>, including for administering the Competition. As a Kaggle.com account holder, you have the right to request access to, review, rectification, portability or deletion of any personal data held by Kaggle about you by logging into your account and/or contacting Kaggle Support at <[www.kaggle.com/contact][7]>.\nb. As part of Competition Sponsor performing this contract between you and the Competition Sponsor, Kaggle will transfer your Personal Information to Competition Sponsor, which acts as an independent controller with regard to this Personal Information. As a controller of such Personal Information, Competition Sponsor agrees to comply with all U.S. and foreign data protection obligations with regard to your Personal Information. Kaggle will transfer your Personal Information to Competition Sponsor in the country specified in the Competition Sponsor Address listed above, which may be a country outside the country of your residence. Such country may not have privacy laws and regulations similar to those of the country of your residence.\n\n####14. WARRANTY, INDEMNITY AND RELEASE \na. You warrant that your Submission is your own original work and, as such, you are the sole and exclusive owner and rights holder of the Submission, and you have the right to make the Submission and grant all required licenses. You agree not to make any Submission that: (i) infringes any third party proprietary rights, intellectual property rights, industrial property rights, personal or moral rights or any other rights, including without limitation, copyright, trademark, patent, trade secret, privacy, publicity or confidentiality obligations, or defames any person; or (ii) otherwise violates any applicable U.S. or foreign state or federal law.\nb. To the maximum extent permitted by law, you indemnify and agree to keep indemnified Competition Entities at all times from and against any liability, claims, demands, losses, damages, costs and expenses resulting from any of your acts, defaults or omissions and/or a breach of any warranty set forth herein. To the maximum extent permitted by law, you agree to defend, indemnify and hold harmless the Competition Entities from and against any and all claims, actions, suits or proceedings, as well as any and all losses, liabilities, damages, costs and expenses (including reasonable attorneys fees) arising out of or accruing from: (a) your Submission or other material uploaded or otherwise provided by you that infringes any third party proprietary rights, intellectual property rights, industrial property rights, personal or moral rights or any other rights, including without limitation, copyright, trademark, patent, trade secret, privacy, publicity or confidentiality obligations, or defames any person; (b) any misrepresentation made by you in connection with the Competition; (c) any non-compliance by you with these Rules or any applicable U.S. or foreign state or federal law; (d) claims brought by persons or entities other than the parties to these Rules arising from or related to your involvement with the Competition; and (e) your acceptance, possession, misuse or use of any Prize, or your participation in the Competition and any Competition-related activity.\nc. You hereby release Competition Entities from any liability associated with: (a) any malfunction or other problem with the Competition Website; (b) any error in the collection, processing, or retention of any Submission; or (c) any typographical or other error in the printing, offering or announcement of any Prize or winners.\n\n####15. INTERNET\na. Competition Entities are not responsible for any malfunction of the Competition Website or any late, lost, damaged, misdirected, incomplete, illegible, undeliverable, or destroyed Submissions or entry materials due to system errors, failed, incomplete or garbled computer or other telecommunication transmission malfunctions, hardware or software failures of any kind, lost or unavailable network connections, typographical or system/human errors and failures, technical malfunction(s) of any telephone network or lines, cable connections, satellite transmissions, servers or providers, or computer equipment, traffic congestion on the Internet or at the Competition Website, or any combination thereof, which may limit a Participant’s ability to participate.\n\n####16. RIGHT TO CANCEL, MODIFY OR DISQUALIFY\na. If for any reason the Competition is not capable of running as planned, including infection by computer virus, bugs, tampering, unauthorized intervention, fraud, technical failures, or any other causes which corrupt or affect the administration, security, fairness, integrity, or proper conduct of the Competition, Competition Sponsor reserves the right to cancel, terminate, modify or suspend the Competition. Competition Sponsor further reserves the right to disqualify any Participant who tampers with the submission process or any other part of the Competition or Competition Website. Any attempt by a Participant to deliberately damage any website, including the Competition Website, or undermine the legitimate operation of the Competition is a violation of criminal and civil laws. Should such an attempt be made, Competition Sponsor and Kaggle each reserves the right to seek damages from any such Participant to the fullest extent of the applicable law.\n\n####17. NOT AN OFFER OR CONTRACT OF EMPLOYMENT\na. Under no circumstances will the entry of a Submission, the awarding of a Prize, or anything in these Rules be construed as an offer or contract of employment with Competition Sponsor or any of the Competition Entities. You acknowledge that you have submitted your Submission voluntarily and not in confidence or in trust. You acknowledge that no confidential, fiduciary, agency, employment or other similar relationship is created between you and Competition Sponsor or any of the Competition Entities by your acceptance of these Rules or your entry of your Submission.\n\n####18. DEFINITIONS\na. \"Competition Data\" are the data or datasets available from the Competition Website for the purpose of use in the Competition, including any prototype or executable code provided on the Competition Website. The Competition Data will contain private and public test sets. Which data belongs to which set will not be made available to Participants. \nb. An “Entry” is when a Participant has joined, signed up, or accepted the rules of a competition. Entry is required to make a Submission to a competition.\nc. A “Final Submission” is the Submission selected by the user, or automatically selected by Kaggle in the event not selected by the user, that is/are used for final placement on the competition leaderboard.\nd. A “Participant” or “Participant User” is an individual who participates in a competition by entering the competition and making a Submission.\ne. The “Private Leaderboard” is a ranked display of Participants’ Submission scores against the private test set. The Private Leaderboard determines the final standing in the competition.\nf. The “Public Leaderboard” is a ranked display of Participants’ Submission scores against a representative sample of the test data. This leaderboard is visible throughout the competition.\ng. A “Sponsor” is responsible for hosting the competition, which includes but is not limited to providing the data for the competition, determining winners, and enforcing competition rules.\nh. A “Submission” is anything provided by the Participant to the Sponsor to be evaluated for competition purposes and determine leaderboard position. A Submission may be made as a model, notebook, prediction file, or other format as determined by the Sponsor.\ni. A “Team” is one or more Participants participating together in a Kaggle competition, by officially merging together as a Team within the competition platform.\n\n [1]: https://www.treasury.gov/resource-center/sanctions/Programs/Pages/Programs.aspx\n [2]: http://www.kaggle.com/terms\n [3]: http://www.opensource.org\n [4]: http://www.opensource.org\n [5]: https://www.kaggle.com/WinningModelDocumentationGuidelines\n [6]: http://www.kaggle.com/privacy\n [7]: http://www.kaggle.com/contact\n", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "THE CHALLENGE", "content": "![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F26097785%2F116b05cf323f8efa040fa3f9b6830adc%2FStats%20(3).jpg?generation=1772057862263667&alt=media)\n\n## 🎯 Competition Objective\n\nYou are provided with historical customer data to develop a **predictive churn model**.\n\n---\n\n## 📌 Your Tasks\n\n### 1️⃣ Model Development\n\n* Train a **binary classification model** using `train.csv`.\n\n### 2️⃣ Probability Prediction\n\n* Generate churn probability predictions for `test.csv`.\n\n### 3️⃣ Submission Format\n\n* Submit **continuous probability scores** for each customer.\n\n> ⚠️ The output must represent probabilities (e.g., 0.73, 0.18, 0.91).\n\n---\n\n## ❗ Important Guidelines\n\n### 🔹 Probability Interpretation\n\n* A **higher probability** indicates a **higher churn risk**.\n* Example:\n\n * `0.85` → Very high churn likelihood\n * `0.12` → Low churn likelihood\n\n### 🔹 Binary Outputs Are Not Allowed\n\n* ❌ Submitting only `0` or `1` predictions is **not acceptable**.\n* ✅ Continuous probability values are required.\n\n### 🔹 Evaluation Criteria\n\n* The competition evaluation focuses on **ranking performance**, not classification threshold accuracy.\n* Models should prioritize **correct ordering of churn risk** rather than fixed cutoff classification.\n\n---\n", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "THE DATA", "content": "![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F26097785%2F772194a4c4b116e3249952af4cfc228a%2FStats%20(4).jpg?generation=1772058065292328&alt=media)\n\n# 🏦 Banking Customer Churn Prediction Challenge\n\nThis competition uses a **structured banking customer dataset** designed to simulate a realistic churn prediction scenario in the financial services domain.\n\nParticipants are expected to develop a production-grade machine learning solution capable of estimating the **probability that a customer will leave the bank**.\n\n---\n\n## 📊 Dataset Overview\n\nThe dataset reflects real-world banking attributes and includes:\n\n### 👤 Demographic Attributes\n\n* Customer profile characteristics\n* Personal segmentation indicators\n\n### 💰 Financial Metrics\n\n* Account balance–related variables\n* Credit and monetary behavior indicators\n\n### 🔁 Account Activity Indicators\n\n* Usage patterns\n* Engagement signals\n* Tenure-related features\n\nThe dataset includes both:\n\n* **Numerical features**\n* **Categorical features**\n\nThis requires robust preprocessing and structured modeling practices.\n\n---\n\n## 🎯 Core Objective\n\nBuild a supervised learning model that:\n\n* Learns from historical customer data\n* Predicts churn probability (continuous values between 0 and 1)\n* Produces reliable ranking of churn risk\n\n---\n\n## 🏗 Engineering Expectations\n\nThis challenge emphasizes more than leaderboard performance. Participants must demonstrate:\n\n* Proper categorical encoding\n* Structured preprocessing pipelines\n* Rigorous validation methodology\n* Clean and modular implementation\n* Full reproducibility\n\nThe goal is to simulate **real-world ML deployment standards**, where engineering discipline is as important as predictive accuracy.\n\n\n", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "EVALUATION", "content": "![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F26097785%2F3fc8cbe1ad3aa7002e71ef05b1d09a4d%2FStats%20(6).jpg?generation=1772058318466051&alt=media)\n\n# 📈 Evaluation & Ranking Framework\n\n---\n\n## 🏆 Primary Metric\n\nSubmissions are evaluated using **ROC AUC (Area Under the Receiver Operating Characteristic Curve)**.\n\n### 🔎 What ROC AUC Measures\n\nROC AUC evaluates a model’s ability to:\n\n* Correctly **rank churn-risk customers higher** than non-risk customers\n* Maintain ranking quality **across all possible classification thresholds**\n\nUnlike accuracy, ROC AUC does **not depend on a fixed cutoff** (e.g., 0.5).\nInstead, it measures how well the model separates positive and negative classes through probability ordering.\n\n> A higher ROC AUC indicates stronger ranking performance.\n\n---\n\n## 🧮 Leaderboard Structure\n\n### 🟢 Public Leaderboard\n\n* Provides **real-time feedback**\n* Based on a **subset of the test dataset**\n* Intended for iterative model improvement\n\n### 🔒 Private Leaderboard\n\n* Based on a **hidden evaluation set**\n* Determines the **final competition ranking**\n\n> Final standings are based **exclusively** on the Private Leaderboard score.\n\n---\n\n## 🥇 Final Winner Determination\n\nWhile predictive performance is critical, the final evaluation also considers engineering quality and methodological rigor.\n\n### Evaluation Criteria Beyond ROC AUC:\n\n* **Validation strategy robustness**\n (e.g., proper cross-validation, no leakage, stratification where appropriate)\n\n* **Code organization & reproducibility**\n (clean structure, deterministic results, modular pipeline)\n\n* **Feature engineering quality**\n (meaningful transformations, justified feature construction)\n\n* **Strict absence of data leakage**\n\n---\n\n## ⚖️ Tie-Breaking Policy\n\nIn the event of identical ROC AUC scores:\n\n> The winner will be determined based on **engineering rigor and methodological discipline**, not randomness.\n\n---\n\n", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "CODE & ENGINEERING STANDARDS", "content": "![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F26097785%2Fbe82e26b5aeda5c8e081dc4b644a16e3%2FStats%20(7).jpg?generation=1772058410261148&alt=media)\n\n# 📓 Notebook Submission Requirements\n\nAll participants are required to submit a **Kaggle Notebook** that meets production-level standards and executes seamlessly within the Kaggle environment.\n\n---\n\n## ⚙️ Execution Requirements\n\nThe notebook must:\n\n* ✅ Execute **end-to-end without manual intervention**\n* ✅ Run from top to bottom without errors\n* ✅ Generate the final `submission.csv` programmatically\n* ✅ Be fully reproducible inside the Kaggle runtime\n\n> No hidden steps. No external dependencies. No manual edits.\n\n---\n\n## 🏗 Engineering & Pipeline Standards\n\nYour notebook must include:\n\n### 🔹 Structured Preprocessing Pipeline\n\n* Proper categorical encoding\n* Missing value handling\n* Feature transformations\n* Clear separation between training and inference logic\n\nPipeline-based implementation (e.g., `sklearn Pipeline`) is strongly recommended.\n\n---\n\n### 🔹 Cross-Validation Strategy\n\n* Use **Stratified K-Fold** (or equivalent stratified validation)\n* Avoid single train/validation splits\n* Ensure no data leakage between folds\n\nCross-validation must be clearly implemented and documented.\n\n---\n\n### 🔹 Deterministic Results\n\n* Fix all relevant random seeds:\n\n * NumPy\n * Python random\n * Model-level seeds\n* Ensure reproducible outputs across multiple runs\n\n---\n\n### 🔹 Validation Metrics\n\nThe notebook must:\n\n* Compute and display **ROC AUC**\n* Show cross-validation mean and standard deviation\n* Clearly distinguish between validation and final predictions\n\n---\n\n### 🔹 Feature Importance / Interpretability\n\nInclude at least one of the following:\n\n* Feature importance visualization (e.g., tree-based models)\n* SHAP analysis\n* Permutation importance\n* Model coefficient analysis (if applicable)\n\nThe goal is to demonstrate **model transparency and reasoning**, not just performance.\n\n---\n\n## 🔁 Reproducibility Requirement\n\nThe entire workflow must be:\n\n* Self-contained\n* Deterministic\n* Fully executable within Kaggle\n\nAny notebook that fails to reproduce results will be disqualified.\n\n---", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "RULES & FAIR PLAY", "content": "![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F26097785%2F891a736132476ceb4fb11953b878ae2a%2FStats%20(8).jpg?generation=1772058486843117&alt=media)\n\n\n# 🚫 Prohibited Practices & Compliance Rules\n\nTo ensure fairness, reproducibility, and methodological integrity, the following practices are **strictly prohibited**.\n\n---\n\n## ❌ Disallowed Activities\n\n### 🔹 Data Leakage (Any Form)\n\n* Using future information during training\n* Encoding target information improperly\n* Fitting preprocessing steps on combined train + test data\n* Any cross-fold contamination\n\n> Any evidence of leakage will result in immediate disqualification.\n\n---\n\n### 🔹 Manual Modification of Prediction Files\n\n* Editing `submission.csv` manually\n* Adjusting probabilities post-training\n* Threshold tuning directly inside the submission file\n\nAll predictions must be generated programmatically.\n\n---\n\n### 🔹 Hardcoded Outputs\n\n* Predefined probability values\n* Static or fabricated prediction arrays\n* Conditional logic that bypasses model inference\n\nThe notebook must compute predictions through a trained model.\n\n---\n\n### 🔹 Use of External Datasets\n\n* No external data sources\n* No pretrained models trained on external customer data\n* No enrichment from third-party APIs\n\nAll modeling must rely exclusively on the provided competition data.\n\n---\n\n### 🔹 Reverse-Engineering the Test Set\n\n* Attempting to infer hidden labels\n* Exploiting leakage patterns in public leaderboard feedback\n* Any manipulation aimed at uncovering private test targets\n\n---\n\n## 📊 Submission Limit\n\nParticipants are limited to:\n\n> **5 submissions per day**\n\nThis encourages thoughtful experimentation rather than leaderboard probing.\n\n---\n\n## ⚖️ Enforcement\n\nViolation of any of the above rules may result in:\n\n* Score invalidation\n* Submission removal\n* Full disqualification from the competition\n\n---\n", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "🎁 Prizes & Recognition", "content": "![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F26097785%2Ff6c7e8bf3366eac038a13b3854a3d762%2FStats%20(9).jpg?generation=1772058626436025&alt=media)\n\n# 🏆 Competition Awards\n\nThis competition recognizes **one winner only**.\n\nFinal selection is based on:\n\n* Private Leaderboard performance\n* Engineering rigor\n* Validation robustness\n* Code quality and reproducibility\n\n---\n\n## 🎁 Winner Rewards\n\nThe selected winner will receive:\n\n### 💰 1,000 EGP Cash Prize\n\nA direct monetary reward in recognition of outstanding performance.\n\n### 🎓 30% Scholarship\n\nA **30% scholarship** on any diploma program offered by AMIT.\n\n### 📜 Official Certificate of Achievement\n\nA formal certificate acknowledging excellence in machine learning and engineering practice.\n\n### 🌍 Public Recognition\n\nFeature and recognition across official AMIT platforms.\n\n---\n\n## 📦 Prize Administration\n\nAll prizes are:\n\n* Managed directly by AMIT\n* Delivered according to official competition policies\n\n---\n\n", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false } ] }