{ "fetched_at": "2026-10-19T09:42:26Z", "source_url": "https://www.kaggle.com/competitions/global-e-commerce-customer-purchase-prediction/data", "pages": [ { "name": "data-description", "content": "# 📂 Dataset Description\n\nThis competition provides a large-scale, multi-table dataset simulating a global e-commerce platform. The data reflects realistic production challenges including messy formatting, inconsistent categorical values, missing data, high-cardinality features, and distribution shifts.\n\nParticipants must integrate and engineer features across multiple relational tables to build customer-level predictive models.\n\n---\n\n# 🎯 What Are You Predicting?\n\nFor each `customer_id` in the test set, you must predict:\n\n> The probability that the customer will make a purchase within the next 30 days.\n\nThis is a **binary classification problem**, and predictions must be probabilities between 0 and 1.\n\n---\n\n# 📁 Files\n\n## Core Competition Files\n\n- **train.csv** — Training labels \n- **test.csv** — Test set for prediction \n- **sample_submission.csv** — Example submission file \n\n## Relational Data Tables\n\n- **customers.csv** — Customer profile and demographic attributes \n- **sessions.csv** — Session-level browsing logs \n- **transactions.csv** — Historical purchase records \n- **marketing_campaigns.csv** — Campaign metadata \n- **geo_data.csv** — Regional socioeconomic attributes \n\nParticipants must join and aggregate these tables to create modeling features.\n\n---\n\n# 📄 File Details\n\n## 1️⃣ train.csv\n\nContains customer IDs and training labels.\n\n### Columns:\n\n- `customer_id` — Unique customer identifier \n- `target` — Binary label \n - 1 = Customer purchased within next 30 days \n - 0 = No purchase \n\n---\n\n## 2️⃣ test.csv\n\nContains customer IDs for which predictions must be generated.\n\n### Columns:\n\n- `customer_id` — Unique customer identifier \n\nThe target column is not included.\n\n---\n\n## 3️⃣ sample_submission.csv\n\nProvides the correct submission format.\n\n### Format:\ncustomer_id,target 100001,0.8234 100002,0.1452\n---\n\n## 4️⃣ customers.csv\n\nCustomer-level profile and demographic information.\n\n### Key Columns:\n\n- `customer_id` — Unique ID \n- `age` — May contain numeric values or non-numeric entries \n- `country_code` — Inconsistent formatting possible \n- `region` — Geographic region identifier \n- `signup_date` — Customer registration date \n- `loyalty_score` — Stored as text \n- `email_open_rate` — Email engagement metric \n- `discount_usage_rate` — Discount behavior metric \n- `avg_review_score` — Historical review average \n- `referral_code` — High-cardinality identifier \n- `customer_tier` — Loyalty program tier \n- `credit_limit` — Currency-formatted string \n- Additional noise columns \n\n---\n\n## 5️⃣ sessions.csv\n\nEach row represents one browsing session.\n\n### Key Columns:\n\n- `session_id` — Unique session identifier \n- `customer_id` — Foreign key \n- `session_timestamp` — Timestamp of session \n- `session_duration` — Session duration \n- `pages_viewed` — May contain non-numeric values \n- `cart_additions` — Number of cart additions \n- `bounce_flag` — Mixed-format indicator \n- `traffic_source` — Acquisition channel \n- `device_type` — Device category \n- `campaign_id` — Associated marketing campaign \n- `geo_ip_region` — Geographic region \n\nThis table contains millions of rows and requires aggregation.\n\n---\n\n## 6️⃣ transactions.csv\n\nHistorical purchase records.\n\n### Key Columns:\n\n- `transaction_id` — Unique transaction identifier \n- `customer_id` — Foreign key \n- `transaction_timestamp` — Purchase time \n- `order_value` — Currency-formatted string \n- `items_count` — Number of items purchased \n- `payment_method` — Payment type \n- `discount_applied` — Mixed-format flag \n- `shipping_speed` — Delivery type \n- Additional noise columns \n\n---\n\n## 7️⃣ marketing_campaigns.csv\n\nMarketing campaign metadata.\n\n### Columns:\n\n- `campaign_id` — Unique campaign identifier \n- `campaign_type` — Campaign category \n- `campaign_budget` — Currency-formatted value \n- `region_target` — Target region \n- `start_date` \n- `end_date` \n\n---\n\n## 8️⃣ geo_data.csv\n\nRegional socioeconomic indicators.\n\n### Columns:\n\n- `geo_ip_region` — Region key \n- `average_income` \n- `urban_ratio` \n- `internet_penetration` \n- `region_tier` \n\n---\n\n# 📊 Data Characteristics\n\nParticipants should expect:\n\n- Missing values \n- Numeric fields stored as strings \n- Currency symbols and formatted numbers \n- Inconsistent categorical casing \n- High-cardinality categorical variables \n- Potential distribution differences between train and test \n- Noise features \n\nNot all columns are predictive.\n\n---\n\n# 📚 Common Acronyms\n\n- **ROC-AUC** — Receiver Operating Characteristic Area Under Curve \n- **CV** — Cross-Validation \n- **ID** — Identifier \n- **LLM** — Large Language Model (allowed under rules) \n\n---\n\n# ⚠ Important Notes\n\n- This is a relational dataset. \n- Aggregation and feature engineering are required. \n- Avoid data leakage when merging transaction and session information. \n- Robust validation strategies are strongly recommended. \n\nParticipants are responsible for careful preprocessing, feature engineering, and modeling.\n\nGood luck!", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Description", "content": "# 📂 Data Description\n\nThis competition provides a large-scale, multi-table dataset simulating a global e-commerce platform. The data reflects real-world challenges such as messy formats, missing values, inconsistent categorical encodings, and distribution shifts.\n\nParticipants must join and aggregate multiple relational tables to construct meaningful customer-level features.\n\n---\n\n## 🗂 File Overview\n\n### 1️⃣ customers.csv\n\nContains customer-level demographic and profile information.\n\n**Key columns include:**\n\n- `customer_id` — Unique customer identifier \n- `age` — Customer age (may contain missing values or non-numeric entries) \n- `country_code` — Country code (inconsistent formatting may exist) \n- `region` — Geographic region identifier \n- `signup_date` — Date customer joined the platform \n- `loyalty_score` — Customer loyalty indicator (stored as text) \n- `email_open_rate` — Email engagement metric \n- `discount_usage_rate` — Discount usage behavior \n- `avg_review_score` — Historical review rating average \n- `referral_code` — High-cardinality referral identifier \n- `customer_tier` — Loyalty tier classification \n- `credit_limit` — Credit limit (stored as currency-formatted string) \n- Noise columns — Additional columns that may or may not provide predictive value \n\n---\n\n### 2️⃣ sessions.csv\n\nContains session-level behavioral logs. Each row represents one browsing session.\n\n**Key columns include:**\n\n- `session_id` — Unique session identifier \n- `customer_id` — Foreign key to customers \n- `session_timestamp` — Timestamp of session \n- `session_duration` — Duration of session \n- `pages_viewed` — Number of pages viewed (may contain non-numeric values) \n- `cart_additions` — Number of items added to cart \n- `bounce_flag` — Session bounce indicator (stored in mixed formats) \n- `traffic_source` — Source of session traffic \n- `device_type` — Device used during session \n- `campaign_id` — Associated marketing campaign \n- `geo_ip_region` — IP-based geographic region \n\nThis file contains millions of rows and requires aggregation at the customer level.\n\n---\n\n### 3️⃣ transactions.csv\n\nContains historical transaction records.\n\n**Key columns include:**\n\n- `transaction_id` — Unique transaction identifier \n- `customer_id` — Foreign key to customers \n- `transaction_timestamp` — Transaction time \n- `order_value` — Purchase amount (stored as currency-formatted string) \n- `items_count` — Number of items purchased \n- `payment_method` — Payment method used \n- `discount_applied` — Discount flag (mixed encoding) \n- `shipping_speed` — Shipping method \n- Additional noise columns \n\n---\n\n### 4️⃣ marketing_campaigns.csv\n\nContains metadata about marketing campaigns.\n\n- `campaign_id` — Unique campaign identifier \n- `campaign_type` — Campaign category \n- `campaign_budget` — Campaign budget (currency-formatted string) \n- `region_target` — Target region \n- `start_date`, `end_date` — Campaign active period \n\n---\n\n### 5️⃣ geo_data.csv\n\nContains regional socioeconomic attributes.\n\n- `geo_ip_region` — Geographic region key \n- `average_income` — Average regional income \n- `urban_ratio` — Urbanization level \n- `internet_penetration` — Internet access rate \n- `region_tier` — Regional classification \n\n---\n\n### 6️⃣ train.csv\n\nContains training labels.\n\n- `customer_id`\n- `target` — Binary variable indicating whether the customer made a purchase in the next 30 days.\n\n---\n\n### 7️⃣ test.csv\n\nContains customer IDs for which participants must generate predictions.\n\n- `customer_id`\n\n---\n\n## ⚠ Important Notes\n\n- The dataset contains missing values.\n- Some numeric values are stored as strings.\n- Categorical values may contain inconsistent casing.\n- High-cardinality features are present.\n- Distribution differences may exist between training and test sets.\n- Not all columns are predictive.\n- Careful preprocessing and feature engineering are required.\n\n---\n\n## 🧠 Expected Workflow\n\nParticipants are expected to:\n\n- Clean and normalize messy data\n- Join relational tables\n- Aggregate session and transaction-level features\n- Engineer behavioral and time-based features\n- Handle high-cardinality variables\n- Implement robust cross-validation strategies\n- Avoid data leakage\n\n---\n\n## 📤 Submission Format\n\nYour submission file must contain:\ncustomer_id,target 100001,0.8234 100002,0.1452\n- `target` must be a probability between 0 and 1.\n- Rows must match the `test.csv` file exactly.", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Evaluation", "content": "Submissions are evaluated using **Area Under the Receiver Operating Characteristic Curve (ROC-AUC)** between the predicted probability and the observed binary target.\n\nROC-AUC measures the model’s ability to rank positive instances higher than negative ones across all possible classification thresholds. A perfect model achieves a score of 1.0, while a model with no predictive power scores approximately 0.5.\n\nHigher scores indicate better ranking performance.\n\n---\n\n## Leaderboard Split\n\nThe leaderboard is divided as follows:\n\n- **Public Leaderboard:** 30% of the test data \n- **Private Leaderboard:** 70% of the test data \n\nDuring the competition, participants see scores based only on the public portion. Final rankings are determined by the private leaderboard after the competition closes.\n\nThis split is designed to reduce leaderboard overfitting and encourage robust modeling.\n\n---\n\n## Submission Requirements\n\nFor each `customer_id` in the test set, you must predict the probability that the customer will make a purchase in the next 30 days.\n\nPredictions must:\n\n- Be numeric probabilities\n- Be between 0 and 1\n- Contain no missing values\n- Match the order and IDs in `test.csv`\n\n---\n\n## Submission File Format\n\nYour submission file must contain a header and follow exactly this structure:\ncustomer_id,target 100001,0.8234 100002,0.1452 100003,0.5127 ...\nWhere:\n\n- `customer_id` corresponds to the IDs in `test.csv`\n- `target` is a predicted probability between 0 and 1\n\nFiles that do not follow this exact format will result in an error.\n\n---\n\n## Important Notes\n\n- Submissions are limited to 5 per day.\n- Up to 3 final submissions will be eligible for private leaderboard scoring.\n- Accuracy is **not** the evaluation metric.\n- Binary predictions (0 or 1) are allowed but strongly discouraged, as ROC-AUC rewards well-calibrated probability ranking.\n\n---\n\nWe recommend validating your model using cross-validation strategies that reflect potential distribution shifts and avoid data leakage.", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "rules", "content": "##ENTRY IN THIS COMPETITION CONSTITUTES YOUR ACCEPTANCE OF THESE OFFICIAL COMPETITION RULES.\n\n**[See Section 3.18 for defined terms](rules#18.-terms)**\n\n*The Competition named below is a skills-based competition to promote and further the field of data science. You must register via the Competition Website to enter. To enter the Competition, you must agree to these Official Competition Rules, which incorporate by reference the provisions and content of the Competition Website and any Specific Competition Rules herein (collectively, the \"Rules\"). Please read these Rules carefully before entry to ensure you understand and agree. You further agree that Submission in the Competition constitutes agreement to these Rules. You may not submit to the Competition and are not eligible to receive the prizes associated with this Competition unless you agree to these Rules. These Rules form a binding legal agreement between you and the Competition Sponsor with respect to the Competition. Your competition Submissions must conform to the requirements stated on the Competition Website. Your Submissions will be scored based on the evaluation metric described on the Competition Website. Subject to compliance with the Competition Rules, Prizes, if any, will be awarded to Participants with the best scores, based on the merits of the data science models submitted. See below for the complete Competition Rules.*\n\n**You cannot sign up to Kaggle from multiple accounts and therefore you cannot enter or submit from multiple accounts.**\n\n

1. COMPETITION-SPECIFIC TERMS

\n\n

1. COMPETITION TITLE

\nGlobal E-Commerce Customer Purchase Prediction\n\n

2. COMPETITION SPONSOR

\nGlobal Data Science Community Challenge (Independent Host)\n\n

3. COMPETITION SPONSOR ADDRESS

\nOnline / Global\n\n

4. COMPETITION WEBSITE

\nhttps://www.kaggle.com/competitions/global-ecommerce-purchase-prediction\n\n

5. TOTAL PRIZES AVAILABLE

\nNo monetary prizes are awarded. This is a skills-based competitive data science challenge.\n\nFirst Prize: Leaderboard Recognition \nSecond Prize: Leaderboard Recognition \nThird Prize: Leaderboard Recognition \n\nNon-monetary recognition may include public acknowledgment and solution spotlight in competition forums.\n\n

6. WINNER LICENSE TYPE

\nNon-Exclusive License.\n\n

7. DATA ACCESS AND USE

\nYou may access and use the Competition Data for non-commercial purposes only, including participating in the Competition, educational use, and academic research. Commercial deployment of models trained on this dataset is not permitted without explicit written permission from the Competition Sponsor.\n\n---\n\n###2. COMPETITION-SPECIFIC RULES\n\nIn addition to the provisions of the General Competition Rules below, you understand and agree to these Competition-Specific Rules required by the Competition Sponsor:\n\n####1. TEAM LIMITS\na. The maximum Team size is three (3).\nb. Team 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.\n\n####2. SUBMISSION LIMITS\na. You may submit a maximum of five (5) Submissions per day.\nb. You may select up to three (3) Final Submissions for judging on the private leaderboard.\n\n####3. COMPETITION TIMELINE\na. Competition Timeline dates (including Entry Deadline, Final Submission Deadline, Start Date, and Team Merger Deadline, as applicable) are reflected on the competition’s Overview > Timeline page.\n\n####4. COMPETITION DATA\n\na. Data Access and Use.\n\nYou may access and use the Competition Data for non-commercial purposes only, including for participating in the Competition, academic research, and education. The Competition Sponsor reserves the right to disqualify any Participant who uses the Competition Data other than as permitted by the Competition Website and these Rules.\n\nb. Data Security.\n\n1. You agree to use reasonable and suitable measures to prevent persons who have not formally agreed to these Rules from gaining access to the Competition Data. You agree not to transmit, duplicate, publish, redistribute or otherwise provide or make available the Competition Data to any party not participating in the Competition. You agree to notify Kaggle immediately upon learning of any possible unauthorized transmission of or unauthorized access to the Competition Data and agree to work with Kaggle to rectify any unauthorized transmission or access.\n\n####5. WINNER LICENSE\n\na. Under Section 2.8 (Winners Obligations) of the General Rules below, you hereby grant and will grant the Competition Sponsor the following license(s) with respect to your Submission if you are a Competition winner:\n\n1. You hereby grant to the Competition Sponsor a worldwide, non-exclusive, sub-licensable, transferable, fully paid-up, royalty-free, perpetual, irrevocable right to use, reproduce, distribute, create derivative works of, publicly perform, publicly display, and digitally perform your winning Submission and the source code used to generate the Submission for educational, research, and competition-related purposes.\n\n2. For generally commercially available software that you used to generate your Submission that is not owned by you, but that can be procured by the Competition Sponsor without undue expense, you do not need to grant the license in the preceding Section for that software.\n\n3. In the event that input data or pretrained models with an incompatible license are used to generate your winning solution, you do not need to grant a license for that data and/or model(s), provided such use complies with these Competition Rules.\n\nb. You may be required by the Sponsor to provide a detailed description of how the winning Submission was generated, including methodology, preprocessing steps, model architecture, hyperparameters, and training details sufficient for reproducibility.\n\n####6. EXTERNAL DATA AND TOOLS\n\na. You may use data other than the Competition Data (“External Data”) to develop and test your Submissions, provided that such data is publicly available, reasonably accessible to all Participants at minimal or no cost, and does not violate these Competition Rules.\n\nb. The use of external models, pretrained models, and large language models is permitted provided their use complies with the Reasonableness Standard and applicable licensing requirements.\n\nc. Automated Machine Learning Tools (“AMLT”)\n\n1. Participants may use automated machine learning tools (e.g., AutoML frameworks) provided they have proper licensing and the resulting Submission complies with all Competition Rules.\n\n####7. ELIGIBILITY\n\na. Employees, interns, contractors, officers and directors of Competition Entities may participate but are not eligible to receive any Prizes. \"Competition Entities\" means the Competition Sponsor, Kaggle Inc., and their respective parent companies, subsidiaries and affiliates.\n\n####8. WINNER’S OBLIGATIONS\n\na. As a condition to being recognized as a winner, a winner must:\n\n1. Deliver the final model code and documentation sufficient to reproduce the winning Submission, including training and inference code.\n\n2. Grant the license specified in Section 5 above.\n\n3. Sign and return any required eligibility certifications or documentation.\n\n####9. GOVERNING LAW\n\na. Unless otherwise provided above, all claims arising out of or relating to these Rules will be governed by California law, excluding its conflict of laws rules, and will be litigated exclusively in the Federal or State courts of Santa Clara County, California, USA. The parties consent to personal jurisdiction in those courts. If any provision of these Rules is held invalid or unenforceable, the remaining provisions will remain in full force and effect.", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "abstract", "content": "# 🌍 Global E-Commerce Customer Purchase Prediction\n\n## Overview\n\nIn today’s competitive digital marketplace, understanding customer purchase behavior is critical. E-commerce platforms generate massive amounts of behavioral, transactional, and marketing data — but converting this raw, messy data into actionable predictions remains a challenging task.\n\nIn this competition, participants are tasked with predicting whether a global e-commerce customer will make a purchase within the next 30 days.\n\nThis is a large-scale, multi-table, intentionally messy dataset designed to simulate real-world industry conditions. Competitors must perform advanced data cleaning, feature engineering, aggregation, and modeling to achieve strong performance.\n\n---\n\n## 🎯 Goal\n\nBuild a machine learning model that predicts the probability that a given customer will make a purchase in the next 30 days.\n\nSubmissions are evaluated using **ROC-AUC (Receiver Operating Characteristic – Area Under Curve)**.\n\nYour objective is to generate accurate probability predictions between 0 and 1 for each customer in the test set.\n\n---\n\n## 📊 The Challenge\n\nUnlike beginner-friendly tabular competitions, this dataset is intentionally complex and realistic:\n\n- Multi-table relational structure (customers, sessions, transactions, marketing, geo)\n- Large-scale data (millions of behavioral records)\n- Missing values (including missing-not-at-random)\n- Numeric values stored as strings\n- Currency-formatted columns\n- Inconsistent categorical casing\n- High-cardinality features\n- Distribution drift between train and test\n- Noise and weakly correlated features\n- Nonlinear target generation\n\nSuccess in this competition requires:\n\n- Efficient data processing\n- Careful preprocessing and normalization\n- Time-aware feature engineering\n- Aggregation of behavioral signals\n- Robust cross-validation strategy\n- Avoidance of data leakage\n- Strong modeling techniques (e.g., gradient boosting, ensemble methods)\n\n---\n\n## 🗂 Dataset Structure\n\nThe competition data includes multiple relational tables:\n\n- **customers.csv** — customer demographic and profile attributes \n- **sessions.csv** — detailed browsing session logs \n- **transactions.csv** — historical purchase activity \n- **marketing_campaigns.csv** — campaign exposure data \n- **geo_data.csv** — regional socioeconomic attributes \n- **train.csv** — training labels \n- **test.csv** — test set for prediction \n\nParticipants must join and engineer features across tables to build effective models.\n\n---\n\n## 📈 Evaluation\n\nSubmissions are scored using:\n\n**ROC-AUC**\n\nPredictions must be probabilities between 0 and 1.\n\nThe public leaderboard reflects 30% of the test data. \nFinal rankings are determined using the remaining 70% private leaderboard split.\n\n---\n\n## 🚀 Recommended Approach\n\nWhile many modeling strategies are possible, competitors may consider:\n\n- Aggregating session-level features (recency, frequency, intensity)\n- Transaction-derived behavioral signals\n- Time-based features and decay modeling\n- Handling high-cardinality categorical variables\n- Robust handling of missing and noisy data\n- Ensemble methods for improved stability\n\nThis competition is designed for intermediate to advanced practitioners seeking to strengthen real-world machine learning skills.\n\n---\n\n## 🏆 Who Should Participate?\n\n- Data scientists looking for realistic industry simulation\n- Kaggle competitors seeking advanced tabular practice\n- Students building production-level ML skills\n- Researchers exploring behavioral modeling\n\nIf you are ready for a serious, high-complexity tabular challenge — this competition is for you.\n\nGood luck, and may the best model win.", "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 } ] }