{ "fetched_at": "2026-10-20T05:12:33Z", "source_url": "https://www.kaggle.com/competitions/apartments-price-prediction-in-almaty/data", "pages": [ { "name": "data-description", "content": "**Data Overview**\n\nThe dataset contains information on 11,884 apartments. Each apartment is described by several features, which can be used to predict its price. The features include details about the apartment’s structure, location, and condition.\n\nThe dataset contains the following columns:\n- **number_of_rooms** (float): The number of rooms in the apartment.\n- **district** (int): The district where the apartment is located (encoded numerically).\n-\t**structure_type** (int): The type of building structure (encoded numerically).\n-\t**year_of_construction** (float): The year the apartment building was constructed.\n-\t**floor** (float): The floor number where the apartment is located.\n-\t**area** (float): The total area of the apartment in square meters.\n-\t**quality** (int): The quality rating of the apartment (encoded numerically).\n-\t**price** (float): The price of the apartment (target variable).\n\n**File**\ntrain_set_almaty_apartments.csv\n\t\t", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Description", "content": "About the Competition\n\nThis competition is designed to help participants learn and apply regression techniques using a straightforward dataset related to apartment prices. The dataset contains information about various features that influence the price of an apartment, such as the number of rooms, floor level, and building type. By participating, you will gain experience in predictive modeling, data visualization, and exploratory data analysis (EDA).\n\nThe Learning Objective\n\nThe primary goal of this competition is to help participants at the entry level understand the process of predicting continuous variables (apartment prices in this case) by applying regression models. Participants are encouraged to explore the data through EDA to uncover relationships between features and the target variable, which will help them build more accurate models.\n\nWhy Participate?\n\nThis competition is ideal for beginners who want to:\n\n- Learn how to apply linear regression and other regression techniques.\n- Gain experience in model tuning.\n- Practice visualizing data using common tools like Matplotlib or Seaborn.\n- Understand the process of building a machine learning model from start to finish.\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F22215069%2Fc7a48abbcf16585bc1730f7fb815cd15%2Fvis_data.jpg?generation=1728988081350051&alt=media)\n\nWhat to Expect\n\nParticipants will be provided with a dataset containing 11,884 apartments, each described by features like the number of rooms, the district, the year of construction, and the apartment’s total area. Your task is to develop a model that can accurately predict the price of apartments based on these features.\n\nThis is a great opportunity for those new to data science or machine learning to practice their skills and learn through hands-on experience. We encourage participants to share their notebooks and insights as part of the learning experience!\n\nFeel free to modify or expand this description as needed!", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Evaluation", "content": "The submissions in this competition will be evaluated using the R-squared (R²) score. The R² score is a common metric used to measure the goodness of fit for regression models. It indicates how well the model’s predictions approximate the actual values. The formula for R² is:\n\n$$ R^2 = 1 - \\frac{ \\sum_{i=1}^{n} \\left( y_i - \\hat{y}i \\right)^2 }{ \\sum{i=1}^{n} \\left( y_i - \\bar{y} \\right)^2 } $$\n\nWhere:\n\n- n is the number of observations,\n- y_i is the actual price of the i-th apartment,\n- \\hat{y}_i is the predicted price of the i-th apartment,\n- \\bar{y} is the mean of the actual prices.\n\n\n\n**Submission Format**\n\nYou need to upload a csv format file merging test_set_almaty_apartments.csv and their predicted values.\n\n`id; price`\n`.1; 1232920`", "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

1. COMPETITION TITLE

Apartments Price Prediction in Almaty\n

2. COMPETITION SPONSOR

Nothing.inc\n

3. COMPETITION SPONSOR ADDRESS

OneOfApartmentsInDataSet\n

4. COMPETITION WEBSITE

https://www.kaggle.com/competitions/apartments-price-prediction-in-almaty\n\n\n###2. COMPETITION-SPECIFIC RULES \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 two (2).\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 eight (8) Submissions per day.\nb. You may select up to four (4) Final Submissions for judging.\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. [CHOOSE ONE OF THE 3 OPTIONS BELOW BASED ON WHAT THE SPONSOR CHOOSES IN THE SOW.]\n\n1. [*Competition Use only is checked*: You may access and use the Competition Data only for participating in the Competition and on Kaggle.com forums. 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\n2. [*Competition Use and Non-Commercial & Academic Research is checked*: You may access and use the Competition Data for non-commercial purposes only, including for participating in the Competition and on Kaggle.com forums, and for 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\n3. [*Competition Use and Commercial are checked*: You may access and use the Competition Data for any purpose, whether commercial or non-commercial, including for participating in the Competition and on Kaggle.com forums, and for 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\n4. [*ADD IN THE FOLLOWING, IN ADDITION TO THE CHOSEN SECTION ABOVE, IF SPECIFIC LICENSE IS CHECKED*: The Competition Data is also subject to the following terms and conditions: [INSERT URL]. \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 [INSERT ONLY THE RELEVANT LICENSE LANGUAGE FOR THE COMPETITION.]\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. [*Non-Exclusive*: You hereby grant and will grant to Competition Sponsor and its designees 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, digitally perform, make, have made, sell, offer for sale and import your winning Submission and the source code used to generate the Submission, in any media now known or developed in the future, for any purpose whatsoever, commercial or otherwise, without further approval by or payment to you.]\n \n2. [*Open Source: You hereby license and will license your winning Submission and the source code used to generate the Submission under an Open Source Initiative-approved license (see [www.opensource.org] (http://www.opensource.org)) that in no event limits commercial use of such code or model containing or depending on such code. \n\n3. 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\n4. 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 an open source license in the preceding Section for that data and/or model(s). \n\nb. You may be required by the Sponsor to provide a detailed description of how the winning Submission was generated, to the Competition Sponsor’s specifications, as outlined in Section 2.8, Winner’s Obligations. This may include a detailed description of methodology, where one must be able to reproduce the approach by reading the description, and includes a detailed explanation of the architecture, preprocessing, loss function, training details, hyper-parameters, etc. The description should also include a link to a code repository with complete and detailed instructions so that the results obtained can be reproduced.\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. However, you will ensure the External Data is either publicly available and equally accessible to use by all Participants of the Competition for purposes of the competition at no cost to the other Participants, or satisfies the Reasonableness criteria as outlined in Section 2.6.b below. The ability to use External Data under this Section does not limit your other obligations under these Competition Rules, including but not limited to Section 2.8 (Winners Obligations). \n\nb. The use of external data and models is acceptable unless specifically prohibited by the Host. Because of the potential costs or restrictions (e.g., “geo restrictions”) associated with obtaining rights to use external data or certain software and associated tools, their use must be “reasonably accessible to all” and of “minimal cost”. Also, regardless of the cost challenges as they might affect all Participants during the course of the competition, the costs of potentially procuring a license for software used to generate a Submission, must also be considered. The Host will employ an assessment of whether or not the following criteria can exclude the use of the particular LLM, data set(s), or tool(s):\n\n1. Are Participants being excluded from a competition because of the \"excessive\" costs for access to certain LLMs, external data, or tools that might be used by other Participants. The Host will assess the excessive cost concern by applying a “Reasonableness” standard (the “Reasonableness Standard”). The Reasonableness Standard will be determined and applied by the Host in light of things like cost thresholds and accessibility.\n\n2. By way of example only, a small subscription charge to use additional elements of a large language model such as Gemini Advanced are acceptable if meeting the Reasonableness Standard of Sec. 8.2. Purchasing a license to use a proprietary dataset that exceeds the cost of a prize in the competition would not be considered reasonable.\n\nc. Automated Machine Learning Tools (“AMLT”)\n\n1. Individual Participants and Teams may use automated machine learning tool(s) (“AMLT”) (e.g., Google toML, H2O Driverless AI, etc.) to create a Submission, provided that the Participant or Team ensures that they have an appropriate license to the AMLT such that they are able to comply with the Competition Rules. \n\n####7. ELIGIBILITY\n\na. Unless otherwise stated in the Competition-Specific Rules above or prohibited by internal policies of the Competition Entities, employees, interns, contractors, officers and directors of Competition Entities may enter and participate in the Competition, but are not eligible to win any Prizes. \"Competition Entities\" means the Competition Sponsor, Kaggle Inc., and their respective parent companies, subsidiaries and affiliates. If you are such a Participant from a Competition Entity, you are subject to all applicable internal policies of your employer with respect to your participation.\n\n####8. WINNER’S OBLIGATIONS\n\na. As a condition to being awarded a Prize, a Prize winner must fulfill the following obligations:\n\n1. Deliver to the Competition Sponsor the final model's software code as used to generate the winning Submission and associated documentation. The delivered software code should follow [these documentation guidelines][5], must be capable of generating the winning Submission, and contain a description of resources required to build and/or run the executable code successfully. For avoidance of doubt, delivered software code should include training code, inference code, and a description of the required computational environment. \n\na. To the extent that the final model’s software code includes generally commercially available software that is not owned by you, but that can be procured by the Competition Sponsor without undue expense, then instead of delivering the code for that software to the Competition Sponsor, you must identify that software, method for procuring it, and any parameters or other information necessary to replicate the winning Submission; Individual Participants and Teams who create a Submission using an AMLT may win a Prize. However, for clarity, the potential winner’s Submission must still meet the requirements of these Rules, including but not limited to Section 2.5 (Winners License), Section 2.8 (Winners Obligations), and Section 3.14 (Warranty, Indemnity, and Release).” \n\nb. Individual Participants and Teams who create a Submission using an AMLT may win a Prize. However, for clarity, the potential winner’s Submission must still meet the requirements of these Rules,\n\n2. Grant to the Competition Sponsor the license to the winning Submission stated in the Competition Specific Rules above, and represent that you have the unrestricted right to grant that license;\n\n3. Sign and return all Prize acceptance documents as may be required by Competition Sponsor or Kaggle, including without limitation: (a) eligibility certifications; (b) licenses, releases and other agreements required under the Rules; and (c) U.S. tax forms (such as IRS Form W-9 if U.S. resident, IRS Form W-8BEN if foreign resident, or future equivalents).\n\n####9. GOVERNING LAW\n\na. Unless otherwise provided in the Competition Specific Rules 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 to be invalid or unenforceable, all remaining provisions of the Rules will remain in full force and effect.\n", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "abstract", "content": "Welcome to the Apartment Price Prediction Competition!\n\nIn this competition, participants are tasked with building a machine learning model to predict the price of apartments based on various features such as the number of rooms, the district, building structure type, and more. The dataset includes information on 11,884 apartments, each with multiple characteristics that influence its price.\n\nGoal\n\nThe goal is to create a model that can accurately predict the price of apartments based on the provided features. Participants will compete to develop the most effective model for this task, using any techniques or algorithms they prefer.\n\nThe winning models will be those that most accurately predict the apartment prices on the test set.\n\nYou can submit your predictions and see how your model performs on the leaderboard. Don’t forget to review the Kaggle competition documentation for guidance on how to participate.\n\nGood luck, and happy modeling!\n\nFeel free to adjust any part of this to better fit your vision for the competition!", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false } ] }