House Rent Prediction Dataset

### Context

The spectrum of housing options in India is incredibly diverse, spanning from the opulent palaces once inhabited by maharajas of yore, to the contemporary high-rise apartment complexes in bustling metropolitan areas, and even to the humble abodes in remote villages, consisting of modest huts. This wide-ranging tapestry of residential choices reflects the significant expansion witnessed in India's housing sector, which has paralleled the upward trajectory of income levels in the country. According to the findings of the Human Rights Measurement Initiative, India currently achieves 60.9% of what is theoretically attainable, considering its current income levels, in ensuring the fundamental right to housing for its citizens. In the realm of housing arrangements, renting, known interchangeably as hiring or letting, constitutes an agreement wherein compensation is provided for the temporary utilization of a resource, service, or property owned by another party. Within this arrangement, a gross lease is one where the tenant is obligated to pay a fixed rental amount, and the landlord assumes responsibility for covering all ongoing property-related expenses. The concept of renting also aligns with the principles of the sharing economy, as it fosters the utilization of assets and resources among individuals or entities, promoting efficiency and access to housing solutions for a broad spectrum of individuals.

### Content

Within this dataset, you will find a comprehensive collection of data pertaining to nearly 4700+ available residential properties, encompassing houses, apartments, and flats offered for rent. This dataset is rich with various attributes, including the number of bedrooms (BHK), rental rates, property size, number of floors, area type, locality, city, furnishing status, tenant preferences, bathroom count, and contact information for the respective point of contact.

### Dataset Glossary (Column-Wise)

* <b>BHK</b>: Number of Bedrooms, Hall, Kitchen.
* <b>Rent</b>: Rent of the Houses/Apartments/Flats.
* <b>Size</b>: Size of the Houses/Apartments/Flats in Square Feet.
* <b>Floor</b>: Houses/Apartments/Flats situated in which Floor and Total Number of Floors (Example: Ground out of 2, 3 out of 5, etc.)
* <b>Area Type</b>: Size of the Houses/Apartments/Flats calculated on either Super Area or Carpet Area or Build Area.
* <b>Area Locality</b>: Locality of the Houses/Apartments/Flats.
* <b>City</b>: City where the Houses/Apartments/Flats are Located.
* <b>Furnishing Status</b>: Furnishing Status of the Houses/Apartments/Flats, either it is Furnished or Semi-Furnished or Unfurnished.
* <b>Tenant Preferred</b>: Type of Tenant Preferred by the Owner or Agent.
* <b>Bathroom</b>: Number of Bathrooms.
* <b>Point of Contact</b>: Whom should you contact for more information regarding the Houses/Apartments/Flats.

### Structure of the Dataset


### Acknowledgement

This Dataset is created from <b>[https://www.magicbricks.com/](https://www.magicbricks.com/)</b>. If you want to learn more, you can visit the Website.

Cover Photo by: <a href="https://unsplash.com/@alex_andrews?utm_source=unsplash&amp;utm_medium=referral&amp;utm_content=creditCopyText"><b>Alexander Andrews</b></a> on <a href="https://unsplash.com/s/photos/house?utm_source=unsplash&amp;utm_medium=referral&amp;utm_content=creditCopyText"><b>Unsplash</b></a>

### 语境
印度的住房选择范围极其广泛，从昔日摩诃罗阁居住的豪华宫殿，到繁华都市中的现代高层公寓综合体，乃至偏远村庄中简朴的茅舍，这一系列丰富多彩的居住选择反映了印度住房行业的显著扩张，这一扩张与国家收入水平的上升轨迹相辅相成。根据人权测量倡议组织的研究结果，印度目前实现了其国民基本住房权利的60.9%，这一比例基于其当前的收入水平理论上的可达到程度。在住房安排领域，租赁，亦称为出租或出租，是一种补偿协议，用于支付他人拥有的资源、服务或财产的临时使用。在此安排中，毛租是指承租人必须支付固定租金，而房东负责承担所有与房产相关的持续费用。租赁的概念也与共享经济的原则相吻合，因为它促进了个人或实体之间资产和资源的利用，促进了效率并提升了广大个人对住房解决方案的获取。

### 内容
在本数据集中，您将发现近4700+套可供出租的住宅物业的全面数据，包括房屋、公寓和公寓。该数据集富含多种属性，包括卧室数量（BHK）、租金、物业面积、楼层数、区域类型、地段、城市、装修状况、租户偏好、浴室数量以及相关联系信息。

### 数据集术语表（按列说明）
* <b>BHK</b>：卧室、客厅、厨房数量。
* <b>租金</b>：房屋、公寓、公寓的租金。
* <b>面积</b>：房屋、公寓、公寓的面积（以平方英尺计）。
* <b>楼层数</b>：房屋、公寓、公寓所在的楼层和总楼层数（例如：2层中的底层，5层中的第3层等）。
* <b>区域类型</b>：房屋、公寓、公寓的面积计算基于超面积、地毯面积或建筑面积。
* <b>区域地段</b>：房屋、公寓、公寓的地段。
* <b>城市</b>：房屋、公寓、公寓所在的城市。
* <b>装修状况</b>：房屋、公寓、公寓的装修状况，无论是装修、半装修还是未装修。
* <b>租户偏好</b>：业主或代理偏好的租户类型。
* <b>浴室</b>：浴室数量。
* <b>联系方式</b>：谁应联系以获取有关房屋、公寓、公寓的更多信息。

### 数据集结构

### 致谢
本数据集由<b>[https://www.magicbricks.com/](https://www.magicbricks.com/)</b>创建。如需了解更多信息，请访问网站。
照片由：<a href="https://unsplash.com/@alex_andrews?utm_source=unsplash&amp;utm_medium=referral&amp;utm_content=creditCopyText"><b>Alexander Andrews</b></a>在<a href="https://unsplash.com/s/photos/house?utm_source=unsplash&amp;utm_medium=referral&amp;utm_content=creditCopyText"><b>Unsplash</b></a>提供。