The Flats Price Dataset provides detailed information on residential properties, focusing primarily on factors that influence flat pricing. It includes various attributes such as the sale price, location, size in square feet, number of rooms, floor level, total number of floors in the building, and the year the property was built. Additional features like the type of building, condition of the flat, distance to the city center, and proximity to amenities such as schools, hospitals, and public transport are also included. This dataset is valuable for real estate market analysis, price prediction using machine learning models, and understanding urban housing trends. It can assist developers, investors, and policymakers in making data-driven decisions related to property investment and urban planning.

Description for Flats Price Dataset

The Flats Price Dataset contains information about real estate prices for flats in urban and suburban areas. The dataset is designed to support analysis of market trends, housing affordability, and real estate investment opportunities. It includes key features like:

• Flat Features: Details such as the number of bedrooms,location, total area (in square feet or meters), and additional amenities.
• Location Data: Information about the city, neighborhood, and proximity to landmarks or transit hubs.
• Price Information: The listed price and price per square foot.

This dataset can be utilized for predictive modeling, trend analysis, and decision-making in real estate or housing markets. It is suitable for analysts, researchers, and developers building tools for real estate insights.