kavyaPratapSingh_Module1
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Apr 3, 2024
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Dataset Proposal
College of Professional Studies, Northeastern University
Professor: Justin Grosz
Feb 25, 2024
House Rent Prediction Dataset Proposal
Dataset Description: Link to Dataset
Our proposed dataset encompasses a comprehensive collection of 265,190 house records, meticulously compiled to include 22 distinct features. These attributes are thoughtfully chosen to provide a holistic view of each listing, ranging from basic information such as the type and size of the housing to amenities and location specifics. Here is a detailed overview of the variables included in our dataset along with their data types:
- Id (Integer): A unique listing identifier.
- URL (String): The URL of the listing.
- Region (String): The Craigslist region of the listing.
- Region URL (String): URL for the region.
- Price (Integer): Monthly rent price (*Target Column*).
- Type (String): Type of housing (e.g., apartment, house).
- SqFeet (Integer): Total square footage of the property.
- Beds (Integer): Number of bedrooms.
- Baths (Float): Number of bathrooms.
- Cats Allowed (Boolean): Whether cats are allowed (1 = yes, 0 = no).
- Dogs Allowed (Boolean): Whether dogs are allowed.
- Smoking Allowed (Boolean): Whether smoking is allowed.
- Wheelchair Access (Boolean): Whether the property is wheelchair accessible.
- Electric_Vehicle_Charge (Boolean): Availability of an electric vehicle charger.
- Comes Furnished (Boolean): Whether the property comes furnished.
- Laundry Options (String): Available laundry options.
- Parking Options (String): Available parking options.
- Image URL (String): URL of the property image.
- Description (String): Property description provided by the poster.
- Lat (Float): Latitude of the property location.
- Long (Float): Longitude of the property location.
- State (String): State where the property is located.
Initial Interest and Goals
Our group's initial interest in this dataset stems from the desire to understand the dynamics influencing house rental prices across different regions and types of properties. By analyzing this dataset, we aim to uncover patterns and insights that could potentially inform both renters and landlords about the current housing market trends. Specifically, we are interested in:
- Identifying factors that significantly affect rental prices.
- Understanding the impact of location and amenities on pricing.
- Exploring the relationship between property size and rental cost.
The primary goal of our analysis is to predict the Price
variable accurately, which represents the monthly rent of a property. By doing so, we hope to develop a model that can assist individuals and families in budgeting for rent and making informed decisions when searching for rental properties.
Group Members
Our group comprises four dedicated members, each bringing unique skills and perspectives to the project:
- Kritika Gehlot
- Reema Mariam Raju
- Sri Harsha Arigapudi
- Kavya Pratap Singh
Conclusion
The house rent prediction dataset is a rich source of information that can provide valuable insights into the rental housing market. Through careful analysis and prediction of the Price variable, our group aims to contribute to a deeper understanding of what influences rental costs and to develop tools that can help stakeholders make informed decisions.
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