Housing Price Data

Summary

Introduction

This dataset is based on Gigasheet(2023) that contains detailed real estate listings, including property type, price, location, bedrooms, bathrooms, area (in Marla), and purpose (rent/sale). Designed for housing market analysis, it supports trend assessment, comparative pricing, and data-driven investment decisions. It is also used for the Housing Price Challenge, enabling predictive modeling and valuation insights.

Dataset Structure

Include

The dataset consists of two files:

train.csv:
- Used for training machine learning models.
- Contains 69,649 entries with detailed property features (e.g., type, price, location) and corresponding sale prices.
test.csv:
- Used for generating predictions to submit to the challenge platform.
- Contains 29,850 entries for evaluation. Submissions are scored and displayed on the Public Leaderboard.

An example from train.csv:

house_index,property_type,price,location,city,baths,purpose,bedrooms,Area_in_Marla
0,Flat,10000000,G-10,Islamabad,2,For Sale,2,4.0

An example from test.csv:

house_index,property_type,location,city,baths,purpose,bedrooms,Area_in_Marla
110571,House,Bahria Town Karachi,Karachi,3,For Sale,3,8.0

Data Fields

The train.csv has the following fields:

House_index: Unique identifier for each property.
Property_type: Type of the property (e.g., House, Apartment, Plot).
Location: Specific location or neighborhood of the property.
City: City in which the property is located.
Bath: Number of bathrooms.
Bedrooms: Number of bedrooms.
Area_in_marla: Total area of the property in marla.
Purpose: Indicates whether the property is for sale or rent.
Price: Target variable representing the property price.

Reference

For more information about the dataset, please visit the gigasheet website.

License

This repository is licensed under the MIT License.