Name: Pakistan House Prices - 2023
Creator: Kaggle
License: https://www.apache.org/licenses/

About this Dataset

Pakistan House Prices - 2023

This is a dataset of house prices in 2023. It has been sourced from here.

There are a lot of possibilities for this dataset and some of them have been listed below:

General Overview:

What is the average price of properties in the dataset?
What is the distribution of property types (e.g., flats, houses, penthouses)?
How many properties are listed for sale, and in which cities?

Location Analysis:

Which locations have the highest and lowest average property prices?
What are the most popular locations based on the number of listings?

Property Characteristics:

What is the average number of bedrooms and bathrooms for listed properties?
How does property size vary across different types and locations?

Price Analysis:

Are there outliers or high-value properties in the dataset?
How does property price correlate with the number of bedrooms and bathrooms?

City Comparison:

How do property prices differ between cities?
Are there specific property types more common in certain cities?

Purpose of Listings:

What is the distribution of properties based on their purpose (e.g., for sale)?
How does the average price vary between different purposes?

Specific Property Types:

What is the average price and size of flats in the dataset?
Are there trends or patterns specific to flats, houses, or other property types?

Popular Locations and Property Types:

Identify popular locations based on the number of listings.
Are certain property types more prevalent in popular locations?

Feature Importance:
- Which features (e.g., location, number of bedrooms) contribute the most to predicting property prices?
- Can we identify the top features that influence the model's predictions?

Property Type Classification:
- Can we use machine learning to classify properties into different types (e.g., flat, house, penthouse) based on their features?
- What is the accuracy of classification models in identifying property types?

Location-based Clustering:
- Are there natural clusters of properties based on their location, and can we identify them using machine learning clustering algorithms?
- How well do clustering algorithms group similar properties together?

Outlier Detection with ML:
- Can machine learning algorithms automatically detect outliers or high-value properties in the dataset?
- How effective are anomaly detection methods in identifying unusual property listings?

Optimal Property Selection:
- Can machine learning help identify the optimal combination of features for a property that maximizes its sale price or rental income?
- How well can models recommend suitable properties based on user preferences?

Customer Segmentation:
- Are there distinct segments of customers with specific preferences for property features?
- Can machine learning algorithms identify and characterize these customer segments?

Property Investment Risk Assessment:
- How can machine learning assist in assessing the risk associated with investing in certain types of properties or locations?
- Can we build a model to predict potential property value fluctuations?

Predictive Modeling:

Can we build a machine learning model to predict property prices based on features such as location, number of bedrooms, and size?
What is the performance (accuracy, RMSE, etc.) of different regression models for predicting property prices?

Happy Processing!!!

Tables

House Prices

@kaggle.manjitbaishya001_house_prices_2023.house_prices

1.06 MB
99499 rows
9 columns


CREATE TABLE house_prices (
  "unnamed_0" BIGINT,
  "property_type" VARCHAR,
  "price" BIGINT,
  "location" VARCHAR,
  "city" VARCHAR,
  "baths" BIGINT,
  "purpose" VARCHAR,
  "bedrooms" BIGINT,
  "area_in_marla" DOUBLE
);