Analysing the price trends for the properties in Kuala Lumpur. For explanation of the project, view the blog post here
We will explore the data for Kuala Lumpur City Propety Listing. Our objective is to find different aspects of the property market in the city and how people like to dwell in this city.
- Let's find the up-scale areas in Kuala Lumpur according to the pricing!
- Where in the city, do people make bigger houses?
- Most expensive areas in Kuala Lumpur to live!
You can download the dataset required from Kaggle at this Link
The data set has multiple columns as follows: - Location -- The neighborhood in which the property is situated. - Price -- The listed sales price in Malaysian Ringgit (RM). - Rooms -- The number of listed rooms. The format N+M indicates N full bedrooms and M additional rooms that cannot count as such for one reason or another. - Bathrooms -- The number of bathrooms in the property. - Car Parks -- The number of car parks on the property. Property Type -- Malaysian properties may fall in one of several property types depending on their characteristics. Size -- The total size of the property. Note that some properties are listed by their land area and others by the built-up size, i.e. the total living space of the property. Furnishing -- Whether or not the property comes pre-furnished.
The main files are in the root directory.
- kuala_lumpur_data_analysis.ipynb (Jupyter file, main file for data analysis)
- property_data_prepare_rough_work.ipynb (Jupyter file for background analysis, rough work and testing. Not required to work upon)
- kl_property_data.csv (data acquired from Kaggle from above url)
- Folder => screenshots (screenshots and images used in the blog)
We analyzed the property trends and found that there are few upscale areas, and most of the upscale areas are due to the larger property sizes. However, the KLCC (twin towers) and the city center areas are quite expensive, even with smaller spaces.
Code and documentation copyright 2020 the code released under the MIT License
Title image courtsey of New Straights Times
Kuala Lumpur Property dataset courtsey of Husmukh at Kaggle