Dataset: Predicting Bangkok Housing Prices

About this Dataset

Predicting Bangkok Housing Prices

Predicting condo prices using web scraped datas

About this dataset

Bangkok is one of the most popular tourist destinations in Southeast Asia, and its condominium market is booming. With so many options to choose from, how can you predict which condominium will be the best investment?

This dataset was collected from hipflat.com, one of the largest condominium listing websites in Thailand, in order to predict the prices of condominiums in Bangkok. The data includes information on location, year built, project area, number of buildings, number of units, price per square meter

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How to use the dataset

This dataset can be used to predict the prices of condominiums in Bangkok, Thailand. The data includes the location, year built, project area, number of buildings, number of floors, number of units, price per square meter, and distance to various amenities

Research Ideas

Predictive pricing for condominiums in Bangkok

Determining the most important features for predicting condominium prices

Identifying trends in the Bangkok condominium market

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: GBoost_best_feature_importances_.csv

Column name	Description
****

File: df_cleaned_for_ML_regression.csv

Column name	Description
district	The district of Bangkok where the condominium is located. (String)
latitude	The latitude of the condominium. (Float)
longitude	The longitude of the condominium. (Float)
year_built	The year the condominium was built. (Integer)
proj_area	The project area of the condominium. (Float)
nbr_buildings	The number of buildings in the condominium. (Integer)
nbr_floors	The number of floors in the condominium. (Integer)
units	The number of units in the condominium. (Integer)
hospital	The distance to the nearest hospital. (Float)
price_sqm	The price per square meter. (Float)
bld_age	The age of the condominium building. (Integer)
dist_shop_1	The distance to the nearest shop. (Float)
dist_shop_2	The distance to the nearest shop. (Float)
dist_shop_3	The distance to the nearest shop. (Float)
dist_shop_4	The distance to the nearest shop. (Float)
dist_shop_5	The distance to the nearest shop. (Float)
dist_school_1	The distance to the nearest school. (Float)
dist_school_2	The distance to the nearest school. (Float)
dist_school_3	The distance to the nearest school. (Float)
dist_school_4	The distance to the nearest school. (Float)
dist_school_5	The distance to the nearest school. (Float)
dist_food_1	The distance to the nearest food. (Float)
dist_food_2	The distance to the nearest food. (Float)
dist_food_3	The distance to the nearest food. (Float)
dist_food_4	The distance to the nearest food. (Float)
dist_food_5	The distance to the nearest food. (Float)
tran_type2	The type of transportation. (String)
tran_type3	The type of transportation. (String)
tran_type4	The type of transportation. (String)
tran_type5	The type of transportation. (String)
tran_name1	The name of the transportation. (String)
tran_name2	The name of the transportation. (String)
tran_name3	The name of the transportation. (String)
tran_name4	The name of the transportation. (String)
tran_name5	The name of the transportation. (String)
dist_tran_1	The distance to the transportation. (Float)
dist_tran_2	The distance to the transportation. (Float)
dist_tran_3	The distance to the transportation. (Float)
dist_tran_4	The distance to the transportation. (Float)
dist_tran_5	The distance to the transportation. (Float)
Elevator	The number of elevators in the condominium. (Integer)
Parking	The number of parking spaces in the condominium. (Integer)
Security	The number of security guards in the condominium. (Integer)
CCTV	The number of CCTV cameras in the condominium. (Integer)
Pool	The number of pools in the condominium. (Integer)
Sauna	The number of saunas in the condominium. (Integer)
Gym	The number of gyms in the condominium. (Integer)
Garden	The number of gardens in the condominium. (Integer)
Playground	The number of playgrounds in the condominium. (Integer)
Shop	The number of shops in the condominium. (Integer)
Restaurant	The number of restaurants in the condominium. (Integer)
Wifi	The number of WiFi hotspots in the condominium. (Integer)
tran_type1	The type of transportation. (String)

File: df_completed.csv

Column name	Description
district	The district of Bangkok where the condominium is located. (String)
latitude	The latitude of the condominium. (Float)
longitude	The longitude of the condominium. (Float)
year_built	The year the condominium was built. (Integer)
proj_area	The project area of the condominium. (Float)
nbr_buildings	The number of buildings in the condominium. (Integer)
nbr_floors	The number of floors in the condominium. (Integer)
units	The number of units in the condominium. (Integer)
hospital	The distance to the nearest hospital. (Float)
price_sqm	The price per square meter. (Float)
shops	The number of shops in the condominium. (Integer)
schools	The number of schools in the condominium. (Integer)
restaurants	The number of restaurants in the condominium. (Integer)
amenities	The number of amenities in the condominium. (Integer)
transportation	The number of transportation options in the condominium. (Integer)
change_last_q	The change in price from the last quarter. (Float)
change_last_y	The change in price from the last year. (Float)
rental_yield	The rental yield of the condominium. (Float)
change_last_y_rental_price	The change in rental price from the last year. (Float)
price_hist	The price history of the condominium. (String)

File: df_0.csv

Column name	Description
Elevator	The number of elevators in the condominium. (Integer)
Garden	The number of gardens in the condominium. (Integer)
Gym	The number of gyms in the condominium. (Integer)
Parking	The number of parking spaces in the condominium. (Integer)
Playground	The number of playgrounds in the condominium. (Integer)
Pool	The number of pools in the condominium. (Integer)
Restaurant	The number of restaurants in the condominium. (Integer)
Sauna	The number of saunas in the condominium. (Integer)
Security	The number of security guards in the condominium. (Integer)
Shop	The number of shops in the condominium. (Integer)
Wifi	The number of WiFi hotspots in the condominium. (Integer)
district	The district of Bangkok where the condominium is located. (String)
latitude	The latitude of the condominium. (Float)
longitude	The longitude of the condominium. (Float)
nbr_buildings	The number of buildings in the condominium. (Integer)
nbr_floors	The number of floors in the condominium. (Integer)
price_hist	The price history of the condominium. (String)
price_sqm	The price per square meter. (Float)
proj_area	The project area of the condominium. (Float)
units	The number of units in the condominium. (Integer)
year_built	The year the condominium was built. (Integer)
CCTV	The number of CCTV cameras in the condominium. (Integer)
date	The date the data was collected. (Date)
name	The name of the condominium. (String)
value	The value of the condominium. (Float)

File: df_100.csv

Column name	Description
CCTV	The number of CCTV cameras in the condominium. (Integer)
Elevator	The number of elevators in the condominium. (Integer)
Garden	The number of gardens in the condominium. (Integer)
Gym	The number of gyms in the condominium. (Integer)
Parking	The number of parking spaces in the condominium. (Integer)
Playground	The number of playgrounds in the condominium. (Integer)
Pool	The number of pools in the condominium. (Integer)
Restaurant	The number of restaurants in the condominium. (Integer)
Sauna	The number of saunas in the condominium. (Integer)
Security	The number of security guards in the condominium. (Integer)
Shop	The number of shops in the condominium. (Integer)
Wifi	The number of WiFi hotspots in the condominium. (Integer)
date	The date the data was collected. (Date)
district	The district of Bangkok where the condominium is located. (String)
latitude	The latitude of the condominium. (Float)
longitude	The longitude of the condominium. (Float)
name	The name of the condominium. (String)
nbr_buildings	The number of buildings in the condominium. (Integer)
nbr_floors	The number of floors in the condominium. (Integer)
price_hist	The price history of the condominium. (String)
price_sqm	The price per square meter. (Float)
proj_area	The project area of the condominium. (Float)
units	The number of units in the condominium. (Integer)
value	The value of the condominium. (Float)
year_built	The year the condominium was built. (Integer)

File: df_1000.csv

Column name	Description
CCTV	The number of CCTV cameras in the condominium. (Integer)
Elevator	The number of elevators in the condominium. (Integer)
Garden	The number of gardens in the condominium. (Integer)
Gym	The number of gyms in the condominium. (Integer)
Parking	The number of parking spaces in the condominium. (Integer)
Playground	The number of playgrounds in the condominium. (Integer)
Pool	The number of pools in the condominium. (Integer)
Restaurant	The number of restaurants in the condominium. (Integer)
Sauna	The number of saunas in the condominium. (Integer)
Security	The number of security guards in the condominium. (Integer)
Shop	The number of shops in the condominium. (Integer)
Wifi	The number of WiFi hotspots in the condominium. (Integer)
date	The date the data was collected. (Date)
district	The district of Bangkok where the condominium is located. (String)
latitude	The latitude of the condominium. (Float)
longitude	The longitude of the condominium. (Float)
name	The name of the condominium. (String)
nbr_buildings	The number of buildings in the condominium. (Integer)
nbr_floors	The number of floors in the condominium. (Integer)
price_hist	The price history of the condominium. (String)
price_sqm	The price per square meter. (Float)
proj_area	The project area of the condominium. (Float)
units	The number of units in the condominium. (Integer)
value	The value of the condominium. (Float)
year_built	The year the condominium was built. (Integer)

File: df_1018.csv

Column name	Description
CCTV	The number of CCTV cameras in the condominium. (Integer)
Elevator	The number of elevators in the condominium. (Integer)
Garden	The number of gardens in the condominium. (Integer)
Gym	The number of gyms in the condominium. (Integer)
Parking	The number of parking spaces in the condominium. (Integer)
Playground	The number of playgrounds in the condominium. (Integer)
Pool	The number of pools in the condominium. (Integer)
Restaurant	The number of restaurants in the condominium. (Integer)
Sauna	The number of saunas in the condominium. (Integer)
Security	The number of security guards in the condominium. (Integer)
Shop	The number of shops in the condominium. (Integer)
Wifi	The number of WiFi hotspots in the condominium. (Integer)
date	The date the data was collected. (Date)
district	The district of Bangkok where the condominium is located. (String)
latitude	The latitude of the condominium. (Float)
longitude	The longitude of the condominium. (Float)
name	The name of the condominium. (String)
nbr_buildings	The number of buildings in the condominium. (Integer)
nbr_floors	The number of floors in the condominium. (Integer)
price_hist	The price history of the condominium. (String)
price_sqm	The price per square meter. (Float)
proj_area	The project area of the condominium. (Float)
units	The number of units in the condominium. (Integer)
value	The value of the condominium. (Float)
year_built	The year the condominium was built. (Integer)

File: df_200.csv

Column name	Description
CCTV	The number of CCTV cameras in the condominium. (Integer)
Elevator	The number of elevators in the condominium. (Integer)
Garden	The number of gardens in the condominium. (Integer)
Gym	The number of gyms in the condominium. (Integer)
Parking	The number of parking spaces in the condominium. (Integer)
Playground	The number of playgrounds in the condominium. (Integer)
Pool	The number of pools in the condominium. (Integer)
Restaurant	The number of restaurants in the condominium. (Integer)
Sauna	The number of saunas in the condominium. (Integer)
Security	The number of security guards in the condominium. (Integer)
Shop	The number of shops in the condominium. (Integer)
Wifi	The number of WiFi hotspots in the condominium. (Integer)
date	The date the data was collected. (Date)
district	The district of Bangkok where the condominium is located. (String)
latitude	The latitude of the condominium. (Float)
longitude	The longitude of the condominium. (Float)
name	The name of the condominium. (String)
nbr_buildings	The number of buildings in the condominium. (Integer)
nbr_floors	The number of floors in the condominium. (Integer)
price_hist	The price history of the condominium. (String)
price_sqm	The price per square meter. (Float)
proj_area	The project area of the condominium. (Float)
units	The number of units in the condominium. (Integer)
value	The value of the condominium. (Float)
year_built	The year the condominium was built. (Integer)

File: df_300.csv

Column name	Description
CCTV	The number of CCTV cameras in the condominium. (Integer)
Elevator	The number of elevators in the condominium. (Integer)
Garden	The number of gardens in the condominium. (Integer)
Gym	The number of gyms in the condominium. (Integer)
Parking	The number of parking spaces in the condominium. (Integer)
Playground	The number of playgrounds in the condominium. (Integer)
Pool	The number of pools in the condominium. (Integer)
Restaurant	The number of restaurants in the condominium. (Integer)
Sauna	The number of saunas in the condominium. (Integer)
Security	The number of security guards in the condominium. (Integer)
Shop	The number of shops in the condominium. (Integer)
Wifi	The number of WiFi hotspots in the condominium. (Integer)
date	The date the data was collected. (Date)
district	The district of Bangkok where the condominium is located. (String)
latitude	The latitude of the condominium. (Float)
longitude	The longitude of the condominium. (Float)
name	The name of the condominium. (String)
nbr_buildings	The number of buildings in the condominium. (Integer)
nbr_floors	The number of floors in the condominium. (Integer)
price_hist	The price history of the condominium. (String)
price_sqm	The price per square meter. (Float)
proj_area	The project area of the condominium. (Float)
units	The number of units in the condominium. (Integer)
value	The value of the condominium. (Float)
year_built	The year the condominium was built. (Integer)

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit .

Tables

Df 0

@kaggle.thedevastator_predicting_bangkok_condominium_prices_using_web.df_0

28.13 KB
42 rows
26 columns


CREATE TABLE df_0 (
  "cctv" BIGINT,
  "elevator" BIGINT,
  "garden" BIGINT,
  "gym" BIGINT,
  "parking" BIGINT,
  "playground" BIGINT,
  "pool" BIGINT,
  "restaurant" BIGINT,
  "sauna" BIGINT,
  "security" BIGINT,
  "shop" BIGINT,
  "wifi" BIGINT,
  "date" TIMESTAMP,
  "district" VARCHAR,
  "id" BIGINT,
  "latitude" DOUBLE,
  "longitude" DOUBLE,
  "name" VARCHAR,
  "nbr_buildings" BIGINT,
  "nbr_floors" BIGINT,
  "price_hist" VARCHAR,
  "price_sqm" BIGINT,
  "proj_area" BIGINT,
  "units" DOUBLE,
  "value" DOUBLE,
  "year_built" BIGINT
);

Df 100

@kaggle.thedevastator_predicting_bangkok_condominium_prices_using_web.df_100

83.64 KB
3303 rows
26 columns


CREATE TABLE df_100 (
  "cctv" BIGINT,
  "elevator" BIGINT,
  "garden" BIGINT,
  "gym" BIGINT,
  "parking" BIGINT,
  "playground" BIGINT,
  "pool" BIGINT,
  "restaurant" BIGINT,
  "sauna" BIGINT,
  "security" BIGINT,
  "shop" BIGINT,
  "wifi" BIGINT,
  "date" TIMESTAMP,
  "district" VARCHAR,
  "id" BIGINT,
  "latitude" DOUBLE,
  "longitude" DOUBLE,
  "name" VARCHAR,
  "nbr_buildings" BIGINT,
  "nbr_floors" BIGINT,
  "price_hist" VARCHAR,
  "price_sqm" BIGINT,
  "proj_area" BIGINT,
  "units" DOUBLE,
  "value" DOUBLE,
  "year_built" BIGINT
);

Df 1000

@kaggle.thedevastator_predicting_bangkok_condominium_prices_using_web.df_1000

85.29 KB
3280 rows
26 columns


CREATE TABLE df_1000 (
  "cctv" BIGINT,
  "elevator" BIGINT,
  "garden" BIGINT,
  "gym" BIGINT,
  "parking" BIGINT,
  "playground" BIGINT,
  "pool" BIGINT,
  "restaurant" BIGINT,
  "sauna" BIGINT,
  "security" BIGINT,
  "shop" BIGINT,
  "wifi" BIGINT,
  "date" TIMESTAMP,
  "district" VARCHAR,
  "id" BIGINT,
  "latitude" DOUBLE,
  "longitude" DOUBLE,
  "name" VARCHAR,
  "nbr_buildings" BIGINT,
  "nbr_floors" BIGINT,
  "price_hist" VARCHAR,
  "price_sqm" BIGINT,
  "proj_area" BIGINT,
  "units" DOUBLE,
  "value" DOUBLE,
  "year_built" BIGINT
);

Df 1018

@kaggle.thedevastator_predicting_bangkok_condominium_prices_using_web.df_1018

36.36 KB
622 rows
26 columns


CREATE TABLE df_1018 (
  "cctv" BIGINT,
  "elevator" BIGINT,
  "garden" BIGINT,
  "gym" BIGINT,
  "parking" BIGINT,
  "playground" BIGINT,
  "pool" BIGINT,
  "restaurant" BIGINT,
  "sauna" BIGINT,
  "security" BIGINT,
  "shop" BIGINT,
  "wifi" BIGINT,
  "date" TIMESTAMP,
  "district" VARCHAR,
  "id" BIGINT,
  "latitude" DOUBLE,
  "longitude" DOUBLE,
  "name" VARCHAR,
  "nbr_buildings" BIGINT,
  "nbr_floors" BIGINT,
  "price_hist" VARCHAR,
  "price_sqm" BIGINT,
  "proj_area" BIGINT,
  "units" DOUBLE,
  "value" DOUBLE,
  "year_built" BIGINT
);

Df 200

@kaggle.thedevastator_predicting_bangkok_condominium_prices_using_web.df_200

79.68 KB
2967 rows
26 columns


CREATE TABLE df_200 (
  "cctv" BIGINT,
  "elevator" BIGINT,
  "garden" BIGINT,
  "gym" BIGINT,
  "parking" BIGINT,
  "playground" BIGINT,
  "pool" BIGINT,
  "restaurant" BIGINT,
  "sauna" BIGINT,
  "security" BIGINT,
  "shop" BIGINT,
  "wifi" BIGINT,
  "date" TIMESTAMP,
  "district" VARCHAR,
  "id" BIGINT,
  "latitude" DOUBLE,
  "longitude" DOUBLE,
  "name" VARCHAR,
  "nbr_buildings" BIGINT,
  "nbr_floors" BIGINT,
  "price_hist" VARCHAR,
  "price_sqm" BIGINT,
  "proj_area" BIGINT,
  "units" DOUBLE,
  "value" DOUBLE,
  "year_built" BIGINT
);

Df 300

@kaggle.thedevastator_predicting_bangkok_condominium_prices_using_web.df_300

84.59 KB
3328 rows
26 columns


CREATE TABLE df_300 (
  "cctv" BIGINT,
  "elevator" BIGINT,
  "garden" BIGINT,
  "gym" BIGINT,
  "parking" BIGINT,
  "playground" BIGINT,
  "pool" BIGINT,
  "restaurant" BIGINT,
  "sauna" BIGINT,
  "security" BIGINT,
  "shop" BIGINT,
  "wifi" BIGINT,
  "date" TIMESTAMP,
  "district" VARCHAR,
  "id" BIGINT,
  "latitude" DOUBLE,
  "longitude" DOUBLE,
  "name" VARCHAR,
  "nbr_buildings" BIGINT,
  "nbr_floors" BIGINT,
  "price_hist" VARCHAR,
  "price_sqm" BIGINT,
  "proj_area" BIGINT,
  "units" DOUBLE,
  "value" DOUBLE,
  "year_built" BIGINT
);

Df 400

@kaggle.thedevastator_predicting_bangkok_condominium_prices_using_web.df_400

82.66 KB
3191 rows
26 columns


CREATE TABLE df_400 (
  "cctv" BIGINT,
  "elevator" BIGINT,
  "garden" BIGINT,
  "gym" BIGINT,
  "parking" BIGINT,
  "playground" BIGINT,
  "pool" BIGINT,
  "restaurant" BIGINT,
  "sauna" BIGINT,
  "security" BIGINT,
  "shop" BIGINT,
  "wifi" BIGINT,
  "date" TIMESTAMP,
  "district" VARCHAR,
  "id" BIGINT,
  "latitude" DOUBLE,
  "longitude" DOUBLE,
  "name" VARCHAR,
  "nbr_buildings" BIGINT,
  "nbr_floors" BIGINT,
  "price_hist" VARCHAR,
  "price_sqm" BIGINT,
  "proj_area" BIGINT,
  "units" DOUBLE,
  "value" DOUBLE,
  "year_built" BIGINT
);

Df 500

@kaggle.thedevastator_predicting_bangkok_condominium_prices_using_web.df_500

85.27 KB
3270 rows
26 columns


CREATE TABLE df_500 (
  "cctv" BIGINT,
  "elevator" BIGINT,
  "garden" BIGINT,
  "gym" BIGINT,
  "parking" BIGINT,
  "playground" BIGINT,
  "pool" BIGINT,
  "restaurant" BIGINT,
  "sauna" BIGINT,
  "security" BIGINT,
  "shop" BIGINT,
  "wifi" BIGINT,
  "date" TIMESTAMP,
  "district" VARCHAR,
  "id" BIGINT,
  "latitude" DOUBLE,
  "longitude" DOUBLE,
  "name" VARCHAR,
  "nbr_buildings" BIGINT,
  "nbr_floors" BIGINT,
  "price_hist" VARCHAR,
  "price_sqm" BIGINT,
  "proj_area" BIGINT,
  "units" DOUBLE,
  "value" DOUBLE,
  "year_built" BIGINT
);

Df 600

@kaggle.thedevastator_predicting_bangkok_condominium_prices_using_web.df_600

81.49 KB
3113 rows
26 columns


CREATE TABLE df_600 (
  "cctv" BIGINT,
  "elevator" BIGINT,
  "garden" BIGINT,
  "gym" BIGINT,
  "parking" BIGINT,
  "playground" BIGINT,
  "pool" BIGINT,
  "restaurant" BIGINT,
  "sauna" BIGINT,
  "security" BIGINT,
  "shop" BIGINT,
  "wifi" BIGINT,
  "date" TIMESTAMP,
  "district" VARCHAR,
  "id" BIGINT,
  "latitude" DOUBLE,
  "longitude" DOUBLE,
  "name" VARCHAR,
  "nbr_buildings" BIGINT,
  "nbr_floors" BIGINT,
  "price_hist" VARCHAR,
  "price_sqm" BIGINT,
  "proj_area" BIGINT,
  "units" DOUBLE,
  "value" DOUBLE,
  "year_built" BIGINT
);

Df 700

@kaggle.thedevastator_predicting_bangkok_condominium_prices_using_web.df_700

80.33 KB
3081 rows
26 columns


CREATE TABLE df_700 (
  "cctv" BIGINT,
  "elevator" BIGINT,
  "garden" BIGINT,
  "gym" BIGINT,
  "parking" BIGINT,
  "playground" BIGINT,
  "pool" BIGINT,
  "restaurant" BIGINT,
  "sauna" BIGINT,
  "security" BIGINT,
  "shop" BIGINT,
  "wifi" BIGINT,
  "date" TIMESTAMP,
  "district" VARCHAR,
  "id" BIGINT,
  "latitude" DOUBLE,
  "longitude" DOUBLE,
  "name" VARCHAR,
  "nbr_buildings" BIGINT,
  "nbr_floors" BIGINT,
  "price_hist" VARCHAR,
  "price_sqm" BIGINT,
  "proj_area" BIGINT,
  "units" DOUBLE,
  "value" DOUBLE,
  "year_built" BIGINT
);

Df 800

@kaggle.thedevastator_predicting_bangkok_condominium_prices_using_web.df_800

86.6 KB
3358 rows
26 columns


CREATE TABLE df_800 (
  "cctv" BIGINT,
  "elevator" BIGINT,
  "garden" BIGINT,
  "gym" BIGINT,
  "parking" BIGINT,
  "playground" BIGINT,
  "pool" BIGINT,
  "restaurant" BIGINT,
  "sauna" BIGINT,
  "security" BIGINT,
  "shop" BIGINT,
  "wifi" BIGINT,
  "date" TIMESTAMP,
  "district" VARCHAR,
  "id" BIGINT,
  "latitude" DOUBLE,
  "longitude" DOUBLE,
  "name" VARCHAR,
  "nbr_buildings" BIGINT,
  "nbr_floors" BIGINT,
  "price_hist" VARCHAR,
  "price_sqm" BIGINT,
  "proj_area" BIGINT,
  "units" DOUBLE,
  "value" DOUBLE,
  "year_built" BIGINT
);

Df 900

@kaggle.thedevastator_predicting_bangkok_condominium_prices_using_web.df_900

82.8 KB
3190 rows
26 columns


CREATE TABLE df_900 (
  "cctv" BIGINT,
  "elevator" BIGINT,
  "garden" BIGINT,
  "gym" BIGINT,
  "parking" BIGINT,
  "playground" BIGINT,
  "pool" BIGINT,
  "restaurant" BIGINT,
  "sauna" BIGINT,
  "security" BIGINT,
  "shop" BIGINT,
  "wifi" BIGINT,
  "date" TIMESTAMP,
  "district" VARCHAR,
  "id" BIGINT,
  "latitude" DOUBLE,
  "longitude" DOUBLE,
  "name" VARCHAR,
  "nbr_buildings" BIGINT,
  "nbr_floors" BIGINT,
  "price_hist" VARCHAR,
  "price_sqm" BIGINT,
  "proj_area" BIGINT,
  "units" DOUBLE,
  "value" DOUBLE,
  "year_built" BIGINT
);

Df Cleaned

@kaggle.thedevastator_predicting_bangkok_condominium_prices_using_web.df_cleaned

292.59 KB
32745 rows
25 columns


CREATE TABLE df_cleaned (
  "id" BIGINT,
  "name" VARCHAR,
  "district" VARCHAR,
  "latitude" DOUBLE,
  "longitude" DOUBLE,
  "year_built" BIGINT,
  "proj_area" BIGINT,
  "nbr_buildings" BIGINT,
  "nbr_floors" BIGINT,
  "units" DOUBLE,
  "price_sqm" BIGINT,
  "date" TIMESTAMP,
  "value" DOUBLE,
  "elevator" BIGINT,
  "parking" BIGINT,
  "security" BIGINT,
  "cctv" BIGINT,
  "pool" BIGINT,
  "sauna" BIGINT,
  "gym" BIGINT,
  "garden" BIGINT,
  "playground" BIGINT,
  "shop" BIGINT,
  "restaurant" BIGINT,
  "wifi" BIGINT
);

Df Cleaned For Ml Regression

@kaggle.thedevastator_predicting_bangkok_condominium_prices_using_web.df_cleaned_for_ml_regression

141.31 KB
1019 rows
55 columns


CREATE TABLE df_cleaned_for_ml_regression (
  "name" VARCHAR,
  "district" VARCHAR,
  "latitude" DOUBLE,
  "longitude" DOUBLE,
  "year_built" BIGINT,
  "proj_area" BIGINT,
  "nbr_buildings" BIGINT,
  "nbr_floors" BIGINT,
  "units" DOUBLE,
  "hospital" DOUBLE,
  "price_sqm" BIGINT,
  "id" BIGINT,
  "bld_age" BIGINT,
  "dist_shop_1" DOUBLE,
  "dist_shop_2" DOUBLE,
  "dist_shop_3" DOUBLE,
  "dist_shop_4" DOUBLE,
  "dist_shop_5" DOUBLE,
  "dist_school_1" DOUBLE,
  "dist_school_2" DOUBLE,
  "dist_school_3" DOUBLE,
  "dist_school_4" DOUBLE,
  "dist_school_5" DOUBLE,
  "dist_food_1" DOUBLE,
  "dist_food_2" DOUBLE,
  "dist_food_3" DOUBLE,
  "dist_food_4" DOUBLE,
  "dist_food_5" DOUBLE,
  "elevator" BIGINT,
  "parking" BIGINT,
  "security" BIGINT,
  "cctv" BIGINT,
  "pool" BIGINT,
  "sauna" BIGINT,
  "gym" BIGINT,
  "garden" BIGINT,
  "playground" BIGINT,
  "shop" BIGINT,
  "restaurant" BIGINT,
  "wifi" BIGINT,
  "tran_type1" VARCHAR,
  "tran_type2" VARCHAR,
  "tran_type3" VARCHAR,
  "tran_type4" VARCHAR,
  "tran_type5" VARCHAR,
  "tran_name1" VARCHAR,
  "tran_name2" VARCHAR,
  "tran_name3" VARCHAR,
  "tran_name4" VARCHAR,
  "tran_name5" VARCHAR,
  "dist_tran_1" DOUBLE,
  "dist_tran_2" DOUBLE,
  "dist_tran_3" DOUBLE,
  "dist_tran_4" DOUBLE,
  "dist_tran_5" DOUBLE
);

Df Completed

@kaggle.thedevastator_predicting_bangkok_condominium_prices_using_web.df_completed

693.16 KB
1019 rows
21 columns


CREATE TABLE df_completed (
  "name" VARCHAR,
  "district" VARCHAR,
  "latitude" VARCHAR,
  "longitude" VARCHAR,
  "year_built" BIGINT,
  "proj_area" BIGINT,
  "nbr_buildings" BIGINT,
  "nbr_floors" BIGINT,
  "units" VARCHAR,
  "shops" VARCHAR,
  "schools" VARCHAR,
  "restaurants" VARCHAR,
  "hospital" VARCHAR,
  "amenities" VARCHAR,
  "transportation" VARCHAR,
  "price_sqm" BIGINT,
  "change_last_q" DOUBLE,
  "change_last_y" VARCHAR,
  "rental_yield" DOUBLE,
  "change_last_y_rental_price" VARCHAR,
  "price_hist" VARCHAR
);

Gboost Best Feature Importances

@kaggle.thedevastator_predicting_bangkok_condominium_prices_using_web.gboost_best_feature_importances

3.61 KB
82 rows
2 columns


CREATE TABLE gboost_best_feature_importances (
  "unnamed_0" VARCHAR,
  "feature_importances" DOUBLE
);

Rf Best Feature Importances

@kaggle.thedevastator_predicting_bangkok_condominium_prices_using_web.rf_best_feature_importances

3.64 KB
82 rows
2 columns


CREATE TABLE rf_best_feature_importances (
  "unnamed_0" VARCHAR,
  "feature_importances" DOUBLE
);

Ridge Coef

@kaggle.thedevastator_predicting_bangkok_condominium_prices_using_web.ridge_coef

3.68 KB
82 rows
2 columns


CREATE TABLE ridge_coef (
  "unnamed_0" VARCHAR,
  "coef" DOUBLE
);

Ridge Result

@kaggle.thedevastator_predicting_bangkok_condominium_prices_using_web.ridge_result

6.33 KB
8 rows
5 columns


CREATE TABLE ridge_result (
  "unnamed_0" BIGINT,
  "ridge_model" VARCHAR,
  "alpha_val" DOUBLE,
  "rmse_test" DOUBLE,
  "r2_test" DOUBLE
);