Datasets:

HathawayLiu
/

housing_dataset

@@ -187,14 +187,15 @@ Seattle Government Open Data Portal and it's keep updating along with time. You
 in different formats. For my own purpose I downloaded the CSV version that updated until the modified time of this repo and you can find it in the following Github Repo:[https://github.com/HathawayLiu/Housing_dataset]
 (File name: `Building_Permits_20240213.csv`). To process and clean the dataset, I did the following steps:
 1. Pre-process the data to make sure that they are in the correct types.
-2. Use the provided `latitude` and `longitude` columns in the dataset along with Google Maps API to fill in the blanks for the `OriginalZip`(Zip code) column.
 3. Use the provided `latitude` and `longitude` columns and the GeoJSon file of Seattle Neighborhood District to assign building permits to their corresponding neighborhood districts.
 4. (The GeoJSon file of Seattle Neighborhood District could be found under this GitHub Repo:[https://github.com/HathawayLiu/Housing_dataset]. You could also download it through Seattle GeoData Portal:https://data-seattlecitygis.opendata.arcgis.com/datasets/SeattleCityGIS::neighborhood-map-atlas-districts/about)
 5. Fill in the blanks left in the dataset with `N/A` for easier future use
 6. Split the dataset into train and test set for future use.
-For more details about data cleaning and processing, you could refer to the `data_cleaning.py` file under this repo. You are more than welcome to download the raw
-data and process the dataset yourself.
 To load the dataset, you could use the following command:
 ```python

 in different formats. For my own purpose I downloaded the CSV version that updated until the modified time of this repo and you can find it in the following Github Repo:[https://github.com/HathawayLiu/Housing_dataset]
 (File name: `Building_Permits_20240213.csv`). To process and clean the dataset, I did the following steps:
 1. Pre-process the data to make sure that they are in the correct types.
+2. Use the provided `latitude` and `longitude` columns in the dataset along with Google GeoCoding API to fill in the blanks for the `OriginalZip`(Zip code) column.
 3. Use the provided `latitude` and `longitude` columns and the GeoJSon file of Seattle Neighborhood District to assign building permits to their corresponding neighborhood districts.
 4. (The GeoJSon file of Seattle Neighborhood District could be found under this GitHub Repo:[https://github.com/HathawayLiu/Housing_dataset]. You could also download it through Seattle GeoData Portal:https://data-seattlecitygis.opendata.arcgis.com/datasets/SeattleCityGIS::neighborhood-map-atlas-districts/about)
 5. Fill in the blanks left in the dataset with `N/A` for easier future use
 6. Split the dataset into train and test set for future use.
+For more details about data cleaning and processing, you could refer to the `data_cleaning.py` file under this repo. Notice that to be able to use the function to get zipcode,
+you need to use your own API Key. Applying for a Google GeoCoding API is free. You could simply follow this link to apply it: https://developers.google.com/maps/documentation/geocoding/get-api-key
+You are more than welcome to download the raw data and process the dataset yourself.
 To load the dataset, you could use the following command:
 ```python