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@@ -138,8 +138,9 @@ as it primarily contains permit-related data.
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  ## Dataset Structure
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- The cleaned and modified full dataset[`Building_Permits_Cleaned.csv`], the splited train[`housing_train_dataset.csv`] and test[`housing_test_dataset.csv`] dataset,
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- and the script to achieve that are provided in the following Github Repo: [https://github.com/HathawayLiu/Housing_dataset]
 
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  The cleaned dataset in total contains 26 columns:
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  - **`PermitNum`(string):** The tracking number used to refer to this permit in SDCI's tracking system.
@@ -178,15 +179,22 @@ Regarding the importance fo 13 neighborhood districts in Seattle, the new added
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  to investigate the building activities and life quality in the aspect of different neighborhood districts.
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  The dataset supports the city's commitment to open data and the promotion of data-driven insights for improving urban infrastructure and living conditions.
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- ### Source Data
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-
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- <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
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  #### Data Collection and Processing
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- <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
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-
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- [More Information Needed]
 
 
 
 
 
 
 
 
 
 
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  #### Who are the source data producers?
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  ## Dataset Structure
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+ The cleaned and modified full dataset[`Building_Permits_Cleaned.csv`], the splited train[`housing_train_dataset.csv`] and test[`housing_test_dataset.csv`] dataset
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+ are provided in the following Github Repo: [https://github.com/HathawayLiu/Housing_dataset]. The cleaned train and test dataset are also provided in the **`data`**
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+ folder of this repo.
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  The cleaned dataset in total contains 26 columns:
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  - **`PermitNum`(string):** The tracking number used to refer to this permit in SDCI's tracking system.
 
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  to investigate the building activities and life quality in the aspect of different neighborhood districts.
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  The dataset supports the city's commitment to open data and the promotion of data-driven insights for improving urban infrastructure and living conditions.
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  #### Data Collection and Processing
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+ The Building Permits dataset is collected by Seattle Government where it contains all of the recent information about housing permits in Seattle. The dataset is published on
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+ Seattle Government Open Data Portal and it's keep updating along with time. You can download the raw data from [Seattle Government Website](https://data.seattle.gov/Permitting/Building-Permits/76t5-zqzr/about_data)
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+ 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]
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+ (which is the same as the one I mentioned above). To process and clean the dataset, I did the following steps:
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+ 1. Pre-process the data to make sure that they are in the correct types.
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+ 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.
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+ 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.
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+ 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)
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+ 5. Fill in the blanks left in the dataset with `N/A` for easier future use
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+ 6. Split the dataset into train and test set for future use.
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+
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+ 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
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+ data and process the dataset yourself.
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  #### Who are the source data producers?
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