Upload data_cleaning.py

data_cleaning.py

@@ -0,0 +1,91 @@
+'''
+git clone https://github.com/geopandas/geopandas.git
+cd geopandas
+pip install .
+'''
+import requests
+import pandas as pd
+import numpy as np
+import requests
+import geopandas as gpd
+from shapely.geometry import Point
+
+# load neighborhood GeoJson file and housing dataset
+neighborhood = gpd.read_file("https://raw.githubusercontent.com/HathawayLiu/Housing_dataset/main/Neighborhood_Map_Atlas_Districts.geojson")
+url = "https://github.com/HathawayLiu/Housing_dataset/raw/main/Building_Permits_20240213.csv"
+df = pd.read_csv(url)
+
+# Pre-processing of data
+df['OriginalZip'] = pd.to_numeric(df['OriginalZip'], errors='coerce').fillna('NA').astype(str)
+df['OriginalZip'] = df['OriginalZip'].replace(0, 'NA')
+df['OriginalCity'] = df['OriginalCity'].fillna('SEATTLE')
+df['OriginalState'] = df['OriginalState'].fillna('WA')
+df['EstProjectCost'] = pd.to_numeric(df['EstProjectCost'], errors='coerce').astype(float)
+df['IssuedDate'] = pd.to_datetime(df['IssuedDate'], errors='coerce')
+df['HousingUnits'] = pd.to_numeric(df['HousingUnits'], errors='coerce').fillna(0).astype(int)
+df['HousingUnitsRemoved'] = pd.to_numeric(df['HousingUnitsRemoved'], errors='coerce').fillna(0).astype(int)
+df['HousingUnitsAdded'] = pd.to_numeric(df['HousingUnitsAdded'], errors='coerce').fillna(0).astype(int)
+df['Longitude'] = pd.to_numeric(df['Longitude'], errors='coerce')
+df['Latitude'] = pd.to_numeric(df['Latitude'], errors='coerce')
+
+# Function to get the zip code from coordinates
+def get_zip_code_from_coordinates(latitude, longitude, api_key):
+    if pd.isna(latitude) or pd.isna(longitude):
+        return 'NA'  # Return 'NA' if latitude or longitude is NaN
+
+    api_url = f"https://maps.googleapis.com/maps/api/geocode/json?latlng={latitude},{longitude}&key={api_key}"
+    response = requests.get(api_url)
+    
+    if response.status_code == 200:
+        data = response.json()
+        if data['results']:
+            for component in data['results'][0]['address_components']:
+                if 'postal_code' in component['types']:
+                    return component['long_name']
+        return 'NA'  # Return 'NA' if no zip code found
+    else:
+        return 'NA'  # Return 'NA' for non-200 responses
+
+# Apply the function only to rows where 'OriginalZip' is 'NA'
+api_key = 'Your Own API Key' 
+for index, row in df.iterrows():
+    if row['OriginalZip'] == 'NA':
+        zip_code = get_zip_code_from_coordinates(row['Latitude'], row['Longitude'], api_key)
+        df.at[index, 'OriginalZip'] = zip_code
+        print(f"Updated row {index} with Zip Code: {zip_code}")
+        
+# Function to get corresponding neighborhood district from coordinates
+gdf = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df.Longitude, df.Latitude), crs='EPSG:4326')
+def get_neighborhood_name(point, neighborhoods):
+    for _, row in neighborhoods.iterrows():
+        if point.within(row['geometry']):
+            print(row['L_HOOD'])
+            return row['L_HOOD']
+    return 'NA'
+# Apply the function to each row
+gdf['NeighborDistrict'] = gdf['geometry'].apply(lambda x: get_neighborhood_name(x, neighborhood) if pd.notna(x) else 'NA')
+# Merge the new column back to the original DataFrame
+df['NeighborDistrict'] = gdf['NeighborDistrict']
+# filtered df to start from year 2000
+df_filtered = df[df['IssuedDate'].dt.year >= 2000]
+df_filtered['IssuedDate'] = df['IssuedDate'].astype(str)
+df_filtered.fillna('NA', inplace=True)
+
+'''
+Following code is for spliting datasets in train and test dataset
+'''
+# Read the dataset
+housing_df = pd.read_csv('https://github.com/HathawayLiu/Housing_dataset/raw/main/Building_Permits_Cleaned.csv')
+# Shuffle the dataset
+housing_df = housing_df.sample(frac=1).reset_index(drop=True)
+
+# Splitting the dataset into training and test sets
+split_ratio = 0.8  # 80% for training, 20% for testing
+split_index = int(len(housing_df) * split_ratio)
+
+train_df = housing_df[:split_index]
+test_df = housing_df[split_index:]
+
+# Export to CSV
+train_df.to_csv('/Users/hathawayliu/Desktop/train_dataset.csv', index=False)
+test_df.to_csv('/Users/hathawayliu/Desktop/test_dataset.csv', index=False)