Spaces:
Sleeping
Sleeping
First Commit
Browse files- .gitattributes +1 -0
- NN_model.h5 +3 -0
- NN_scaler.pkl +3 -0
- Resale Flat Price.csv +3 -0
- app.py +865 -0
- requirements.txt +5 -0
- street_name_categories.pkl +3 -0
- town_categories.pkl +3 -0
.gitattributes
CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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Resale[[:space:]]Flat[[:space:]]Price.csv filter=lfs diff=lfs merge=lfs -text
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NN_model.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:de214846be63b8501d636c921649bc2407d50e40d112acbd9e72cc2f0eef564f
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size 531272
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NN_scaler.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:fa213f00aa5bc69036412219154864191cb95b83578de0f375f2e132b48a02b5
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size 1087
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Resale Flat Price.csv
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version https://git-lfs.github.com/spec/v1
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oid sha256:074c329ef187737178d36e149023db28f226b05192332ebcf20b04022bdd0838
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size 18807467
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app.py
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import numpy as np
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import pandas as pd
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import joblib
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from tensorflow.keras.models import load_model
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import matplotlib.pyplot as plt
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# Load the trained model
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model = load_model('NN_model.h5')
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# Load category mappings
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town_categories = joblib.load('town_categories.pkl')
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street_name_categories = joblib.load('street_name_categories.pkl')
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# Load the scaler used during training
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scaler = joblib.load('NN_scaler.pkl')
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# Load the data
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town_data = pd.read_csv('Resale Flat Price.csv')
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from openai import OpenAI
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from dotenv import load_dotenv
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load_dotenv()
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client = OpenAI()
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def get_advice(monthly_avg, year, predicted_price, town, street_name, floor, flat_type, flat_model, floor_area_sqm, lease_commence_date):
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"""
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Calls OpenAI API to get advice on whether the property is a good buy.
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Parameters:
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- monthly_avg (float): The average resale price of all house types sold in the area from 2017 to 2024.
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- year (int): The current year when the property is being evaluated.
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- predicted_price (float): The predicted resale price of the property.
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- town (str): The town where the property is located.
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- street_name (str): The street name of the property location.
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- floor (int): The floor level of the property.
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- flat_type (str): The type of flat (e.g., '2 ROOM', '3 ROOM').
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- flat_model (str): The model of the flat (e.g., 'Improved', 'Model A').
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- floor_area_sqm (float): The floor area of the flat in square meters.
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- lease_commence_date (int): The year the lease of the property commenced.
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Returns:
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- str: Advice on whether the property is a good buy, based on resale trends and investment potential.
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"""
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# Calculate remaining lease
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remaining_years = 99 - (year - lease_commence_date)
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# Prepare a prompt with the property details
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prompt = f"""I have a property with the following details:
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- Predicted resale price: S${predicted_price} at {year}
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- Monthly Average of all types of houses sold in the same area from 2017 to 2024: {monthly_avg}
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(Note that the difference in predicted price and monthly average might be because of the nature of the flat type/size of house)
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- Town: {town}
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- Street Name: {street_name}
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- Floor: {floor}
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- Flat Type: {flat_type}
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- Flat Model: {flat_model}
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- Floor Area (sqm): {floor_area_sqm}
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- Remaining Lease: {remaining_years}
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Based on these details, please advise whether this is a good buy in Singapore, considering resale trends and investment potential in point form. Talk about monthly average trends.
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Do not ask me to check for anything.
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Give a concise response.
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"""
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# Make the OpenAI API call
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response = client.chat.completions.create(
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model="gpt-4o-mini",
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temperature=0.7,
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messages=[
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{"role": "system", "content": "You are a helpful real estate advisor"},
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{"role": "user", "content": prompt}
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]
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)
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# Extract and return the response text
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advice = response.choices[0].message.content
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advice = advice.replace("*", "").replace("#", "")
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return advice
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def NN_predict(year, month, town, street_name, floor, flat_type, flat_model, floor_area_sqm, lease_commence_date):
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"""
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84 |
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Predicts the resale price of a property using a neural network model.
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Parameters:
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- year (int): The current year when the property is being evaluated.
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- month (int): The current month when the property is being evaluated.
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- town (str): The town where the property is located.
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- street_name (str): The street name of the property location.
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- floor (int): The floor level of the property.
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- flat_type (str): The type of flat (e.g., '2 ROOM', '3 ROOM').
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93 |
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- flat_model (str): The model of the flat (e.g., 'Improved', 'Model A').
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- floor_area_sqm (float): The floor area of the flat in square meters.
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95 |
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- lease_commence_date (int): The year the lease of the property commenced.
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96 |
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Returns:
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98 |
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- str: The predicted resale price in Singapore dollars, formatted as a string.
|
99 |
+
"""
|
100 |
+
|
101 |
+
# Calculate remaining lease
|
102 |
+
remaining_years = 99 - (year - lease_commence_date)
|
103 |
+
|
104 |
+
# Encode 'town' and 'street_name'
|
105 |
+
if town in town_categories and street_name in street_name_categories:
|
106 |
+
town_code = town_categories.index(town)
|
107 |
+
street_name_code = street_name_categories.index(street_name)
|
108 |
+
else:
|
109 |
+
return "Error: Town or Street Name not found in mappings."
|
110 |
+
|
111 |
+
# Prepare inputs
|
112 |
+
town_embedding_vector = np.array([[town_code]], dtype=np.int32)
|
113 |
+
street_name_embedding_vector = np.array([[street_name_code]], dtype=np.int32)
|
114 |
+
|
115 |
+
# Map flat_type and flat_model to encoded values
|
116 |
+
flat_type_mapping = {
|
117 |
+
'1 ROOM': 1,
|
118 |
+
'2 ROOM': 2,
|
119 |
+
'3 ROOM': 3,
|
120 |
+
'4 ROOM': 4,
|
121 |
+
'5 ROOM': 5,
|
122 |
+
'EXECUTIVE': 6,
|
123 |
+
'MULTI-GENERATION': 7
|
124 |
+
}
|
125 |
+
flat_model_mapping = {
|
126 |
+
'2-room': 1,
|
127 |
+
'Improved': 2,
|
128 |
+
'Simplified': 3,
|
129 |
+
'Standard': 4,
|
130 |
+
'Apartment': 5,
|
131 |
+
'Type S1': 6,
|
132 |
+
'Type S2': 7,
|
133 |
+
'Model A': 8,
|
134 |
+
'Model A2': 9,
|
135 |
+
'New Generation': 10,
|
136 |
+
'Adjoined flat': 11,
|
137 |
+
'Improved-Maisonette': 12,
|
138 |
+
'Maisonette': 13,
|
139 |
+
'Model A-Maisonette': 14,
|
140 |
+
'Multi Generation': 15,
|
141 |
+
'Premium Apartment': 16,
|
142 |
+
'Premium Maisonette': 17,
|
143 |
+
'DBSS': 18,
|
144 |
+
'Terrace': 19,
|
145 |
+
'Premium Apartment Loft': 20,
|
146 |
+
'3Gen': 21
|
147 |
+
}
|
148 |
+
|
149 |
+
encoded_flat_type = flat_type_mapping[flat_type]
|
150 |
+
encoded_flat_model = flat_model_mapping[flat_model]
|
151 |
+
|
152 |
+
# Convert inputs to correct types
|
153 |
+
year = int(year)
|
154 |
+
month = int(month)
|
155 |
+
floor = int(floor)
|
156 |
+
floor_area_sqm = float(floor_area_sqm)
|
157 |
+
remaining_years = int(remaining_years)
|
158 |
+
|
159 |
+
# Scale continuous features using the same scaler as during training
|
160 |
+
numeric_features = np.array([[year, month, floor, floor_area_sqm, remaining_years]], dtype=np.float32)
|
161 |
+
scaled_numeric_features = scaler.transform(numeric_features)
|
162 |
+
|
163 |
+
# Prepare the numeric input by concatenating scaled continuous and categorical features
|
164 |
+
numeric_input = np.concatenate([scaled_numeric_features, [[encoded_flat_type, encoded_flat_model]]], axis=1).astype(np.float32) # Shape (1, 7)
|
165 |
+
|
166 |
+
# Pass the three separate inputs as required by the model
|
167 |
+
prediction = model.predict([town_embedding_vector, street_name_embedding_vector, numeric_input])
|
168 |
+
|
169 |
+
# Format prediction output
|
170 |
+
predicted_price = prediction[0][0]
|
171 |
+
output = f"S${predicted_price:,.2f}"
|
172 |
+
|
173 |
+
return output
|
174 |
+
|
175 |
+
def plot(town_data, street_name, forecast_years=1):
|
176 |
+
"""
|
177 |
+
Generates a time series plot for the resale price trend over time for a specified street name.
|
178 |
+
|
179 |
+
Parameters:
|
180 |
+
- town_data (DataFrame): The DataFrame containing the data with columns 'street_name', 'time', and 'resale_price'.
|
181 |
+
- street_name (str): The street name to filter the data by.
|
182 |
+
|
183 |
+
Returns:
|
184 |
+
- tuple: A tuple containing the formatted prediction as a string and the file path to the saved plot image.
|
185 |
+
"""
|
186 |
+
|
187 |
+
# Filter data for the specified street name
|
188 |
+
filtered_town_data = town_data[town_data['street_name'] == street_name]
|
189 |
+
|
190 |
+
# Check if the filtered dataset is empty
|
191 |
+
if filtered_town_data.empty:
|
192 |
+
raise ValueError("No flat type in the area")
|
193 |
+
|
194 |
+
# Extract sale year and month from the 'month' column
|
195 |
+
filtered_town_data['sale_year'] = filtered_town_data['month'].apply(lambda x: int(x.split('-')[0])) # Get year from 'month'
|
196 |
+
filtered_town_data['sale_month'] = filtered_town_data['month'].apply(lambda x: int(x.split('-')[1])) # Get month from 'month'
|
197 |
+
|
198 |
+
# Convert the 'month' column to a numerical representation
|
199 |
+
filtered_town_data['time'] = filtered_town_data['sale_year'] + (filtered_town_data['sale_month'] - 1) / 12
|
200 |
+
|
201 |
+
# Sort data by 'time'
|
202 |
+
filtered_data = filtered_town_data.sort_values(by='time')
|
203 |
+
|
204 |
+
# Group by month and calculate the average resale price
|
205 |
+
monthly_avg = filtered_data.groupby('time')['resale_price'].mean().reset_index()
|
206 |
+
monthly_avg = monthly_avg[:-1]
|
207 |
+
|
208 |
+
# Plot the average monthly resale price trend
|
209 |
+
plt.figure(figsize=(10, 6))
|
210 |
+
plt.plot(monthly_avg['time'], monthly_avg['resale_price'], color='blue', label='Average Monthly Resale Price')
|
211 |
+
plt.xlabel('Time (Year)')
|
212 |
+
plt.ylabel('Average Resale Price')
|
213 |
+
plt.title(f'Average Monthly Resale Price Trend Over Time for {street_name}')
|
214 |
+
plt.legend()
|
215 |
+
|
216 |
+
# Save the plot and close it
|
217 |
+
plot_path = "resale_price_trend.png"
|
218 |
+
plt.savefig(plot_path)
|
219 |
+
plt.close()
|
220 |
+
|
221 |
+
# Format prediction and return results
|
222 |
+
return plot_path, monthly_avg
|
223 |
+
|
224 |
+
|
225 |
+
def predict_price(year, month, town, street_name, floor, flat_type, flat_model, floor_area_sqm, lease_commence_date):
|
226 |
+
|
227 |
+
predicted_price = NN_predict(year, month, town, street_name, floor, flat_type, flat_model, floor_area_sqm, lease_commence_date)
|
228 |
+
plot_path, monthly_avg = plot(town_data, street_name)
|
229 |
+
recommendation = get_advice(monthly_avg, year, predicted_price, town, street_name, floor, flat_type, flat_model, floor_area_sqm, lease_commence_date)
|
230 |
+
|
231 |
+
return predicted_price, plot_path, recommendation
|
232 |
+
|
233 |
+
# # Example usage
|
234 |
+
# predicted = predict_price(
|
235 |
+
# year=2024,
|
236 |
+
# month=10,
|
237 |
+
# town='ANG MO KIO',
|
238 |
+
# street_name='ANG MO KIO AVE 10',
|
239 |
+
# floor=10,
|
240 |
+
# flat_type='2 ROOM',
|
241 |
+
# flat_model='Improved',
|
242 |
+
# floor_area_sqm=44,
|
243 |
+
# lease_commence_date=1979
|
244 |
+
# )
|
245 |
+
|
246 |
+
# print(predicted)
|
247 |
+
|
248 |
+
# Gradio
|
249 |
+
import gradio as gr
|
250 |
+
|
251 |
+
interface = gr.Interface(
|
252 |
+
fn=predict_price,
|
253 |
+
title="NeuralNest",
|
254 |
+
inputs=[
|
255 |
+
gr.components.Number(label="Year", value = 2024),
|
256 |
+
gr.components.Number(label="Month", value = 11),
|
257 |
+
gr.components.Dropdown(choices=['ANG MO KIO',
|
258 |
+
'BEDOK',
|
259 |
+
'BISHAN',
|
260 |
+
'BUKIT BATOK',
|
261 |
+
'BUKIT MERAH',
|
262 |
+
'BUKIT PANJANG',
|
263 |
+
'BUKIT TIMAH',
|
264 |
+
'CENTRAL AREA',
|
265 |
+
'CHOA CHU KANG',
|
266 |
+
'CLEMENTI',
|
267 |
+
'GEYLANG',
|
268 |
+
'HOUGANG',
|
269 |
+
'JURONG EAST',
|
270 |
+
'JURONG WEST',
|
271 |
+
'KALLANG/WHAMPOA',
|
272 |
+
'MARINE PARADE',
|
273 |
+
'PASIR RIS',
|
274 |
+
'PUNGGOL',
|
275 |
+
'QUEENSTOWN',
|
276 |
+
'SEMBAWANG',
|
277 |
+
'SENGKANG',
|
278 |
+
'SERANGOON',
|
279 |
+
'TAMPINES',
|
280 |
+
'TOA PAYOH',
|
281 |
+
'WOODLANDS',
|
282 |
+
'YISHUN'], value = 'ANG MO KIO', label="Town"),
|
283 |
+
gr.components.Dropdown(choices=['ANG MO KIO AVE 10',
|
284 |
+
'ANG MO KIO AVE 4',
|
285 |
+
'ANG MO KIO AVE 5',
|
286 |
+
'ANG MO KIO AVE 1',
|
287 |
+
'ANG MO KIO AVE 3',
|
288 |
+
'ANG MO KIO AVE 9',
|
289 |
+
'ANG MO KIO AVE 8',
|
290 |
+
'ANG MO KIO AVE 6',
|
291 |
+
'ANG MO KIO ST 52',
|
292 |
+
'BEDOK NTH AVE 4',
|
293 |
+
'BEDOK NTH AVE 1',
|
294 |
+
'BEDOK NTH RD',
|
295 |
+
'BEDOK STH AVE 1',
|
296 |
+
'BEDOK RESERVOIR RD',
|
297 |
+
'CHAI CHEE ST',
|
298 |
+
'BEDOK NTH ST 3',
|
299 |
+
'BEDOK STH RD',
|
300 |
+
'CHAI CHEE AVE',
|
301 |
+
'NEW UPP CHANGI RD',
|
302 |
+
'CHAI CHEE DR',
|
303 |
+
'BEDOK STH AVE 2',
|
304 |
+
'BEDOK NTH AVE 3',
|
305 |
+
'BEDOK RESERVOIR VIEW',
|
306 |
+
'CHAI CHEE RD',
|
307 |
+
'LENGKONG TIGA',
|
308 |
+
'BEDOK CTRL',
|
309 |
+
'JLN DAMAI',
|
310 |
+
'BEDOK NTH AVE 2',
|
311 |
+
'BEDOK STH AVE 3',
|
312 |
+
'SIN MING RD',
|
313 |
+
'SIN MING AVE',
|
314 |
+
'BISHAN ST 12',
|
315 |
+
'BISHAN ST 13',
|
316 |
+
'BISHAN ST 22',
|
317 |
+
'BISHAN ST 24',
|
318 |
+
'BISHAN ST 23',
|
319 |
+
'BRIGHT HILL DR',
|
320 |
+
'SHUNFU RD',
|
321 |
+
'BT BATOK ST 34',
|
322 |
+
'BT BATOK ST 51',
|
323 |
+
'BT BATOK ST 11',
|
324 |
+
'BT BATOK ST 52',
|
325 |
+
'BT BATOK ST 21',
|
326 |
+
'BT BATOK EAST AVE 5',
|
327 |
+
'BT BATOK WEST AVE 6',
|
328 |
+
'BT BATOK CTRL',
|
329 |
+
'BT BATOK WEST AVE 8',
|
330 |
+
'BT BATOK EAST AVE 4',
|
331 |
+
'BT BATOK ST 31',
|
332 |
+
'BT BATOK ST 25',
|
333 |
+
'BT BATOK EAST AVE 3',
|
334 |
+
'BT BATOK WEST AVE 5',
|
335 |
+
'BT BATOK ST 24',
|
336 |
+
'JLN BT HO SWEE',
|
337 |
+
'TELOK BLANGAH DR',
|
338 |
+
'BEO CRES',
|
339 |
+
'TELOK BLANGAH CRES',
|
340 |
+
'TAMAN HO SWEE',
|
341 |
+
'TELOK BLANGAH RISE',
|
342 |
+
'TELOK BLANGAH WAY',
|
343 |
+
'JLN BT MERAH',
|
344 |
+
'JLN KLINIK',
|
345 |
+
'TELOK BLANGAH HTS',
|
346 |
+
'BT MERAH VIEW',
|
347 |
+
'INDUS RD',
|
348 |
+
'BT MERAH LANE 1',
|
349 |
+
'TELOK BLANGAH ST 31',
|
350 |
+
'MOH GUAN TER',
|
351 |
+
'HAVELOCK RD',
|
352 |
+
'HENDERSON CRES',
|
353 |
+
'BT PURMEI RD',
|
354 |
+
'KIM TIAN RD',
|
355 |
+
'DEPOT RD',
|
356 |
+
'JLN RUMAH TINGGI',
|
357 |
+
'DELTA AVE',
|
358 |
+
'JLN MEMBINA',
|
359 |
+
'REDHILL RD',
|
360 |
+
'LENGKOK BAHRU',
|
361 |
+
'ZION RD',
|
362 |
+
'PETIR RD',
|
363 |
+
'PENDING RD',
|
364 |
+
'BANGKIT RD',
|
365 |
+
'SEGAR RD',
|
366 |
+
'JELAPANG RD',
|
367 |
+
'SENJA RD',
|
368 |
+
'FAJAR RD',
|
369 |
+
'BT PANJANG RING RD',
|
370 |
+
'SENJA LINK',
|
371 |
+
'LOMPANG RD',
|
372 |
+
'GANGSA RD',
|
373 |
+
'TOH YI DR',
|
374 |
+
'FARRER RD',
|
375 |
+
'JLN KUKOH',
|
376 |
+
'ROWELL RD',
|
377 |
+
'WATERLOO ST',
|
378 |
+
'NEW MKT RD',
|
379 |
+
'TG PAGAR PLAZA',
|
380 |
+
'QUEEN ST',
|
381 |
+
'BAIN ST',
|
382 |
+
'CANTONMENT RD',
|
383 |
+
'TECK WHYE LANE',
|
384 |
+
'CHOA CHU KANG AVE 4',
|
385 |
+
'CHOA CHU KANG AVE 3',
|
386 |
+
'CHOA CHU KANG CRES',
|
387 |
+
'CHOA CHU KANG ST 54',
|
388 |
+
'CHOA CHU KANG CTRL',
|
389 |
+
'JLN TECK WHYE',
|
390 |
+
'CHOA CHU KANG ST 62',
|
391 |
+
'CHOA CHU KANG NTH 6',
|
392 |
+
'CHOA CHU KANG DR',
|
393 |
+
'CHOA CHU KANG NTH 5',
|
394 |
+
'CHOA CHU KANG ST 52',
|
395 |
+
'CHOA CHU KANG AVE 2',
|
396 |
+
'CLEMENTI WEST ST 2',
|
397 |
+
'WEST COAST RD',
|
398 |
+
'CLEMENTI WEST ST 1',
|
399 |
+
'CLEMENTI AVE 4',
|
400 |
+
'CLEMENTI AVE 5',
|
401 |
+
'CLEMENTI ST 11',
|
402 |
+
'CLEMENTI AVE 2',
|
403 |
+
'CLEMENTI AVE 3',
|
404 |
+
'CLEMENTI AVE 1',
|
405 |
+
"C'WEALTH AVE WEST",
|
406 |
+
'CIRCUIT RD',
|
407 |
+
'BALAM RD',
|
408 |
+
'MACPHERSON LANE',
|
409 |
+
'EUNOS CRES',
|
410 |
+
'UBI AVE 1',
|
411 |
+
'HAIG RD',
|
412 |
+
'OLD AIRPORT RD',
|
413 |
+
'GEYLANG EAST AVE 1',
|
414 |
+
'SIMS DR',
|
415 |
+
'PIPIT RD',
|
416 |
+
'GEYLANG EAST CTRL',
|
417 |
+
'EUNOS RD 5',
|
418 |
+
'CASSIA CRES',
|
419 |
+
'BUANGKOK CRES',
|
420 |
+
'HOUGANG AVE 3',
|
421 |
+
'HOUGANG AVE 8',
|
422 |
+
'HOUGANG AVE 1',
|
423 |
+
'HOUGANG AVE 5',
|
424 |
+
'HOUGANG ST 61',
|
425 |
+
'HOUGANG ST 11',
|
426 |
+
'HOUGANG AVE 7',
|
427 |
+
'HOUGANG AVE 4',
|
428 |
+
'HOUGANG AVE 2',
|
429 |
+
'LOR AH SOO',
|
430 |
+
'HOUGANG ST 92',
|
431 |
+
'HOUGANG ST 52',
|
432 |
+
'HOUGANG AVE 10',
|
433 |
+
'HOUGANG ST 51',
|
434 |
+
'UPP SERANGOON RD',
|
435 |
+
'HOUGANG CTRL',
|
436 |
+
'HOUGANG ST 91',
|
437 |
+
'BUANGKOK LINK',
|
438 |
+
'HOUGANG ST 31',
|
439 |
+
'PANDAN GDNS',
|
440 |
+
'TEBAN GDNS RD',
|
441 |
+
'JURONG EAST ST 24',
|
442 |
+
'JURONG EAST ST 21',
|
443 |
+
'JURONG EAST AVE 1',
|
444 |
+
'JURONG EAST ST 13',
|
445 |
+
'JURONG EAST ST 32',
|
446 |
+
'TOH GUAN RD',
|
447 |
+
'JURONG WEST ST 93',
|
448 |
+
'BOON LAY AVE',
|
449 |
+
'HO CHING RD',
|
450 |
+
'BOON LAY DR',
|
451 |
+
'TAO CHING RD',
|
452 |
+
'JURONG WEST ST 91',
|
453 |
+
'JURONG WEST ST 42',
|
454 |
+
'JURONG WEST ST 92',
|
455 |
+
'BOON LAY PL',
|
456 |
+
'JURONG WEST ST 52',
|
457 |
+
'TAH CHING RD',
|
458 |
+
'JURONG WEST ST 81',
|
459 |
+
'YUNG SHENG RD',
|
460 |
+
'JURONG WEST ST 25',
|
461 |
+
'JURONG WEST ST 73',
|
462 |
+
'JURONG WEST ST 72',
|
463 |
+
'JURONG WEST AVE 3',
|
464 |
+
'JURONG WEST AVE 5',
|
465 |
+
'YUNG HO RD',
|
466 |
+
'JURONG WEST ST 74',
|
467 |
+
'JURONG WEST AVE 1',
|
468 |
+
'JURONG WEST ST 71',
|
469 |
+
'JURONG WEST ST 61',
|
470 |
+
'JURONG WEST ST 65',
|
471 |
+
'JURONG WEST CTRL 1',
|
472 |
+
'JURONG WEST ST 64',
|
473 |
+
'JURONG WEST ST 62',
|
474 |
+
'JURONG WEST ST 41',
|
475 |
+
'JURONG WEST ST 24',
|
476 |
+
'JLN BATU',
|
477 |
+
'JLN BAHAGIA',
|
478 |
+
'LOR LIMAU',
|
479 |
+
"ST. GEORGE'S RD",
|
480 |
+
'KALLANG BAHRU',
|
481 |
+
'DORSET RD',
|
482 |
+
'GEYLANG BAHRU',
|
483 |
+
'BENDEMEER RD',
|
484 |
+
'WHAMPOA DR',
|
485 |
+
'UPP BOON KENG RD',
|
486 |
+
'RACE COURSE RD',
|
487 |
+
'OWEN RD',
|
488 |
+
'NTH BRIDGE RD',
|
489 |
+
'TOWNER RD',
|
490 |
+
'FARRER PK RD',
|
491 |
+
'MCNAIR RD',
|
492 |
+
'JLN TENTERAM',
|
493 |
+
'BOON KENG RD',
|
494 |
+
'JLN RAJAH',
|
495 |
+
'MARINE DR',
|
496 |
+
'MARINE CRES',
|
497 |
+
'MARINE TER',
|
498 |
+
'CHANGI VILLAGE RD',
|
499 |
+
'PASIR RIS ST 71',
|
500 |
+
'PASIR RIS ST 11',
|
501 |
+
'PASIR RIS DR 3',
|
502 |
+
'PASIR RIS DR 6',
|
503 |
+
'PASIR RIS ST 21',
|
504 |
+
'PASIR RIS DR 4',
|
505 |
+
'PASIR RIS ST 53',
|
506 |
+
'PASIR RIS DR 10',
|
507 |
+
'PASIR RIS ST 52',
|
508 |
+
'PASIR RIS ST 12',
|
509 |
+
'PASIR RIS ST 51',
|
510 |
+
'PASIR RIS ST 72',
|
511 |
+
'PASIR RIS DR 1',
|
512 |
+
'PUNGGOL FIELD',
|
513 |
+
'EDGEDALE PLAINS',
|
514 |
+
'PUNGGOL FIELD WALK',
|
515 |
+
'EDGEFIELD PLAINS',
|
516 |
+
'PUNGGOL RD',
|
517 |
+
'PUNGGOL EAST',
|
518 |
+
'PUNGGOL DR',
|
519 |
+
'PUNGGOL CTRL',
|
520 |
+
'PUNGGOL PL',
|
521 |
+
"C'WEALTH CL",
|
522 |
+
'STIRLING RD',
|
523 |
+
'MEI LING ST',
|
524 |
+
"C'WEALTH CRES",
|
525 |
+
"C'WEALTH DR",
|
526 |
+
'GHIM MOH RD',
|
527 |
+
'DOVER RD',
|
528 |
+
'HOLLAND AVE',
|
529 |
+
'STRATHMORE AVE',
|
530 |
+
'HOLLAND DR',
|
531 |
+
'GHIM MOH LINK',
|
532 |
+
'CLARENCE LANE',
|
533 |
+
'DOVER CRES',
|
534 |
+
'SEMBAWANG DR',
|
535 |
+
'SEMBAWANG CL',
|
536 |
+
'MONTREAL DR',
|
537 |
+
'ADMIRALTY LINK',
|
538 |
+
'ADMIRALTY DR',
|
539 |
+
'SEMBAWANG CRES',
|
540 |
+
'CANBERRA RD',
|
541 |
+
'FERNVALE RD',
|
542 |
+
'COMPASSVALE LANE',
|
543 |
+
'ANCHORVALE RD',
|
544 |
+
'RIVERVALE DR',
|
545 |
+
'RIVERVALE CRES',
|
546 |
+
'SENGKANG EAST WAY',
|
547 |
+
'RIVERVALE ST',
|
548 |
+
'RIVERVALE WALK',
|
549 |
+
'FERNVALE LANE',
|
550 |
+
'ANCHORVALE LINK',
|
551 |
+
'COMPASSVALE RD',
|
552 |
+
'COMPASSVALE CRES',
|
553 |
+
'JLN KAYU',
|
554 |
+
'COMPASSVALE WALK',
|
555 |
+
'COMPASSVALE DR',
|
556 |
+
'COMPASSVALE LINK',
|
557 |
+
'COMPASSVALE BOW',
|
558 |
+
'SENGKANG CTRL',
|
559 |
+
'ANCHORVALE LANE',
|
560 |
+
'ANCHORVALE DR',
|
561 |
+
'COMPASSVALE ST',
|
562 |
+
'SERANGOON AVE 4',
|
563 |
+
'LOR LEW LIAN',
|
564 |
+
'SERANGOON AVE 2',
|
565 |
+
'SERANGOON NTH AVE 1',
|
566 |
+
'SERANGOON AVE 1',
|
567 |
+
'SERANGOON CTRL',
|
568 |
+
'SERANGOON NTH AVE 4',
|
569 |
+
'TAMPINES ST 22',
|
570 |
+
'TAMPINES ST 41',
|
571 |
+
'TAMPINES AVE 4',
|
572 |
+
'TAMPINES ST 44',
|
573 |
+
'TAMPINES ST 81',
|
574 |
+
'TAMPINES ST 11',
|
575 |
+
'TAMPINES ST 23',
|
576 |
+
'TAMPINES ST 91',
|
577 |
+
'TAMPINES ST 21',
|
578 |
+
'TAMPINES ST 83',
|
579 |
+
'TAMPINES ST 42',
|
580 |
+
'TAMPINES ST 71',
|
581 |
+
'TAMPINES ST 45',
|
582 |
+
'TAMPINES ST 34',
|
583 |
+
'TAMPINES ST 82',
|
584 |
+
'TAMPINES AVE 9',
|
585 |
+
'SIMEI ST 1',
|
586 |
+
'SIMEI ST 5',
|
587 |
+
'TAMPINES ST 72',
|
588 |
+
'TAMPINES ST 84',
|
589 |
+
'SIMEI ST 2',
|
590 |
+
'TAMPINES CTRL 7',
|
591 |
+
'TAMPINES ST 33',
|
592 |
+
'TAMPINES ST 32',
|
593 |
+
'TAMPINES AVE 5',
|
594 |
+
'LOR 5 TOA PAYOH',
|
595 |
+
'LOR 7 TOA PAYOH',
|
596 |
+
'LOR 4 TOA PAYOH',
|
597 |
+
'LOR 1 TOA PAYOH',
|
598 |
+
'TOA PAYOH EAST',
|
599 |
+
'POTONG PASIR AVE 1',
|
600 |
+
'TOA PAYOH NTH',
|
601 |
+
'LOR 8 TOA PAYOH',
|
602 |
+
'LOR 3 TOA PAYOH',
|
603 |
+
'POTONG PASIR AVE 3',
|
604 |
+
'JOO SENG RD',
|
605 |
+
'LOR 2 TOA PAYOH',
|
606 |
+
'TOA PAYOH CTRL',
|
607 |
+
'MARSILING DR',
|
608 |
+
'WOODLANDS ST 13',
|
609 |
+
'WOODLANDS DR 52',
|
610 |
+
'WOODLANDS ST 41',
|
611 |
+
'MARSILING CRES',
|
612 |
+
'WOODLANDS ST 83',
|
613 |
+
'WOODLANDS CIRCLE',
|
614 |
+
'WOODLANDS DR 40',
|
615 |
+
'WOODLANDS ST 31',
|
616 |
+
'WOODLANDS DR 16',
|
617 |
+
'WOODLANDS ST 81',
|
618 |
+
'WOODLANDS RING RD',
|
619 |
+
'WOODLANDS DR 53',
|
620 |
+
'WOODLANDS DR 62',
|
621 |
+
'WOODLANDS DR 70',
|
622 |
+
'WOODLANDS DR 42',
|
623 |
+
'WOODLANDS DR 50',
|
624 |
+
'WOODLANDS AVE 6',
|
625 |
+
'WOODLANDS DR 14',
|
626 |
+
'WOODLANDS AVE 1',
|
627 |
+
'WOODLANDS AVE 5',
|
628 |
+
'MARSILING RISE',
|
629 |
+
'WOODLANDS CRES',
|
630 |
+
'WOODLANDS DR 73',
|
631 |
+
'WOODLANDS DR 44',
|
632 |
+
'YISHUN RING RD',
|
633 |
+
'YISHUN AVE 3',
|
634 |
+
'YISHUN ST 11',
|
635 |
+
'YISHUN AVE 4',
|
636 |
+
'YISHUN ST 22',
|
637 |
+
'YISHUN ST 71',
|
638 |
+
'YISHUN AVE 5',
|
639 |
+
'YISHUN ST 21',
|
640 |
+
'YISHUN ST 41',
|
641 |
+
'YISHUN ST 61',
|
642 |
+
'YISHUN AVE 6',
|
643 |
+
'YISHUN AVE 11',
|
644 |
+
'YISHUN CTRL',
|
645 |
+
'YISHUN ST 81',
|
646 |
+
'YISHUN ST 72',
|
647 |
+
'YISHUN AVE 2',
|
648 |
+
'ANG MO KIO ST 32',
|
649 |
+
'ANG MO KIO ST 31',
|
650 |
+
'BEDOK NTH ST 2',
|
651 |
+
'BEDOK NTH ST 1',
|
652 |
+
'JLN TENAGA',
|
653 |
+
'BEDOK NTH ST 4',
|
654 |
+
'BT BATOK WEST AVE 4',
|
655 |
+
'CANTONMENT CL',
|
656 |
+
'BOON TIONG RD',
|
657 |
+
'SPOTTISWOODE PK RD',
|
658 |
+
'REDHILL CL',
|
659 |
+
'KIM TIAN PL',
|
660 |
+
'CASHEW RD',
|
661 |
+
"QUEEN'S RD",
|
662 |
+
'CHANDER RD',
|
663 |
+
'KELANTAN RD',
|
664 |
+
'SAGO LANE',
|
665 |
+
'UPP CROSS ST',
|
666 |
+
'CHIN SWEE RD',
|
667 |
+
'SMITH ST',
|
668 |
+
'TECK WHYE AVE',
|
669 |
+
'CHOA CHU KANG ST 51',
|
670 |
+
'CHOA CHU KANG AVE 5',
|
671 |
+
'CHOA CHU KANG AVE 1',
|
672 |
+
'WEST COAST DR',
|
673 |
+
'PAYA LEBAR WAY',
|
674 |
+
'ALJUNIED CRES',
|
675 |
+
'JOO CHIAT RD',
|
676 |
+
'PINE CL',
|
677 |
+
'HOUGANG ST 22',
|
678 |
+
'HOUGANG AVE 9',
|
679 |
+
'HOUGANG AVE 6',
|
680 |
+
'HOUGANG ST 21',
|
681 |
+
'JURONG WEST ST 75',
|
682 |
+
'KANG CHING RD',
|
683 |
+
'KG KAYU RD',
|
684 |
+
'CRAWFORD LANE',
|
685 |
+
'WHAMPOA WEST',
|
686 |
+
'BEACH RD',
|
687 |
+
'CAMBRIDGE RD',
|
688 |
+
"ST. GEORGE'S LANE",
|
689 |
+
'JELLICOE RD',
|
690 |
+
'ELIAS RD',
|
691 |
+
'HOLLAND CL',
|
692 |
+
'TANGLIN HALT RD',
|
693 |
+
"C'WEALTH AVE",
|
694 |
+
'WELLINGTON CIRCLE',
|
695 |
+
'CANBERRA LINK',
|
696 |
+
'SENGKANG WEST AVE',
|
697 |
+
'SENGKANG EAST RD',
|
698 |
+
'SERANGOON CTRL DR',
|
699 |
+
'SERANGOON AVE 3',
|
700 |
+
'SERANGOON NTH AVE 3',
|
701 |
+
'TAMPINES AVE 8',
|
702 |
+
'TAMPINES ST 24',
|
703 |
+
'TAMPINES ST 12',
|
704 |
+
'SIMEI LANE',
|
705 |
+
'SIMEI ST 4',
|
706 |
+
'LOR 6 TOA PAYOH',
|
707 |
+
'KIM KEAT LINK',
|
708 |
+
'MARSILING LANE',
|
709 |
+
'WOODLANDS ST 82',
|
710 |
+
'WOODLANDS DR 60',
|
711 |
+
'WOODLANDS AVE 3',
|
712 |
+
'WOODLANDS DR 75',
|
713 |
+
'WOODLANDS AVE 4',
|
714 |
+
'WOODLANDS ST 32',
|
715 |
+
'YISHUN AVE 7',
|
716 |
+
'ANG MO KIO ST 11',
|
717 |
+
'BISHAN ST 11',
|
718 |
+
'BT BATOK WEST AVE 2',
|
719 |
+
'BT BATOK ST 32',
|
720 |
+
'BT BATOK ST 33',
|
721 |
+
'BT BATOK ST 22',
|
722 |
+
'BT BATOK WEST AVE 7',
|
723 |
+
'HOY FATT RD',
|
724 |
+
'SILAT AVE',
|
725 |
+
'EVERTON PK',
|
726 |
+
'BT MERAH CTRL',
|
727 |
+
'JELEBU RD',
|
728 |
+
'EMPRESS RD',
|
729 |
+
'VEERASAMY RD',
|
730 |
+
'CHOA CHU KANG ST 64',
|
731 |
+
'CHOA CHU KANG ST 53',
|
732 |
+
'CHOA CHU KANG NTH 7',
|
733 |
+
'CLEMENTI AVE 6',
|
734 |
+
'CLEMENTI ST 13',
|
735 |
+
'GEYLANG SERAI',
|
736 |
+
'JLN TIGA',
|
737 |
+
'ALJUNIED RD',
|
738 |
+
'YUNG LOH RD',
|
739 |
+
'YUNG AN RD',
|
740 |
+
"JLN MA'MOR",
|
741 |
+
'WHAMPOA RD',
|
742 |
+
'LOR 3 GEYLANG',
|
743 |
+
'PASIR RIS ST 13',
|
744 |
+
"QUEEN'S CL",
|
745 |
+
'DOVER CL EAST',
|
746 |
+
'SEMBAWANG VISTA',
|
747 |
+
'TAMPINES ST 43',
|
748 |
+
'SIMEI RD',
|
749 |
+
'KIM KEAT AVE',
|
750 |
+
'UPP ALJUNIED LANE',
|
751 |
+
'POTONG PASIR AVE 2',
|
752 |
+
'WOODLANDS DR 72',
|
753 |
+
'MARSILING RD',
|
754 |
+
'WOODLANDS DR 71',
|
755 |
+
'YISHUN AVE 9',
|
756 |
+
'YISHUN ST 20',
|
757 |
+
'ANG MO KIO ST 21',
|
758 |
+
'TIONG BAHRU RD',
|
759 |
+
'KLANG LANE',
|
760 |
+
'CHOA CHU KANG LOOP',
|
761 |
+
'CLEMENTI ST 14',
|
762 |
+
'SIMS PL',
|
763 |
+
'JURONG EAST ST 31',
|
764 |
+
'YUAN CHING RD',
|
765 |
+
'CORPORATION DR',
|
766 |
+
'YUNG PING RD',
|
767 |
+
'WHAMPOA STH',
|
768 |
+
'TESSENSOHN RD',
|
769 |
+
'JLN DUSUN',
|
770 |
+
'QUEENSWAY',
|
771 |
+
'FERNVALE LINK',
|
772 |
+
'KIM PONG RD',
|
773 |
+
'KIM CHENG ST',
|
774 |
+
'SAUJANA RD',
|
775 |
+
'BUFFALO RD',
|
776 |
+
'CLEMENTI ST 12',
|
777 |
+
'DAKOTA CRES',
|
778 |
+
'JURONG WEST ST 51',
|
779 |
+
'FRENCH RD',
|
780 |
+
'GLOUCESTER RD',
|
781 |
+
'KG ARANG RD',
|
782 |
+
'MOULMEIN RD',
|
783 |
+
'KENT RD',
|
784 |
+
'AH HOOD RD',
|
785 |
+
'SERANGOON NTH AVE 2',
|
786 |
+
'TAMPINES CTRL 1',
|
787 |
+
'TAMPINES AVE 7',
|
788 |
+
'LOR 1A TOA PAYOH',
|
789 |
+
'WOODLANDS AVE 9',
|
790 |
+
'YISHUN CTRL 1',
|
791 |
+
'LOWER DELTA RD',
|
792 |
+
'JLN DUA',
|
793 |
+
'WOODLANDS ST 11',
|
794 |
+
'ANG MO KIO AVE 2',
|
795 |
+
'SELEGIE RD',
|
796 |
+
'SIMS AVE',
|
797 |
+
'REDHILL LANE',
|
798 |
+
"KING GEORGE'S AVE",
|
799 |
+
'PASIR RIS ST 41',
|
800 |
+
'PUNGGOL WALK',
|
801 |
+
'LIM LIAK ST',
|
802 |
+
'JLN BERSEH',
|
803 |
+
'SENGKANG WEST WAY',
|
804 |
+
'BUANGKOK GREEN',
|
805 |
+
'SEMBAWANG WAY',
|
806 |
+
'PUNGGOL WAY',
|
807 |
+
'YISHUN ST 31',
|
808 |
+
'TECK WHYE CRES',
|
809 |
+
'KRETA AYER RD',
|
810 |
+
'MONTREAL LINK',
|
811 |
+
'UPP SERANGOON CRES',
|
812 |
+
'SUMANG LINK',
|
813 |
+
'SENGKANG EAST AVE',
|
814 |
+
'YISHUN AVE 1',
|
815 |
+
'ANCHORVALE CRES',
|
816 |
+
'YUNG KUANG RD',
|
817 |
+
'ANCHORVALE ST',
|
818 |
+
'TAMPINES CTRL 8',
|
819 |
+
'YISHUN ST 51',
|
820 |
+
'UPP SERANGOON VIEW',
|
821 |
+
'TAMPINES AVE 1',
|
822 |
+
'BEDOK RESERVOIR CRES',
|
823 |
+
'ANG MO KIO ST 61',
|
824 |
+
'DAWSON RD',
|
825 |
+
'FERNVALE ST',
|
826 |
+
'SENG POH RD',
|
827 |
+
'HOUGANG ST 32',
|
828 |
+
'TAMPINES ST 86',
|
829 |
+
'HENDERSON RD',
|
830 |
+
'SUMANG WALK',
|
831 |
+
'CHOA CHU KANG AVE 7',
|
832 |
+
'KEAT HONG CL',
|
833 |
+
'JURONG WEST CTRL 3',
|
834 |
+
'KEAT HONG LINK',
|
835 |
+
'ALJUNIED AVE 2',
|
836 |
+
'CANBERRA CRES',
|
837 |
+
'SUMANG LANE',
|
838 |
+
'CANBERRA ST',
|
839 |
+
'ANG MO KIO ST 44',
|
840 |
+
'ANG MO KIO ST 51',
|
841 |
+
'BT BATOK EAST AVE 6',
|
842 |
+
'BT BATOK WEST AVE 9',
|
843 |
+
'GEYLANG EAST AVE 2',
|
844 |
+
'MARINE PARADE CTRL',
|
845 |
+
'CANBERRA WALK',
|
846 |
+
'WOODLANDS RISE',
|
847 |
+
'TAMPINES ST 61',
|
848 |
+
'YISHUN ST 43',
|
849 |
+
'SENGKANG WEST RD',
|
850 |
+
'BIDADARI PK DR',
|
851 |
+
'CANBERRA VIEW'], value = "ANG MO KIO AVE 10", label = "Street Name"),
|
852 |
+
gr.components.Number(label="Floor", value = 1),
|
853 |
+
gr.components.Dropdown(choices=['1 ROOM', '2 ROOM', '3 ROOM', '4 ROOM', '5 ROOM', 'EXECUTIVE', 'MULTI-GENERATION'], value='1 ROOM', label="Flat Type"),
|
854 |
+
gr.components.Dropdown(choices=['2-room', 'Improved', 'Simplified', 'Standard', 'Apartment', 'Type S1', 'Type S2', 'Model A', 'Model A2', 'New Generation', 'Adjoined flat', 'Improved-Maisonette', 'Maisonette', 'Model A-Maisonette', 'Multi Generation', 'Premium Apartment', 'Premium Maisonette', 'DBSS', 'Terrace', 'Premium Apartment Loft', '3Gen'], value='Improved', label="Flat Model"),
|
855 |
+
gr.components.Number(label="Floor Area (sqm)"),
|
856 |
+
gr.components.Number(label="Lease Commence Year")
|
857 |
+
],
|
858 |
+
outputs=[gr.components.Textbox(label="Predicted Resale Price"),
|
859 |
+
gr.components.Image(type="filepath", label="Resale Price Trend Timeseries with following year prediction"),
|
860 |
+
gr.components.Textbox(label="AI Real Estate Advisor")],
|
861 |
+
allow_flagging="never"
|
862 |
+
)
|
863 |
+
|
864 |
+
# Launch the interface
|
865 |
+
interface.launch()
|
requirements.txt
ADDED
@@ -0,0 +1,5 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
numpy
|
2 |
+
pandas
|
3 |
+
joblib
|
4 |
+
tensorflow
|
5 |
+
matplotlib
|
street_name_categories.pkl
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:3684a67eecc5152581360954458444c27e6ffc0878d41ba1ed10c1ada4b32004
|
3 |
+
size 9406
|
town_categories.pkl
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:b871705543a5ea22be51bfb06cbbc82944a05f636f62c536199a760410e13e0f
|
3 |
+
size 342
|