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import json |
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import requests |
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from misc import nearest_mrt |
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import pickle |
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import os |
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import pandas as pd |
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import datetime |
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from datetime import datetime |
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def findlast10(postal): |
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df = pd.read_json("data/data_features.json", lines=True) |
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df_filtered = df[df['Postal']==str(postal)] |
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df_output=df_filtered.sort_values(by='transaction_yr', ascending=False).head(10).reset_index(drop=True) |
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storey_dict = { |
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'01 TO 03': 1, '04 TO 06': 2, '07 TO 09': 3, '10 TO 12': 4, |
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'13 TO 15': 5, '16 TO 18': 6, '19 TO 21': 7, '22 TO 24': 8, |
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'25 TO 27': 9, '28 TO 30': 10, '31 TO 33': 11, '34 TO 36': 12, |
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'37 TO 39': 13, '40 TO 42': 14, '43 TO 45': 15, '46 TO 48': 16, |
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'49 TO 51': 17 |
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} |
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swapped_dict = {value: key for key, value in storey_dict.items()} |
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df_output['storey_height']=df_output['storey_height'].apply(lambda x: swapped_dict[x]) |
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df_out = df_output[['transaction','area','storey_height','resale_price']] |
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return df_out |
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def main_fn(Postal_,age_,town_,storey_,room_): |
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filename = 'finalized_model2.sav' |
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if os.path.exists("./finalized_model2.sav"): |
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model = pickle.load(open(filename, 'rb')) |
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print('loaded model') |
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else: |
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print('failed loading model') |
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feature_names = model.feature_names_in_.tolist() |
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input = [0]*len(feature_names) |
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mrt_name = [] |
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mrt_loc = [] |
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with open('data/mrt_list.json', 'r') as file: |
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for line in file: |
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item = json.loads(line) |
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mrt_name.append(item['MRT']) |
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loc = tuple([float(i) for i in item['location']]) |
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mrt_loc.append(loc) |
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Postal_input = int(Postal_) |
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input[feature_names.index('Postal')] = Postal_input |
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search_term = Postal_input |
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query_string= 'https://www.onemap.gov.sg/api/common/elastic/search?searchVal={}&returnGeom=Y&getAddrDetails=Y&pageNum=1'.format(search_term) |
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resp = requests.get(query_string) |
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data = json.loads(resp.content) |
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print(query_string) |
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print(data) |
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chosen_result = data['results'][0] |
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distance_km, nearest_mr = nearest_mrt(chosen_result['LATITUDE'], chosen_result['LONGITUDE'], mrt_name, mrt_loc) |
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input[feature_names.index('distance_mrt')] = distance_km |
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height_input = int(storey_) |
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Height = (height_input+2)//3 |
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input[feature_names.index('storey_height')] = Height |
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input[feature_names.index("town")]=town_ |
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input[feature_names.index("flat_num")]=room_ |
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age_input = int(age_) |
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current_date = datetime.now() |
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input[feature_names.index('age_transation')] = age_input |
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input[feature_names.index('transaction_yr')] = current_date.year |
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Actual = dict(zip(feature_names,input)) |
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Actual_df = pd.DataFrame(Actual, index=[0]) |
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resale_adj_price = model.predict(Actual_df)[0] |
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quarter = (current_date.month - 1) // 3 + 1 |
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formatted_quarter = f"{quarter}Q{current_date.year}" |
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RPI_pd = pd.read_csv('data/RPI_dict.csv', header=None) |
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RPI_dict = dict(zip(RPI_pd[0], RPI_pd[1])) |
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RPI = float(RPI_dict[formatted_quarter]) |
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price = resale_adj_price*(RPI/133.9) |
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df = findlast10(Postal_input) |
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return (int(price), df) |
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if __name__ == "__main__": |
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Postal_,age_,town_,storey_,room_ = 680705, 30, 'CHOA CHU KANG', 12, '5 ROOM' |
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item = main_fn(Postal_,age_,town_,storey_,room_) |
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print(item) |