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2021-055
Commit dd22e39d authored by IT18174236(Gunarathna D.M.G.S)'s avatar IT18174236(Gunarathna D.M.G.S)
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Merge branch 'IT18174236(Content_Creation_Component)' into 'master' 

It18174236(content creation component)

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parents 102afa59 8ac75838
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Prediction Componant/Prediction/dataset.csv 0 → 100644
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Time Date,Code,Topic,Value
1012018,2667437,QLD_CW_ST0203,2926
2012018,2667437,QLD_CW_ST0203,2687.531
3012018,2667437,QLD_CW_ST0203,2793
4012018,2667437,QLD_CW_ST0203,2394
5012018,2667437,QLD_CW_ST0203,2660
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7012018,2667437,QLD_CW_ST0203,2527
8012018,2667437,QLD_CW_ST0203,2793
9012018,2667437,QLD_CW_ST0203,2793
10012018,2667437,QLD_CW_ST0203,2793
11012018,2667437,QLD_CW_ST0203,2926
12012018,2667437,QLD_CW_ST0203,2793
13012018,2667437,QLD_CW_ST0203,2869.8208
14012018,2667437,QLD_CW_ST0203,3325
15012018,2667437,QLD_CW_ST0203,2261
16012018,2667437,QLD_CW_ST0203,2261
17012018,2667437,QLD_CW_ST0203,2253.685
18012018,2667437,QLD_CW_ST0203,2394
19012018,2667437,QLD_CW_ST0203,2394
20012018,2667437,QLD_CW_ST0203,2394
21012018,2667437,QLD_CW_ST0203,2527
22012018,2667437,QLD_CW_ST0203,2793
23012018,2667437,QLD_CW_ST0203,2660
24012018,2667437,QLD_CW_ST0203,2547.1362
25012018,2667437,QLD_CW_ST0203,2926
26012018,2667437,QLD_CW_ST0203,2926
27012018,2667437,QLD_CW_ST0203,3059
28012018,2667437,QLD_CW_ST0203,2853.1027
29012018,2667437,QLD_CW_ST0203,2793
30012018,2667437,QLD_CW_ST0203,2793
31012018,2667437,QLD_CW_ST0203,2816.4745
1022018,2667437,QLD_CW_ST0203,2231.1548
2022018,2667437,QLD_CW_ST0203,5212.004
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6032018,2667437,QLD_CW_ST0203,2082.514
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24032018,2667437,QLD_CW_ST0203,2527
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25042018,2667437,QLD_CW_ST0203,2128
26042018,2667437,QLD_CW_ST0203,2261
27042018,2667437,QLD_CW_ST0203,2261
28042018,2667437,QLD_CW_ST0203,5187
29042018,2667437,QLD_CW_ST0203,5187
30042018,2667437,QLD_CW_ST0203,5187
1052018,2667437,QLD_CW_ST0203,5306.434
2052018,2667437,QLD_CW_ST0203,2121.0175
3052018,2667437,QLD_CW_ST0203,5320
4052018,2667437,QLD_CW_ST0203,2128
5052018,2667437,QLD_CW_ST0203,2128
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11052018,2667437,QLD_CW_ST0203,5054
12052018,2667437,QLD_CW_ST0203,4921
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21062018,2667437,QLD_CW_ST0203,4517.345
22062018,2667437,QLD_CW_ST0203,4788
23062018,2667437,QLD_CW_ST0203,4522
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27062018,2667437,QLD_CW_ST0203,4723.096
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6122020,2667437,QLD_CW_ST0203,2684.2592
7122020,2667437,QLD_CW_ST0203,2741.2896
8122020,2667437,QLD_CW_ST0203,3029.8464
9122020,2667437,QLD_CW_ST0203,2603.3154
10122020,2667437,QLD_CW_ST0203,2997.0486
11122020,2667437,QLD_CW_ST0203,2921.6376
12122020,2667437,QLD_CW_ST0203,3167.5945
13122020,2667437,QLD_CW_ST0203,3167.5945
14122020,2667437,QLD_CW_ST0203,3383.354511
15122020,2667437,QLD_CW_ST0203,2714.591329
16122020,2667437,QLD_CW_ST0203,2585.035107
\ No newline at end of file
Prediction Componant/Prediction/prediction.py 0 → 100644
View file @ dd22e39d
import warnings;
warnings.simplefilter('ignore')
import pandas as pd
import fbprophet
from fbprophet import Prophet
import json
from prophet.serialize import model_to_json, model_from_json
from matplotlib import pyplot
df = pd.read_csv('dataset.csv')
# data preprocessing
df['Year'] = df['Time Date'].apply(lambda x: str(x)[-4:])
df['Month'] = df['Time Date'].apply(lambda x: str(x)[-6:-4])
df['Day'] = df['Time Date'].apply(lambda x: str(x)[:-6])
df['ds'] = pd.DatetimeIndex(df['Year'] + '-' + df['Month'] + '-' + df['Day'])
df = df.loc[(df['Code'] == 2667437) & (df['Topic'] == 'QLD_CW_ST0203')]
df.drop(['Time Date', 'Code', 'Topic', 'Year', 'Month', 'Day'], axis=1, inplace=True)
df.columns = ['y', 'ds']
# sample print
print(df.head())
# train model
m = Prophet(interval_width=0.95, daily_seasonality=True)
model = m.fit(df)
# save trained model
with open('serialized_model.json', 'w') as fout:
json.dump(model_to_json(m), fout) # Save model
# future prediction (next 100 days)
future = m.make_future_dataframe(periods=100, freq='D')
forecast = m.predict(future)
# print predicted data
print(forecast)
# show charts prediction
model.plot(forecast)
pyplot.show()
plot1 = m.plot(forecast)
Prediction Componant/Prediction/serialized_model.json 0 → 100644
View file @ dd22e39d
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