Upload 4 files
Browse files- app.py +107 -0
- insurance.csv +1339 -0
- requirements.txt +1 -0
- train.py +57 -0
app.py
ADDED
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# Import the libraries
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import os
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import uuid
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import joblib
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importjson
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import gradio as gr
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import pandas as pd
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from huggingface_hub import CommitScheduler
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from pathlib import Path
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# Run the training script placed in the same directory as app.py
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# The training script will train and persist a linear regression
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# model with the filename 'model.joblib'
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os.system("python train.py")
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# Load the freshly trained model from disk
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Insurance_charge_predictor = joblib.load('model.joblib')
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# Prepare the logging functionality
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log_file = Path("logs/") / f"data_{uuid.uuid4()}.json"
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log_folder = log_file.parent
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scheduler = CommitScheduler(
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repo_id="-----------", # provide a name "insurance-charge-mlops-logs" for the repo_id
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repo_type="dataset",
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folder_path=log_folder,
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path_in_repo="data",
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every=2
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)
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# Define the predict function which will take features, convert to dataframe and make predictions using the saved model
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# the functions runs when 'Submit' is clicked or when a API request is made
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def predict_insurance_charge(age,sex,bmi,children,smoker,region):
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sample = {
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'age':age,
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'bmi':bmi,
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'children':children,
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'sex':sex,
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'smoker':smoker,
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'region':region
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}
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data_point = pd.DataFrame([sample])
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prediction = insurance_charge_predictor(data_point).tolist()
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# While the prediction is made, log both the inputs and outputs to a log file
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# While writing to the log file, ensure that the commit scheduler is locked to avoid parallel
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# access
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with scheduler.lock:
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with log_file.open("a") as f:
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f.write(json.dumps(
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{
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'age': age,
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'bmi': bmi,
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'children': children,
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'sex': sex,
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'smoker': smoker,
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'region': region,
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'prediction': prediction[0]
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}
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))
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f.write("\n")
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return prediction[0]
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# Set up UI components for input and output
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age_input = gr.Number(label='age')
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bmi_input = gr.Number(label='bmi')
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children_input = gr.Number(label='children')
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sex_ipnut = gr.Dropdown(['male','female'],label='sex')
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smoker_input = gr.Dropdown(['yes','no'],label='smoker')
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region_input = gr.Dropdown(
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['southeast','southwest','northwest','northeast'],
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lable='region'
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)
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model_output = gr.label(label="Insurace Charges")
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# Create the gradio interface, make title "HealthyLife Insurance Charge Prediction"
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demo = gr.interface(
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fn=predict_insurance_charge,
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inputs=[age_input,bmi_input,children_input,sex_input,smoker_input,region_input],
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output=model_output,
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title="HealthyLife Insurance Charge Prediction",
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description="This API allows you to predict the estimating insurnace charges based on customer attributes",
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examples=[[33,33.44,5,'male','no','southeast'],
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[58,25.177,0,'male','no','northeast'],
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[52,38.380,2,'female','no','northeast']],
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concurrency_limit=16
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)
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# Launch with a load balancer
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demo.queue()
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demo.launch(share=False)
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insurance.csv
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@@ -0,0 +1,1339 @@
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|
1 |
+
index,age,sex,bmi,children,smoker,region,charges
|
2 |
+
0,19,female,27.9,0,yes,southwest,16884.924
|
3 |
+
1,18,male,33.77,1,no,southeast,1725.5523
|
4 |
+
2,28,male,33.0,3,no,southeast,4449.462
|
5 |
+
3,33,male,22.705,0,no,northwest,21984.47061
|
6 |
+
4,32,male,28.88,0,no,northwest,3866.8552
|
7 |
+
5,31,female,25.74,0,no,southeast,3756.6216
|
8 |
+
6,46,female,33.44,1,no,southeast,8240.5896
|
9 |
+
7,37,female,27.74,3,no,northwest,7281.5056
|
10 |
+
8,37,male,29.83,2,no,northeast,6406.4107
|
11 |
+
9,60,female,25.84,0,no,northwest,28923.13692
|
12 |
+
10,25,male,26.22,0,no,northeast,2721.3208
|
13 |
+
11,62,female,26.29,0,yes,southeast,27808.7251
|
14 |
+
12,23,male,34.4,0,no,southwest,1826.843
|
15 |
+
13,56,female,39.82,0,no,southeast,11090.7178
|
16 |
+
14,27,male,42.13,0,yes,southeast,39611.7577
|
17 |
+
15,19,male,24.6,1,no,southwest,1837.237
|
18 |
+
16,52,female,30.78,1,no,northeast,10797.3362
|
19 |
+
17,23,male,23.845,0,no,northeast,2395.17155
|
20 |
+
18,56,male,40.3,0,no,southwest,10602.385
|
21 |
+
19,30,male,35.3,0,yes,southwest,36837.467
|
22 |
+
20,60,female,36.005,0,no,northeast,13228.84695
|
23 |
+
21,30,female,32.4,1,no,southwest,4149.736
|
24 |
+
22,18,male,34.1,0,no,southeast,1137.011
|
25 |
+
23,34,female,31.92,1,yes,northeast,37701.8768
|
26 |
+
24,37,male,28.025,2,no,northwest,6203.90175
|
27 |
+
25,59,female,27.72,3,no,southeast,14001.1338
|
28 |
+
26,63,female,23.085,0,no,northeast,14451.83515
|
29 |
+
27,55,female,32.775,2,no,northwest,12268.63225
|
30 |
+
28,23,male,17.385,1,no,northwest,2775.19215
|
31 |
+
29,31,male,36.3,2,yes,southwest,38711.0
|
32 |
+
30,22,male,35.6,0,yes,southwest,35585.576
|
33 |
+
31,18,female,26.315,0,no,northeast,2198.18985
|
34 |
+
32,19,female,28.6,5,no,southwest,4687.797
|
35 |
+
33,63,male,28.31,0,no,northwest,13770.0979
|
36 |
+
34,28,male,36.4,1,yes,southwest,51194.55914
|
37 |
+
35,19,male,20.425,0,no,northwest,1625.43375
|
38 |
+
36,62,female,32.965,3,no,northwest,15612.19335
|
39 |
+
37,26,male,20.8,0,no,southwest,2302.3
|
40 |
+
38,35,male,36.67,1,yes,northeast,39774.2763
|
41 |
+
39,60,male,39.9,0,yes,southwest,48173.361
|
42 |
+
40,24,female,26.6,0,no,northeast,3046.062
|
43 |
+
41,31,female,36.63,2,no,southeast,4949.7587
|
44 |
+
42,41,male,21.78,1,no,southeast,6272.4772
|
45 |
+
43,37,female,30.8,2,no,southeast,6313.759
|
46 |
+
44,38,male,37.05,1,no,northeast,6079.6715
|
47 |
+
45,55,male,37.3,0,no,southwest,20630.28351
|
48 |
+
46,18,female,38.665,2,no,northeast,3393.35635
|
49 |
+
47,28,female,34.77,0,no,northwest,3556.9223
|
50 |
+
48,60,female,24.53,0,no,southeast,12629.8967
|
51 |
+
49,36,male,35.2,1,yes,southeast,38709.176
|
52 |
+
50,18,female,35.625,0,no,northeast,2211.13075
|
53 |
+
51,21,female,33.63,2,no,northwest,3579.8287
|
54 |
+
52,48,male,28.0,1,yes,southwest,23568.272
|
55 |
+
53,36,male,34.43,0,yes,southeast,37742.5757
|
56 |
+
54,40,female,28.69,3,no,northwest,8059.6791
|
57 |
+
55,58,male,36.955,2,yes,northwest,47496.49445
|
58 |
+
56,58,female,31.825,2,no,northeast,13607.36875
|
59 |
+
57,18,male,31.68,2,yes,southeast,34303.1672
|
60 |
+
58,53,female,22.88,1,yes,southeast,23244.7902
|
61 |
+
59,34,female,37.335,2,no,northwest,5989.52365
|
62 |
+
60,43,male,27.36,3,no,northeast,8606.2174
|
63 |
+
61,25,male,33.66,4,no,southeast,4504.6624
|
64 |
+
62,64,male,24.7,1,no,northwest,30166.61817
|
65 |
+
63,28,female,25.935,1,no,northwest,4133.64165
|
66 |
+
64,20,female,22.42,0,yes,northwest,14711.7438
|
67 |
+
65,19,female,28.9,0,no,southwest,1743.214
|
68 |
+
66,61,female,39.1,2,no,southwest,14235.072
|
69 |
+
67,40,male,26.315,1,no,northwest,6389.37785
|
70 |
+
68,40,female,36.19,0,no,southeast,5920.1041
|
71 |
+
69,28,male,23.98,3,yes,southeast,17663.1442
|
72 |
+
70,27,female,24.75,0,yes,southeast,16577.7795
|
73 |
+
71,31,male,28.5,5,no,northeast,6799.458
|
74 |
+
72,53,female,28.1,3,no,southwest,11741.726
|
75 |
+
73,58,male,32.01,1,no,southeast,11946.6259
|
76 |
+
74,44,male,27.4,2,no,southwest,7726.854
|
77 |
+
75,57,male,34.01,0,no,northwest,11356.6609
|
78 |
+
76,29,female,29.59,1,no,southeast,3947.4131
|
79 |
+
77,21,male,35.53,0,no,southeast,1532.4697
|
80 |
+
78,22,female,39.805,0,no,northeast,2755.02095
|
81 |
+
79,41,female,32.965,0,no,northwest,6571.02435
|
82 |
+
80,31,male,26.885,1,no,northeast,4441.21315
|
83 |
+
81,45,female,38.285,0,no,northeast,7935.29115
|
84 |
+
82,22,male,37.62,1,yes,southeast,37165.1638
|
85 |
+
83,48,female,41.23,4,no,northwest,11033.6617
|
86 |
+
84,37,female,34.8,2,yes,southwest,39836.519
|
87 |
+
85,45,male,22.895,2,yes,northwest,21098.55405
|
88 |
+
86,57,female,31.16,0,yes,northwest,43578.9394
|
89 |
+
87,56,female,27.2,0,no,southwest,11073.176
|
90 |
+
88,46,female,27.74,0,no,northwest,8026.6666
|
91 |
+
89,55,female,26.98,0,no,northwest,11082.5772
|
92 |
+
90,21,female,39.49,0,no,southeast,2026.9741
|
93 |
+
91,53,female,24.795,1,no,northwest,10942.13205
|
94 |
+
92,59,male,29.83,3,yes,northeast,30184.9367
|
95 |
+
93,35,male,34.77,2,no,northwest,5729.0053
|
96 |
+
94,64,female,31.3,2,yes,southwest,47291.055
|
97 |
+
95,28,female,37.62,1,no,southeast,3766.8838
|
98 |
+
96,54,female,30.8,3,no,southwest,12105.32
|
99 |
+
97,55,male,38.28,0,no,southeast,10226.2842
|
100 |
+
98,56,male,19.95,0,yes,northeast,22412.6485
|
101 |
+
99,38,male,19.3,0,yes,southwest,15820.699
|
102 |
+
100,41,female,31.6,0,no,southwest,6186.127
|
103 |
+
101,30,male,25.46,0,no,northeast,3645.0894
|
104 |
+
102,18,female,30.115,0,no,northeast,21344.8467
|
105 |
+
103,61,female,29.92,3,yes,southeast,30942.1918
|
106 |
+
104,34,female,27.5,1,no,southwest,5003.853
|
107 |
+
105,20,male,28.025,1,yes,northwest,17560.37975
|
108 |
+
106,19,female,28.4,1,no,southwest,2331.519
|
109 |
+
107,26,male,30.875,2,no,northwest,3877.30425
|
110 |
+
108,29,male,27.94,0,no,southeast,2867.1196
|
111 |
+
109,63,male,35.09,0,yes,southeast,47055.5321
|
112 |
+
110,54,male,33.63,1,no,northwest,10825.2537
|
113 |
+
111,55,female,29.7,2,no,southwest,11881.358
|
114 |
+
112,37,male,30.8,0,no,southwest,4646.759
|
115 |
+
113,21,female,35.72,0,no,northwest,2404.7338
|
116 |
+
114,52,male,32.205,3,no,northeast,11488.31695
|
117 |
+
115,60,male,28.595,0,no,northeast,30259.99556
|
118 |
+
116,58,male,49.06,0,no,southeast,11381.3254
|
119 |
+
117,29,female,27.94,1,yes,southeast,19107.7796
|
120 |
+
118,49,female,27.17,0,no,southeast,8601.3293
|
121 |
+
119,37,female,23.37,2,no,northwest,6686.4313
|
122 |
+
120,44,male,37.1,2,no,southwest,7740.337
|
123 |
+
121,18,male,23.75,0,no,northeast,1705.6245
|
124 |
+
122,20,female,28.975,0,no,northwest,2257.47525
|
125 |
+
123,44,male,31.35,1,yes,northeast,39556.4945
|
126 |
+
124,47,female,33.915,3,no,northwest,10115.00885
|
127 |
+
125,26,female,28.785,0,no,northeast,3385.39915
|
128 |
+
126,19,female,28.3,0,yes,southwest,17081.08
|
129 |
+
127,52,female,37.4,0,no,southwest,9634.538
|
130 |
+
128,32,female,17.765,2,yes,northwest,32734.1863
|
131 |
+
129,38,male,34.7,2,no,southwest,6082.405
|
132 |
+
130,59,female,26.505,0,no,northeast,12815.44495
|
133 |
+
131,61,female,22.04,0,no,northeast,13616.3586
|
134 |
+
132,53,female,35.9,2,no,southwest,11163.568
|
135 |
+
133,19,male,25.555,0,no,northwest,1632.56445
|
136 |
+
134,20,female,28.785,0,no,northeast,2457.21115
|
137 |
+
135,22,female,28.05,0,no,southeast,2155.6815
|
138 |
+
136,19,male,34.1,0,no,southwest,1261.442
|
139 |
+
137,22,male,25.175,0,no,northwest,2045.68525
|
140 |
+
138,54,female,31.9,3,no,southeast,27322.73386
|
141 |
+
139,22,female,36.0,0,no,southwest,2166.732
|
142 |
+
140,34,male,22.42,2,no,northeast,27375.90478
|
143 |
+
141,26,male,32.49,1,no,northeast,3490.5491
|
144 |
+
142,34,male,25.3,2,yes,southeast,18972.495
|
145 |
+
143,29,male,29.735,2,no,northwest,18157.876
|
146 |
+
144,30,male,28.69,3,yes,northwest,20745.9891
|
147 |
+
145,29,female,38.83,3,no,southeast,5138.2567
|
148 |
+
146,46,male,30.495,3,yes,northwest,40720.55105
|
149 |
+
147,51,female,37.73,1,no,southeast,9877.6077
|
150 |
+
148,53,female,37.43,1,no,northwest,10959.6947
|
151 |
+
149,19,male,28.4,1,no,southwest,1842.519
|
152 |
+
150,35,male,24.13,1,no,northwest,5125.2157
|
153 |
+
151,48,male,29.7,0,no,southeast,7789.635
|
154 |
+
152,32,female,37.145,3,no,northeast,6334.34355
|
155 |
+
153,42,female,23.37,0,yes,northeast,19964.7463
|
156 |
+
154,40,female,25.46,1,no,northeast,7077.1894
|
157 |
+
155,44,male,39.52,0,no,northwest,6948.7008
|
158 |
+
156,48,male,24.42,0,yes,southeast,21223.6758
|
159 |
+
157,18,male,25.175,0,yes,northeast,15518.18025
|
160 |
+
158,30,male,35.53,0,yes,southeast,36950.2567
|
161 |
+
159,50,female,27.83,3,no,southeast,19749.38338
|
162 |
+
160,42,female,26.6,0,yes,northwest,21348.706
|
163 |
+
161,18,female,36.85,0,yes,southeast,36149.4835
|
164 |
+
162,54,male,39.6,1,no,southwest,10450.552
|
165 |
+
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1111,38,male,38.39,3,yes,southeast,41949.2441
|
1114 |
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1112,48,female,25.85,3,yes,southeast,24180.9335
|
1115 |
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1113,28,female,26.315,3,no,northwest,5312.16985
|
1116 |
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1114,23,male,24.51,0,no,northeast,2396.0959
|
1117 |
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1115,55,male,32.67,1,no,southeast,10807.4863
|
1118 |
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1116,41,male,29.64,5,no,northeast,9222.4026
|
1119 |
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1117,25,male,33.33,2,yes,southeast,36124.5737
|
1120 |
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1118,33,male,35.75,1,yes,southeast,38282.7495
|
1121 |
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1119,30,female,19.95,3,no,northwest,5693.4305
|
1122 |
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1120,23,female,31.4,0,yes,southwest,34166.273
|
1123 |
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1121,46,male,38.17,2,no,southeast,8347.1643
|
1124 |
+
1122,53,female,36.86,3,yes,northwest,46661.4424
|
1125 |
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1123,27,female,32.395,1,no,northeast,18903.49141
|
1126 |
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1124,23,female,42.75,1,yes,northeast,40904.1995
|
1127 |
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1125,63,female,25.08,0,no,northwest,14254.6082
|
1128 |
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1126,55,male,29.9,0,no,southwest,10214.636
|
1129 |
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1127,35,female,35.86,2,no,southeast,5836.5204
|
1130 |
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1128,34,male,32.8,1,no,southwest,14358.36437
|
1131 |
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1129,19,female,18.6,0,no,southwest,1728.897
|
1132 |
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1130,39,female,23.87,5,no,southeast,8582.3023
|
1133 |
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1131,27,male,45.9,2,no,southwest,3693.428
|
1134 |
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1132,57,male,40.28,0,no,northeast,20709.02034
|
1135 |
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1133,52,female,18.335,0,no,northwest,9991.03765
|
1136 |
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1134,28,male,33.82,0,no,northwest,19673.33573
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1137 |
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1135,50,female,28.12,3,no,northwest,11085.5868
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1138 |
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1136,44,female,25.0,1,no,southwest,7623.518
|
1139 |
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1137,26,female,22.23,0,no,northwest,3176.2877
|
1140 |
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1138,33,male,30.25,0,no,southeast,3704.3545
|
1141 |
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1139,19,female,32.49,0,yes,northwest,36898.73308
|
1142 |
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1140,50,male,37.07,1,no,southeast,9048.0273
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1143 |
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1141,41,female,32.6,3,no,southwest,7954.517
|
1144 |
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1142,52,female,24.86,0,no,southeast,27117.99378
|
1145 |
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1143,39,male,32.34,2,no,southeast,6338.0756
|
1146 |
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1144,50,male,32.3,2,no,southwest,9630.397
|
1147 |
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1145,52,male,32.775,3,no,northwest,11289.10925
|
1148 |
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1146,60,male,32.8,0,yes,southwest,52590.82939
|
1149 |
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1147,20,female,31.92,0,no,northwest,2261.5688
|
1150 |
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1148,55,male,21.5,1,no,southwest,10791.96
|
1151 |
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1149,42,male,34.1,0,no,southwest,5979.731
|
1152 |
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1150,18,female,30.305,0,no,northeast,2203.73595
|
1153 |
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1151,58,female,36.48,0,no,northwest,12235.8392
|
1154 |
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1152,43,female,32.56,3,yes,southeast,40941.2854
|
1155 |
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1153,35,female,35.815,1,no,northwest,5630.45785
|
1156 |
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1154,48,female,27.93,4,no,northwest,11015.1747
|
1157 |
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1155,36,female,22.135,3,no,northeast,7228.21565
|
1158 |
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1156,19,male,44.88,0,yes,southeast,39722.7462
|
1159 |
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1157,23,female,23.18,2,no,northwest,14426.07385
|
1160 |
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1158,20,female,30.59,0,no,northeast,2459.7201
|
1161 |
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1159,32,female,41.1,0,no,southwest,3989.841
|
1162 |
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1160,43,female,34.58,1,no,northwest,7727.2532
|
1163 |
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1161,34,male,42.13,2,no,southeast,5124.1887
|
1164 |
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1162,30,male,38.83,1,no,southeast,18963.17192
|
1165 |
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1163,18,female,28.215,0,no,northeast,2200.83085
|
1166 |
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1164,41,female,28.31,1,no,northwest,7153.5539
|
1167 |
+
1165,35,female,26.125,0,no,northeast,5227.98875
|
1168 |
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1166,57,male,40.37,0,no,southeast,10982.5013
|
1169 |
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1167,29,female,24.6,2,no,southwest,4529.477
|
1170 |
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1168,32,male,35.2,2,no,southwest,4670.64
|
1171 |
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1169,37,female,34.105,1,no,northwest,6112.35295
|
1172 |
+
1170,18,male,27.36,1,yes,northeast,17178.6824
|
1173 |
+
1171,43,female,26.7,2,yes,southwest,22478.6
|
1174 |
+
1172,56,female,41.91,0,no,southeast,11093.6229
|
1175 |
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1173,38,male,29.26,2,no,northwest,6457.8434
|
1176 |
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1174,29,male,32.11,2,no,northwest,4433.9159
|
1177 |
+
1175,22,female,27.1,0,no,southwest,2154.361
|
1178 |
+
1176,52,female,24.13,1,yes,northwest,23887.6627
|
1179 |
+
1177,40,female,27.4,1,no,southwest,6496.886
|
1180 |
+
1178,23,female,34.865,0,no,northeast,2899.48935
|
1181 |
+
1179,31,male,29.81,0,yes,southeast,19350.3689
|
1182 |
+
1180,42,female,41.325,1,no,northeast,7650.77375
|
1183 |
+
1181,24,female,29.925,0,no,northwest,2850.68375
|
1184 |
+
1182,25,female,30.3,0,no,southwest,2632.992
|
1185 |
+
1183,48,female,27.36,1,no,northeast,9447.3824
|
1186 |
+
1184,23,female,28.49,1,yes,southeast,18328.2381
|
1187 |
+
1185,45,male,23.56,2,no,northeast,8603.8234
|
1188 |
+
1186,20,male,35.625,3,yes,northwest,37465.34375
|
1189 |
+
1187,62,female,32.68,0,no,northwest,13844.7972
|
1190 |
+
1188,43,female,25.27,1,yes,northeast,21771.3423
|
1191 |
+
1189,23,female,28.0,0,no,southwest,13126.67745
|
1192 |
+
1190,31,female,32.775,2,no,northwest,5327.40025
|
1193 |
+
1191,41,female,21.755,1,no,northeast,13725.47184
|
1194 |
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1192,58,female,32.395,1,no,northeast,13019.16105
|
1195 |
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1193,48,female,36.575,0,no,northwest,8671.19125
|
1196 |
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1194,31,female,21.755,0,no,northwest,4134.08245
|
1197 |
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1195,19,female,27.93,3,no,northwest,18838.70366
|
1198 |
+
1196,19,female,30.02,0,yes,northwest,33307.5508
|
1199 |
+
1197,41,male,33.55,0,no,southeast,5699.8375
|
1200 |
+
1198,40,male,29.355,1,no,northwest,6393.60345
|
1201 |
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1199,31,female,25.8,2,no,southwest,4934.705
|
1202 |
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1200,37,male,24.32,2,no,northwest,6198.7518
|
1203 |
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1201,46,male,40.375,2,no,northwest,8733.22925
|
1204 |
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1202,22,male,32.11,0,no,northwest,2055.3249
|
1205 |
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1203,51,male,32.3,1,no,northeast,9964.06
|
1206 |
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1204,18,female,27.28,3,yes,southeast,18223.4512
|
1207 |
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1205,35,male,17.86,1,no,northwest,5116.5004
|
1208 |
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1206,59,female,34.8,2,no,southwest,36910.60803
|
1209 |
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1207,36,male,33.4,2,yes,southwest,38415.474
|
1210 |
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1208,37,female,25.555,1,yes,northeast,20296.86345
|
1211 |
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1209,59,male,37.1,1,no,southwest,12347.172
|
1212 |
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1210,36,male,30.875,1,no,northwest,5373.36425
|
1213 |
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1211,39,male,34.1,2,no,southeast,23563.01618
|
1214 |
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1212,18,male,21.47,0,no,northeast,1702.4553
|
1215 |
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1213,52,female,33.3,2,no,southwest,10806.839
|
1216 |
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1214,27,female,31.255,1,no,northwest,3956.07145
|
1217 |
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1215,18,male,39.14,0,no,northeast,12890.05765
|
1218 |
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1216,40,male,25.08,0,no,southeast,5415.6612
|
1219 |
+
1217,29,male,37.29,2,no,southeast,4058.1161
|
1220 |
+
1218,46,female,34.6,1,yes,southwest,41661.602
|
1221 |
+
1219,38,female,30.21,3,no,northwest,7537.1639
|
1222 |
+
1220,30,female,21.945,1,no,northeast,4718.20355
|
1223 |
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1221,40,male,24.97,2,no,southeast,6593.5083
|
1224 |
+
1222,50,male,25.3,0,no,southeast,8442.667
|
1225 |
+
1223,20,female,24.42,0,yes,southeast,26125.67477
|
1226 |
+
1224,41,male,23.94,1,no,northeast,6858.4796
|
1227 |
+
1225,33,female,39.82,1,no,southeast,4795.6568
|
1228 |
+
1226,38,male,16.815,2,no,northeast,6640.54485
|
1229 |
+
1227,42,male,37.18,2,no,southeast,7162.0122
|
1230 |
+
1228,56,male,34.43,0,no,southeast,10594.2257
|
1231 |
+
1229,58,male,30.305,0,no,northeast,11938.25595
|
1232 |
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1230,52,male,34.485,3,yes,northwest,60021.39897
|
1233 |
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1231,20,female,21.8,0,yes,southwest,20167.33603
|
1234 |
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1232,54,female,24.605,3,no,northwest,12479.70895
|
1235 |
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1233,58,male,23.3,0,no,southwest,11345.519
|
1236 |
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1234,45,female,27.83,2,no,southeast,8515.7587
|
1237 |
+
1235,26,male,31.065,0,no,northwest,2699.56835
|
1238 |
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1236,63,female,21.66,0,no,northeast,14449.8544
|
1239 |
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1237,58,female,28.215,0,no,northwest,12224.35085
|
1240 |
+
1238,37,male,22.705,3,no,northeast,6985.50695
|
1241 |
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1239,25,female,42.13,1,no,southeast,3238.4357
|
1242 |
+
1240,52,male,41.8,2,yes,southeast,47269.854
|
1243 |
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1241,64,male,36.96,2,yes,southeast,49577.6624
|
1244 |
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1242,22,female,21.28,3,no,northwest,4296.2712
|
1245 |
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1243,28,female,33.11,0,no,southeast,3171.6149
|
1246 |
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1244,18,male,33.33,0,no,southeast,1135.9407
|
1247 |
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1245,28,male,24.3,5,no,southwest,5615.369
|
1248 |
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1246,45,female,25.7,3,no,southwest,9101.798
|
1249 |
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1247,33,male,29.4,4,no,southwest,6059.173
|
1250 |
+
1248,18,female,39.82,0,no,southeast,1633.9618
|
1251 |
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1249,32,male,33.63,1,yes,northeast,37607.5277
|
1252 |
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1250,24,male,29.83,0,yes,northeast,18648.4217
|
1253 |
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1251,19,male,19.8,0,no,southwest,1241.565
|
1254 |
+
1252,20,male,27.3,0,yes,southwest,16232.847
|
1255 |
+
1253,40,female,29.3,4,no,southwest,15828.82173
|
1256 |
+
1254,34,female,27.72,0,no,southeast,4415.1588
|
1257 |
+
1255,42,female,37.9,0,no,southwest,6474.013
|
1258 |
+
1256,51,female,36.385,3,no,northwest,11436.73815
|
1259 |
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1257,54,female,27.645,1,no,northwest,11305.93455
|
1260 |
+
1258,55,male,37.715,3,no,northwest,30063.58055
|
1261 |
+
1259,52,female,23.18,0,no,northeast,10197.7722
|
1262 |
+
1260,32,female,20.52,0,no,northeast,4544.2348
|
1263 |
+
1261,28,male,37.1,1,no,southwest,3277.161
|
1264 |
+
1262,41,female,28.05,1,no,southeast,6770.1925
|
1265 |
+
1263,43,female,29.9,1,no,southwest,7337.748
|
1266 |
+
1264,49,female,33.345,2,no,northeast,10370.91255
|
1267 |
+
1265,64,male,23.76,0,yes,southeast,26926.5144
|
1268 |
+
1266,55,female,30.5,0,no,southwest,10704.47
|
1269 |
+
1267,24,male,31.065,0,yes,northeast,34254.05335
|
1270 |
+
1268,20,female,33.3,0,no,southwest,1880.487
|
1271 |
+
1269,45,male,27.5,3,no,southwest,8615.3
|
1272 |
+
1270,26,male,33.915,1,no,northwest,3292.52985
|
1273 |
+
1271,25,female,34.485,0,no,northwest,3021.80915
|
1274 |
+
1272,43,male,25.52,5,no,southeast,14478.33015
|
1275 |
+
1273,35,male,27.61,1,no,southeast,4747.0529
|
1276 |
+
1274,26,male,27.06,0,yes,southeast,17043.3414
|
1277 |
+
1275,57,male,23.7,0,no,southwest,10959.33
|
1278 |
+
1276,22,female,30.4,0,no,northeast,2741.948
|
1279 |
+
1277,32,female,29.735,0,no,northwest,4357.04365
|
1280 |
+
1278,39,male,29.925,1,yes,northeast,22462.04375
|
1281 |
+
1279,25,female,26.79,2,no,northwest,4189.1131
|
1282 |
+
1280,48,female,33.33,0,no,southeast,8283.6807
|
1283 |
+
1281,47,female,27.645,2,yes,northwest,24535.69855
|
1284 |
+
1282,18,female,21.66,0,yes,northeast,14283.4594
|
1285 |
+
1283,18,male,30.03,1,no,southeast,1720.3537
|
1286 |
+
1284,61,male,36.3,1,yes,southwest,47403.88
|
1287 |
+
1285,47,female,24.32,0,no,northeast,8534.6718
|
1288 |
+
1286,28,female,17.29,0,no,northeast,3732.6251
|
1289 |
+
1287,36,female,25.9,1,no,southwest,5472.449
|
1290 |
+
1288,20,male,39.4,2,yes,southwest,38344.566
|
1291 |
+
1289,44,male,34.32,1,no,southeast,7147.4728
|
1292 |
+
1290,38,female,19.95,2,no,northeast,7133.9025
|
1293 |
+
1291,19,male,34.9,0,yes,southwest,34828.654
|
1294 |
+
1292,21,male,23.21,0,no,southeast,1515.3449
|
1295 |
+
1293,46,male,25.745,3,no,northwest,9301.89355
|
1296 |
+
1294,58,male,25.175,0,no,northeast,11931.12525
|
1297 |
+
1295,20,male,22.0,1,no,southwest,1964.78
|
1298 |
+
1296,18,male,26.125,0,no,northeast,1708.92575
|
1299 |
+
1297,28,female,26.51,2,no,southeast,4340.4409
|
1300 |
+
1298,33,male,27.455,2,no,northwest,5261.46945
|
1301 |
+
1299,19,female,25.745,1,no,northwest,2710.82855
|
1302 |
+
1300,45,male,30.36,0,yes,southeast,62592.87309
|
1303 |
+
1301,62,male,30.875,3,yes,northwest,46718.16325
|
1304 |
+
1302,25,female,20.8,1,no,southwest,3208.787
|
1305 |
+
1303,43,male,27.8,0,yes,southwest,37829.7242
|
1306 |
+
1304,42,male,24.605,2,yes,northeast,21259.37795
|
1307 |
+
1305,24,female,27.72,0,no,southeast,2464.6188
|
1308 |
+
1306,29,female,21.85,0,yes,northeast,16115.3045
|
1309 |
+
1307,32,male,28.12,4,yes,northwest,21472.4788
|
1310 |
+
1308,25,female,30.2,0,yes,southwest,33900.653
|
1311 |
+
1309,41,male,32.2,2,no,southwest,6875.961
|
1312 |
+
1310,42,male,26.315,1,no,northwest,6940.90985
|
1313 |
+
1311,33,female,26.695,0,no,northwest,4571.41305
|
1314 |
+
1312,34,male,42.9,1,no,southwest,4536.259
|
1315 |
+
1313,19,female,34.7,2,yes,southwest,36397.576
|
1316 |
+
1314,30,female,23.655,3,yes,northwest,18765.87545
|
1317 |
+
1315,18,male,28.31,1,no,northeast,11272.33139
|
1318 |
+
1316,19,female,20.6,0,no,southwest,1731.677
|
1319 |
+
1317,18,male,53.13,0,no,southeast,1163.4627
|
1320 |
+
1318,35,male,39.71,4,no,northeast,19496.71917
|
1321 |
+
1319,39,female,26.315,2,no,northwest,7201.70085
|
1322 |
+
1320,31,male,31.065,3,no,northwest,5425.02335
|
1323 |
+
1321,62,male,26.695,0,yes,northeast,28101.33305
|
1324 |
+
1322,62,male,38.83,0,no,southeast,12981.3457
|
1325 |
+
1323,42,female,40.37,2,yes,southeast,43896.3763
|
1326 |
+
1324,31,male,25.935,1,no,northwest,4239.89265
|
1327 |
+
1325,61,male,33.535,0,no,northeast,13143.33665
|
1328 |
+
1326,42,female,32.87,0,no,northeast,7050.0213
|
1329 |
+
1327,51,male,30.03,1,no,southeast,9377.9047
|
1330 |
+
1328,23,female,24.225,2,no,northeast,22395.74424
|
1331 |
+
1329,52,male,38.6,2,no,southwest,10325.206
|
1332 |
+
1330,57,female,25.74,2,no,southeast,12629.1656
|
1333 |
+
1331,23,female,33.4,0,no,southwest,10795.93733
|
1334 |
+
1332,52,female,44.7,3,no,southwest,11411.685
|
1335 |
+
1333,50,male,30.97,3,no,northwest,10600.5483
|
1336 |
+
1334,18,female,31.92,0,no,northeast,2205.9808
|
1337 |
+
1335,18,female,36.85,0,no,southeast,1629.8335
|
1338 |
+
1336,21,female,25.8,0,no,southwest,2007.945
|
1339 |
+
1337,61,female,29.07,0,yes,northwest,29141.3603
|
requirements.txt
ADDED
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scikit-learn==1.4.2
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train.py
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import joblib
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import pandas as pd
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from sklearn.preprocessing import StandardScaler, OneHotEncoder
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from sklearn.compose import make_column_transformer
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from sklearn.pipeline import make_pipeline
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from sklearn.model_selection import train_test_split
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from sklearn.linear_model import LinearRegression
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from sklearn.metrics import mean_squared_error, r2_score
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data_df = pd.read_csv("insurance.csv")
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target = 'charges'
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numeric_features = ['age', 'bmi', 'children']
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categorical_features = ['sex', 'smoker', 'region']
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print("Creating data subsets")
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X = data_df[numeric_features + categorical_features]
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y = data_df[target]
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Xtrain, Xtest, ytrain, ytest = train_test_split(
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X, y,
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test_size=0.2,
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random_state=42
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)
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preprocessor = make_column_transformer(
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(StandardScaler(), numeric_features),
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(OneHotEncoder(handle_unknown='ignore'), categorical_features)
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)
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model_linear_regression = LinearRegression(n_jobs=-1)
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print("Estimating Model Pipeline")
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model_pipeline = make_pipeline(
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preprocessor,
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model_linear_regression
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)
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model_pipeline.fit(Xtrain, ytrain)
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print("Logging Metrics")
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print(f"R-squared: {r2_score(ytest, model_pipeline.predict(Xtest))}")
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print("Serializing Model")
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saved_model_path = "model.joblib"
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joblib.dump(model_pipeline, saved_model_path)
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