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Files changed (8) hide show
  1. CC_STREAMLIT.py +348 -0
  2. assurance.pkl +3 -0
  3. cc_streamlit_notebook.ipynb +0 -0
  4. image1.jpg +0 -0
  5. image2.jpg +0 -0
  6. insurance.csv +1339 -0
  7. requirements.txt +23 -0
  8. test.csv +1339 -0
CC_STREAMLIT.py ADDED
@@ -0,0 +1,348 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import streamlit as st
2
+ import pandas as pd
3
+ import seaborn as sns
4
+ import matplotlib.pyplot as plt
5
+ import numpy as np
6
+ import pickle
7
+ import base64
8
+ from plotly import graph_objs as go
9
+
10
+ from streamlit_option_menu import option_menu
11
+ #st.set_page_config(layout='centered')
12
+ st.set_option('deprecation.showPyplotGlobalUse', False)
13
+
14
+ @st.cache_data
15
+ def load_data (dataset):
16
+ df= pd.read_csv(dataset)
17
+ return df
18
+
19
+ def filedownload(df):
20
+ csv = df.to_csv(index=False)
21
+ b64 = base64.b64encode(csv.encode()).decode() # strings <-> bytes conversions
22
+ href = f'<a href="data:file/csv;base64,{b64}" download="diabete_predictions.csv">Download CSV File</a>'
23
+ return href
24
+ st.set_page_config(
25
+ page_title="Insurance Predicting Application",
26
+ )
27
+
28
+ def user_input_feature():
29
+ age= st.slider("Age", 1, 65,step=1)
30
+ sex= st.selectbox("Quel est votre sexe? ", ["Masculin", "Feminin"])
31
+ sex=0
32
+ if sex== "Masculin":
33
+ sex=0
34
+ else:
35
+ sex=1
36
+ bmi = st.slider("Select your BMI:e", 0.0, max_value=250.0,step=0.1)
37
+ children = st.slider("Combien d'enfant avez-vous? ", 0, step=1)
38
+ smoker= st.selectbox("Fumez-vous? ",["Oui", "Non"])
39
+ smoker=1 #on pars sur la base qu'il ne fume pas
40
+ if smoker=="Oui":
41
+ smoker=0
42
+ else:
43
+ smoker=1
44
+ region = 1
45
+ region= st.selectbox("Quelle est votre région d'origine? ",['southwest','southeast','northwest','northeast'])
46
+
47
+ if region=="northeast":
48
+ region= 0
49
+ elif region=="northwest":
50
+ region=1
51
+ elif region=="southwest":
52
+ region=2
53
+ elif region=="southeast":
54
+ region=3
55
+
56
+ data = {
57
+ 'age':age,
58
+ 'sex':sex,
59
+ 'bmi':bmi,
60
+ 'children':children ,
61
+ "smoker": smoker,
62
+ "region":region
63
+
64
+
65
+ }
66
+
67
+ features= pd.DataFrame(data, index=[0])
68
+ return features
69
+
70
+
71
+ st.sidebar.image('image2.jpg', width=300)
72
+
73
+ #class MultiApp:
74
+
75
+ #def __init__(self):
76
+ #self.apps = []
77
+
78
+ #def add_app(self, title, func):
79
+
80
+ #self.apps.append({
81
+ #"title": title,
82
+ #"function": func
83
+ #})
84
+
85
+ def main():
86
+ with st.sidebar:
87
+ st.write('')
88
+
89
+ choice = option_menu(
90
+ menu_title='Menu bar ',
91
+ options=['Home','Analysis','Data virtualisation','Machine Learning','Contact Us'],
92
+ icons=['house-fill','person-circle','trophy-fill','chat-fill','info-circle-fill'],
93
+ #menu_icon='chat-text-fill',
94
+ default_index=0,
95
+ styles={
96
+ "container": {"padding": "5!important","background-color":'#333333'},
97
+ "icon": {"color": "white", "font-size": "15px"},
98
+ "nav-link": {"color":"white","font-size": "15px", "text-align": "left", "margin":"0px", },
99
+ #"--hover-color": "blue"},
100
+ "nav-link-selected": {"background-color": "#005580"},}
101
+
102
+ )
103
+
104
+ #menu = ['Home', 'Analysis', 'Data virtualisation','Machine Learning']
105
+ #choice = st.sidebar.selectbox('Select Menu', menu)
106
+
107
+ if choice =='Home':
108
+
109
+ st.markdown(
110
+ """
111
+ <style>
112
+ .centered {
113
+ display: flex;
114
+ justify-content: center;
115
+ align-items: center;
116
+ height: 15vh;
117
+ flex-direction: column;
118
+ }
119
+ </style>
120
+ """,
121
+ unsafe_allow_html=True
122
+ )
123
+
124
+ st.markdown(
125
+ """
126
+ <div class="centered">
127
+ <h1 style="color:#005580;">Welcome to Our Insurance Enterprise!</h1>
128
+ <p style="font-size:18px; color:#808080;">Providing reliable protection for your future.</p>
129
+ </div>
130
+ """,
131
+ unsafe_allow_html=True
132
+ )
133
+ st.markdown(
134
+ """
135
+ <style>
136
+ .centered {
137
+ display: flex;
138
+ justify-content: center;
139
+ align-items: center;
140
+ height: 25vh;
141
+ flex-direction: column;
142
+ }
143
+ </style>
144
+ """,
145
+ unsafe_allow_html=True
146
+ )
147
+
148
+ st.markdown(
149
+ """
150
+ <div class="centered">
151
+ <p style="text-align:justify;">Safeguarding Your Tomorrow, Today. Here, we are dedicated to protecting your future by offering comprehensive insurance solutions tailored to your individual needs. With a focus on trust, reliability, and personalized service, we strive to be your trusted partner in navigating the complexities of insurance. From safeguarding your home and vehicles to ensuring your health and financial security, we are here to support you every step of the way.</p>
152
+
153
+ </div>
154
+ """,
155
+ unsafe_allow_html=True
156
+ )
157
+
158
+
159
+
160
+ st.markdown(
161
+ """
162
+ <div class="centered">
163
+ <h1 style="color:#D0AFAE;">Company Overview</h1>
164
+ <p style="font-size:18px; color:#808080;">At our insurance enterprise, let's provide you with a concise overview of our company.</p>
165
+ </div>
166
+ """,
167
+ unsafe_allow_html=True
168
+ )
169
+
170
+
171
+ st.markdown("<h4 color: white;'>Types of Insurance Offered.</h4>",unsafe_allow_html=True)
172
+
173
+
174
+ # Liste des types d'assurances
175
+ insurance_types = [
176
+ " Car Insurance",
177
+ "Home Insurance",
178
+ "Health Insurance",
179
+ "Life Insurance",
180
+ "Travel Insurance",
181
+ "Liability Insurance",
182
+ "Business Insurance",
183
+ "Pet Insurance",
184
+ "Legal Protection Insurance",
185
+ # Ajoutez d'autres types d'assurances selon les besoins de votre entreprise
186
+ ]
187
+
188
+ # Affichage des types d'assurances avec mise en forme CSS
189
+ st.markdown(
190
+ """
191
+ <style>
192
+ .insurance-type {
193
+ background-color: black;
194
+ padding: 10px;
195
+ margin-bottom: 10px;
196
+ border-radius: 5px;
197
+ box-shadow: 0px 2px 5px rgba(0, 0, 0, 0.1);
198
+ }
199
+ </style>
200
+ """,
201
+ unsafe_allow_html=True
202
+ )
203
+
204
+ # Affichage de chaque type d'assurance avec la mise en forme CSS
205
+ for insurance_type in insurance_types:
206
+ st.markdown(f'<div class="insurance-type">{insurance_type}</div>', unsafe_allow_html=True)
207
+
208
+ if choice =='Analysis':
209
+ st.markdown("<h1 style='text-align:center;color:#005580;'>Data Analysis</h1>",unsafe_allow_html=True)
210
+ #st.header('Insurances Dataset')
211
+ st.write("")
212
+ st.write("")
213
+ st.write("")
214
+ data= load_data("insurance.csv")
215
+ if st.checkbox('Dataset preview'):
216
+ st.write(data.head(15))
217
+ elif st.checkbox('Summary'):
218
+ st.write(data.describe())
219
+ elif st.checkbox("Missing values"):
220
+ st.write(data.isna().sum())
221
+ elif st.checkbox("Data types"):
222
+ st.write(data.dtypes)
223
+ elif st.checkbox("Duplicated lignes"):
224
+ st.write(data.duplicated())
225
+ elif st.checkbox("Dataset Shape"):
226
+ st.write(data.shape)
227
+
228
+ elif choice =='Data virtualisation':
229
+ st.markdown("<h1 style='text-align:center;color:#005580;'>Data virtualisation </h1>",unsafe_allow_html=True)
230
+ data= load_data("insurance.csv")
231
+
232
+
233
+ st.write('Do you want to visualize some plots?')
234
+ option = st.selectbox("Options", ["Yes", "No"])
235
+
236
+ if option == "Yes":
237
+ st.markdown("<h4 style='text-align:left;color:white;'>Data presentation </h4>",unsafe_allow_html=True)
238
+ st.write(data)
239
+ graph = st.selectbox('What kind of plot do you want?',['Non Interactive','Interactive'])
240
+ if graph == 'Non Interactive':
241
+
242
+ fig = plt.figure(figsize=(10,5))
243
+ sns.countplot(x='age',data=data)
244
+ st.pyplot(fig)
245
+
246
+ fig = plt.figure(figsize=(10,5))
247
+ sns.histplot(x='age',data=data)
248
+ st.pyplot(fig)
249
+
250
+ fig = plt.figure(figsize=(10,5))
251
+ sns.lineplot(x='age',y="bmi",data=data)
252
+ st.pyplot(fig)
253
+
254
+ plt.figure(figsize = (10,5))
255
+ plt.scatter(data.charges,data.age)
256
+ plt.xlabel('charges')
257
+ plt.ylabel('age')
258
+ st.pyplot()
259
+
260
+ if graph == 'Interactive':
261
+
262
+ layout = go.Layout(xaxis = dict(range=[0,6000]),
263
+ yaxis = dict(range=[0,60]))
264
+ fig = go.Figure(data=go.Scatter(x=data.charges,y=data.age,
265
+ mode='markers'),layout=layout)
266
+ st.plotly_chart(fig)
267
+
268
+ layout = go.Layout(
269
+ xaxis=dict(range=[0, 100]) )
270
+ fig = go.Figure(data=go.Histogram(x=data['age']),layout=layout)
271
+ st.plotly_chart(fig)
272
+
273
+
274
+ layout = go.Layout(xaxis=dict(range=[0, 6000]), yaxis=dict(range=[0, 60]))
275
+ fig = go.Figure(data=go.Line(x=data['age'], y=data['bmi']), layout=layout)
276
+ st.plotly_chart(fig)
277
+
278
+
279
+
280
+ layout = go.Layout(xaxis=dict(range=[0, 6000]), yaxis=dict(range=[0, 60]))
281
+ fig = go.Figure(data=go.Surface (x=data['age'], y=data['bmi']), layout=layout)
282
+ st.plotly_chart(fig)
283
+
284
+
285
+ else:
286
+ st.write("You selected: No, so you can't visualize")
287
+
288
+
289
+ elif choice =='Machine Learning':
290
+ tab1, tab2 =st.tabs([":clipboard: Simulation of your informations",":clipboard: Prediction of the dataset" ])
291
+
292
+ with tab1:
293
+ st.subheader("Simulation of your informations")
294
+ st.write("User parameters")
295
+ data=user_input_feature()
296
+ model = pickle.load(open('assurance.pkl', 'rb'))
297
+ prediction = model.predict(data)
298
+ string="Le montant de vos frais d'assurance est de "+ str(np.round(prediction[0],2))+"£"
299
+ st.success(string)
300
+
301
+
302
+ with tab2:
303
+
304
+ data= load_data("test.csv")
305
+ model = pickle.load(open('assurance.pkl', 'rb'))
306
+ prediction = model.predict(data)
307
+ st.subheader('Prediction of the CSV')
308
+ pp = pd.DataFrame({"Prediction":prediction})
309
+ ndf = pd.concat([data,pp],axis=1)
310
+ st.write(ndf)
311
+
312
+ st.write("Do you want to download the csv predicted ? ")
313
+ option = st.selectbox("Options", ["No", "Yes"])
314
+ button=""
315
+ if option== "No":
316
+ st.write("You selected No!!!")
317
+ else:
318
+ button = st.button("Download")
319
+ if button:
320
+ st.markdown(filedownload(ndf), unsafe_allow_html=True)
321
+
322
+
323
+
324
+
325
+
326
+
327
+ elif choice =='Contact Us':
328
+ st.markdown("<h1 style='text-align:center;color:#005580;'>Our contact</h1>",unsafe_allow_html=True)
329
+
330
+ st.write('If you have any questions or concerns about our products or services, we are here to help. Please feel free to contact us using any of the following methods:')
331
+
332
+ st.markdown("<h5 style='text-align:center;color:white;'>Customer Service</h5>",unsafe_allow_html=True)
333
+ st.write('You can also reach our customer service team Monday through Friday, from 9:00 AM to 5:00 PM:')
334
+ st.markdown('📞 Phone:+237 694 89 99 77')
335
+ st.markdown('📧 Contact via mail: [[email protected]]')
336
+ st.markdown("<h5 style='text-align:center;color:white;'>Branches</h5>",unsafe_allow_html=True)
337
+ st.write('If you prefer to speak in person, you can visit one of our branches located throughout the country. Find the nearest branch to you:')
338
+ st.markdown('🌐 Titi Garage')
339
+ st.markdown('🌐 Bastos')
340
+ st.markdown("🌐 Essos")
341
+
342
+
343
+
344
+
345
+ if __name__ == "__main__":
346
+ main()
347
+
348
+
assurance.pkl ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:73add7c34b1f9a5372e0ab848ba881e74f25f648d52406b7a3471570c56911df
3
+ size 134272
cc_streamlit_notebook.ipynb ADDED
The diff for this file is too large to render. See raw diff
 
image1.jpg ADDED
image2.jpg ADDED
insurance.csv ADDED
@@ -0,0 +1,1339 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ age,sex,bmi,children,smoker,region,charges
2
+ 19,female,27.9,0,yes,southwest,16884.924
3
+ 18,male,33.77,1,no,southeast,1725.5523
4
+ 28,male,33.0,3,no,southeast,4449.462
5
+ 33,male,22.705,0,no,northwest,21984.47061
6
+ 32,male,28.88,0,no,northwest,3866.8552
7
+ 31,female,25.74,0,no,southeast,3756.6216
8
+ 46,female,33.44,1,no,southeast,8240.5896
9
+ 37,female,27.74,3,no,northwest,7281.5056
10
+ 37,male,29.83,2,no,northeast,6406.4107
11
+ 60,female,25.84,0,no,northwest,28923.13692
12
+ 25,male,26.22,0,no,northeast,2721.3208
13
+ 62,female,26.29,0,yes,southeast,27808.7251
14
+ 23,male,34.4,0,no,southwest,1826.843
15
+ 56,female,39.82,0,no,southeast,11090.7178
16
+ 27,male,42.13,0,yes,southeast,39611.7577
17
+ 19,male,24.6,1,no,southwest,1837.237
18
+ 52,female,30.78,1,no,northeast,10797.3362
19
+ 23,male,23.845,0,no,northeast,2395.17155
20
+ 56,male,40.3,0,no,southwest,10602.385
21
+ 30,male,35.3,0,yes,southwest,36837.467
22
+ 60,female,36.005,0,no,northeast,13228.84695
23
+ 30,female,32.4,1,no,southwest,4149.736
24
+ 18,male,34.1,0,no,southeast,1137.011
25
+ 34,female,31.92,1,yes,northeast,37701.8768
26
+ 37,male,28.025,2,no,northwest,6203.90175
27
+ 59,female,27.72,3,no,southeast,14001.1338
28
+ 63,female,23.085,0,no,northeast,14451.83515
29
+ 55,female,32.775,2,no,northwest,12268.63225
30
+ 23,male,17.385,1,no,northwest,2775.19215
31
+ 31,male,36.3,2,yes,southwest,38711.0
32
+ 22,male,35.6,0,yes,southwest,35585.576
33
+ 18,female,26.315,0,no,northeast,2198.18985
34
+ 19,female,28.6,5,no,southwest,4687.797
35
+ 63,male,28.31,0,no,northwest,13770.0979
36
+ 28,male,36.4,1,yes,southwest,51194.55914
37
+ 19,male,20.425,0,no,northwest,1625.43375
38
+ 62,female,32.965,3,no,northwest,15612.19335
39
+ 26,male,20.8,0,no,southwest,2302.3
40
+ 35,male,36.67,1,yes,northeast,39774.2763
41
+ 60,male,39.9,0,yes,southwest,48173.361
42
+ 24,female,26.6,0,no,northeast,3046.062
43
+ 31,female,36.63,2,no,southeast,4949.7587
44
+ 41,male,21.78,1,no,southeast,6272.4772
45
+ 37,female,30.8,2,no,southeast,6313.759
46
+ 38,male,37.05,1,no,northeast,6079.6715
47
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