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Browse files- CC_STREAMLIT.py +348 -0
- assurance.pkl +3 -0
- cc_streamlit_notebook.ipynb +0 -0
- image1.jpg +0 -0
- image2.jpg +0 -0
- insurance.csv +1339 -0
- requirements.txt +23 -0
- test.csv +1339 -0
CC_STREAMLIT.py
ADDED
@@ -0,0 +1,348 @@
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1 |
+
import streamlit as st
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2 |
+
import pandas as pd
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3 |
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import seaborn as sns
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4 |
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import matplotlib.pyplot as plt
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5 |
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import numpy as np
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6 |
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import pickle
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7 |
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import base64
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8 |
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from plotly import graph_objs as go
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9 |
+
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from streamlit_option_menu import option_menu
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11 |
+
#st.set_page_config(layout='centered')
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12 |
+
st.set_option('deprecation.showPyplotGlobalUse', False)
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+
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14 |
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@st.cache_data
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15 |
+
def load_data (dataset):
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df= pd.read_csv(dataset)
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return df
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+
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def filedownload(df):
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csv = df.to_csv(index=False)
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b64 = base64.b64encode(csv.encode()).decode() # strings <-> bytes conversions
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href = f'<a href="data:file/csv;base64,{b64}" download="diabete_predictions.csv">Download CSV File</a>'
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return href
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st.set_page_config(
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page_title="Insurance Predicting Application",
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)
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def user_input_feature():
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age= st.slider("Age", 1, 65,step=1)
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sex= st.selectbox("Quel est votre sexe? ", ["Masculin", "Feminin"])
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sex=0
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if sex== "Masculin":
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sex=0
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else:
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sex=1
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bmi = st.slider("Select your BMI:e", 0.0, max_value=250.0,step=0.1)
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children = st.slider("Combien d'enfant avez-vous? ", 0, step=1)
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smoker= st.selectbox("Fumez-vous? ",["Oui", "Non"])
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smoker=1 #on pars sur la base qu'il ne fume pas
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if smoker=="Oui":
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smoker=0
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else:
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smoker=1
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region = 1
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region= st.selectbox("Quelle est votre région d'origine? ",['southwest','southeast','northwest','northeast'])
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if region=="northeast":
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region= 0
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elif region=="northwest":
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region=1
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elif region=="southwest":
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region=2
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elif region=="southeast":
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region=3
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data = {
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'age':age,
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58 |
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'sex':sex,
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'bmi':bmi,
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'children':children ,
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"smoker": smoker,
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"region":region
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}
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features= pd.DataFrame(data, index=[0])
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return features
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+
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+
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71 |
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st.sidebar.image('image2.jpg', width=300)
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72 |
+
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73 |
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#class MultiApp:
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74 |
+
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75 |
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#def __init__(self):
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76 |
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#self.apps = []
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77 |
+
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78 |
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#def add_app(self, title, func):
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79 |
+
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80 |
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#self.apps.append({
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81 |
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#"title": title,
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82 |
+
#"function": func
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83 |
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#})
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84 |
+
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85 |
+
def main():
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86 |
+
with st.sidebar:
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87 |
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st.write('')
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88 |
+
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89 |
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choice = option_menu(
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90 |
+
menu_title='Menu bar ',
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91 |
+
options=['Home','Analysis','Data virtualisation','Machine Learning','Contact Us'],
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92 |
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icons=['house-fill','person-circle','trophy-fill','chat-fill','info-circle-fill'],
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93 |
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#menu_icon='chat-text-fill',
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94 |
+
default_index=0,
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95 |
+
styles={
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96 |
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"container": {"padding": "5!important","background-color":'#333333'},
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97 |
+
"icon": {"color": "white", "font-size": "15px"},
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98 |
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"nav-link": {"color":"white","font-size": "15px", "text-align": "left", "margin":"0px", },
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99 |
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#"--hover-color": "blue"},
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100 |
+
"nav-link-selected": {"background-color": "#005580"},}
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101 |
+
|
102 |
+
)
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103 |
+
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104 |
+
#menu = ['Home', 'Analysis', 'Data virtualisation','Machine Learning']
|
105 |
+
#choice = st.sidebar.selectbox('Select Menu', menu)
|
106 |
+
|
107 |
+
if choice =='Home':
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108 |
+
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109 |
+
st.markdown(
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110 |
+
"""
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111 |
+
<style>
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112 |
+
.centered {
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113 |
+
display: flex;
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114 |
+
justify-content: center;
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115 |
+
align-items: center;
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116 |
+
height: 15vh;
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117 |
+
flex-direction: column;
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118 |
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}
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119 |
+
</style>
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120 |
+
""",
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121 |
+
unsafe_allow_html=True
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122 |
+
)
|
123 |
+
|
124 |
+
st.markdown(
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125 |
+
"""
|
126 |
+
<div class="centered">
|
127 |
+
<h1 style="color:#005580;">Welcome to Our Insurance Enterprise!</h1>
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128 |
+
<p style="font-size:18px; color:#808080;">Providing reliable protection for your future.</p>
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129 |
+
</div>
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130 |
+
""",
|
131 |
+
unsafe_allow_html=True
|
132 |
+
)
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133 |
+
st.markdown(
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134 |
+
"""
|
135 |
+
<style>
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136 |
+
.centered {
|
137 |
+
display: flex;
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138 |
+
justify-content: center;
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139 |
+
align-items: center;
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140 |
+
height: 25vh;
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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>
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154 |
+
""",
|
155 |
+
unsafe_allow_html=True
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156 |
+
)
|
157 |
+
|
158 |
+
|
159 |
+
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160 |
+
st.markdown(
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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;
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196 |
+
border-radius: 5px;
|
197 |
+
box-shadow: 0px 2px 5px rgba(0, 0, 0, 0.1);
|
198 |
+
}
|
199 |
+
</style>
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200 |
+
""",
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201 |
+
unsafe_allow_html=True
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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("")
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213 |
+
st.write("")
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214 |
+
data= load_data("insurance.csv")
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215 |
+
if st.checkbox('Dataset preview'):
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216 |
+
st.write(data.head(15))
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217 |
+
elif st.checkbox('Summary'):
|
218 |
+
st.write(data.describe())
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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"):
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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 |
+
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233 |
+
st.write('Do you want to visualize some plots?')
|
234 |
+
option = st.selectbox("Options", ["Yes", "No"])
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235 |
+
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236 |
+
if option == "Yes":
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237 |
+
st.markdown("<h4 style='text-align:left;color:white;'>Data presentation </h4>",unsafe_allow_html=True)
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238 |
+
st.write(data)
|
239 |
+
graph = st.selectbox('What kind of plot do you want?',['Non Interactive','Interactive'])
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240 |
+
if graph == 'Non Interactive':
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241 |
+
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242 |
+
fig = plt.figure(figsize=(10,5))
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243 |
+
sns.countplot(x='age',data=data)
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244 |
+
st.pyplot(fig)
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245 |
+
|
246 |
+
fig = plt.figure(figsize=(10,5))
|
247 |
+
sns.histplot(x='age',data=data)
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248 |
+
st.pyplot(fig)
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249 |
+
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250 |
+
fig = plt.figure(figsize=(10,5))
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251 |
+
sns.lineplot(x='age',y="bmi",data=data)
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252 |
+
st.pyplot(fig)
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253 |
+
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254 |
+
plt.figure(figsize = (10,5))
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255 |
+
plt.scatter(data.charges,data.age)
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256 |
+
plt.xlabel('charges')
|
257 |
+
plt.ylabel('age')
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258 |
+
st.pyplot()
|
259 |
+
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260 |
+
if graph == 'Interactive':
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261 |
+
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262 |
+
layout = go.Layout(xaxis = dict(range=[0,6000]),
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263 |
+
yaxis = dict(range=[0,60]))
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264 |
+
fig = go.Figure(data=go.Scatter(x=data.charges,y=data.age,
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265 |
+
mode='markers'),layout=layout)
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266 |
+
st.plotly_chart(fig)
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267 |
+
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268 |
+
layout = go.Layout(
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269 |
+
xaxis=dict(range=[0, 100]) )
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270 |
+
fig = go.Figure(data=go.Histogram(x=data['age']),layout=layout)
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271 |
+
st.plotly_chart(fig)
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272 |
+
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273 |
+
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274 |
+
layout = go.Layout(xaxis=dict(range=[0, 6000]), yaxis=dict(range=[0, 60]))
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275 |
+
fig = go.Figure(data=go.Line(x=data['age'], y=data['bmi']), layout=layout)
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276 |
+
st.plotly_chart(fig)
|
277 |
+
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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")
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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 @@
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|
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 |
+
55,male,37.3,0,no,southwest,20630.28351
|
48 |
+
18,female,38.665,2,no,northeast,3393.35635
|
49 |
+
28,female,34.77,0,no,northwest,3556.9223
|
50 |
+
60,female,24.53,0,no,southeast,12629.8967
|
51 |
+
36,male,35.2,1,yes,southeast,38709.176
|
52 |
+
18,female,35.625,0,no,northeast,2211.13075
|
53 |
+
21,female,33.63,2,no,northwest,3579.8287
|
54 |
+
48,male,28.0,1,yes,southwest,23568.272
|
55 |
+
36,male,34.43,0,yes,southeast,37742.5757
|
56 |
+
40,female,28.69,3,no,northwest,8059.6791
|
57 |
+
58,male,36.955,2,yes,northwest,47496.49445
|
58 |
+
58,female,31.825,2,no,northeast,13607.36875
|
59 |
+
18,male,31.68,2,yes,southeast,34303.1672
|
60 |
+
53,female,22.88,1,yes,southeast,23244.7902
|
61 |
+
34,female,37.335,2,no,northwest,5989.52365
|
62 |
+
43,male,27.36,3,no,northeast,8606.2174
|
63 |
+
25,male,33.66,4,no,southeast,4504.6624
|
64 |
+
64,male,24.7,1,no,northwest,30166.61817
|
65 |
+
28,female,25.935,1,no,northwest,4133.64165
|
66 |
+
20,female,22.42,0,yes,northwest,14711.7438
|
67 |
+
19,female,28.9,0,no,southwest,1743.214
|
68 |
+
61,female,39.1,2,no,southwest,14235.072
|
69 |
+
40,male,26.315,1,no,northwest,6389.37785
|
70 |
+
40,female,36.19,0,no,southeast,5920.1041
|
71 |
+
28,male,23.98,3,yes,southeast,17663.1442
|
72 |
+
27,female,24.75,0,yes,southeast,16577.7795
|
73 |
+
31,male,28.5,5,no,northeast,6799.458
|
74 |
+
53,female,28.1,3,no,southwest,11741.726
|
75 |
+
58,male,32.01,1,no,southeast,11946.6259
|
76 |
+
44,male,27.4,2,no,southwest,7726.854
|
77 |
+
57,male,34.01,0,no,northwest,11356.6609
|
78 |
+
29,female,29.59,1,no,southeast,3947.4131
|
79 |
+
21,male,35.53,0,no,southeast,1532.4697
|
80 |
+
22,female,39.805,0,no,northeast,2755.02095
|
81 |
+
41,female,32.965,0,no,northwest,6571.02435
|
82 |
+
31,male,26.885,1,no,northeast,4441.21315
|
83 |
+
45,female,38.285,0,no,northeast,7935.29115
|
84 |
+
22,male,37.62,1,yes,southeast,37165.1638
|
85 |
+
48,female,41.23,4,no,northwest,11033.6617
|
86 |
+
37,female,34.8,2,yes,southwest,39836.519
|
87 |
+
45,male,22.895,2,yes,northwest,21098.55405
|
88 |
+
57,female,31.16,0,yes,northwest,43578.9394
|
89 |
+
56,female,27.2,0,no,southwest,11073.176
|
90 |
+
46,female,27.74,0,no,northwest,8026.6666
|
91 |
+
55,female,26.98,0,no,northwest,11082.5772
|
92 |
+
21,female,39.49,0,no,southeast,2026.9741
|
93 |
+
53,female,24.795,1,no,northwest,10942.13205
|
94 |
+
59,male,29.83,3,yes,northeast,30184.9367
|
95 |
+
35,male,34.77,2,no,northwest,5729.0053
|
96 |
+
64,female,31.3,2,yes,southwest,47291.055
|
97 |
+
28,female,37.62,1,no,southeast,3766.8838
|
98 |
+
54,female,30.8,3,no,southwest,12105.32
|
99 |
+
55,male,38.28,0,no,southeast,10226.2842
|
100 |
+
56,male,19.95,0,yes,northeast,22412.6485
|
101 |
+
38,male,19.3,0,yes,southwest,15820.699
|
102 |
+
41,female,31.6,0,no,southwest,6186.127
|
103 |
+
30,male,25.46,0,no,northeast,3645.0894
|
104 |
+
18,female,30.115,0,no,northeast,21344.8467
|
105 |
+
61,female,29.92,3,yes,southeast,30942.1918
|
106 |
+
34,female,27.5,1,no,southwest,5003.853
|
107 |
+
20,male,28.025,1,yes,northwest,17560.37975
|
108 |
+
19,female,28.4,1,no,southwest,2331.519
|
109 |
+
26,male,30.875,2,no,northwest,3877.30425
|
110 |
+
29,male,27.94,0,no,southeast,2867.1196
|
111 |
+
63,male,35.09,0,yes,southeast,47055.5321
|
112 |
+
54,male,33.63,1,no,northwest,10825.2537
|
113 |
+
55,female,29.7,2,no,southwest,11881.358
|
114 |
+
37,male,30.8,0,no,southwest,4646.759
|
115 |
+
21,female,35.72,0,no,northwest,2404.7338
|
116 |
+
52,male,32.205,3,no,northeast,11488.31695
|
117 |
+
60,male,28.595,0,no,northeast,30259.99556
|
118 |
+
58,male,49.06,0,no,southeast,11381.3254
|
119 |
+
29,female,27.94,1,yes,southeast,19107.7796
|
120 |
+
49,female,27.17,0,no,southeast,8601.3293
|
121 |
+
37,female,23.37,2,no,northwest,6686.4313
|
122 |
+
44,male,37.1,2,no,southwest,7740.337
|
123 |
+
18,male,23.75,0,no,northeast,1705.6245
|
124 |
+
20,female,28.975,0,no,northwest,2257.47525
|
125 |
+
44,male,31.35,1,yes,northeast,39556.4945
|
126 |
+
47,female,33.915,3,no,northwest,10115.00885
|
127 |
+
26,female,28.785,0,no,northeast,3385.39915
|
128 |
+
19,female,28.3,0,yes,southwest,17081.08
|
129 |
+
52,female,37.4,0,no,southwest,9634.538
|
130 |
+
32,female,17.765,2,yes,northwest,32734.1863
|
131 |
+
38,male,34.7,2,no,southwest,6082.405
|
132 |
+
59,female,26.505,0,no,northeast,12815.44495
|
133 |
+
61,female,22.04,0,no,northeast,13616.3586
|
134 |
+
53,female,35.9,2,no,southwest,11163.568
|
135 |
+
19,male,25.555,0,no,northwest,1632.56445
|
136 |
+
20,female,28.785,0,no,northeast,2457.21115
|
137 |
+
22,female,28.05,0,no,southeast,2155.6815
|
138 |
+
19,male,34.1,0,no,southwest,1261.442
|
139 |
+
22,male,25.175,0,no,northwest,2045.68525
|
140 |
+
54,female,31.9,3,no,southeast,27322.73386
|
141 |
+
22,female,36.0,0,no,southwest,2166.732
|
142 |
+
34,male,22.42,2,no,northeast,27375.90478
|
143 |
+
26,male,32.49,1,no,northeast,3490.5491
|
144 |
+
34,male,25.3,2,yes,southeast,18972.495
|
145 |
+
29,male,29.735,2,no,northwest,18157.876
|
146 |
+
30,male,28.69,3,yes,northwest,20745.9891
|
147 |
+
29,female,38.83,3,no,southeast,5138.2567
|
148 |
+
46,male,30.495,3,yes,northwest,40720.55105
|
149 |
+
51,female,37.73,1,no,southeast,9877.6077
|
150 |
+
53,female,37.43,1,no,northwest,10959.6947
|
151 |
+
19,male,28.4,1,no,southwest,1842.519
|
152 |
+
35,male,24.13,1,no,northwest,5125.2157
|
153 |
+
48,male,29.7,0,no,southeast,7789.635
|
154 |
+
32,female,37.145,3,no,northeast,6334.34355
|
155 |
+
42,female,23.37,0,yes,northeast,19964.7463
|
156 |
+
40,female,25.46,1,no,northeast,7077.1894
|
157 |
+
44,male,39.52,0,no,northwest,6948.7008
|
158 |
+
48,male,24.42,0,yes,southeast,21223.6758
|
159 |
+
18,male,25.175,0,yes,northeast,15518.18025
|
160 |
+
30,male,35.53,0,yes,southeast,36950.2567
|
161 |
+
50,female,27.83,3,no,southeast,19749.38338
|
162 |
+
42,female,26.6,0,yes,northwest,21348.706
|
163 |
+
18,female,36.85,0,yes,southeast,36149.4835
|
164 |
+
54,male,39.6,1,no,southwest,10450.552
|
165 |
+
32,female,29.8,2,no,southwest,5152.134
|
166 |
+
37,male,29.64,0,no,northwest,5028.1466
|
167 |
+
47,male,28.215,4,no,northeast,10407.08585
|
168 |
+
20,female,37.0,5,no,southwest,4830.63
|
169 |
+
32,female,33.155,3,no,northwest,6128.79745
|
170 |
+
19,female,31.825,1,no,northwest,2719.27975
|
171 |
+
27,male,18.905,3,no,northeast,4827.90495
|
172 |
+
63,male,41.47,0,no,southeast,13405.3903
|
173 |
+
49,male,30.3,0,no,southwest,8116.68
|
174 |
+
18,male,15.96,0,no,northeast,1694.7964
|
175 |
+
35,female,34.8,1,no,southwest,5246.047
|
176 |
+
24,female,33.345,0,no,northwest,2855.43755
|
177 |
+
63,female,37.7,0,yes,southwest,48824.45
|
178 |
+
38,male,27.835,2,no,northwest,6455.86265
|
179 |
+
54,male,29.2,1,no,southwest,10436.096
|
180 |
+
46,female,28.9,2,no,southwest,8823.279
|
181 |
+
41,female,33.155,3,no,northeast,8538.28845
|
182 |
+
58,male,28.595,0,no,northwest,11735.87905
|
183 |
+
18,female,38.28,0,no,southeast,1631.8212
|
184 |
+
22,male,19.95,3,no,northeast,4005.4225
|
185 |
+
44,female,26.41,0,no,northwest,7419.4779
|
186 |
+
44,male,30.69,2,no,southeast,7731.4271
|
187 |
+
36,male,41.895,3,yes,northeast,43753.33705
|
188 |
+
26,female,29.92,2,no,southeast,3981.9768
|
189 |
+
30,female,30.9,3,no,southwest,5325.651
|
190 |
+
41,female,32.2,1,no,southwest,6775.961
|
191 |
+
29,female,32.11,2,no,northwest,4922.9159
|
192 |
+
61,male,31.57,0,no,southeast,12557.6053
|
193 |
+
36,female,26.2,0,no,southwest,4883.866
|
194 |
+
25,male,25.74,0,no,southeast,2137.6536
|
195 |
+
56,female,26.6,1,no,northwest,12044.342
|
196 |
+
18,male,34.43,0,no,southeast,1137.4697
|
197 |
+
19,male,30.59,0,no,northwest,1639.5631
|
198 |
+
39,female,32.8,0,no,southwest,5649.715
|
199 |
+
45,female,28.6,2,no,southeast,8516.829
|
200 |
+
51,female,18.05,0,no,northwest,9644.2525
|
201 |
+
64,female,39.33,0,no,northeast,14901.5167
|
202 |
+
19,female,32.11,0,no,northwest,2130.6759
|
203 |
+
48,female,32.23,1,no,southeast,8871.1517
|
204 |
+
60,female,24.035,0,no,northwest,13012.20865
|
205 |
+
27,female,36.08,0,yes,southeast,37133.8982
|
206 |
+
46,male,22.3,0,no,southwest,7147.105
|
207 |
+
28,female,28.88,1,no,northeast,4337.7352
|
208 |
+
59,male,26.4,0,no,southeast,11743.299
|
209 |
+
35,male,27.74,2,yes,northeast,20984.0936
|
210 |
+
63,female,31.8,0,no,southwest,13880.949
|
211 |
+
40,male,41.23,1,no,northeast,6610.1097
|
212 |
+
20,male,33.0,1,no,southwest,1980.07
|
213 |
+
40,male,30.875,4,no,northwest,8162.71625
|
214 |
+
24,male,28.5,2,no,northwest,3537.703
|
215 |
+
34,female,26.73,1,no,southeast,5002.7827
|
216 |
+
45,female,30.9,2,no,southwest,8520.026
|
217 |
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41,female,37.1,2,no,southwest,7371.772
|
218 |
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53,female,26.6,0,no,northwest,10355.641
|
219 |
+
27,male,23.1,0,no,southeast,2483.736
|
220 |
+
26,female,29.92,1,no,southeast,3392.9768
|
221 |
+
24,female,23.21,0,no,southeast,25081.76784
|
222 |
+
34,female,33.7,1,no,southwest,5012.471
|
223 |
+
53,female,33.25,0,no,northeast,10564.8845
|
224 |
+
32,male,30.8,3,no,southwest,5253.524
|
225 |
+
19,male,34.8,0,yes,southwest,34779.615
|
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45,female,27.83,2,no,southeast,8515.7587
|
1237 |
+
26,male,31.065,0,no,northwest,2699.56835
|
1238 |
+
63,female,21.66,0,no,northeast,14449.8544
|
1239 |
+
58,female,28.215,0,no,northwest,12224.35085
|
1240 |
+
37,male,22.705,3,no,northeast,6985.50695
|
1241 |
+
25,female,42.13,1,no,southeast,3238.4357
|
1242 |
+
52,male,41.8,2,yes,southeast,47269.854
|
1243 |
+
64,male,36.96,2,yes,southeast,49577.6624
|
1244 |
+
22,female,21.28,3,no,northwest,4296.2712
|
1245 |
+
28,female,33.11,0,no,southeast,3171.6149
|
1246 |
+
18,male,33.33,0,no,southeast,1135.9407
|
1247 |
+
28,male,24.3,5,no,southwest,5615.369
|
1248 |
+
45,female,25.7,3,no,southwest,9101.798
|
1249 |
+
33,male,29.4,4,no,southwest,6059.173
|
1250 |
+
18,female,39.82,0,no,southeast,1633.9618
|
1251 |
+
32,male,33.63,1,yes,northeast,37607.5277
|
1252 |
+
24,male,29.83,0,yes,northeast,18648.4217
|
1253 |
+
19,male,19.8,0,no,southwest,1241.565
|
1254 |
+
20,male,27.3,0,yes,southwest,16232.847
|
1255 |
+
40,female,29.3,4,no,southwest,15828.82173
|
1256 |
+
34,female,27.72,0,no,southeast,4415.1588
|
1257 |
+
42,female,37.9,0,no,southwest,6474.013
|
1258 |
+
51,female,36.385,3,no,northwest,11436.73815
|
1259 |
+
54,female,27.645,1,no,northwest,11305.93455
|
1260 |
+
55,male,37.715,3,no,northwest,30063.58055
|
1261 |
+
52,female,23.18,0,no,northeast,10197.7722
|
1262 |
+
32,female,20.52,0,no,northeast,4544.2348
|
1263 |
+
28,male,37.1,1,no,southwest,3277.161
|
1264 |
+
41,female,28.05,1,no,southeast,6770.1925
|
1265 |
+
43,female,29.9,1,no,southwest,7337.748
|
1266 |
+
49,female,33.345,2,no,northeast,10370.91255
|
1267 |
+
64,male,23.76,0,yes,southeast,26926.5144
|
1268 |
+
55,female,30.5,0,no,southwest,10704.47
|
1269 |
+
24,male,31.065,0,yes,northeast,34254.05335
|
1270 |
+
20,female,33.3,0,no,southwest,1880.487
|
1271 |
+
45,male,27.5,3,no,southwest,8615.3
|
1272 |
+
26,male,33.915,1,no,northwest,3292.52985
|
1273 |
+
25,female,34.485,0,no,northwest,3021.80915
|
1274 |
+
43,male,25.52,5,no,southeast,14478.33015
|
1275 |
+
35,male,27.61,1,no,southeast,4747.0529
|
1276 |
+
26,male,27.06,0,yes,southeast,17043.3414
|
1277 |
+
57,male,23.7,0,no,southwest,10959.33
|
1278 |
+
22,female,30.4,0,no,northeast,2741.948
|
1279 |
+
32,female,29.735,0,no,northwest,4357.04365
|
1280 |
+
39,male,29.925,1,yes,northeast,22462.04375
|
1281 |
+
25,female,26.79,2,no,northwest,4189.1131
|
1282 |
+
48,female,33.33,0,no,southeast,8283.6807
|
1283 |
+
47,female,27.645,2,yes,northwest,24535.69855
|
1284 |
+
18,female,21.66,0,yes,northeast,14283.4594
|
1285 |
+
18,male,30.03,1,no,southeast,1720.3537
|
1286 |
+
61,male,36.3,1,yes,southwest,47403.88
|
1287 |
+
47,female,24.32,0,no,northeast,8534.6718
|
1288 |
+
28,female,17.29,0,no,northeast,3732.6251
|
1289 |
+
36,female,25.9,1,no,southwest,5472.449
|
1290 |
+
20,male,39.4,2,yes,southwest,38344.566
|
1291 |
+
44,male,34.32,1,no,southeast,7147.4728
|
1292 |
+
38,female,19.95,2,no,northeast,7133.9025
|
1293 |
+
19,male,34.9,0,yes,southwest,34828.654
|
1294 |
+
21,male,23.21,0,no,southeast,1515.3449
|
1295 |
+
46,male,25.745,3,no,northwest,9301.89355
|
1296 |
+
58,male,25.175,0,no,northeast,11931.12525
|
1297 |
+
20,male,22.0,1,no,southwest,1964.78
|
1298 |
+
18,male,26.125,0,no,northeast,1708.92575
|
1299 |
+
28,female,26.51,2,no,southeast,4340.4409
|
1300 |
+
33,male,27.455,2,no,northwest,5261.46945
|
1301 |
+
19,female,25.745,1,no,northwest,2710.82855
|
1302 |
+
45,male,30.36,0,yes,southeast,62592.87309
|
1303 |
+
62,male,30.875,3,yes,northwest,46718.16325
|
1304 |
+
25,female,20.8,1,no,southwest,3208.787
|
1305 |
+
43,male,27.8,0,yes,southwest,37829.7242
|
1306 |
+
42,male,24.605,2,yes,northeast,21259.37795
|
1307 |
+
24,female,27.72,0,no,southeast,2464.6188
|
1308 |
+
29,female,21.85,0,yes,northeast,16115.3045
|
1309 |
+
32,male,28.12,4,yes,northwest,21472.4788
|
1310 |
+
25,female,30.2,0,yes,southwest,33900.653
|
1311 |
+
41,male,32.2,2,no,southwest,6875.961
|
1312 |
+
42,male,26.315,1,no,northwest,6940.90985
|
1313 |
+
33,female,26.695,0,no,northwest,4571.41305
|
1314 |
+
34,male,42.9,1,no,southwest,4536.259
|
1315 |
+
19,female,34.7,2,yes,southwest,36397.576
|
1316 |
+
30,female,23.655,3,yes,northwest,18765.87545
|
1317 |
+
18,male,28.31,1,no,northeast,11272.33139
|
1318 |
+
19,female,20.6,0,no,southwest,1731.677
|
1319 |
+
18,male,53.13,0,no,southeast,1163.4627
|
1320 |
+
35,male,39.71,4,no,northeast,19496.71917
|
1321 |
+
39,female,26.315,2,no,northwest,7201.70085
|
1322 |
+
31,male,31.065,3,no,northwest,5425.02335
|
1323 |
+
62,male,26.695,0,yes,northeast,28101.33305
|
1324 |
+
62,male,38.83,0,no,southeast,12981.3457
|
1325 |
+
42,female,40.37,2,yes,southeast,43896.3763
|
1326 |
+
31,male,25.935,1,no,northwest,4239.89265
|
1327 |
+
61,male,33.535,0,no,northeast,13143.33665
|
1328 |
+
42,female,32.87,0,no,northeast,7050.0213
|
1329 |
+
51,male,30.03,1,no,southeast,9377.9047
|
1330 |
+
23,female,24.225,2,no,northeast,22395.74424
|
1331 |
+
52,male,38.6,2,no,southwest,10325.206
|
1332 |
+
57,female,25.74,2,no,southeast,12629.1656
|
1333 |
+
23,female,33.4,0,no,southwest,10795.93733
|
1334 |
+
52,female,44.7,3,no,southwest,11411.685
|
1335 |
+
50,male,30.97,3,no,northwest,10600.5483
|
1336 |
+
18,female,31.92,0,no,northeast,2205.9808
|
1337 |
+
18,female,36.85,0,no,southeast,1629.8335
|
1338 |
+
21,female,25.8,0,no,southwest,2007.945
|
1339 |
+
61,female,29.07,0,yes,northwest,29141.3603
|
requirements.txt
ADDED
@@ -0,0 +1,23 @@
|
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|
1 |
+
|
2 |
+
matplotlib==3.7.5
|
3 |
+
numpy==1.24.4
|
4 |
+
pandas==1.5.3
|
5 |
+
|
6 |
+
plotly==5.20.0
|
7 |
+
pybase64==1.3.2
|
8 |
+
pycaret==3.3.0
|
9 |
+
scikit-learn==1.4.1.post1
|
10 |
+
seaborn==0.12.2
|
11 |
+
streamlit==1.33.0
|
12 |
+
streamlit-camera-input-live==0.2.0
|
13 |
+
streamlit-card==1.0.0
|
14 |
+
streamlit-embedcode==0.1.2
|
15 |
+
streamlit-extras==0.4.2
|
16 |
+
streamlit-faker==0.0.3
|
17 |
+
streamlit-image-coordinates==0.1.6
|
18 |
+
streamlit-keyup==0.2.4
|
19 |
+
streamlit-option-menu==0.3.12
|
20 |
+
streamlit-player==0.1.5
|
21 |
+
streamlit-toggle-switch==1.0.2
|
22 |
+
streamlit-vertical-slider==2.5.5
|
23 |
+
|
test.csv
ADDED
@@ -0,0 +1,1339 @@
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|
1 |
+
age,sex,bmi,children,smoker,region
|
2 |
+
19,1,27.9,0,0,0
|
3 |
+
18,0,33.77,1,1,1
|
4 |
+
28,0,33.0,3,1,1
|
5 |
+
33,0,22.705,0,1,2
|
6 |
+
32,0,28.88,0,1,2
|
7 |
+
31,1,25.74,0,1,1
|
8 |
+
46,1,33.44,1,1,1
|
9 |
+
37,1,27.74,3,1,2
|
10 |
+
37,0,29.83,2,1,3
|
11 |
+
60,1,25.84,0,1,2
|
12 |
+
25,0,26.22,0,1,3
|
13 |
+
62,1,26.29,0,0,1
|
14 |
+
23,0,34.4,0,1,0
|
15 |
+
56,1,39.82,0,1,1
|
16 |
+
27,0,42.13,0,0,1
|
17 |
+
19,0,24.6,1,1,0
|
18 |
+
52,1,30.78,1,1,3
|
19 |
+
23,0,23.845,0,1,3
|
20 |
+
56,0,40.3,0,1,0
|
21 |
+
30,0,35.3,0,0,0
|
22 |
+
60,1,36.005,0,1,3
|
23 |
+
30,1,32.4,1,1,0
|
24 |
+
18,0,34.1,0,1,1
|
25 |
+
34,1,31.92,1,0,3
|
26 |
+
37,0,28.025,2,1,2
|
27 |
+
59,1,27.72,3,1,1
|
28 |
+
63,1,23.085,0,1,3
|
29 |
+
55,1,32.775,2,1,2
|
30 |
+
23,0,17.385,1,1,2
|
31 |
+
31,0,36.3,2,0,0
|
32 |
+
22,0,35.6,0,0,0
|
33 |
+
18,1,26.315,0,1,3
|
34 |
+
19,1,28.6,5,1,0
|
35 |
+
63,0,28.31,0,1,2
|
36 |
+
28,0,36.4,1,0,0
|
37 |
+
19,0,20.425,0,1,2
|
38 |
+
62,1,32.965,3,1,2
|
39 |
+
26,0,20.8,0,1,0
|
40 |
+
35,0,36.67,1,0,3
|
41 |
+
60,0,39.9,0,0,0
|
42 |
+
24,1,26.6,0,1,3
|
43 |
+
31,1,36.63,2,1,1
|
44 |
+
41,0,21.78,1,1,1
|
45 |
+
37,1,30.8,2,1,1
|
46 |
+
38,0,37.05,1,1,3
|
47 |
+
55,0,37.3,0,1,0
|
48 |
+
18,1,38.665,2,1,3
|
49 |
+
28,1,34.77,0,1,2
|
50 |
+
60,1,24.53,0,1,1
|
51 |
+
36,0,35.2,1,0,1
|
52 |
+
18,1,35.625,0,1,3
|
53 |
+
21,1,33.63,2,1,2
|
54 |
+
48,0,28.0,1,0,0
|
55 |
+
36,0,34.43,0,0,1
|
56 |
+
40,1,28.69,3,1,2
|
57 |
+
58,0,36.955,2,0,2
|
58 |
+
58,1,31.825,2,1,3
|
59 |
+
18,0,31.68,2,0,1
|
60 |
+
53,1,22.88,1,0,1
|
61 |
+
34,1,37.335,2,1,2
|
62 |
+
43,0,27.36,3,1,3
|
63 |
+
25,0,33.66,4,1,1
|
64 |
+
64,0,24.7,1,1,2
|
65 |
+
28,1,25.935,1,1,2
|
66 |
+
20,1,22.42,0,0,2
|
67 |
+
19,1,28.9,0,1,0
|
68 |
+
61,1,39.1,2,1,0
|
69 |
+
40,0,26.315,1,1,2
|
70 |
+
40,1,36.19,0,1,1
|
71 |
+
28,0,23.98,3,0,1
|
72 |
+
27,1,24.75,0,0,1
|
73 |
+
31,0,28.5,5,1,3
|
74 |
+
53,1,28.1,3,1,0
|
75 |
+
58,0,32.01,1,1,1
|
76 |
+
44,0,27.4,2,1,0
|
77 |
+
57,0,34.01,0,1,2
|
78 |
+
29,1,29.59,1,1,1
|
79 |
+
21,0,35.53,0,1,1
|
80 |
+
22,1,39.805,0,1,3
|
81 |
+
41,1,32.965,0,1,2
|
82 |
+
31,0,26.885,1,1,3
|
83 |
+
45,1,38.285,0,1,3
|
84 |
+
22,0,37.62,1,0,1
|
85 |
+
48,1,41.23,4,1,2
|
86 |
+
37,1,34.8,2,0,0
|
87 |
+
45,0,22.895,2,0,2
|
88 |
+
57,1,31.16,0,0,2
|
89 |
+
56,1,27.2,0,1,0
|
90 |
+
46,1,27.74,0,1,2
|
91 |
+
55,1,26.98,0,1,2
|
92 |
+
21,1,39.49,0,1,1
|
93 |
+
53,1,24.795,1,1,2
|
94 |
+
59,0,29.83,3,0,3
|
95 |
+
35,0,34.77,2,1,2
|
96 |
+
64,1,31.3,2,0,0
|
97 |
+
28,1,37.62,1,1,1
|
98 |
+
54,1,30.8,3,1,0
|
99 |
+
55,0,38.28,0,1,1
|
100 |
+
56,0,19.95,0,0,3
|
101 |
+
38,0,19.3,0,0,0
|
102 |
+
41,1,31.6,0,1,0
|
103 |
+
30,0,25.46,0,1,3
|
104 |
+
18,1,30.115,0,1,3
|
105 |
+
61,1,29.92,3,0,1
|
106 |
+
34,1,27.5,1,1,0
|
107 |
+
20,0,28.025,1,0,2
|
108 |
+
19,1,28.4,1,1,0
|
109 |
+
26,0,30.875,2,1,2
|
110 |
+
29,0,27.94,0,1,1
|
111 |
+
63,0,35.09,0,0,1
|
112 |
+
54,0,33.63,1,1,2
|
113 |
+
55,1,29.7,2,1,0
|
114 |
+
37,0,30.8,0,1,0
|
115 |
+
21,1,35.72,0,1,2
|
116 |
+
52,0,32.205,3,1,3
|
117 |
+
60,0,28.595,0,1,3
|
118 |
+
58,0,49.06,0,1,1
|
119 |
+
29,1,27.94,1,0,1
|
120 |
+
49,1,27.17,0,1,1
|
121 |
+
37,1,23.37,2,1,2
|
122 |
+
44,0,37.1,2,1,0
|
123 |
+
18,0,23.75,0,1,3
|
124 |
+
20,1,28.975,0,1,2
|
125 |
+
44,0,31.35,1,0,3
|
126 |
+
47,1,33.915,3,1,2
|
127 |
+
26,1,28.785,0,1,3
|
128 |
+
19,1,28.3,0,0,0
|
129 |
+
52,1,37.4,0,1,0
|
130 |
+
32,1,17.765,2,0,2
|
131 |
+
38,0,34.7,2,1,0
|
132 |
+
59,1,26.505,0,1,3
|
133 |
+
61,1,22.04,0,1,3
|
134 |
+
53,1,35.9,2,1,0
|
135 |
+
19,0,25.555,0,1,2
|
136 |
+
20,1,28.785,0,1,3
|
137 |
+
22,1,28.05,0,1,1
|
138 |
+
19,0,34.1,0,1,0
|
139 |
+
22,0,25.175,0,1,2
|
140 |
+
54,1,31.9,3,1,1
|
141 |
+
22,1,36.0,0,1,0
|
142 |
+
34,0,22.42,2,1,3
|
143 |
+
26,0,32.49,1,1,3
|
144 |
+
34,0,25.3,2,0,1
|
145 |
+
29,0,29.735,2,1,2
|
146 |
+
30,0,28.69,3,0,2
|
147 |
+
29,1,38.83,3,1,1
|
148 |
+
46,0,30.495,3,0,2
|
149 |
+
51,1,37.73,1,1,1
|
150 |
+
53,1,37.43,1,1,2
|
151 |
+
19,0,28.4,1,1,0
|
152 |
+
35,0,24.13,1,1,2
|
153 |
+
48,0,29.7,0,1,1
|
154 |
+
32,1,37.145,3,1,3
|
155 |
+
42,1,23.37,0,0,3
|
156 |
+
40,1,25.46,1,1,3
|
157 |
+
44,0,39.52,0,1,2
|
158 |
+
48,0,24.42,0,0,1
|
159 |
+
18,0,25.175,0,0,3
|
160 |
+
30,0,35.53,0,0,1
|
161 |
+
50,1,27.83,3,1,1
|
162 |
+
42,1,26.6,0,0,2
|
163 |
+
18,1,36.85,0,0,1
|
164 |
+
54,0,39.6,1,1,0
|
165 |
+
32,1,29.8,2,1,0
|
166 |
+
37,0,29.64,0,1,2
|
167 |
+
47,0,28.215,4,1,3
|
168 |
+
20,1,37.0,5,1,0
|
169 |
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751 |
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752 |
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37,1,26.4,0,0,1
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754 |
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755 |
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58,1,22.77,0,1,1
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31,0,27.645,2,1,3
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758 |
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47,1,27.83,0,0,1
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763 |
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23,0,35.2,1,1,0
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33,0,27.1,1,0,0
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765 |
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27,0,26.03,0,1,3
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766 |
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45,1,25.175,2,1,3
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767 |
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57,1,31.825,0,1,2
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768 |
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47,0,32.3,1,1,0
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769 |
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42,1,29.0,1,1,0
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770 |
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64,1,39.7,0,1,0
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771 |
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38,1,19.475,2,1,2
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772 |
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61,0,36.1,3,1,0
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773 |
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53,1,26.7,2,1,0
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774 |
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44,1,36.48,0,1,3
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775 |
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19,1,28.88,0,0,2
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776 |
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41,0,34.2,2,1,2
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777 |
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51,0,33.33,3,1,1
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778 |
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40,0,32.3,2,1,2
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779 |
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45,0,39.805,0,1,3
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780 |
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35,0,34.32,3,1,1
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781 |
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53,0,28.88,0,1,2
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782 |
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30,0,24.4,3,0,0
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783 |
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18,0,41.14,0,1,1
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784 |
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51,0,35.97,1,1,1
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785 |
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50,1,27.6,1,0,0
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786 |
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31,1,29.26,1,1,1
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787 |
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35,1,27.7,3,1,0
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788 |
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60,0,36.955,0,1,3
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789 |
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21,0,36.86,0,1,2
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790 |
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29,0,22.515,3,1,3
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791 |
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62,1,29.92,0,1,1
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792 |
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39,1,41.8,0,1,1
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793 |
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19,0,27.6,0,1,0
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794 |
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22,1,23.18,0,1,3
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795 |
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53,0,20.9,0,0,1
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796 |
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39,1,31.92,2,1,2
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797 |
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27,0,28.5,0,0,2
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798 |
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30,0,44.22,2,1,1
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799 |
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30,1,22.895,1,1,3
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800 |
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58,1,33.1,0,1,0
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801 |
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33,0,24.795,0,0,3
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802 |
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42,1,26.18,1,1,1
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803 |
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64,1,35.97,0,1,1
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804 |
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21,0,22.3,1,1,0
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805 |
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18,1,42.24,0,0,1
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806 |
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23,0,26.51,0,1,1
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807 |
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45,1,35.815,0,1,2
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808 |
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40,1,41.42,1,1,2
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809 |
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19,1,36.575,0,1,2
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810 |
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18,0,30.14,0,1,1
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811 |
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25,0,25.84,1,1,3
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812 |
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46,1,30.8,3,1,0
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813 |
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33,1,42.94,3,1,2
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814 |
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54,0,21.01,2,1,1
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815 |
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28,0,22.515,2,1,3
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816 |
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36,0,34.43,2,1,1
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817 |
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20,1,31.46,0,1,1
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818 |
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24,1,24.225,0,1,2
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819 |
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23,0,37.1,3,1,0
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820 |
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47,1,26.125,1,0,3
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821 |
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56,1,28.31,0,1,3
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51,1,34.2,1,1,0
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61,0,32.3,2,1,2
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53,1,23.75,2,1,3
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64,1,26.885,0,0,2
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54,0,24.035,0,1,3
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39,1,34.1,3,1,0
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63,1,36.85,0,1,1
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33,1,36.29,3,1,3
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54,1,35.815,3,1,2
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54,0,31.6,0,1,0
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55,1,35.2,0,0,1
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54,1,23.0,3,1,0
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64,0,26.41,0,1,3
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27,1,21.47,0,1,2
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55,0,27.645,0,1,2
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48,1,28.9,0,1,0
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57,0,27.94,1,1,1
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29,1,25.6,4,1,0
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42,1,25.3,1,1,0
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48,0,37.29,2,1,1
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39,0,42.655,0,1,3
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63,0,21.66,1,1,2
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54,1,31.9,1,1,1
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37,0,37.07,1,0,1
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63,0,31.445,0,1,3
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21,0,31.255,0,1,2
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54,1,28.88,2,1,3
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60,1,18.335,0,1,3
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32,1,29.59,1,1,1
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21,0,26.03,0,1,3
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1080 |
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28,0,31.68,0,0,1
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63,0,33.66,3,1,1
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18,0,21.78,2,1,1
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32,0,27.835,1,1,2
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32,0,31.5,1,1,0
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62,1,30.495,2,1,2
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39,1,18.3,5,0,0
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55,0,28.975,0,1,3
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57,0,31.54,0,1,2
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52,0,47.74,1,1,1
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56,0,22.1,0,1,0
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55,1,29.83,0,1,3
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23,0,32.7,3,1,0
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22,1,30.4,0,0,2
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50,1,33.7,4,1,0
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18,1,31.35,4,1,3
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51,1,34.96,2,0,3
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22,0,33.77,0,1,1
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1100 |
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52,1,30.875,0,1,3
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1101 |
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25,1,33.99,1,1,1
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1102 |
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33,1,19.095,2,0,3
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1103 |
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53,0,28.6,3,1,0
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1104 |
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29,0,38.94,1,1,1
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1105 |
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58,0,36.08,0,1,1
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1106 |
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37,0,29.8,0,1,0
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54,1,31.24,0,1,1
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1108 |
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49,1,29.925,0,1,2
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50,1,26.22,2,1,2
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1110 |
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26,0,30.0,1,1,0
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1111 |
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45,0,20.35,3,1,1
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54,1,32.3,1,1,3
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1113 |
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38,0,38.39,3,0,1
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48,1,25.85,3,0,1
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1115 |
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28,1,26.315,3,1,2
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23,0,24.51,0,1,3
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55,0,32.67,1,1,1
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41,0,29.64,5,1,3
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1119 |
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25,0,33.33,2,0,1
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1120 |
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33,0,35.75,1,0,1
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1121 |
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30,1,19.95,3,1,2
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1122 |
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23,1,31.4,0,0,0
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1123 |
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46,0,38.17,2,1,1
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1124 |
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53,1,36.86,3,0,2
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1125 |
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27,1,32.395,1,1,3
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1126 |
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23,1,42.75,1,0,3
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1127 |
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63,1,25.08,0,1,2
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1128 |
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55,0,29.9,0,1,0
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1129 |
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35,1,35.86,2,1,1
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1130 |
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34,0,32.8,1,1,0
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1131 |
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19,1,18.6,0,1,0
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1132 |
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39,1,23.87,5,1,1
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1133 |
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27,0,45.9,2,1,0
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1134 |
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57,0,40.28,0,1,3
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1135 |
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52,1,18.335,0,1,2
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1136 |
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28,0,33.82,0,1,2
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1137 |
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50,1,28.12,3,1,2
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1138 |
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44,1,25.0,1,1,0
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1139 |
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26,1,22.23,0,1,2
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1140 |
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33,0,30.25,0,1,1
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1141 |
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1142 |
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1150 |
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1153 |
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43,1,32.56,3,0,1
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1155 |
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1156 |
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1159 |
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1160 |
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32,1,41.1,0,1,0
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1162 |
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1164 |
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1165 |
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1166 |
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41,1,28.31,1,1,2
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1168 |
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29,1,24.6,2,1,0
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1170 |
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32,0,35.2,2,1,0
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43,1,26.7,2,0,0
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56,1,41.91,0,1,1
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1175 |
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38,0,29.26,2,1,2
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1176 |
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29,0,32.11,2,1,2
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1177 |
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22,1,27.1,0,1,0
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1178 |
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52,1,24.13,1,0,2
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1179 |
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40,1,27.4,1,1,0
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1180 |
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23,1,34.865,0,1,3
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1181 |
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31,0,29.81,0,0,1
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1182 |
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42,1,41.325,1,1,3
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1183 |
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24,1,29.925,0,1,2
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1184 |
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48,1,27.36,1,1,3
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23,1,28.49,1,0,1
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1187 |
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45,0,23.56,2,1,3
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1188 |
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20,0,35.625,3,0,2
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1189 |
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62,1,32.68,0,1,2
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1190 |
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43,1,25.27,1,0,3
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1191 |
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23,1,28.0,0,1,0
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1192 |
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31,1,32.775,2,1,2
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1193 |
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41,1,21.755,1,1,3
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1194 |
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58,1,32.395,1,1,3
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1195 |
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48,1,36.575,0,1,2
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1196 |
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31,1,21.755,0,1,2
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1197 |
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19,1,27.93,3,1,2
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1198 |
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19,1,30.02,0,0,2
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1199 |
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41,0,33.55,0,1,1
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1200 |
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40,0,29.355,1,1,2
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1201 |
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31,1,25.8,2,1,0
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1202 |
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37,0,24.32,2,1,2
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1203 |
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46,0,40.375,2,1,2
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1204 |
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22,0,32.11,0,1,2
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1205 |
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51,0,32.3,1,1,3
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1206 |
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18,1,27.28,3,0,1
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1207 |
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35,0,17.86,1,1,2
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1208 |
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59,1,34.8,2,1,0
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1209 |
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36,0,33.4,2,0,0
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1210 |
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37,1,25.555,1,0,3
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1211 |
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59,0,37.1,1,1,0
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1212 |
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36,0,30.875,1,1,2
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1213 |
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39,0,34.1,2,1,1
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1214 |
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18,0,21.47,0,1,3
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1215 |
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52,1,33.3,2,1,0
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1216 |
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27,1,31.255,1,1,2
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1217 |
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18,0,39.14,0,1,3
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1218 |
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40,0,25.08,0,1,1
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1219 |
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29,0,37.29,2,1,1
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1220 |
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46,1,34.6,1,0,0
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1221 |
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38,1,30.21,3,1,2
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1222 |
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30,1,21.945,1,1,3
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1223 |
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40,0,24.97,2,1,1
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1224 |
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50,0,25.3,0,1,1
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1225 |
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20,1,24.42,0,0,1
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1226 |
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41,0,23.94,1,1,3
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1227 |
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33,1,39.82,1,1,1
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1228 |
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38,0,16.815,2,1,3
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1229 |
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42,0,37.18,2,1,1
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1230 |
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56,0,34.43,0,1,1
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1231 |
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58,0,30.305,0,1,3
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1232 |
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52,0,34.485,3,0,2
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1233 |
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20,1,21.8,0,0,0
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1234 |
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54,1,24.605,3,1,2
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1235 |
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58,0,23.3,0,1,0
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1236 |
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45,1,27.83,2,1,1
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1237 |
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26,0,31.065,0,1,2
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1238 |
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63,1,21.66,0,1,3
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1239 |
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58,1,28.215,0,1,2
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1240 |
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37,0,22.705,3,1,3
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1241 |
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25,1,42.13,1,1,1
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1242 |
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52,0,41.8,2,0,1
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1243 |
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64,0,36.96,2,0,1
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1244 |
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22,1,21.28,3,1,2
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1245 |
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28,1,33.11,0,1,1
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1246 |
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18,0,33.33,0,1,1
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1247 |
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28,0,24.3,5,1,0
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1248 |
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45,1,25.7,3,1,0
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1249 |
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33,0,29.4,4,1,0
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1250 |
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18,1,39.82,0,1,1
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1251 |
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32,0,33.63,1,0,3
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1252 |
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24,0,29.83,0,0,3
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1253 |
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19,0,19.8,0,1,0
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1254 |
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20,0,27.3,0,0,0
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1255 |
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40,1,29.3,4,1,0
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1256 |
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34,1,27.72,0,1,1
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1257 |
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42,1,37.9,0,1,0
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1258 |
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51,1,36.385,3,1,2
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1259 |
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54,1,27.645,1,1,2
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1260 |
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55,0,37.715,3,1,2
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1261 |
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52,1,23.18,0,1,3
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1262 |
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32,1,20.52,0,1,3
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1263 |
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28,0,37.1,1,1,0
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1264 |
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41,1,28.05,1,1,1
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1265 |
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43,1,29.9,1,1,0
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1266 |
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49,1,33.345,2,1,3
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1267 |
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64,0,23.76,0,0,1
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1268 |
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55,1,30.5,0,1,0
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1269 |
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24,0,31.065,0,0,3
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1270 |
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20,1,33.3,0,1,0
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1271 |
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45,0,27.5,3,1,0
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1272 |
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26,0,33.915,1,1,2
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1273 |
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25,1,34.485,0,1,2
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1274 |
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43,0,25.52,5,1,1
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1275 |
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35,0,27.61,1,1,1
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1276 |
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26,0,27.06,0,0,1
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1277 |
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57,0,23.7,0,1,0
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1278 |
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22,1,30.4,0,1,3
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1279 |
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32,1,29.735,0,1,2
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1280 |
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39,0,29.925,1,0,3
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1281 |
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25,1,26.79,2,1,2
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1282 |
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48,1,33.33,0,1,1
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1283 |
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47,1,27.645,2,0,2
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1284 |
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18,1,21.66,0,0,3
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1285 |
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18,0,30.03,1,1,1
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1286 |
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61,0,36.3,1,0,0
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1287 |
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47,1,24.32,0,1,3
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1288 |
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28,1,17.29,0,1,3
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1289 |
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36,1,25.9,1,1,0
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1290 |
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20,0,39.4,2,0,0
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1291 |
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44,0,34.32,1,1,1
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1292 |
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38,1,19.95,2,1,3
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1293 |
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19,0,34.9,0,0,0
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1294 |
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21,0,23.21,0,1,1
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1295 |
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46,0,25.745,3,1,2
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1296 |
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58,0,25.175,0,1,3
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1297 |
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20,0,22.0,1,1,0
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1298 |
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18,0,26.125,0,1,3
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1299 |
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28,1,26.51,2,1,1
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1300 |
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33,0,27.455,2,1,2
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1301 |
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19,1,25.745,1,1,2
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1302 |
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45,0,30.36,0,0,1
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1303 |
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62,0,30.875,3,0,2
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1304 |
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25,1,20.8,1,1,0
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1305 |
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43,0,27.8,0,0,0
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1306 |
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42,0,24.605,2,0,3
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1307 |
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24,1,27.72,0,1,1
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1308 |
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29,1,21.85,0,0,3
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1309 |
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32,0,28.12,4,0,2
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1310 |
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25,1,30.2,0,0,0
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1311 |
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41,0,32.2,2,1,0
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1312 |
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42,0,26.315,1,1,2
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1313 |
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33,1,26.695,0,1,2
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1314 |
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34,0,42.9,1,1,0
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1315 |
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19,1,34.7,2,0,0
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1316 |
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30,1,23.655,3,0,2
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1317 |
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18,0,28.31,1,1,3
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1318 |
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19,1,20.6,0,1,0
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1319 |
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18,0,53.13,0,1,1
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1320 |
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35,0,39.71,4,1,3
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1321 |
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39,1,26.315,2,1,2
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1322 |
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31,0,31.065,3,1,2
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1323 |
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62,0,26.695,0,0,3
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1324 |
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62,0,38.83,0,1,1
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1325 |
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42,1,40.37,2,0,1
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1326 |
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31,0,25.935,1,1,2
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1327 |
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61,0,33.535,0,1,3
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1328 |
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42,1,32.87,0,1,3
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1329 |
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51,0,30.03,1,1,1
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1330 |
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23,1,24.225,2,1,3
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1331 |
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52,0,38.6,2,1,0
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1332 |
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57,1,25.74,2,1,1
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1333 |
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23,1,33.4,0,1,0
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1334 |
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52,1,44.7,3,1,0
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1335 |
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50,0,30.97,3,1,2
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1336 |
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18,1,31.92,0,1,3
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1337 |
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18,1,36.85,0,1,1
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1338 |
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21,1,25.8,0,1,0
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1339 |
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61,1,29.07,0,0,2
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