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| import streamlit as st | |
| import numpy as np | |
| import pickle | |
| import streamlit.components.v1 as components | |
| from sklearn.preprocessing import LabelEncoder | |
| le = LabelEncoder() | |
| # Load the pickled model | |
| def load_model(): | |
| return pickle.load(open('Insurance_Prediction_kn.pkl', 'rb')) | |
| # Function for model prediction | |
| def model_prediction(model, features): | |
| predicted = str(model.predict(features)[0]) | |
| return predicted | |
| def transform(text): | |
| text = le.fit_transform(text) | |
| return text[0] | |
| def app_design(): | |
| # Add input fields for High, Open, and Low values | |
| image = '20.png' | |
| st.image(image, use_column_width=True) | |
| st.subheader("Enter the following values:") | |
| #Encoding categorical data values | |
| Age = st.number_input("Age") | |
| EmploymentType = st.text_input("Employment Type") | |
| EmploymentType = transform([EmploymentType]) | |
| sex = st.selectbox("Sex",('Male','Female')) | |
| if sex == 'Male': | |
| sex = 1 | |
| else: | |
| sex = 0 | |
| GraduateOrNot = st.selectbox("Graduate Or Not",('Yes','No')) | |
| if GraduateOrNot == 'Yes': | |
| GraduateOrNot = 1 | |
| else: | |
| GraduateOrNot = 0 | |
| AnnualIncome = st.number_input("Annual Income") | |
| FamilyMembers = st.number_input("Family Members") | |
| FrequentFlyer = st.selectbox("Frequent Flyer",('Yes','No')) | |
| if FrequentFlyer == 'Yes': | |
| FrequentFlyer = 1 | |
| else: | |
| FrequentFlyer = 0 | |
| EverTravelledAbroad = st.selectbox("Ever Travelled Abroad",('Yes','No')) | |
| if EverTravelledAbroad == 'Yes': | |
| EverTravelledAbroad = 1 | |
| else: | |
| EverTravelledAbroad = 0 | |
| # Create a feature list from the user inputs | |
| features = [[Age, EmploymentType, GraduateOrNot, AnnualIncome, FamilyMembers, FrequentFlyer, EverTravelledAbroad]] | |
| # Load the model | |
| model = load_model() | |
| # Make a prediction when the user clicks the "Predict" button | |
| if st.button('Predict Insurance'): | |
| predicted_value = model_prediction(model, features) | |
| if predicted_value==1: | |
| st.success("The Travel Insurance is taken by person") | |
| else: | |
| st.success("The Travel Insurance is not taken by person") | |
| def about_hidevs(): | |
| components.html(""" | |
| <div> | |
| <h4>🚀 Unlock Your Dream Job with HiDevs Community!</h4> | |
| <p class="subtitle">🔍 Seeking the perfect job? HiDevs Community is your gateway to career success in the tech industry. Explore free expert courses, job-seeking support, and career transformation tips.</p> | |
| <p class="subtitle">💼 We offer an upskill program in <b>Gen AI, Data Science, Machine Learning</b>, and assist startups in adopting <b>Gen AI</b> at minimal development costs.</p> | |
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| <p class="subtitle">💡 Join us now, and turbocharge your career!</p> | |
| <p class="subtitle"><a class="link" href="https://hidevscommunity.wixsite.com/hidevs" target="__blank">Website</a> | |
| <a class="link" href="https://www.youtube.com/@HidevsCommunity1307/" target="__blank">YouTube</a> | |
| <a class="link" href="https://www.instagram.com/hidevs_community/" target="__blank">Instagram</a> | |
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| <a class="link" href="https://www.linkedin.com/company/hidevs-community/" target="__blank">LinkedIn</a> | |
| <a class="link" href="https://github.com/hidevscommunity" target="__blank">GitHub</a></p> | |
| </div> | |
| """, | |
| height=600) | |
| def main(): | |
| # Set the app title and add your website name and logo | |
| st.set_page_config( | |
| page_title="Insurance Prediction", | |
| page_icon=":chart_with_upwards_trend:", | |
| ) | |
| st.title("Welcome to our Insurance Prediction App!") | |
| app_design() | |
| st.header("About HiDevs Community") | |
| about_hidevs() | |
| if __name__ == '__main__': | |
| main() | |