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import streamlit as st |
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import numpy as np |
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import pickle |
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import streamlit.components.v1 as components |
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from sklearn.preprocessing import LabelEncoder |
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le = LabelEncoder() |
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def load_model(): |
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return pickle.load(open('Income_classification_ra.pkl', 'rb')) |
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def model_prediction(model, features): |
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predicted = str(model.predict(features)[0]) |
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return predicted |
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def transform(text): |
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text = le.fit_transform(text) |
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return text[0] |
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def app_design(): |
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image = '18.png' |
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st.image(image, use_column_width=True) |
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st.subheader("Enter the following values:") |
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Age = st.number_input("Age") |
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Workclass = st.text_input("Workclass") |
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Workclass = transform([Workclass]) |
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Final_weight = st.number_input("Final_weight") |
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Education = st.text_input("Education") |
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Education=transform([Education]) |
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Education_Num = st.number_input("Education_Num") |
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Marital_status = st.text_input("Marital_status") |
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Marital_status=transform([Marital_status]) |
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Occupation = st.text_input("Occupation") |
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Occupation=transform([Occupation]) |
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Relationship = st.text_input("Relationship") |
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Relationship=transform([Relationship]) |
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Race = st.text_input("Race") |
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Race=transform([Race]) |
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Sex = st.selectbox("Sex",('Male','Female')) |
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if Sex == 'Male': |
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Sex = 1 |
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else: |
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Sex = 0 |
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Capital_gain = st.number_input("Capital_gain") |
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Capital_loss = st.number_input("Capital_loss") |
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Hours_per_week = st.number_input("Hours_per_week") |
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Native_country = st.text_input("Native_country") |
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Native_country=transform([Native_country]) |
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features = [[Age, Workclass, Final_weight, Education, Education_Num, Marital_status, Occupation,Relationship,Race,Sex,Capital_gain,Capital_loss,Hours_per_week,Native_country]] |
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model = load_model() |
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if st.button('Predict Income'): |
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predicted_value = model_prediction(model, features) |
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if predicted_value=='1': |
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predicted_value='greater then 50000' |
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else: |
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predicted_value='less then or equal to 50000' |
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st.success(f"The Income is: {predicted_value}") |
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def about_hidevs(): |
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components.html(""" |
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<div> |
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<h4>🚀 Unlock Your Dream Job with HiDevs Community!</h4> |
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<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> |
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<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">🆓 Best of all, everything we offer is <b>completely free</b>! We are dedicated to helping society.</p> |
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<p class="subtitle">Book free of cost 1:1 mentorship on any topic of your choice — <a class="link" href="https://topmate.io/deepakchawla1307">topmate</a></p> |
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<p class="subtitle">✨ We dedicate over 30 minutes to each applicant’s resume, LinkedIn profile, mock interview, and upskill program. If you’d like our guidance, check out our services <a class="link" href="https://hidevscommunity.wixsite.com/hidevs">here</a></p> |
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<p class="subtitle">💡 Join us now, and turbocharge your career!</p> |
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<p class="subtitle"><a class="link" href="https://hidevscommunity.wixsite.com/hidevs" target="__blank">Website</a> |
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<a class="link" href="https://www.youtube.com/@HidevsCommunity1307/" target="__blank">YouTube</a> |
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<a class="link" href="https://www.instagram.com/hidevs_community/" target="__blank">Instagram</a> |
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<a class="link" href="https://medium.com/@hidevscommunity" target="__blank">Medium</a> |
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<a class="link" href="https://www.linkedin.com/company/hidevs-community/" target="__blank">LinkedIn</a> |
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<a class="link" href="https://github.com/hidevscommunity" target="__blank">GitHub</a></p> |
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</div> |
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""", |
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height=600) |
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def main(): |
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st.set_page_config( |
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page_title="Income Classification", |
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page_icon=":chart_with_upwards_trend:", |
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) |
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st.title("Welcome to our Income Classification App!") |
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app_design() |
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st.header("About HiDevs Community") |
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about_hidevs() |
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if __name__ == '__main__': |
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main() |
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