Spaces:
Sleeping
Sleeping
mbabazif
commited on
Commit
•
f8409da
1
Parent(s):
da36e5e
App
Browse files
app.py
CHANGED
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@@ -22,69 +22,76 @@ options = st.sidebar.radio("Select a page:", ["Prediction", "Model Information",
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# Prediction Page
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if options == "Prediction":
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st.title("Income Classification - Prediction")
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# Input fields
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input_data = pd.DataFrame([[
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age, gender, education, worker_class, marital_status, race, is_hispanic, employment_commitment,
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employment_stat, wage_per_hour, working_week_per_year, industry_code, industry_code_main, occupation_code,
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# Prediction Page
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if options == "Prediction":
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st.title("Income Classification - Prediction")
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with st.form('input feature'):
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# Form inputs...
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col1, col2, col3 = st.columns(3)
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with col1:
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# Input fields
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age = st.number_input("Age", min_value=0)
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gender = st.selectbox("Gender", ['Female', 'Male'])
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education = st.selectbox("Education", ['High School', 'Children', 'Middle School', 'Masters', 'Bachelors Degree',
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'Elementary', 'College Dropout', 'Associates Degree', 'Professional Degree',
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'Doctorate'])
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worker_class = st.selectbox("Worker Class", ['Private', 'Federal government', 'Never worked', 'Local government',
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'Self-employed-incorporated', 'Self-employed-not incorporated',
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'State government', 'Without pay'])
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marital_status = st.selectbox("Marital Status", ['Widowed', 'Never married', 'Married-civilian spouse present',
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'Divorced', 'Married-spouse absent', 'Separated',
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'Married-A F spouse present'])
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race = st.selectbox("Race", ['White', 'Black', 'Asian or Pacific Islander', 'Amer Indian Aleut or Eskimo', 'Other'])
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is_hispanic = st.selectbox("Is Hispanic", ['All other', 'Mexican-American', 'Central or South American',
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'Mexican (Mexicano)', 'Puerto Rican', 'Other Spanish', 'Cuban',
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'Do not know', 'Chicano'])
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employment_commitment = st.selectbox("Employment Commitment", ['Not in labor force', 'Children or Armed Forces',
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'Full-time schedules', 'PT for econ reasons usually PT',
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'Unemployed full-time',
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'PT for non-econ reasons usually FT',
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'PT for econ reasons usually FT',
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'Unemployed part- time'])
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with col2:
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employment_stat = st.number_input("Employment Status", min_value=0, max_value=2, step=1)
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wage_per_hour = st.number_input("Wage per Hour", min_value=0)
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working_week_per_year = st.number_input("Working Week per Year", min_value=0)
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industry_code = st.number_input("Industry Code", min_value=0)
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industry_code_main = st.selectbox("Industry Code Main", ['Not in universe or children', 'Hospital services',
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'Retail trade', 'Finance insurance and real estate',
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'Manufacturing-nondurable goods', 'Transportation',
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'Business and repair services', 'Medical except hospital',
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'Education', 'Construction', 'Manufacturing-durable goods',
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'Public administration', 'Agriculture',
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'Other professional services', 'Mining',
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'Utilities and sanitary services', 'Private household services',
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'Personal services except private HH', 'Wholesale trade',
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'Communications', 'Entertainment', 'Social services',
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'Forestry and fisheries', 'Armed Forces'])
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occupation_code = st.number_input("Occupation Code", min_value=0)
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occupation_code_main = st.selectbox("Occupation Code Main", ['Unknown', 'Adm support including clerical',
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'Executive admin and managerial', 'Sales',
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'Machine operators assmblrs & inspctrs', 'Other service',
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'Precision production craft & repair',
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'Professional specialty', 'Handlers equip cleaners etc',
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'Transportation and material moving',
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'Farming forestry and fishing', 'Private household services',
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'Technicians and related support', 'Protective services',
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'Armed Forces'])
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total_employed = st.selectbox("Total Employed", [0, 1, 2, 3, 4, 5, 6])
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with col3:
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household_summary = st.selectbox("Household Summary", ['Householder', 'Child 18 or older',
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'Child under 18 never married', 'Spouse of householder',
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'Nonrelative of householder', 'Other relative of householder',
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'Group Quarters- Secondary individual', 'Child under 18 ever married'])
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vet_benefit = st.number_input("Vet Benefit", min_value=0, max_value=2, step=1)
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tax_status = st.selectbox("Tax Status", ['Head of household', 'Single', 'Nonfiler', 'Joint both 65+',
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'Joint both under 65', 'Joint one under 65 & one 65+'])
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gains = st.number_input("Gains", min_value=0)
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losses = st.number_input("Losses", min_value=0)
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stocks_status = st.number_input("Stocks Status", min_value=0)
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citizenship = st.selectbox("Citizenship", ['citizen', 'foreigner'])
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importance_of_record = st.number_input("Importance of Record", min_value=0.0, format='%f')
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submit_button = st.form_submit_button('Predict Income Level')
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if st.button:
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input_data = pd.DataFrame([[
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age, gender, education, worker_class, marital_status, race, is_hispanic, employment_commitment,
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employment_stat, wage_per_hour, working_week_per_year, industry_code, industry_code_main, occupation_code,
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