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import joblib |
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import pandas as pd |
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import streamlit as st |
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EDU_DICT = {'some high school': 1, |
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'some college': 2, |
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'high school': 3, |
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"associate's degree": 4, |
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"bachelor's degree": 5, |
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"master's degree": 6 |
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} |
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race = {'group A': 0, |
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'group B': 1, |
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'group C': 2, |
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'group D': 3, |
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'group E': 4 |
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} |
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model = joblib.load('modelN.joblib') |
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unique_values = joblib.load('unique_valuesN.joblib') |
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unique_gender = unique_values["gender"] |
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unique_lunch = unique_values["lunch"] |
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unique_test_preparation_course = unique_values["test preparation course"] |
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unique_education = unique_values["parental level of education"] |
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def main(): |
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st.title("Race Prediction") |
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with st.form("questionaire"): |
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gender = st.selectbox("gender", options = unique_gender) |
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lunch = st.selectbox("lunch", options = unique_lunch) |
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test = st.selectbox("test preparation course", options = unique_test_preparation_course) |
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education = st.selectbox("parental level of education", options = unique_education) |
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math_score = st.slider("math score", min_value = 1, max_value = 100) |
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reading_score = st.slider("reading score", min_value = 1, max_value = 100) |
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writing_score = st.slider("writing score", min_value = 1, max_value = 100) |
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clicked = st.form_submit_button("Predict income") |
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if clicked: |
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result=model.predict(pd.DataFrame({"gender": [gender], |
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"lunch": [lunch], |
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"test preparation course": [test], |
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"parental level of education": [EDU_DICT[education]], |
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"math score": [math_score], |
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"reading score": [reading_score], |
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"writing score": [writing_score]})) |
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if result[0] == 0: |
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result = 'group A' |
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elif result[0] == 1: |
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result = 'group B' |
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elif result[0] == 2: |
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result = 'group C' |
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elif result[0] == 3: |
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result = 'group D' |
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elif result[0] == 4: |
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result = 'group E' |
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else: |
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'ERROR' |
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st.success("Race Prediction is "+result) |
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if __name__ == "__main__": |
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main() |
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