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app.py

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+# -*- coding: utf-8 -*-
+"""app
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1FxKIGQzWkYp6AR9LCDf1hHJfeiAgni2N
+"""
+
+import joblib
+import pandas as pd
+import streamlit as st
+
+model = joblib.load('model.joblib')
+unique_values = joblib.load('unique_values.joblib')
+
+unique_class = unique_values["workclass"]
+unique_education = unique_values["education"]
+unique_marital_status = unique_values["marital.status"]
+unique_relationship = unique_values["relationship"]
+unique_occupation = unique_values["occupation"]
+unique_sex = unique_values["sex"]
+unique_race = unique_values["race"]
+unique_country = unique_values["native.country"]
+def main():
+  st.title("Adult Income Analysis")
+  with st.form("questionaire"):
+    age = st.slider("Age", min_value=10, max_value=100)
+    workclass = st.selectbox("Workclass", unique_class)
+    education = st.selectbox("Education", unique_education)
+    Marital_Status = st.selectbox("Marital Status", unique_marital_status)
+    occupation = st.selectbox("Occupation", unique_occupation)
+    relationship = st.selectbox("Relationship", unique_relationship)
+    race = st.selectbox("Race", unique_race)
+    sex = st.selectbox("Sex", unique_sex)
+    hours_per_week = st.slider("Hours per week", min_value=1, max_value=100)
+    native_country = st.selectbox("Country", unique_country)
+    clicked = st.form_submit_button("Predict income")
+    if clicked:
+      result=model.predict(pd.DataFrame({"age": [age],
+                                         "workclass": [workclass],
+                                         "education": [education],
+                                         "marital.status": [Marital_Status],
+                                         "occupation": [occupation],
+                                         "relationship": [relationship],
+                                         "sex": [sex],
+                                         "race": [race],
+                                         "hours.per.week": [hours_per_week],
+                                         "native.country": [native_country]}))
+      result = '>50K' if result[0] == 1 else '<=50K'
+      st.success('The predicted income is {}'.format(result))
+if __name__=='__main__':
+  main()

kaggle.json

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+{"username":"johnsonjoestar","key":"fce749cf190f160ba66388ab15d8b8f2"}

model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:25d3439c05d7692f6a9c5410744a98d72f757ef6f40836dd57b7292c1d543b35
+size 300569

unique_values.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6d860365c89dab77a8fc16932d4aec81300745cd8a8c9831acc683f3e9335ee2
+size 3835