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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+micro_world_139countries.csv filter=lfs diff=lfs merge=lfs -text

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import joblib
+from xgboost import XGBRegressor
+from sklearn.preprocessing import StandardScaler, OneHotEncoder
+import shap
+from streamlit_shap import st_shap
+
+
+
+# Page configuration
+st.set_page_config(
+    page_title="Medical Costs Concern Prediction",)
+ 
+st.title('Predict Medical Costs Concern')
+ 
+# Load model and preprocessing objects
+@st.cache_resource
+def load_model_objects():
+    model_xgb = joblib.load('model_best.joblib')
+    scaler = joblib.load('scaler.joblib')
+    return model_xgb, scaler
+ 
+model_xgb, scaler = load_model_objects()
+# Create SHAP explainer
+explainer = shap.TreeExplainer(model_xgb)
+ 
+# App description
+with st.expander("What's this app?"):
+    st.markdown("""
+    This app predicts how worried a person is about medical costs, based on factors like age, education, income, and employment status.
+    We've trained an AI model to analyze these inputs and give a prediction.
+    """)
+ 
+st.subheader('Describe yourself')
+ 
+# User inputs
+col1, col2 = st.columns(2)
+ 
+with col1:
+    age = st.number_input('Age', min_value=18, max_value=100, value=30)
+    education = st.selectbox('Education Level', options=['Primary', 'Secondary', 'Tertiary'], index=1)
+    income_quartile = st.radio('Income Quartile', options=['Lowest', 'Second', 'Third', 'Highest'])
+ 
+with col2:
+    employment_status = st.selectbox('Employment Status', options=['Unemployed', 'Employed', 'Self-employed', 'Student'], index=1)
+ 
+# Map user inputs to numerical and categorical features
+education_mapping = {'Primary': 1, 'Secondary': 2, 'Tertiary': 3}
+income_mapping = {'Lowest': 1, 'Second': 2, 'Third': 3, 'Highest': 4}
+employment_mapping = {'Unemployed': 0, 'Employed': 1, 'Self-employed': 2, 'Student': 3}
+ 
+# Transform user input into a feature vector
+education_num = education_mapping[education]
+income_num = income_mapping[income_quartile]
+employment_num = employment_mapping[employment_status]
+ 
+# Prepare features for the model
+num_features = pd.DataFrame({
+    'age': [age],
+    'educ': [education_num],
+    'inc_q': [income_num],
+    'emp_in': [employment_num]
+})
+num_scaled = pd.DataFrame(scaler.transform(num_features), columns=num_features.columns)
+ 
+# Prediction button
+if st.button('Predict Concern Level'):
+    # Make prediction
+    predicted_concern = model_xgb.predict(num_scaled)[0]
+    # Display prediction
+    st.metric(label="Predicted concern level", value=f'{round(predicted_concern)} (1: Not Worried, 3: Very Worried)')
+    # SHAP explanation
+    st.subheader('Concern Factors Explained')
+    shap_values = explainer.shap_values(num_scaled)
+    st_shap(shap.force_plot(explainer.expected_value, shap_values, num_scaled), height=400, width=600)
+    st.markdown("""
+    This plot shows how each feature contributes to the predicted concern level:
+    - Blue bars push the concern level lower
+    - Red bars push the concern level higher
+    - The length of each bar indicates the strength of the feature's impact
+    """)
+ 
+# Footer
+st.markdown("---")

best.reg.joblib

@@ -0,0 +1,3 @@
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+oid sha256:c26ee9bad4da71cbed2f0d14951c9c2190dd0aa38f8ce5ea1a958deca783b193
+size 15738433

micro_world_139countries.csv

@@ -0,0 +1,3 @@
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+size 40174289

model.joblib

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+oid sha256:d6b19da76907416fb6f5ddbfd53d2fcf4b13750fb06085a849548fc9fdaf4260
+size 649

model_best.joblib

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+oid sha256:5b74d7f67e3cebb5a921fd0e7bb84350f3e34ef31932ec2022c4f91d5cec95dc
+size 199835

model_xgb.joblib

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+size 460166

requirements.txt

@@ -0,0 +1,11 @@
+numpy
+streamlit
+pandas
+altair
+matplotlib
+seaborn
+scipy
+joblib
+xgboost
+shap
+streamlit_shap

scaler.joblib

@@ -0,0 +1,3 @@
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+oid sha256:81342eb8c5ddbf89d9b2b7e14d5e3c3ae0c0adb8d85c6332b78c139983c13a5e
+size 1047