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@@ -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
+kiva_loans.csv filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,129 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+import joblib
+import shap
+import xgboost as xgb
+
+# Load the saved model and preprocessing objects
+model_xgb = joblib.load('model_xgb.joblib')
+scaler = joblib.load('scaler.joblib')
+ohe = joblib.load('ohe.joblib')
+
+# Extract the unique values for 'country' and 'sector' from the OneHotEncoder (ohe) object
+unique_countries = ohe.categories_[0]  # Assuming 'country' is the first categorical feature
+unique_sectors = ohe.categories_[1]  # Assuming 'sector' is the second categorical feature
+
+# Define a mapping of encoded values to repayment interval labels
+repayment_interval_mapping = {0: '🚅 Bullet Repayment Interval', 1: '🪙 Irregular Repayment Interval', 2: '📅 Monthly Repayment Interval'}
+
+# Title with emojis and colors
+st.markdown("<h1 style='text-align: center; color: blue;'>📊 Loan Repayment Interval Prediction 📈</h1>", unsafe_allow_html=True)
+
+# Input Features Section with emojis and description
+st.write("## 🎯 Input Features")
+st.markdown("Here you can choose the variables to predict the repayment interval based on historical data. Please provide the following details:")
+
+# User input fields using unique country and sector values from the OneHotEncoder object
+country = st.selectbox('🌍 Country', unique_countries)
+sector = st.selectbox('🏢 Sector', unique_sectors)
+funded_amount = st.number_input('💰 Funded Amount', min_value=0, max_value=10000, value=1000, step=50)
+lender_count = st.number_input('👥 Lender Count', min_value=1, max_value=100, value=2, step=1)
+
+# Create a sample observation from the user input
+sample_listing = pd.DataFrame({
+    'country': [country],
+    'sector': [sector],
+    'funded_amount': [funded_amount],
+    'lender_count': [lender_count],
+})
+
+# Separate categorical and numerical features
+cat_features = ['country', 'sector']
+num_features = ['funded_amount', 'lender_count']
+
+# Get the feature names from the OneHotEncoder
+ohe_feature_names = ohe.get_feature_names_out(cat_features)
+
+# Combine numerical feature names with encoded categorical feature names
+feature_names = np.concatenate([num_features, ohe_feature_names])
+
+# One-hot encode categorical features
+X_cat = pd.DataFrame(ohe.transform(sample_listing[cat_features]), columns=ohe_feature_names)
+
+# Scale numerical features
+X_num = pd.DataFrame(scaler.transform(sample_listing[num_features]), columns=num_features)
+
+# Combine processed features
+X_processed = pd.concat([X_num, X_cat], axis=1)
+
+# Make a prediction (returns the encoded value)
+predicted_encoded_repayment_interval = model_xgb.predict(X_processed)[0]
+
+# Map the encoded value back to the actual repayment interval label
+predicted_repayment_interval = repayment_interval_mapping.get(int(predicted_encoded_repayment_interval), "Unknown")
+
+# Display the actual repayment interval label with more style
+st.title("✅ Predicted Repayment Interval:")
+st.markdown(f"<h2 style='color:green;'>{predicted_repayment_interval}</h2>", unsafe_allow_html=True)
+
+# Explanation for SHAP force plots
+st.write("## 🔍 SHAP Explanation")
+st.markdown("The following SHAP plots explain the model's decision for each repayment interval type. These visualizations help you understand the key features that influenced the model's prediction.")
+
+# SHAP explanations
+explainer = shap.TreeExplainer(model_xgb)
+shap_values = explainer.shap_values(X_processed)
+
+# Function to add background color to SHAP plots
+def add_background(html_content):
+    white_background_style = "<style>body { background-color: white; }</style>"
+    return white_background_style + html_content
+
+# Generate and save SHAP force plot for Class 0 (Bullet)
+st.write("### 🚅 SHAP Force Plot for Class 0: Bullet Repayment Interval")
+st.markdown("This plot explains the factors influencing the Bullet repayment interval prediction. Bullet repayment means paying off the loan in one lump sum at the end of the loan period.")
+shap_html_path_0 = "shap_force_plot_class_0.html"
+shap.save_html(shap_html_path_0, shap.force_plot(
+    explainer.expected_value[0],
+    shap_values[0][:, 0],
+    X_processed.iloc[0, :].values,
+    feature_names,
+    show=False,
+    matplotlib=False
+))
+with open(shap_html_path_0, 'r', encoding='utf-8') as f:
+    shap_html_0 = f.read()
+st.components.v1.html(add_background(shap_html_0), height=130)
+
+# Generate and save SHAP force plot for Class 1 (Irregular)
+st.write("### 🪙 SHAP Force Plot for Class 1: Irregular Repayment Interval")
+st.markdown("This plot explains the factors influencing the Irregular repayment interval prediction. Irregular repayment means paying off the loan at irregular intervals based on specific conditions.")
+shap_html_path_1 = "shap_force_plot_class_1.html"
+shap.save_html(shap_html_path_1, shap.force_plot(
+    explainer.expected_value[1],
+    shap_values[0][:, 1],
+    X_processed.iloc[0, :].values,
+    feature_names,
+    show=False,
+    matplotlib=False
+))
+with open(shap_html_path_1, 'r', encoding='utf-8') as f:
+    shap_html_1 = f.read()
+st.components.v1.html(add_background(shap_html_1), height=130)
+
+# Generate and save SHAP force plot for Class 2 (Monthly)
+st.write("### 📅 SHAP Force Plot for Class 2: Monthly Repayment Interval")
+st.markdown("This plot explains the factors influencing the Monthly repayment interval prediction. Monthly repayment means paying off the loan in equal monthly installments.")
+shap_html_path_2 = "shap_force_plot_class_2.html"
+shap.save_html(shap_html_path_2, shap.force_plot(
+    explainer.expected_value[2],
+    shap_values[0][:, 2],
+    X_processed.iloc[0, :].values,
+    feature_names,
+    show=False,
+    matplotlib=False
+))
+with open(shap_html_path_2, 'r', encoding='utf-8') as f:
+    shap_html_2 = f.read()
+st.components.v1.html(add_background(shap_html_2), height=130)

kiva_loans.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b20efc20de600b27608d69fe07e728b00a075c3db29849e146b717098f778d92
+size 195852823

model_xgb.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:98dd28b386001fc78e8261ec56685bd758c88b9ec0c5bedb73554c5b3953cf45
+size 1648037

ohe.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6a42f3b58afdd17d47b9a67203087a2619a51c65ad916d9b44eb172c078c0a6a
+size 2809

requirements.txt

@@ -0,0 +1,9 @@
+
+streamlit
+pandas
+numpy
+xgboost
+scikit-learn
+shap
+matplotlib
+joblib

scaler.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:82b8e1f41898b2d08758feeed3e15d37e15d577a0f5a701d931a611c39774546
+size 999