Update app.py

app.py

@@ -1,133 +1,79 @@
+import streamlit as st
 import pandas as pd
 import numpy as np
-import streamlit as st  # Streamlit for UI
 import matplotlib.pyplot as plt
 import seaborn as sns
-from sklearn.model_selection import train_test_split, GridSearchCV
+from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import StandardScaler
 from sklearn.svm import SVC
-from sklearn.ensemble import RandomForestClassifier
 from sklearn.linear_model import LogisticRegression
 from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
 
 # Load Dataset
-df = pd.read_csv("bank_loan(1).csv")
+df = pd.read_csv("bank_loan.csv")
 
-# Preprocessing
-df['CCAvg'] = df['CCAvg'].astype('float64')
-
-# Drop Unnecessary Columns
+# Data Preprocessing
+df['CCAvg'] = df['CCAvg'].astype(float)
+df[df['Experience'] < 0] = df[df['Experience'] < 0].abs()
 df.drop(['ID', 'ZIP Code'], axis=1, inplace=True)
+df['Income'] = round(df['Income']/12, 2)
 
-# Convert annual income to monthly
-df['Income'] = round(df['Income'] / 12, 2)
-
-# Define X and y
+# Define Features and Target Variable
 X = df.drop('Personal Loan', axis=1)
 y = df['Personal Loan']
 
-# Standardize the features
+# Standardize Features
 scaler = StandardScaler()
 X_scaled = scaler.fit_transform(X)
 
-# Train-Validation-Test Split
+# Split Data into Train, Validation, and Test Sets
 X_train, X_temp, y_train, y_temp = train_test_split(X_scaled, y, test_size=0.3, stratify=y, random_state=42)
 X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, stratify=y_temp, random_state=42)
 
-# Train Models
-svm_rbf = SVC(kernel='rbf', C=10, gamma=0.1, probability=True)
-svm_linear = SVC(kernel='linear', C=1, probability=True)
-logistic_regression = LogisticRegression(max_iter=500)
-
-models = {"SVM (RBF Kernel)": svm_rbf, "SVM (Linear Kernel)": svm_linear, "Logistic Regression": logistic_regression}
+# Train Model
+svm_model = SVC(kernel='rbf', C=10, gamma=0.1, probability=True)
+svm_model.fit(X_train, y_train)
 
-# Train and Select the Best Model
-best_model, best_acc = None, 0
-for name, model in models.items():
-    model.fit(X_train, y_train)
-    acc = accuracy_score(y_val, model.predict(X_val))
-    if acc > best_acc:
-        best_acc = acc
-        best_model = model
+def predict_loan_acceptance(sample):
+    sample_scaled = scaler.transform(sample)
+    prediction = svm_model.predict(sample_scaled)[0]
+    probability = svm_model.predict_proba(sample_scaled)[0][1]
+    return prediction, probability
 
 # Streamlit UI
-st.title("🏦 Personal Loan Prediction System")
-st.write("Answer the questions below to check your **loan eligibility.**")
-
-# Collect User Inputs
-st.subheader("📋 Personal & Financial Information")
-
-age = st.slider("📌 Age", min_value=18, max_value=100, value=30)
-experience = st.slider("📌 Years of Experience", min_value=0, max_value=50, value=5)
-income = st.slider("📌 Monthly Income (in thousands)", min_value=0, max_value=50, value=5)
-family = st.selectbox("📌 Number of Family Members", [1, 2, 3, 4])
-
-st.subheader("💳 Credit & Banking Behavior")
-
-ccavg = st.slider("📌 Avg Monthly Credit Card Spending", min_value=0, max_value=10, value=1)
-education = st.radio("📌 Education Level", ["Undergraduate (1)", "Graduate (2)", "Advanced/Professional (3)"])
-mortgage = st.slider("📌 Mortgage Amount (in thousands)", min_value=0, max_value=500, value=0)
-
-st.subheader("📡 Account Information")
-
-securities = st.radio("📌 Do you have a Securities Account?", ["No", "Yes"])
-cd_account = st.radio("📌 Do you have a Certificate of Deposit (CD) Account?", ["No", "Yes"])
-online = st.radio("📌 Do you use Online Banking?", ["No", "Yes"])
-credit_card = st.radio("📌 Do you have a Credit Card with the bank?", ["No", "Yes"])
-
-# Convert Inputs for Model
-education = 1 if "Undergraduate" in education else (2 if "Graduate" in education else 3)
-securities = 1 if securities == "Yes" else 0
-cd_account = 1 if cd_account == "Yes" else 0
-online = 1 if online == "Yes" else 0
-credit_card = 1 if credit_card == "Yes" else 0
-
-# Submit Button
-if st.button("🔮 Predict Loan Approval"):
-    user_data = np.array([[age, experience, income, family, ccavg, education, mortgage, securities, cd_account, online, credit_card]])
-    user_scaled = scaler.transform(user_data)
-
-    prediction = best_model.predict(user_scaled)[0]
-    probability = best_model.predict_proba(user_scaled)[0][1]
-
-    st.subheader("📊 Prediction Result")
+st.title("Personal Loan Eligibility Checker")
+st.write("Answer the following questions to check if you're eligible for a personal loan.")
+
+age = st.number_input("Enter your age:", min_value=18, max_value=100, step=1)
+experience = st.number_input("Enter your years of experience:", min_value=0, max_value=80, step=1)
+income = st.number_input("Enter your monthly income (in thousands):", min_value=0.0, step=0.1)
+family = st.selectbox("Select number of family members:", [1, 2, 3, 4])
+ccavg = st.number_input("Enter your average monthly credit card spending:", min_value=0.0, step=0.1)
+education = st.selectbox("Select your education level:", [1, 2, 3], format_func=lambda x: ["Undergraduate", "Graduate", "Advanced/Professional"][x-1])
+mortgage = st.number_input("Enter your mortgage amount (Enter 0 if none):", min_value=0.0, step=0.1)
+securities_account = st.radio("Do you have a securities account?", [0, 1], format_func=lambda x: "Yes" if x else "No")
+cd_account = st.radio("Do you have a certificate of deposit (CD) account?", [0, 1], format_func=lambda x: "Yes" if x else "No")
+online = st.radio("Do you use online banking?", [0, 1], format_func=lambda x: "Yes" if x else "No")
+credit_card = st.radio("Do you have a credit card with the bank?", [0, 1], format_func=lambda x: "Yes" if x else "No")
+
+if st.button("Check Eligibility"):
+    sample = pd.DataFrame({
+        'Age': [age],
+        'Experience': [experience],
+        'Income': [income],
+        'Family': [family],
+        'CCAvg': [ccavg],
+        'Education': [education],
+        'Mortgage': [mortgage],
+        'Securities Account': [securities_account],
+        'CD Account': [cd_account],
+        'Online': [online],
+        'CreditCard': [credit_card]
+    })
+    prediction, probability = predict_loan_acceptance(sample)
+    
+    st.subheader("Prediction Result")
     if prediction == 1:
-        st.success(f"✅ Loan Approved! (Probability: {probability:.2f})")
+        st.success(f"Loan Accepted ✅\nProbability of Acceptance: {probability:.4f}")
     else:
-        st.error(f"❌ Loan Not Approved. (Probability: {probability:.2f})")
-
-# Show User Input Data in a Table
-st.subheader("📌 Your Entered Information")
-user_df = pd.DataFrame({
-    "Feature": ["Age", "Experience", "Income", "Family", "CCAvg", "Education", "Mortgage",
-                "Securities Account", "CD Account", "Online", "Credit Card"],
-    "Your Answer": [age, experience, income, family, ccavg, education, mortgage, securities, cd_account, online, credit_card]
-})
-
-st.dataframe(user_df)
-
-# Show Summary Visuals
-st.subheader("📊 Feature Distributions in Dataset")
-
-fig, ax = plt.subplots(3, 2, figsize=(12, 10))
-
-sns.histplot(df["Age"], kde=True, bins=20, ax=ax[0, 0])
-ax[0, 0].set_title("Distribution of Age")
-
-sns.histplot(df["Experience"], kde=True, bins=20, ax=ax[0, 1])
-ax[0, 1].set_title("Distribution of Experience")
-
-sns.histplot(df["Income"], kde=True, bins=20, ax=ax[1, 0])
-ax[1, 0].set_title("Distribution of Income")
-
-sns.histplot(df["CCAvg"], kde=True, bins=20, ax=ax[1, 1])
-ax[1, 1].set_title("Distribution of Credit Card Avg Spending")
-
-sns.histplot(df["Mortgage"], kde=True, bins=20, ax=ax[2, 0])
-ax[2, 0].set_title("Distribution of Mortgage")
-
-sns.histplot(df["Personal Loan"], kde=True, bins=2, ax=ax[2, 1])
-ax[2, 1].set_title("Loan Approvals (Target Variable)")
-
-plt.tight_layout()
-st.pyplot(fig)
+        st.error(f"Loan Not Accepted ❌\nProbability of Acceptance: {probability:.4f}")