other files

app.py

@@ -0,0 +1,179 @@
+import streamlit as st
+import pickle
+from sklearn.preprocessing import StandardScaler
+import random
+
+# Title of the app
+st.title('Credit Default Risk Prediction')
+
+# Updated list of column names
+fin_cols = ['MONTHS_BALANCE_mean', 'CNT_INSTALMENT_mean', 'CNT_INSTALMENT_FUTURE_mean', 'SK_DPD_mean', 'SK_DPD_DEF_mean', 
+            'DAYS_CREDIT_MEAN', 'CREDIT_DAY_OVERDUE_MEAN', 'AMT_CREDIT_MAX_OVERDUE_MEAN', 'AMT_CREDIT_SUM_MEAN', 
+            'AMT_CREDIT_SUM_DEBT_MEAN', 'AMT_CREDIT_SUM_OVERDUE_MEAN', 'DAYS_CREDIT_ENDDATE_MEAN', 
+            'DAYS_ENDDATE_FACT_MEAN', 'AMT_CREDIT_SUM_LIMIT_MEAN', 'CREDIT_LEFT', 'AMT_ANNUITY_mean', 'AMT_APPLICATION_mean', 
+            'AMT_CREDIT_mean', 'AMT_DOWN_PAYMENT_mean', 'AMT_GOODS_PRICE_mean', 'HOUR_APPR_PROCESS_START_mean', 
+            'RATE_DOWN_PAYMENT_mean', 'SELLERPLACE_AREA_mean', 'amt_income_total', 'amt_credit', 'amt_goods_price', 
+            'days_birth', 'days_employed', 'days_registration', 'days_id_publish', 'hour_appr_process_start', 
+            'days_last_phone_change', 'amt_req_credit_bureau_year', 'NUM_LATE_PAYMENTS_MEAN', 'DIFF_AMOUNT_MORE_OR_LESS_MEAN', 
+            'def_60_cnt_social_circle (0-5)', 'name_contract_type(cash_loans/revolving_loans)', 
+            'organization_type(check document to know your organisation type in our github page)', 
+            'loan_purpose(Business development/Medicine/Other/celebration/Cancelled/Unused offer/Unknown)', 
+            'payment_type(Cashless from the account of the employer/Non-cash from your account/Unknown)', 
+            'channel_type(AP+(Cash loan)/Contact center/Unknown)']
+
+num_cols = ['MONTHS_BALANCE_mean', 'CNT_INSTALMENT_mean', 'CNT_INSTALMENT_FUTURE_mean', 'SK_DPD_mean', 'SK_DPD_DEF_mean', 
+            'DAYS_CREDIT_MEAN', 'CREDIT_DAY_OVERDUE_MEAN', 'AMT_CREDIT_MAX_OVERDUE_MEAN', 'AMT_CREDIT_SUM_MEAN', 
+            'AMT_CREDIT_SUM_DEBT_MEAN', 'AMT_CREDIT_SUM_OVERDUE_MEAN', 'DAYS_CREDIT_ENDDATE_MEAN', 
+            'DAYS_ENDDATE_FACT_MEAN', 'AMT_CREDIT_SUM_LIMIT_MEAN', 'CREDIT_LEFT', 'AMT_ANNUITY_mean', 'AMT_APPLICATION_mean', 
+            'AMT_CREDIT_mean', 'AMT_DOWN_PAYMENT_mean', 'AMT_GOODS_PRICE_mean', 'HOUR_APPR_PROCESS_START_mean', 
+            'RATE_DOWN_PAYMENT_mean', 'SELLERPLACE_AREA_mean', 'amt_income_total', 'amt_credit', 'amt_goods_price', 
+            'days_birth', 'days_employed', 'days_registration', 'days_id_publish', 'hour_appr_process_start', 
+            'days_last_phone_change', 'amt_req_credit_bureau_year', 'NUM_LATE_PAYMENTS_MEAN', 'DIFF_AMOUNT_MORE_OR_LESS_MEAN']
+
+cat_cols = ['def_60_cnt_social_circle (0-5)', 'name_contract_type(cash_loans/revolving_loans)', 
+            'organization_type(check document to know your organisation type in our github page)', 
+            'loan_purpose(Business development/Medicine/Other/celebration/Cancelled/Unused offer/Unknown)', 
+            'payment_type(Cashless from the account of the employer/Non-cash from your account/Unknown)', 
+            'channel_type(AP+(Cash loan)/Contact center/Unknown)']
+
+# Encoding dictionaries
+loan_purpose_dict = {'Business development': 0, 'Medicine': 1, 'Other': 2, 'celebration': 3, 'Cancelled': 4, 'Unused offer': 5, 'Unknown': 6}
+payment_type_dict = {'Cashless from the account of the employer': 0, 'Non-cash from your account': 1, 'Unknown': 3}
+channel_type_dict = {'AP+ (Cash loan)': 0, 'Contact center': 1, 'Unknown': 2}
+name_contract_type_dict = {'Cash loans': 0, 'revolving loans': 1}
+
+# Initialize session state for random value filling
+if 'random_values_filled' not in st.session_state:
+    st.session_state.random_values_filled = False
+
+# Collecting user inputs
+inputs = {}
+for i in range(len(fin_cols)):
+    if st.session_state.random_values_filled:
+        inputs[f"input_{i}"] = st.text_input(f'Enter {fin_cols[i]}', value=st.session_state.get(f"input_{i}", ""))
+    else:
+        inputs[f"input_{i}"] = st.text_input(f'Enter {fin_cols[i]}')
+
+# Function to fill random values
+def fill_random_values():
+    st.session_state.random_values_filled = True
+    for i in range(len(fin_cols)):
+        if fin_cols[i] in num_cols:
+            st.session_state[f"input_{i}"] = str(random.uniform(0, 100))  # Random float value for numerical columns
+        elif 'def_60_cnt_social_circle' in fin_cols[i]:
+            st.session_state[f"input_{i}"] = str(random.randint(0, 5))
+        elif 'name_contract_type' in fin_cols[i]:
+            st.session_state[f"input_{i}"] = str(random.randint(0, 1))
+        elif 'organization_type' in fin_cols[i]:
+            st.session_state[f"input_{i}"] = str(random.randint(0, 57))
+        elif 'loan_purpose' in fin_cols[i]:
+            st.session_state[f"input_{i}"] = str(random.randint(0, 6))
+        elif 'payment_type' in fin_cols[i]:
+            st.session_state[f"input_{i}"] = str(random.randint(0, 3))
+        elif 'channel_type' in fin_cols[i]:
+            st.session_state[f"input_{i}"] = str(random.randint(0, 3))
+
+
+
+# Repopulating the fields if they were randomly filled
+if st.session_state.random_values_filled:
+    for i in range(len(fin_cols)):
+        inputs[f"input_{i}"] = st.text_input(f'Enter {fin_cols[i]}', value=st.session_state.get(f"input_{i}", ""))
+
+
+
+# Button to estimate
+if st.button('Estimate'):
+    # Encoding categorical variables
+    for i in range(len(fin_cols)):
+        if 'loan_purpose' in fin_cols[i]:
+            inputs[f"input_{i}"] = loan_purpose_dict.get(inputs[f"input_{i}"], 6)  # Default to 6 ('Unknown')
+        elif 'payment_type' in fin_cols[i]:
+            inputs[f"input_{i}"] = payment_type_dict.get(inputs[f"input_{i}"], 3)  # Default to 3 ('Unknown')
+        elif 'channel_type' in fin_cols[i]:
+            inputs[f"input_{i}"] = channel_type_dict.get(inputs[f"input_{i}"], 2)  # Default to 2 ('Unknown')
+        elif 'name_contract_type' in fin_cols[i]:
+            inputs[f"input_{i}"] = name_contract_type_dict.get(inputs[f"input_{i}"], 1)  # Default to 1
+        elif 'def_60_cnt_social_circle' in fin_cols[i]:
+            try:
+                inputs[f"input_{i}"] = int(inputs[f"input_{i}"])
+            except ValueError:
+                inputs[f"input_{i}"] = 0  # Default value
+        elif 'organization_type' in fin_cols[i]:
+            try:
+                inputs[f"input_{i}"] = int(inputs[f"input_{i}"])
+            except ValueError:
+                inputs[f"input_{i}"] = 0  # Default value
+
+    # Collecting and validating numerical data
+    num_data = []
+    for i in range(len(fin_cols)):
+        if fin_cols[i] in num_cols:
+            value = inputs[f"input_{i}"]
+            if value != '':
+                try:
+                    num_data.append(float(value))
+                except ValueError:
+                    num_data.append(0.0)  # Default value for invalid inputs
+            else:
+                num_data.append(0.0)  # Default value for empty input
+
+    # Collecting encoded categorical data
+    cat_data = []
+    for i in range(len(fin_cols)):
+        if fin_cols[i] in cat_cols:
+            value = inputs[f"input_{i}"]
+            # Ensure the value is numerical
+            if value != '':
+                try:
+                    cat_data.append(float(value))
+                except ValueError:
+                    cat_data.append(0.0)  # Default value
+            else:
+                cat_data.append(0.0)  # Default value
+
+    # Standardizing numerical data
+    ss = StandardScaler()
+    num_tra = ss.fit_transform([num_data])
+
+    # Merging num_tra and cat_data in the correct order of fin_cols
+    final_data = []
+    num_idx = 0
+    cat_idx = 0
+    for col in fin_cols:
+        if col in num_cols:
+            final_data.append(num_tra[0][num_idx])
+            num_idx += 1
+        else:
+            final_data.append(cat_data[cat_idx])
+            cat_idx += 1
+
+    # Convert final_data to a numpy array with a numeric data type
+    import numpy as np
+    final_data = np.array(final_data, dtype=np.float32)
+
+    # Verify data types
+    # st.write("Final data types:", final_data.dtype)
+    # st.write("Final data:", final_data)
+
+    # Loading the model from the pickle file
+    model = pickle.load(open('stacking_model_dec.pkl', 'rb'))
+
+    # Making a prediction
+    # Making a prediction
+    try:
+        prediction = model.predict([final_data])
+
+        # Displaying more descriptive output based on the prediction value
+        if prediction[0] == 0:
+            st.success("The customer will **NOT** default on the loan.")
+        elif prediction[0] == 1:
+            st.warning("The customer **WILL** default on the loan.")
+        else:
+            st.error("Unexpected prediction value.")
+            
+    except Exception as e:
+        st.error(f"Error during prediction: {e}")
+
+
+        

requirements.txt

@@ -0,0 +1,8 @@
+streamlit
+scikit-learn
+pandas
+numpy
+matplotlib
+seaborn
+scipy
+xgboost