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best_svm_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a0424332e1e8ce9f2ede32649fd900be18620105e9d4b30f9024ef474302547f
+size 134201

edaanalisis.py

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+# streamlit
+import streamlit as st
+# pandas
+import pandas as pd
+# visualisasi
+import seaborn as sns
+import matplotlib.pyplot as plt
+import plotly.express as px
+from PIL import Image
+
+st.set_page_config(
+    page_title = 'Predict Credit Card Default',
+    layout =  'wide',
+    initial_sidebar_state='expanded'
+)
+
+def run():
+    
+    st.title('Predict Credit Card')
+
+    st.subheader('EDA untuk analisis dataset default credit card')
+
+    st.image('https://image.cermati.com/v1536918930/kcqxfjxwseh5e6kyrrpv.jpg',
+             caption= 'CREDIT CARD')
+    
+    st.write('This page is made by badriahnursakinah')
+    st.write('# Hello')
+
+    st.markdown('---')
+
+    '''
+    Pada page kali ini, penulis akan melakukan
+    eksplorasi sederhana untuk memprediksi kemungkinan default pada pembayaran kartu kredit
+    Dataset yg digunakan adalah dataset predict default payment next month
+    Dataset ini berasal dari website Big Query
+    '''
+            
+    data = pd.read_csv('P1G5_Set_1_badriah_nursakinah.csv')
+    st.dataframe(data)
+
+    st.write('#### Plot education_level')
+    fig = plt.figure(figsize=(15,5))
+    sns.countplot(x='education_level', data= data)
+    st.pyplot(fig)
+
+    st.write('### Histogram')
+    options = st.selectbox('Pilih kolom:',
+                            ('sex',
+                            'education_level', 'marital_status',
+                            'age'))
+    fig = plt.figure(figsize=(15,5))
+    sns.histplot(data[options], bins=30,kde=True)
+    st.pyplot(fig)
+
+    st.write('#### Plotly Plot - limit_balance dengan Overall')
+    fig = px.scatter(data,x='limit_balance',y='default_payment_next_month', hover_data=['pay_0','pay_2','pay_3','pay_4','pay_5','pay_6',])
+    st.plotly_chart(fig)
+
+if __name__ == '__main__':
+    run()

predict_credit_card_default.py

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+import streamlit as st
+import pandas as pd
+import pickle
+import streamlit as st
+
+import pandas as pd
+import numpy as np
+import pickle
+import json
+
+# Load the pre-trained model using pickle
+with open('best_svm_model.pkl', 'rb') as file_1:
+  model = pickle.load(file_1)
+
+# Create a Streamlit web app
+def run():
+    st.title("Credit Card Default Prediction Dashboard")
+
+    # Add input fields for user input
+    st.header("User Input Features")
+
+    with st.form('predict player'):
+            st.title('Playernya aja')
+
+    # Input fields for each feature
+            limit_balance = st.slider("LIMIT_BAL (Amount of Credit in NT dollars)", 0, 1000000, 50000)
+            sex = st.radio("SEX (Gender)", ["Male", "Female"])
+            education = st.radio("EDUCATION (Education Level)", ["Graduate School", "University", "High School", "Others"])
+            marriage = st.radio("MARRIAGE (Marital Status)", ["Married", "Single", "Others"])
+            age = st.slider("AGE (Age in years)", 20, 80, 30)
+            st.write('Payment Status')
+            st.write("-1 : Sudah Dibayar Sebelum H-1")
+            st.write("-2 : Sudah Dibayar Sebelum H-2")
+            st.write("0 : Dibayar Tepat Waktu")
+            st.write("2 : Telat Pembayaran 2 Bulan")
+            st.write("3 : Telat Pembayaran 3 Bulan")
+            st.write("4 : Telat Pembayaran 4 Bulan")
+            st.write("5 : Telat Pembayaran 5 Bulan")
+            st.write("6 : Telat Pembayaran 6 Bulan")
+            pay_status_sept = st.slider("PAY_0 (Repayment status in September, 2005)", -2, 8, 0)
+            pay_status_aug = st.slider("PAY_2 (Repayment status in August, 2005)", -2, 8, 0)
+            pay_status_jul = st.slider("PAY_3 (Repayment status in July, 2005)", -2, 8, 0)
+            pay_status_jun = st.slider("PAY_4 (Repayment status in June, 2005)", -2, 8, 0)
+            pay_status_may = st.slider("PAY_5 (Repayment status in May, 2005)", -2, 8, 0)
+            pay_status_apr = st.slider("PAY_6 (Repayment status in April, 2005)", -2, 8, 0)
+            bill_amt_sept = st.slider("BILL_AMT1 (Bill statement in September, 2005 - NT dollar)", 0, 1000000, 5000)
+            bill_amt_aug = st.slider("BILL_AMT2 (Bill statement in August, 2005 - NT dollar)", 0, 1000000, 5000)
+            bill_amt_jul = st.slider("BILL_AMT3 (Bill statement in July, 2005 - NT dollar)", 0, 1000000, 5000)
+            bill_amt_jun = st.slider("BILL_AMT4 (Bill statement in June, 2005 - NT dollar)", 0, 1000000, 5000)
+            bill_amt_may = st.slider("BILL_AMT5 (Bill statement in May, 2005 - NT dollar)", 0, 1000000, 5000)
+            bill_amt_apr = st.slider("BILL_AMT6 (Bill statement in April, 2005 - NT dollar)", 0, 1000000, 5000)
+            pay_amt_sept = st.slider("PAY_AMT1 (Previous payment in September, 2005 - NT dollar)", 0, 100000, 500)
+            pay_amt_aug = st.slider("PAY_AMT2 (Previous payment in August, 2005 - NT dollar)", 0, 100000, 500)
+            pay_amt_jul = st.slider("PAY_AMT3 (Previous payment in July, 2005 - NT dollar)", 0, 100000, 500)
+            pay_amt_jun = st.slider("PAY_AMT4 (Previous payment in June, 2005 - NT dollar)", 0, 100000, 500)
+            pay_amt_may = st.slider("PAY_AMT5 (Previous payment in May, 2005 - NT dollar)", 0, 100000, 500)
+            pay_amt_apr = st.slider("PAY_AMT6 (Previous payment in April, 2005 - NT dollar)", 0, 100000, 500)
+
+            submit = st.form_submit_button("Predict Player Rating")
+
+    # Define mappings for education and marriage
+    education_mapping = {
+        "Graduate School": 1,
+        "University": 2,
+        "High School": 3,
+        "Others": 4
+    }
+
+    marriage_mapping = {
+        "Married": 1,
+        "Single": 2,
+        "Others": 3
+    }
+
+    # Create a DataFrame with user input data
+    user_input_data = pd.DataFrame({
+        "limit_balance": [limit_balance],
+        "sex": [1 if sex == "Male" else 2],  # Map 'Male' to 1 and 'Female' to 2
+        "education_level": [education_mapping[education]],
+        "marital_status": [marriage_mapping[marriage]],
+        "age": [age],
+        "pay_1": [pay_status_sept],
+        "pay_2": [pay_status_aug],
+        "pay_3": [pay_status_jul],
+        "pay_4": [pay_status_jun],
+        "pay_5": [pay_status_may],
+        "pay_6": [pay_status_apr],
+        "bill_amt_1": [bill_amt_sept],
+        "bill_amt_2": [bill_amt_aug],
+        "bill_amt_3": [bill_amt_jul],
+        "bill_amt_4": [bill_amt_jun],
+        "bill_amt_5": [bill_amt_may],
+        "bill_amt_6": [bill_amt_apr],
+        "pay_amt_1": [pay_amt_sept],
+        "pay_amt_2": [pay_amt_aug],
+        "pay_amt_3": [pay_amt_jul],
+        "pay_amt_4": [pay_amt_jun],
+        "pay_amt_5": [pay_amt_may],
+        "pay_amt_6": [pay_amt_apr]
+    })
+
+    # Predict button
+    if submit:
+        # Make predictions using the loaded model
+        predicted_default = model.predict(user_input_data)
+
+        # Display the prediction result
+        st.subheader("Prediction Result")
+        if predicted_default[0] == 1:
+            st.write("The model predicts that the client may default on their credit card payment.")
+        else:
+            st.write("The model predicts that the client is unlikely to default on their credit card payment.")
+
+
+
+
+
+            #limit_balance,sex,education_level,marital_status,age,pay_1,pay_2,pay_3,pay_4,pay_5,pay_6,bill_amt_1,bill_amt_2,bill_amt_3,bill_amt_4,bill_amt_5,bill_amt_6,pay_amt_1,pay_amt_2,pay_amt_3,pay_amt_4,pay_amt_5,pay_amt_6,default_payment_next_month,Klasifikasi
+    #0,240000.0,2,2,1,41.0,1.0,-1.0,-1.0,-1.0,-1,-1,0.0,40529.0,3211.0,9795.0,11756.0,12522.0,40529.0,3211.0,9795.0,11756.0,12522.0,6199.0,0,Dewasa

predict_eda.py

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+import streamlit as st
+
+import edaanalisis
+import predict_credit_card_default
+
+# setting page configuration
+# st.set_page_config(page_title= 'FIFA 2022 Data',
+#                 page_icon= ':bar_chart:',
+#                 layout= 'wide',
+#                 initial_sidebar_state='auto')
+
+navigation = st.sidebar.selectbox("Select Page", 
+                                  options=['EDA', 'Predict'])
+st.sidebar.write('# About')
+st.sidebar.write('''
+This page is created to predict credit card default data
+                 ''')
+
+
+if navigation == 'EDA':
+    edaanalisis.run()
+else:
+    predict_credit_card_default.run()