first deploy

app.py

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+import streamlit as st
+import eda
+import prediction
+
+page = st.sidebar.selectbox('Pilih Halaman : ', ('EDA', 'Prediction'))
+
+if page == 'EDA' : 
+    eda.run()
+else:
+    prediction.run()

eda.py

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+import streamlit as st
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+import plotly.express as px
+from PIL import Image
+
+def run():
+    #Membuat title
+    st.title('Credit Cards Simulation')
+
+    #Tambahkan gambar
+    image = Image.open('CC.jpeg')
+    st.image(image, caption = 'Credit Cards')
+
+    #Menambahkan deskripsi
+    st.write('Page ini dibuat oleh Mardhya Malik Nurbani')
+
+    #Membuat garis
+    st.markdown('----')
+
+    #Masukkan pandas dataframe
+
+    #Show dataframe
+    df = pd.read_csv('P1G5_Set_1_mardhya.csv')
+    st.dataframe(df)
+
+    #Membuat bar plot
+    # st.write('#### Plot AttackingWorkRate')
+    # fig = plt.figure(figsize=(15,5))
+    # sns.countplot(x='AttackingWorkRate', data = df)
+    # st.pyplot(fig)
+
+    # #Membuat histogram
+    # st.write('#### Histogram of Age')
+    # fig = plt.figure(figsize=(15,5))
+    # sns.histplot(df['Overall'], bins = 30, kde = True)
+    # st.pyplot(fig)
+
+    # #membuat histogram berdasarkan inputan user
+    # st.write('#### Histogram berdasarkan input user')
+    # #kalo mau pake radio button, ganti selectbox jadi radio
+    # option = st.selectbox('Pilih Column : ', ('Age', 'Weight', 'Height', 'ShootingTotal'))
+    # fig = plt.figure(figsize= (15,5))
+    # sns.histplot(df[option], bins = 30, kde = True)
+    # st.pyplot(fig)
+
+    # #Membuat Plotly plot
+
+    # st.write('#### Plotly Plot - ValueEUR vs Overall')
+    # fig = px.scatter(df, x = 'ValueEUR', y = 'Overall', hover_data = ['Name', 'Age'])
+    # st.plotly_chart(fig)
+
+if __name__ == '__main__':
+    run()

list_cat_cols.txt

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+[]

list_num_cols.txt

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+["limit_balance", "sex", "education_level", "marital_status", "age", "pay_0", "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"]

model_svm.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:63e0a4df641d81ccff26ff89e72467ab90030be0a39b977d33a1e7c8b0c7e245
+size 124872

prediction.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import pickle
+import json
+
+#Load All Files
+
+#load model dan files yang sudah di save pada framing
+
+with open('list_num_cols.txt', 'r') as file_1:
+  list_num_cols = json.load(file_1)
+with open('list_cat_cols.txt', 'r') as file_2:
+  list_cat_cols = json.load(file_2)
+with open('model_svm.pkl', 'rb') as file_3:
+    model_svm = pickle.load(file_3)
+
+def run():
+
+    with st.form('credit_cards_simulation'):
+        #Field Nama
+        LimitBalance = st.number_input('Limit Balance', value = 0)
+        #Field Umur
+        Sex = st.number_input('Sex', min_value = 1, max_value = 2, value = 1, step = 1, help = '1 = Male, 2 = Female')
+        #Field Tinggi badan
+        EducationLevel = st.number_input('Education', min_value = 1, max_value = 6, value = 1, step = 1, help = '1 = Graduate School, 2 = University, 3 = High School, 4 = Other, 5 = Unknown, 6 = Unknown')
+        #Field Weight
+        MaritalStatus = st.number_input('Marital Status', min_value = 1, max_value = 3, value = 1, step = 1, help = '1 = Married, 2 = Single, 3 = Other')
+        #field price
+        Age = st.slider('Age', 18, 70, 25)
+
+        st.markdown('----')
+
+        #Field Attacking Work Rate
+        Pay_0 = st.number_input('Pay 1', min_value = -2, max_value = 9, value = 0, step = 1, help = 'Repayment Status in September, 2025 (-2 = No Transaction, -1 = Pay Duly, 1-9 = Payment Delay 1-9 Month)')
+        #Field Defensive Work Rate
+        Pay_2 = st.number_input('Pay 2',  min_value = -2, max_value = 9, value = 0, step = 1, help = 'Repayment status in August, 2005 (scale same as above)')
+        #Field Pace Total
+        Pay_3 = st.number_input('Pay 3',  min_value = -2, max_value = 9, value = 0, step = 1, help = 'Repayment status in July, 2005 (scale same as above)')
+        #Field Shooting Total
+        Pay_4 = st.number_input('Pay 4',  min_value = -2, max_value = 9, value = 0, step = 1, help = 'Repayment status in June, 2005 (scale same as above)')
+        #Field Passing Total
+        Pay_5 = st.number_input('Pay 5',  min_value = -2, max_value = 9, value = 0, step = 1, help = 'Repayment status in May, 2005 (scale same as above)')
+        #Field Dribbling Total
+        Pay_6 = st.number_input('Pay 6',  min_value = -2, max_value = 9, value = 0, step = 1, help = 'Repayment status in April, 2005 (scale same as above)')
+        #Field Defending Total
+        Bill_amt_1 = st.number_input('Amount Of Bill 1', value = 0 ,  help = 'Amount of bill statement in September, 2005 (NT dollar)')
+        #Field Physicality Total
+        Bill_amt_2 = st.number_input('Amount Of Bill 2', value = 0, help = 'Amount of bill statement in August, 2005 (NT dollar)')
+        Bill_amt_3 = st.number_input('Amount Of Bill 3', value = 0, help = 'Amount of bill statement in July, 2005 (NT dollar)')
+        Bill_amt_4 = st.number_input('Amount Of Bill 4', value = 0, help = 'Amount of bill statement in June, 2005 (NT dollar)')
+        Bill_amt_5 = st.number_input('Amount Of Bill 5', value = 0, help = 'Amount of bill statement in May, 2005 (NT dollar)')
+        Bill_amt_6 = st.number_input('Amount Of Bill 6', value = 0, help = 'Amount of bill statement in April, 2005 (NT dollar)')
+        #Field Pay AMT
+        Pay_amt_1 = st.number_input('Amount of previous payment 1', value = 0, help = 'Amount of previous payment in September, 2005 (NT dollar)')
+        Pay_amt_2 = st.number_input('Amount of previous payment 2', value = 0, help = 'Amount of previous payment in August, 2005 (NT dollar)')
+        Pay_amt_3 = st.number_input('Amount of previous payment 3', value = 0, help = 'Amount of previous payment in July, 2005 (NT dollar)')
+        Pay_amt_4 = st.number_input('Amount of previous payment 4', value = 0, help = 'Amount of previous payment in June, 2005 (NT dollar)')
+        Pay_amt_5 = st.number_input('Amount of previous payment 5', value = 0, help = 'Amount of previous payment in May, 2005 (NT dollar)')
+        Pay_amt_6 = st.number_input('Amount of previous payment 6', value = 0, help = 'Amount of previous payment in April, 2005 (NT dollar)')
+
+
+        #bikin submit button
+        submitted = st.form_submit_button('Predict')
+
+    #Inference
+    data_inf = {
+        'limit_balance': LimitBalance,
+        'sex': Sex,
+        'education_level': EducationLevel,
+        'marital_status': MaritalStatus,
+        'age': Age,
+        'pay_0' : Pay_0,
+        'pay_2' : Pay_2,
+        'pay_3' :Pay_3,
+        'pay_4': Pay_4,
+        'pay_5' : Pay_5,
+        'pay_6' :Pay_6,
+        'bill_amt_1' :Bill_amt_1,
+        'bill_amt_2':Bill_amt_2,
+        'bill_amt_3':Bill_amt_3,
+        'bill_amt_4':Bill_amt_4,
+        'bill_amt_5':Bill_amt_5,
+        'bill_amt_6':Bill_amt_6,
+        'pay_amt_1':Pay_amt_1,
+        'pay_amt_2':Pay_amt_2,
+        'pay_amt_3':Pay_amt_3,
+        'pay_amt_4':Pay_amt_4,
+        'pay_amt_5':Pay_amt_5,
+        'pay_amt_6':Pay_amt_6,
+
+    }
+
+    data_inf = pd.DataFrame([data_inf])
+    st.dataframe(data_inf)
+
+    #Logic ketika predic button ditekan
+
+    if submitted:
+        #split between numerical and categorical columns
+        data_inf_num = data_inf[list_num_cols]
+        data_inf_cat = data_inf[list_cat_cols]
+        
+        #Scaling & Encoding
+        data_inf_final = data_inf_num
+
+        #predict using linear reg model
+        y_pred_inf = model_svm.predict(data_inf_final)
+        
+        st.write('## Payment : ', str(int(y_pred_inf)))
+
+if __name__ == '__main__':
+   run()

requirements.txt

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+streamlit
+pandas
+seaborn
+matplotlib
+numpy
+plotly
+pillow
+scikit-learn==1,2.2