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app.py

@@ -1,35 +1,35 @@
-import streamlit as st
-import numpy as np
-import pandas as pd
-import joblib
-
-from eda import eda_page
-from prediction import model_page
-
-#Load data
-data = pd.read_csv("fraud_test.csv")
-
-st.header('Milestone 2')
-st.write("""
-Created by Reski Hidayat - HCK015 """)
-
-st.write("This program is made to predict Credit Card Fraud using Model Classification.")
-st.write("Dataset `fraud_test`")
-data
-
-def main():
-    # Define menu options
-    menu_options = ["Data Analysis", "Model Prediction"]
-
-    # Create sidebar menu
-    selected_option = st.sidebar.radio("Menu", menu_options)
-
-    # Display selected page
-    if selected_option == "Data Analysis":
-        eda_page()
-    elif selected_option == "Model Prediction":
-        model_page()
-
-
-if __name__ == "__main__":
+import streamlit as st
+import numpy as np
+import pandas as pd
+import joblib
+
+from eda import eda_page
+from prediction import model_page
+
+#Load data
+data = pd.read_csv("fraud_test.csv")
+
+st.header('Milestone 2')
+st.write("""
+Created by Reski Hidayat - HCK015 """)
+
+st.write("This program is made to predict Credit Card Fraud using Model Classification.")
+st.write("Dataset `fraud_test`")
+data
+
+def main():
+    # Define menu options
+    menu_options = ["Data Analysis", "Model Prediction"]
+
+    # Create sidebar menu
+    selected_option = st.sidebar.radio("Menu", menu_options)
+
+    # Display selected page
+    if selected_option == "Data Analysis":
+        eda_page()
+    elif selected_option == "Model Prediction":
+        model_page()
+
+
+if __name__ == "__main__":
     main()

prediction.py

@@ -1,153 +1,153 @@
-import streamlit as st
-import numpy as np
-import pandas as pd
-import joblib
-
-# Library Random Data
-from scipy.stats import randint
-
-from datetime import datetime, timedelta
-from sklearn.utils import shuffle
-
-def model_page():
-    st.title("Model Prediction of Credit Card Fault")
-    st.write("The model predicts whether the customer's transaction is fraud or not")
-    st.sidebar.header('User Input Features')
-
-    input_data = user_input()
-
-    st.subheader('User Input')
-    st.write(input_data)
-
-    # Load the model using a context manager to ensure the file is closed
-    with open("XGB_best_model.pkl", "rb") as f:
-        load_model = joblib.load(f)
-
-    prediction = load_model.predict(input_data)
-
-    if prediction == 1:
-        prediction = 'The Transaction is Fraud'
-    else:
-        prediction = 'The Transaction is Legit'
-
-    st.write('Based on user input, the model predicted: ')
-    st.write(prediction)
-
-def user_input(num_rows=1):
-    data = generate_data(num_rows)
-    return data
-
-def generate_data(num_rows=555719):
-    trans_date_trans_time = st.sidebar.date_input("Transaction Date", value=datetime.now(), min_value=datetime.now() - timedelta(days=365), max_value=datetime.now())
-    trans_date_trans_time = [trans_date_trans_time for _ in range(num_rows)]
-
-    cc_num = st.sidebar.number_input("Credit Card Number", value=500000, min_value=100000, max_value=999999)
-    cc_num = [cc_num for _ in range(num_rows)]
-
-    merchant = st.sidebar.selectbox("Merchant", ['Merchant1', 'Merchant2', 'Merchant3'])
-    merchant = [merchant for _ in range(num_rows)]
-
-    category = st.sidebar.selectbox("Category", ['Personal', 'Childcare', 'Food', 'Transportation'])
-    category = [category for _ in range(num_rows)]
-
-    amt = st.sidebar.number_input("Amount", value=500, min_value=0, max_value=1000)
-    amt = [amt for _ in range(num_rows)]
-
-    first = st.sidebar.text_input("First Name")
-    first = [first for _ in range(num_rows)]
-
-    last = st.sidebar.text_input("Last Name")
-    last = [last for _ in range(num_rows)]
-
-    gender = st.sidebar.selectbox("Gender", ['Male', 'Female'])
-    gender = [gender for _ in range(num_rows)]
-
-    street = st.sidebar.text_input("Street")
-    street = [street for _ in range(num_rows)]
-
-    city = st.sidebar.text_input("City")
-    city = [city for _ in range(num_rows)]
-
-    state = st.sidebar.selectbox("State", ['NY', 'CA', 'IL', 'TX'])
-    state = [state for _ in range(num_rows)]
-
-    zip_code = st.sidebar.text_input("Zip Code")
-    zip_code = [zip_code for _ in range(num_rows)]
-
-    lat = st.sidebar.number_input("Latitude", value=40.7128, min_value=-90., max_value=90.)
-    lat = [lat for _ in range(num_rows)]
-
-    long_ = st.sidebar.number_input("Longitude", value=-74.0060, min_value=-180., max_value=180.)
-    long_ = [long_ for _ in range(num_rows)]
-
-    city_pop = st.sidebar.number_input("City Population", value=10000, min_value=10000, max_value=1000000)
-    city_pop = [city_pop for _ in range(num_rows)]
-
-    job = st.sidebar.selectbox("Job", ['Software Engineer', 'Doctor', 'Lawyer', 'Teacher'])
-    job = [job for _ in range(num_rows)]
-
-    dob = st.sidebar.date_input("Date of Birth", value=datetime.now() - timedelta(days=365*70), min_value=datetime.now() - timedelta(days=365*100), max_value=datetime.now())
-    dob = [dob for _ in range(num_rows)]
-
-    trans_num = np.arange(1, num_rows + 1)
-
-    unix_time = st.sidebar.number_input("Unix Time", value=int(datetime.now().timestamp()), min_value=0, max_value=int(datetime.now().timestamp()))
-    unix_time = [unix_time for _ in range(num_rows)]
-
-    merch_lat = st.sidebar.number_input("Merchant Latitude", value=40.7128, min_value=-90., max_value=90.)
-    merch_lat = [merch_lat for _ in range(num_rows)]
-
-    merch_long = st.sidebar.number_input("Merchant Longitude", value=-74.0060, min_value=-180., max_value=180.)
-    merch_long = [merch_long for _ in range(num_rows)]
-
-    age = st.sidebar.number_input("Age", value=30, min_value=18, max_value=80)
-    age = [age for _ in range(num_rows)]
-
-    
-
-    data = {
-        'Trans_date_trans_time': trans_date_trans_time,
-        'Cc_num': cc_num,
-        'Merchant': merchant,
-        'Category': category,
-        'Amt': amt,
-        'First': first,
-        'Last': last,
-        'Gender': gender,
-        'Street': street,
-        'City': city,
-        'State': state,
-        'Zip': zip_code,
-        'Lat': lat,
-        'Long': long_,
-        'City_pop': city_pop,
-        'Job': job,
-        'Dob': dob,
-        'Trans_num': trans_num,
-        'Unix_time': unix_time,
-        'Merch_lat': merch_lat,
-        'Merch_long': merch_long,
-        'age': age,
-        'category': category,
-        'amt': amt,
-        'state': state,
-        'job': job
-    }
-
-    # Create a Pandas DataFrame
-    df = pd.DataFrame(data)
-
-    return df
-
-# def main():
-#     st.title("Credit Card Transaction Data")
-#     st.write("This app generates random credit card transaction data.")
-
-#     num_rows = st.slider("Number of rows", 100, 100000, 555719)
-
-#     df = generate_data(num_rows)
-
-#     st.write(df)
-
-# if __name__ == "__main__":
+import streamlit as st
+import numpy as np
+import pandas as pd
+import joblib
+
+# Library Random Data
+from scipy.stats import randint
+
+from datetime import datetime, timedelta
+from sklearn.utils import shuffle
+
+def model_page():
+    st.title("Model Prediction of Credit Card Fault")
+    st.write("The model predicts whether the customer's transaction is fraud or not")
+    st.sidebar.header('User Input Features')
+
+    input_data = user_input()
+
+    st.subheader('User Input')
+    st.write(input_data)
+
+    # Load the model using a context manager to ensure the file is closed
+    with open("XGB_best_model.pkl", "rb") as f:
+        load_model = joblib.load(f)
+
+    prediction = load_model.predict(input_data)
+
+    if prediction == 1:
+        prediction = 'The Transaction is Fraud'
+    else:
+        prediction = 'The Transaction is Legit'
+
+    st.write('Based on user input, the model predicted: ')
+    st.write(prediction)
+
+def user_input(num_rows=1):
+    data = generate_data(num_rows)
+    return data
+
+def generate_data(num_rows=555719):
+    trans_date_trans_time = st.sidebar.date_input("Transaction Date", value=datetime.now(), min_value=datetime.now() - timedelta(days=365), max_value=datetime.now())
+    trans_date_trans_time = [trans_date_trans_time for _ in range(num_rows)]
+
+    cc_num = st.sidebar.number_input("Credit Card Number", value=500000, min_value=100000, max_value=999999)
+    cc_num = [cc_num for _ in range(num_rows)]
+
+    merchant = st.sidebar.selectbox("Merchant", ['Merchant1', 'Merchant2', 'Merchant3'])
+    merchant = [merchant for _ in range(num_rows)]
+
+    category = st.sidebar.selectbox("Category", ['Personal', 'Childcare', 'Food', 'Transportation'])
+    category = [category for _ in range(num_rows)]
+
+    amt = st.sidebar.number_input("Amount", value=500, min_value=0, max_value=100000)
+    amt = [amt for _ in range(num_rows)]
+
+    first = st.sidebar.text_input("First Name")
+    first = [first for _ in range(num_rows)]
+
+    last = st.sidebar.text_input("Last Name")
+    last = [last for _ in range(num_rows)]
+
+    gender = st.sidebar.selectbox("Gender", ['Male', 'Female'])
+    gender = [gender for _ in range(num_rows)]
+
+    street = st.sidebar.text_input("Street")
+    street = [street for _ in range(num_rows)]
+
+    city = st.sidebar.text_input("City")
+    city = [city for _ in range(num_rows)]
+
+    state = st.sidebar.selectbox("State", ['NY', 'CA', 'IL', 'TX'])
+    state = [state for _ in range(num_rows)]
+
+    zip_code = st.sidebar.text_input("Zip Code")
+    zip_code = [zip_code for _ in range(num_rows)]
+
+    lat = st.sidebar.number_input("Latitude", value=40.7128, min_value=-90., max_value=90.)
+    lat = [lat for _ in range(num_rows)]
+
+    long_ = st.sidebar.number_input("Longitude", value=-74.0060, min_value=-180., max_value=180.)
+    long_ = [long_ for _ in range(num_rows)]
+
+    city_pop = st.sidebar.number_input("City Population", value=10000, min_value=10000, max_value=1000000)
+    city_pop = [city_pop for _ in range(num_rows)]
+
+    job = st.sidebar.selectbox("Job", ['Software Engineer', 'Doctor', 'Lawyer', 'Teacher'])
+    job = [job for _ in range(num_rows)]
+
+    dob = st.sidebar.date_input("Date of Birth", value=datetime.now() - timedelta(days=365*70), min_value=datetime.now() - timedelta(days=365*100), max_value=datetime.now())
+    dob = [dob for _ in range(num_rows)]
+
+    trans_num = np.arange(1, num_rows + 1)
+
+    unix_time = st.sidebar.number_input("Unix Time", value=int(datetime.now().timestamp()), min_value=0, max_value=int(datetime.now().timestamp()))
+    unix_time = [unix_time for _ in range(num_rows)]
+
+    merch_lat = st.sidebar.number_input("Merchant Latitude", value=40.7128, min_value=-90., max_value=90.)
+    merch_lat = [merch_lat for _ in range(num_rows)]
+
+    merch_long = st.sidebar.number_input("Merchant Longitude", value=-74.0060, min_value=-180., max_value=180.)
+    merch_long = [merch_long for _ in range(num_rows)]
+
+    age = st.sidebar.number_input("Age", value=30, min_value=18, max_value=80)
+    age = [age for _ in range(num_rows)]
+
+    
+
+    data = {
+        'Trans_date_trans_time': trans_date_trans_time,
+        'Cc_num': cc_num,
+        'Merchant': merchant,
+        'Category': category,
+        'Amt': amt,
+        'First': first,
+        'Last': last,
+        'Gender': gender,
+        'Street': street,
+        'City': city,
+        'State': state,
+        'Zip': zip_code,
+        'Lat': lat,
+        'Long': long_,
+        'City_pop': city_pop,
+        'Job': job,
+        'Dob': dob,
+        'Trans_num': trans_num,
+        'Unix_time': unix_time,
+        'Merch_lat': merch_lat,
+        'Merch_long': merch_long,
+        'age': age,
+        'category': category,
+        'amt': amt,
+        'state': state,
+        'job': job
+    }
+
+    # Create a Pandas DataFrame
+    df = pd.DataFrame(data)
+
+    return df
+
+# def main():
+#     st.title("Credit Card Transaction Data")
+#     st.write("This app generates random credit card transaction data.")
+
+#     num_rows = st.slider("Number of rows", 100, 100000, 555719)
+
+#     df = generate_data(num_rows)
+
+#     st.write(df)
+
+# if __name__ == "__main__":
 #     main()

requirements.txt

@@ -1,7 +1,6 @@
-scikit-learn
-pandas
-matplotlib
-joblib
-transformers
-seaborn
-numpy
+scikit-learn
+pandas
+matplotlib
+joblib
+transformers
+seaborn