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.gitattributes

@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+card.csv filter=lfs diff=lfs merge=lfs -text

Procfile

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+web: sh setup.sh && streamlit run app.py

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import pickle
+import base64
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+st.write("""
+# Detection Fraud Credit Card
+
+Kartu kredit adalah sebuah alat pembayaran menggunakan kartu yang berfungsi sebagai pengganti uang tunai.
+""")
+
+url_dataset = f'<a href="card.csv">Download Dataset CSV File</a>'
+st.markdown(url_dataset, unsafe_allow_html=True)
+
+def user_input_features() :
+    distance_from_home = st.sidebar.slider('distance_from_home', 0.004874, 10632.723672)
+    distance_from_last_transaction = st.sidebar.slider('distance_from_last_transaction', 0.000118, 11851.104565)
+    ratio_to_median_purchase_price = st.sidebar.slider('ratio_to_median_purchase_price', 0.004399, 267.802942)
+    repeat_retailer = st.sidebar.slider('repeat_retailer', 0.0, 1.0)
+    used_chip = st.sidebar.slider('used_chip', 0.0, 1.0)
+    used_pin_number  = st.sidebar.slider('used_pin_number ', 0.0, 1.0)
+    online_order  = st.sidebar.slider('online_order ', 0.0, 1.0)
+
+    data = {
+    'distance_from_home':[distance_from_home],
+    'distance_from_last_transaction':[distance_from_last_transaction],
+    'ratio_to_median_purchase_price':[ratio_to_median_purchase_price],
+    'repeat_retailer':[repeat_retailer],
+    'used_pin_number':[used_pin_number],
+    'online_order':[online_order],
+    'used_chip':[used_chip]
+    }
+
+    features = pd.DataFrame(data)
+    return features
+
+input_df = user_input_features()
+
+card_raw = pd.read_csv('card.csv')
+card_raw.fillna(0, inplace=True)
+card = card_raw.drop(columns=['fraud'])
+df = pd.concat([input_df, card],axis=0)
+
+df = df[:1] # Selects only the first row (the user input data)
+df.fillna(0, inplace=True)
+
+features = ['distance_from_home', 'distance_from_last_transaction',
+       'ratio_to_median_purchase_price', 'repeat_retailer', 'used_chip',
+       'used_pin_number', 'online_order']
+
+df = df[features]
+
+st.subheader('User Input features')
+st.write(df)
+load_clf = pickle.load(open('card_clf.pkl', 'rb'))
+detection = load_clf.predict(df)
+if(detection > 0) :
+    detection = 1
+detection_proba = load_clf.predict_proba(df)
+knee_labels = np.array(['Normal','Penipuan'])
+st.subheader('Detection')
+st.write(knee_labels[detection])
+st.subheader('Detection Probability')
+df_prob = pd.DataFrame(data=detection_proba, index=['Probability'], columns=knee_labels)
+st.write(df_prob)

card.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7013c329bae9ef0ef32d65dbeb095694f0c7cd6c00ff74b2d0087fa1c67b8717
+size 76277977

card_clf.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:92834859475433d0b26912608c842357f4f49fe381c5ffa9f4ca4caaf10f3bf8
+size 3198324

requirements.txt

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+matplotlib==3.6.0
+numpy==1.23.3
+pandas==1.5.0
+seaborn==0.12.0
+streamlit==1.12.2
+sklearn

setup.sh

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+mkdir -p ~/.streamlit/
+
+echo "\
+[general]\n\
+email = \"your-email@domain.com\"\n\
+" > ~/.streamlit/credentials.toml
+
+echo "\
+[server]\n\
+headless = true\n\
+enableCORS=false\n\
+port = $PORT\n\
+" > ~/.streamlit/config.toml