Update app.py

app.py

@@ -1,85 +1,88 @@
-import streamlit as st
-import pickle
-import pandas as pd
-
-# Load model from file
-model_path = 'model_rfbest_pipe_rfbest_pipe_rfbest_pipe_rf.pkl'
-with open(model_path, 'rb') as file:
-    model = pickle.load(file)
-
-# Judul aplikasi
-st.title("Prediksi Churn Pelanggan")
-
-# Form untuk input data
-st.subheader("Masukkan Data Pelanggan")
-
-# Input data pelanggan
-gender = st.selectbox('Gender', ['Female', 'Male'])
-senior_citizen = st.selectbox('Senior Citizen', [0, 1])
-partner = st.selectbox('Partner', ['Yes', 'No'])
-dependents = st.selectbox('Dependents', ['Yes', 'No'])
-tenure = st.number_input('Tenure (bulan)', min_value=0, max_value=72, value=45)
-phone_service = st.selectbox('Phone Service', ['Yes', 'No'])
-multiple_lines = st.selectbox('Multiple Lines', ['Yes', 'No'])
-internet_service = st.selectbox('Internet Service', ['DSL', 'Fiber optic', 'No'])
-online_security = st.selectbox('Online Security', ['Yes', 'No'])
-online_backup = st.selectbox('Online Backup', ['Yes', 'No'])
-device_protection = st.selectbox('Device Protection', ['Yes', 'No'])
-tech_support = st.selectbox('Tech Support', ['Yes', 'No'])
-streaming_tv = st.selectbox('Streaming TV', ['Yes', 'No'])
-streaming_movies = st.selectbox('Streaming Movies', ['Yes', 'No'])
-contract = st.selectbox('Contract', ['Month-to-month', 'One year', 'Two year'])
-paperless_billing = st.selectbox('Paperless Billing', ['Yes', 'No'])
-payment_method = st.selectbox('Payment Method', ['Electronic check', 'Mailed check', 'Bank transfer (automatic)', 'Credit card (automatic)'])
-monthly_charges = st.number_input('Monthly Charges', min_value=0.0, value=70.35)
-total_charges = st.number_input('Total Charges', min_value=0.0, value=346.45)
-
-# Membuat DataFrame dari input
-data_baru = {
-    'gender': [gender],
-    'SeniorCitizen': [senior_citizen],
-    'Partner': [partner],
-    'Dependents': [dependents],
-    'tenure': [tenure],
-    'PhoneService': [phone_service],
-    'MultipleLines': [multiple_lines],
-    'InternetService': [internet_service],
-    'OnlineSecurity': [online_security],
-    'OnlineBackup': [online_backup],
-    'DeviceProtection': [device_protection],
-    'TechSupport': [tech_support],
-    'StreamingTV': [streaming_tv],
-    'StreamingMovies': [streaming_movies],
-    'Contract': [contract],
-    'PaperlessBilling': [paperless_billing],
-    'PaymentMethod': [payment_method],
-    'MonthlyCharges': [monthly_charges],
-    'TotalCharges': [total_charges]
-}
-
-df_baru = pd.DataFrame(data_baru)
-
-# Melakukan encoding pada data kategorikal
-categorical_columns = df_baru.select_dtypes(include=['object']).columns
-df_baru = pd.get_dummies(df_baru, columns=categorical_columns, drop_first=True)
-
-# Menampilkan data yang dimasukkan pengguna
-st.subheader("Data Pelanggan yang Dimasukkan:")
-st.write(df_baru)
-
-# Tombol untuk melakukan prediksi
-if st.button('Prediction'):
-    # Prediksi churn
-    prediksi = model.predict(df_baru)
-
-    # Menampilkan hasil prediksi
-    if prediksi[0] == 1:
-        hasil = 'Yes'
-    else:
-        hasil = 'No'
-
-    st.subheader(f"Hasil Prediksi Churn: {hasil}")
-
-    # Probabilitas churn
-    probabilitas = model.predict_proba(df_baru)[:, 1]
-    st.subheader(f"Probabilitas Churn: {probabilitas[0]:.2f}")
+import streamlit as st
+import joblib
+import pandas as pd
+
+# Load model from file
+model_path = 'joblibmodel_rfbest_pipe_rfbest_pipe_rfbest_pipe_rf.pkl'
+with open(model_path, 'rb') as file:
+    model = joblib.load(file)
+
+# model_path = 'joblibmodel_rfbest_pipe_rfbest_pipe_rfbest_pipe_rf.pkl'
+# model = joblib.load(model_path)
+
+# Judul aplikasi
+st.title("Prediksi Churn Pelanggan")
+
+# Form untuk input data
+st.subheader("Masukkan Data Pelanggan")
+
+# Input data pelanggan
+gender = st.selectbox('Gender', ['Female', 'Male'])
+senior_citizen = st.selectbox('Senior Citizen', [0, 1])
+partner = st.selectbox('Partner', ['Yes', 'No'])
+dependents = st.selectbox('Dependents', ['Yes', 'No'])
+tenure = st.number_input('Tenure (bulan)', min_value=0, max_value=72, value=45)
+phone_service = st.selectbox('Phone Service', ['Yes', 'No'])
+multiple_lines = st.selectbox('Multiple Lines', ['Yes', 'No'])
+internet_service = st.selectbox('Internet Service', ['DSL', 'Fiber optic', 'No'])
+online_security = st.selectbox('Online Security', ['Yes', 'No'])
+online_backup = st.selectbox('Online Backup', ['Yes', 'No'])
+device_protection = st.selectbox('Device Protection', ['Yes', 'No'])
+tech_support = st.selectbox('Tech Support', ['Yes', 'No'])
+streaming_tv = st.selectbox('Streaming TV', ['Yes', 'No'])
+streaming_movies = st.selectbox('Streaming Movies', ['Yes', 'No'])
+contract = st.selectbox('Contract', ['Month-to-month', 'One year', 'Two year'])
+paperless_billing = st.selectbox('Paperless Billing', ['Yes', 'No'])
+payment_method = st.selectbox('Payment Method', ['Electronic check', 'Mailed check', 'Bank transfer (automatic)', 'Credit card (automatic)'])
+monthly_charges = st.number_input('Monthly Charges', min_value=0.0, value=70.35)
+total_charges = st.number_input('Total Charges', min_value=0.0, value=346.45)
+
+# Membuat DataFrame dari input
+data_baru = {
+    'gender': [gender],
+    'SeniorCitizen': [senior_citizen],
+    'Partner': [partner],
+    'Dependents': [dependents],
+    'tenure': [tenure],
+    'PhoneService': [phone_service],
+    'MultipleLines': [multiple_lines],
+    'InternetService': [internet_service],
+    'OnlineSecurity': [online_security],
+    'OnlineBackup': [online_backup],
+    'DeviceProtection': [device_protection],
+    'TechSupport': [tech_support],
+    'StreamingTV': [streaming_tv],
+    'StreamingMovies': [streaming_movies],
+    'Contract': [contract],
+    'PaperlessBilling': [paperless_billing],
+    'PaymentMethod': [payment_method],
+    'MonthlyCharges': [monthly_charges],
+    'TotalCharges': [total_charges]
+}
+
+df_baru = pd.DataFrame(data_baru)
+
+# Melakukan encoding pada data kategorikal
+categorical_columns = df_baru.select_dtypes(include=['object']).columns
+df_baru = pd.get_dummies(df_baru, columns=categorical_columns, drop_first=True)
+
+# Menampilkan data yang dimasukkan pengguna
+st.subheader("Data Pelanggan yang Dimasukkan:")
+st.write(df_baru)
+
+# Tombol untuk melakukan prediksi
+if st.button('Prediction'):
+    # Prediksi churn
+    prediksi = model.predict(df_baru)
+
+    # Menampilkan hasil prediksi
+    if prediksi[0] == 1:
+        hasil = 'Yes'
+    else:
+        hasil = 'No'
+
+    st.subheader(f"Hasil Prediksi Churn: {hasil}")
+
+    # Probabilitas churn
+    probabilitas = model.predict_proba(df_baru)[:, 1]
+    st.subheader(f"Probabilitas Churn: {probabilitas[0]:.2f}")