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app.py
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@@ -15,6 +15,12 @@ import joblib
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model_path = hf_hub_download(repo_id="harishsohani/MLOP-Project-Tourism", filename="best_tourism_model.joblib")
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model = joblib.load(model_path)
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# ---------------------------------------------------------
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# Define Unique Values for each column
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@@ -24,7 +30,7 @@ TypeofContact_vals = ['Self Enquiry', 'Company Invited']
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Occupation_vals = ['Salaried', 'Free Lancer', 'Small Business', 'Large Business']
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Gender_vals = ['Female', 'Male'
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ProductPitched_vals = ['Deluxe', 'Basic', 'Standard', 'Super Deluxe', 'King']
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Designation_vals = ['Manager', 'Executive', 'Senior Manager', 'AVP', 'VP']
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CityTier_vals = [1, 2, 3]
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PreferredPropertyStar_vals = [3.0, 4.0, 5.0]
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PitchSatisfactionScore_vals = [1, 2, 3, 4, 5]
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st.
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# ----- COMPACT CSS -----
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st.markdown("""
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<style>
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.card {
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background-color: #ffffff;
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padding: 15px;
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border-radius: 12px;
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box-shadow: 0 2px 8px rgba(0,0,0,0.06);
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margin-bottom: 12px;
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}
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.section-title {
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font-size: 20px;
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font-weight: 700;
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margin-bottom: 6px;
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color: #1a73e8;
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}
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h1 {
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font-size: 26px !important;
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margin-bottom: 8px;
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}
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label, .stSelectbox label, .stNumberInput label {
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font-size: 14px !important;
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font-weight: 600 !important;
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}
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padding: 10px 20px;
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font-size: 16px;
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width: 40%;
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border-radius: 8px;
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}
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border-left: 4px solid #1a73e8;
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font-size: 16px;
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border-radius: 6px;
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margin-top: 10px;
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}
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</style>
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""", unsafe_allow_html=True)
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# ---- TITLE ----
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st.markdown("<h1 style='text-align:center;'>🏖️ Tourism App – Customer Input</h1>", unsafe_allow_html=True)
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# -----------------------
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#
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# -----------------------
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st.
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st.
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MonthlyIncome = st.number_input("Monthly Income", 0, 500000, 50000)
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with col2:
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Gender = st.selectbox("Gender", ["Male", "Female"])
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MaritalStatus = st.selectbox("Marital Status", ["Single", "Married", "Divorced"])
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col1, col2 = st.columns(2)
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with col1:
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Occupation = st.selectbox("Occupation", ["Salaried", "Business", "Retired"])
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Designation = st.selectbox("Designation", ["Executive", "Manager", "Senior Manager"])
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with col2:
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CityTier_display = st.selectbox("City Tier", ["Tier 1", "Tier 2", "Tier 3"])
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Passport_display = st.selectbox("Passport", ["Yes", "No"])
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col1, col2 = st.columns(2)
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with col1:
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NumberOfTrips = st.number_input("Travel Persons", 1, 10, 2)
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NumberOfChildrenVisiting = st.number_input("Children", 0, 10, 0)
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with col2:
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duration = st.number_input("Trip Days", 1, 60, 5)
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property_type = st.selectbox("Preferred Stay", ["Hotel", "Resort", "Homestay"])
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with col1:
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ProductPitched = st.selectbox("Product Pitched", ["Basic", "Standard", "Premium"])
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with col2:
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DurationOfPitch = st.number_input("Pitch Duration (min)", 0, 120, 30)
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#
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model_path = hf_hub_download(repo_id="harishsohani/MLOP-Project-Tourism", filename="best_tourism_model.joblib")
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model = joblib.load(model_path)
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# Streamlit UI for Machine Failure Prediction
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st.title("Tourism App - Input form for Prediction")
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st.write("""
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This application predicts the likelihood of whether a customer would take the product based on following set of parameters.
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Please provide the following details.
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""")
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# ---------------------------------------------------------
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# Define Unique Values for each column
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Occupation_vals = ['Salaried', 'Free Lancer', 'Small Business', 'Large Business']
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Gender_vals = ['Female', 'Male']
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ProductPitched_vals = ['Deluxe', 'Basic', 'Standard', 'Super Deluxe', 'King']
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Designation_vals = ['Manager', 'Executive', 'Senior Manager', 'AVP', 'VP']
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CityType = [ "Tier 1", "Tier 2", "Tier3"]
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CityTier_vals = [1, 2, 3]
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PreferredPropertyStar_vals = [3.0, 4.0, 5.0]
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PitchSatisfactionScore_vals = [1, 2, 3, 4, 5]
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# ---------------------------------------------------------
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# SECTION 1: Personal Information
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# ---------------------------------------------------------
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st.header("1️⃣ Personal Information")
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col1, col2 = st.columns(2)
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with col1:
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Age = st.number_input("Age", min_value=1, max_value=100, value=30)
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Gender = st.selectbox("Gender", Gender_vals)
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with col2:
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MaritalStatus = st.selectbox("Marital Status", MaritalStatus_vals)
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MonthlyIncome = st.number_input("Monthly Income", min_value=0, value=50000)
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# ---------------------------------------------------------
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# Section 2: Customer Profile
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# ---------------------------------------------------------
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st.header("2️⃣ Customer Background & Profile")
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col3, col4 = st.columns(2)
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with col3:
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Occupation = st.selectbox("Occupation", Occupation_vals)
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Designation = st.selectbox("Designation", Designation_vals)
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with col4:
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CityTier = st.selectbox("City Tier", sorted(CityTier_vals))
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OwnCar_display = st.radio("Own Car?", ["Yes", "No"])
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Passport_display = st.radio("Passport?", ["Yes", "No"])
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# Convert Yes/No → 1/0
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OwnCar = 1 if OwnCar_display == "Yes" else 0
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Passport = 1 if Passport_display == "Yes" else 0
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# ---------------------------------------------------------
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# SECTION 3: Travel & Vacation Behavior
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# ---------------------------------------------------------
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st.header("3️⃣ Travel & Vacation Behavior")
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col5, col6 = st.columns(2)
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with col5:
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NumberOfPersonVisiting = st.number_input(
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"Number of Persons Visiting", min_value=1, max_value=10, value=2
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)
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NumberOfChildrenVisiting = st.number_input(
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"Number of Children Visiting", min_value=0, max_value=10, value=0
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)
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with col6:
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NumberOfTrips = st.number_input(
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"Number of Trips per Year",
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min_value=0,
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max_value=50,
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value=1
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)
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PreferredPropertyStar = st.selectbox(
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"Preferred Property Star", PreferredPropertyStar_vals
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)
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# ---------------------------------------------------------
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# SECTION 4: Sales Interaction Details
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# ---------------------------------------------------------
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st.header("4️⃣ Sales Interaction Details")
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col7, col8 = st.columns(2)
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with col7:
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TypeofContact = st.selectbox("Type of Contact", TypeofContact_vals)
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ProductPitched = st.selectbox("Product Pitched", ProductPitched_vals)
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with col8:
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DurationOfPitch = st.number_input(
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"Duration of Pitch (minutes)", min_value=0.0, max_value=60.0, value=10.0
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)
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PitchSatisfactionScore = st.selectbox(
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"Pitch Satisfaction Score", sorted(PitchSatisfactionScore_vals)
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)
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NumberOfFollowups = st.number_input(
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"Number of Follow-ups", min_value=0, max_value=20, value=2
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)
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# ---------------------------------------------------------
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# Prepare Input for Model
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# ---------------------------------------------------------
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input_data = {
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"Age": Age,
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"TypeofContact": TypeofContact,
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"CityTier": CityTier,
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"DurationOfPitch": DurationOfPitch,
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"Occupation": Occupation,
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"Gender": Gender,
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"NumberOfPersonVisiting": NumberOfPersonVisiting,
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"NumberOfFollowups": NumberOfFollowups,
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"ProductPitched": ProductPitched,
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"PreferredPropertyStar": PreferredPropertyStar,
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"MaritalStatus": MaritalStatus,
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"NumberOfTrips": NumberOfTrips,
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"Passport": Passport, # now 0/1
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"PitchSatisfactionScore": PitchSatisfactionScore,
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"OwnCar": OwnCar, # now 0/1
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"NumberOfChildrenVisiting": NumberOfChildrenVisiting,
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"Designation": Designation,
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"MonthlyIncome": MonthlyIncome
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}
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import_data_df = pd.DataFrame([input_data])
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# The following code can be enabled to see the etails of data frame prepared from user input
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# This code was used for debugging and now disabled
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## st.subheader("📦 Input Data Summary")
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## st.json(input_data)
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# ---------------------------------------------------------
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# Prediction Button
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# ---------------------------------------------------------
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if st.button("Predict"):
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st.success("Prediction logic goes here (connect your model).")
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prediction = model.predict(import_data_df)[0]
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result = "Customer is likely to Take Product" if prediction == 1 else "Customer will not Take the Product"
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st.subheader("Prediction Result:")
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st.success(f"Prediction as per Model: **{result}**")
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