Spaces:
Sleeping
Sleeping
File size: 3,507 Bytes
747fef8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 |
import streamlit as st
import pandas as pd
from huggingface_hub import hf_hub_download
import joblib
# Download and load the model
model_path = hf_hub_download(repo_id="PSstark/Machine-Learning-Prediction", filename="best_prediction_model_v1.joblib")
model = joblib.load(model_path)
# Streamlit UI for Machine Failure Prediction
st.title("Tourism Product Purchase Prediction App")
st.write("""
Welcome to the **Tourism Product Purchase Prediction App**! 🌍✨
This tool predicts whether a customer is likely to purchase a tourism product based on their personal details, preferences, and interaction history.
Please provide the customer information below, and the model will estimate the likelihood of them taking the product.
""")
# Basic demographic info
age = st.number_input("Customer Age", min_value=18, max_value=80, value=35)
gender = st.selectbox("Gender", ["Male", "Female"])
marital_status = st.selectbox("Marital Status", ["Single", "Married", "Divorced"])
# Contact and occupation info
typeof_contact = st.selectbox("Type of Contact", ["Self Enquiry", "Company Invited"])
occupation = st.selectbox("Occupation", ["Salaried", "Small Business", "Large Business", "Free Lancer"])
# Travel and product preferences
city_tier = st.selectbox("City Tier", [1, 2, 3])
product_pitched = st.selectbox("Product Pitched", ["Basic", "Deluxe", "Standard", "Super Deluxe", "King"])
designation = st.selectbox("Designation", ["Executive", "Manager", "Senior Manager", "AVP", "VP"])
# Numeric customer interaction details
duration_of_pitch = st.number_input("Duration of Pitch (minutes)", min_value=0.0, max_value=100.0, value=10.0)
number_of_person_visiting = st.number_input("Number of Persons Visiting", min_value=1, max_value=10, value=2)
number_of_followups = st.number_input("Number of Follow-ups", min_value=0, max_value=20, value=2)
preferred_property_star = st.selectbox("Preferred Property Star", [1, 2, 3, 4, 5])
number_of_trips = st.number_input("Number of Trips Taken", min_value=0, max_value=50, value=5)
pitch_satisfaction_score = st.slider("Pitch Satisfaction Score", min_value=1, max_value=5, value=3)
# Additional info
passport = st.selectbox("Passport", [0, 1])
own_car = st.selectbox("Own Car", [0, 1,2,3])
number_of_children_visiting = st.number_input("Number of Children Visiting", min_value=0, max_value=10, value=0)
monthly_income = st.number_input("Monthly Income", min_value=0.0, max_value=1000000.0, value=25000.0)
# 📊 Assemble all inputs into a DataFrame
input_data = pd.DataFrame([{
'Age': age,
'TypeofContact': typeof_contact,
'CityTier': city_tier,
'DurationOfPitch': duration_of_pitch,
'Occupation': occupation,
'Gender': gender,
'NumberOfPersonVisiting': number_of_person_visiting,
'NumberOfFollowups': number_of_followups,
'ProductPitched': product_pitched,
'PreferredPropertyStar': preferred_property_star,
'MaritalStatus': marital_status,
'NumberOfTrips': number_of_trips,
'Passport': passport,
'PitchSatisfactionScore': pitch_satisfaction_score,
'OwnCar': own_car,
'NumberOfChildrenVisiting': number_of_children_visiting,
'Designation': designation,
'MonthlyIncome': monthly_income
}])
# 🔮 Make prediction
if st.button("Predict Purchase"):
prediction = model.predict(input_data)[0]
result = "✅ Customer is Likely to Purchase the Product" if prediction == 1 else "❌ Customer is Unlikely to Purchase the Product"
st.subheader("Prediction Result:")
st.success(result)
|