File size: 3,643 Bytes
3246ac5 47333c3 3f80d78 3246ac5 3f80d78 3246ac5 e2cd39f 3246ac5 3e145f2 ecb9ee2 3246ac5 3e145f2 988f453 47333c3 988f453 47333c3 988f453 | 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 77 78 79 80 81 82 83 | import streamlit as st
import pandas as pd
from huggingface_hub import hf_hub_download
import joblib
# Download and load the model from Hugging Face Hub
model_path = hf_hub_download(
repo_id="Sandhya777/tourism_package_prediction_model1",
filename="best_tourism_package_prediction_v2.joblib"
)
model = joblib.load(model_path)
# Streamlit UI for Insurance Charges Prediction
st.title("🌴Tourism Package Prediction App🌴")
st.write("Fill in the customer information below and click **Predict**.")
# User input
col1, col2 = st.columns(2)
with col1:
age= st.number_input("Age", min_value=18, max_value=100, value=30, step=1)
typeofcontact = st.selectbox("Type of Contact", ["Company Invited", "Self Inquiry"])
citytier = st.selectbox("City Tier", [1, 2, 3])
durationofpitch = st.number_input("Duration of Pitch", min_value=1, max_value=100, value=10, step=1)
occupation= st.selectbox("Occupation", ["Salaried", "Free Lancer","Small Business","Large Business"])
gender = st.selectbox("Gender", ["Male", "Female"])
numberofpersonvisiting = st.number_input("Number of People Visiting", min_value=1, max_value=10, value=2, step=1)
numberoffollowups = st.number_input("Number of Follow-ups", min_value=1, max_value=10, value=2, step=1)
productpitched= st.selectbox("Product Pitched", ["Basic", "Deluxe","Standard","King","Super Deluxe"])
with col2:
preferredpropertystar= st.number_input("Preferred Property Star", min_value=1, max_value=5, value=3, step=1)
maritalstatus = st.selectbox("Marital Status", ["Single", "Married", "Divorced","Unmarried"])
numberoftrips = st.number_input("Number of Trips", min_value=1, max_value=10, value=2, step=1)
passport = st.selectbox("Passport", [0, 1])
pitchsatisfactionscore = st.number_input("Pitch Satisfaction Score", min_value=1, max_value=5, value=3, step=1)
owncar = st.selectbox("Own Car", [0, 1])
numberofchildrenvisiting = st.number_input("Number of Children Visiting", min_value=0, max_value=10, value=0, step=1)
designation = st.selectbox("Designation", ["Executive", "Manager", "VP", "AVP","Senior Manager"])
monthlyincome = st.number_input("Monthly Income", min_value=1000, max_value=100000, value=5000, step=100)
# Assemble input into DataFrame
input_data = pd.DataFrame([{
'age': age,
'typeofcontact': typeofcontact,
'citytier': citytier,
'durationofpitch': durationofpitch,
'occupation': occupation,
'gender': gender,
'numberofpersonvisiting': numberofpersonvisiting,
'numberoffollowups': numberoffollowups,
'productpitched': productpitched,
'preferredpropertystar': preferredpropertystar,
'maritalstatus': maritalstatus,
'numberoftrips': numberoftrips,
'passport': passport,
'pitchsatisfactionscore': pitchsatisfactionscore,
'owncar': owncar,
'numberofchildrenvisiting': numberofchildrenvisiting,
'designation': designation,
'monthlyincome': monthlyincome
}])
# Set the classification threshold
classification_threshold = 0.45
# Predict button
if st.button("Predict"):
prediction_proba = model.predict_proba(input_data)[0, 1]
prediction = (prediction_proba >= classification_threshold).astype(int)
result = "Take the Tourism Package" if prediction == 1 else "Not to Take the Tourism Package"
#st.write(f"Based on the information provided, the customer is likely to {result}.")
color = "#16a34a" if prediction == 1 else "#dc2626" # nice green/red
st.markdown(
f"""
Based on the information provided, the customer is likely to
<b><span style='color:{color}; font-size:18px;'>{result}</span></b>.
""",
unsafe_allow_html=True
)
|