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import streamlit as st |
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import pandas as pd |
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import joblib |
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from huggingface_hub import hf_hub_download |
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from sklearn.preprocessing import LabelEncoder |
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model_path = hf_hub_download(repo_id="Vaddiritz/Tourism-Package-Prediction-rithika", filename="best_tourism_model_v1.joblib") |
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model = joblib.load(model_path) |
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st.title("Tourism Package Recommendation App") |
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st.write(""" |
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This application predicts whether a customer is likely to purchase a **tourism package** |
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based on their profile and preferences. |
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Fill in the details below to get a prediction. |
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""") |
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age = st.number_input("Age", min_value=18, max_value=100, value=30) |
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typeofcontact = st.selectbox("Type of Contact", ["Company Invited", "Self Inquiry"]) |
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citytier = st.selectbox("City Tier", [1, 2, 3]) |
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occupation = st.selectbox("Occupation", ["Salaried", "Freelancer"]) |
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gender = st.selectbox("Gender", ["Male", "Female"]) |
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numberofpersonvisiting = st.number_input("Number Of Person Visiting", min_value=1, max_value=10, value=1) |
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preferredpropertystar = st.selectbox("Preferred Property Star", [1, 2, 3, 4, 5]) |
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maritalstatus = st.selectbox("Marital Status", ["Single", "Married", "Divorced"]) |
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numberoftrips = st.number_input("Number Of Trips", min_value=0, max_value=20, value=1) |
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passport = st.selectbox("Passport", [0, 1]) |
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owncar = st.selectbox("Own Car", [0, 1]) |
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numberofchildrenvisiting = st.number_input("Number Of Children Visiting", min_value=0, max_value=10, value=0) |
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designation = st.selectbox("Designation", ["Manager", "Executive", "Senior Manager", "AVP"]) |
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monthlyincome = st.number_input("Monthly Income", min_value=1000, value=50000) |
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pitchsatisfactionscore = st.slider("Pitch Satisfaction Score", 1, 5, 3) |
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productpitched = st.selectbox("Product Pitched", ["Basic", "Deluxe", "Super Deluxe", "King", "Standard"]) |
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numberoffollowups = st.number_input("Number Of Followups", min_value=0, max_value=20, value=2) |
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durationofpitch = st.number_input("Duration Of Pitch (minutes)", min_value=0, max_value=60, value=10) |
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input_data = pd.DataFrame([[age, typeofcontact, citytier, occupation, gender,numberofpersonvisiting, preferredpropertystar, |
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maritalstatus,numberoftrips, passport, owncar, numberofchildrenvisiting, designation, |
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monthlyincome, pitchsatisfactionscore, productpitched,numberoffollowups, durationofpitch]], |
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columns=["Age", "TypeofContact", "CityTier", "Occupation", "Gender", |
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"NumberOfPersonVisiting", "PreferredPropertyStar", "MaritalStatus", |
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"NumberOfTrips", "Passport", "OwnCar", "NumberOfChildrenVisiting", |
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"Designation", "MonthlyIncome", "PitchSatisfactionScore", "ProductPitched", |
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"NumberOfFollowups", "DurationOfPitch"]) |
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st.subheader("Entered Details:") |
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st.write(input_data) |
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if st.button("Predict Package Purchase"): |
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prediction = model.predict(input_data)[0] |
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result = "Likely to Purchase Package" if prediction == 1 else "Unlikely to Purchase" |
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st.subheader("Prediction Result:") |
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st.success(result) |
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