first commit

tourism_package_prediction/data/test_data.csv

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+CustomerID,Age,TypeofContact,CityTier,DurationOfPitch,Occupation,Gender,NumberOfPersonVisiting,NumberOfFollowups,ProductPitched,PreferredPropertyStar,MaritalStatus,NumberOfTrips,Passport,PitchSatisfactionScore,OwnCar,NumberOfChildrenVisiting,Designation,MonthlyIncome,IncomeCategory,AgeGroup,ProdTaken
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tourism_package_prediction/data/train_data.csv

@@ -0,0 +1,865 @@
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tourism_package_prediction/deployment/Dockerfile

@@ -0,0 +1,23 @@
+# Use a minimal base image with Python 3.9 installed
+FROM python:3.9
+
+# Set the working directory inside the container to /app
+WORKDIR /app
+
+# Copy all files from the current directory on the host to the container's /app directory
+COPY . .
+
+# Install Python dependencies listed in requirements.txt
+RUN pip3 install -r requirements.txt
+
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH
+
+WORKDIR $HOME/app
+
+COPY --chown=user . $HOME/app
+
+# Define the command to run the Streamlit app on port "8501" and make it accessible externally
+CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0", "--server.enableXsrfProtection=false"]

tourism_package_prediction/deployment/app.py

@@ -0,0 +1,201 @@
+#!/usr/bin/env python3
+"""
+Streamlit App for Tourism Package Prediction
+"""
+
+import streamlit as st
+import pandas as pd
+import numpy as np
+import joblib
+from huggingface_hub import hf_hub_download
+
+# Page configuration
+st.set_page_config(
+    page_title="Tourism Package Prediction",
+    page_icon="🏖️",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+@st.cache_resource
+def load_model():
+    """Load the trained model from HuggingFace Hub"""
+    try:
+        model_path = hf_hub_download(
+            repo_id="RavindraDubey12/Tourism-Package-Prediction-model",
+            filename="best_model.joblib"
+        )
+        model = joblib.load(model_path)
+        return model
+    except Exception as e:
+        st.error(f"Error loading model: {e}")
+        return None
+
+def prepare_input_data(age, gender, marital_status, city_tier, type_of_contact,
+                      occupation, designation, monthly_income, num_person_visiting,
+                      num_children_visiting, preferred_property_star, num_trips,
+                      passport, own_car, duration_of_pitch, product_pitched,
+                      num_followups, pitch_satisfaction_score):
+    """Prepare input data for model prediction"""
+
+    # Create mapping dictionaries
+    gender_map = {"Male": 1, "Female": 0}
+    marital_map = {"Single": 2, "Married": 1, "Divorced": 0, "Unmarried": 3}
+    contact_map = {"Self Enquiry": 1, "Company Invited": 0}
+    occupation_map = {"Salaried": 2, "Small Business": 1, "Free Lancer": 0}
+    designation_map = {"Executive": 0, "Manager": 1, "Senior Manager": 2, "AVP": 3, "VP": 4}
+    product_map = {"Basic": 0, "Standard": 1, "Deluxe": 2, "Super Deluxe": 3}
+    passport_map = {"Yes": 1, "No": 0}
+    car_map = {"Yes": 1, "No": 0}
+
+    # Feature engineering (matching training data encoding)
+    if monthly_income <= 15000:
+        income_category = 0  # Low
+    elif monthly_income <= 25000:
+        income_category = 1  # Medium
+    elif monthly_income <= 35000:
+        income_category = 2  # High
+    else:
+        income_category = 3  # Very High
+
+    if age <= 25:
+        age_group = 0  # Young
+    elif age <= 35:
+        age_group = 1  # Adult
+    elif age <= 45:
+        age_group = 2  # Middle-aged
+    elif age <= 55:
+        age_group = 3  # Senior
+    else:
+        age_group = 4  # Elderly
+
+    # Create input array
+    input_array = np.array([[
+        age, contact_map[type_of_contact], city_tier, duration_of_pitch,
+        occupation_map[occupation], gender_map[gender], num_person_visiting,
+        num_followups, product_map[product_pitched], preferred_property_star,
+        marital_map[marital_status], num_trips, passport_map[passport],
+        pitch_satisfaction_score, car_map[own_car], num_children_visiting,
+        designation_map[designation], monthly_income, income_category, age_group
+    ]])
+
+    return input_array
+
+def main():
+    """Main Streamlit app"""
+
+    st.title("Tourism Package Prediction")
+    st.markdown("### Predict Customer Purchase Likelihood for Wellness Tourism Package")
+    st.markdown("---")
+
+    # Load model
+    model = load_model()
+    if model is None:
+        st.error("Failed to load the prediction model.")
+        return
+
+    # Sidebar inputs
+    st.sidebar.header("Customer Information")
+
+    # Demographics
+    st.sidebar.subheader("Demographics")
+    age = st.sidebar.slider("Age", 18, 80, 35)
+    gender = st.sidebar.selectbox("Gender", ["Male", "Female"])
+    marital_status = st.sidebar.selectbox("Marital Status", ["Single", "Married", "Divorced", "Unmarried"])
+
+    # Location & Contact
+    st.sidebar.subheader("Location & Contact")
+    city_tier = st.sidebar.selectbox("City Tier", [1, 2, 3])
+    type_of_contact = st.sidebar.selectbox("Type of Contact", ["Self Enquiry", "Company Invited"])
+
+    # Professional Info
+    st.sidebar.subheader("Professional Info")
+    occupation = st.sidebar.selectbox("Occupation", ["Salaried", "Small Business", "Free Lancer"])
+    designation = st.sidebar.selectbox("Designation", ["Executive", "Manager", "Senior Manager", "AVP", "VP"])
+    monthly_income = st.sidebar.number_input("Monthly Income", 10000, 50000, 20000)
+
+    # Travel Preferences
+    st.sidebar.subheader("Travel Preferences")
+    num_person_visiting = st.sidebar.slider("Number of Persons Visiting", 1, 5, 2)
+    num_children_visiting = st.sidebar.slider("Number of Children Visiting", 0, 3, 0)
+    preferred_property_star = st.sidebar.slider("Preferred Property Star Rating", 1.0, 5.0, 3.0, 0.5)
+    num_trips = st.sidebar.slider("Number of Trips per Year", 0.0, 10.0, 2.0, 0.5)
+
+    # Additional Info
+    st.sidebar.subheader("Additional Info")
+    passport = st.sidebar.selectbox("Has Passport", ["Yes", "No"])
+    own_car = st.sidebar.selectbox("Owns Car", ["Yes", "No"])
+
+    # Sales Interaction
+    st.sidebar.subheader("Sales Interaction")
+    duration_of_pitch = st.sidebar.slider("Duration of Pitch (minutes)", 5, 60, 15)
+    product_pitched = st.sidebar.selectbox("Product Pitched", ["Basic", "Standard", "Deluxe", "Super Deluxe"])
+    num_followups = st.sidebar.slider("Number of Followups", 0.0, 6.0, 3.0, 0.5)
+    pitch_satisfaction_score = st.sidebar.slider("Pitch Satisfaction Score", 1, 5, 3)
+
+    # Main content
+    col1, col2 = st.columns([2, 1])
+
+    with col1:
+        st.subheader("Customer Profile Summary")
+        profile_data = {
+            "Age": age,
+            "Gender": gender,
+            "Marital Status": marital_status,
+            "City Tier": city_tier,
+            "Occupation": occupation,
+            "Monthly Income": f"₹{monthly_income:,}",
+            "Number of Persons": num_person_visiting,
+            "Preferred Star Rating": preferred_property_star,
+            "Annual Trips": num_trips,
+            "Has Passport": passport,
+            "Owns Car": own_car
+        }
+
+        for key, value in profile_data.items():
+            st.write(f"**{key}:** {value}")
+
+    with col2:
+        st.subheader("Prediction")
+
+        if st.button("Predict Purchase Likelihood", type="primary"):
+            input_data = prepare_input_data(
+                age, gender, marital_status, city_tier, type_of_contact,
+                occupation, designation, monthly_income, num_person_visiting,
+                num_children_visiting, preferred_property_star, num_trips,
+                passport, own_car, duration_of_pitch, product_pitched,
+                num_followups, pitch_satisfaction_score
+            )
+
+            try:
+                prediction = model.predict(input_data)[0]
+                prediction_proba = model.predict_proba(input_data)[0]
+
+                if prediction == 1:
+                    st.success("High likelihood of purchase!")
+                    st.write(f"**Confidence:** {prediction_proba[1]:.2%}")
+                    st.balloons()
+                else:
+                    st.warning("Low likelihood of purchase")
+                    st.write(f"**Confidence:** {prediction_proba[0]:.2%}")
+
+                # Probability breakdown
+                st.subheader("Probability Breakdown")
+                prob_df = pd.DataFrame({
+                    'Outcome': ['Will Not Purchase', 'Will Purchase'],
+                    'Probability': [prediction_proba[0], prediction_proba[1]]
+                })
+                st.bar_chart(prob_df.set_index('Outcome'))
+
+            except Exception as e:
+                st.error(f"Prediction error: {e}")
+
+    st.markdown("---")
+    st.markdown("### About This Model")
+    st.info("""
+    This ML model predicts customer purchase likelihood for the Wellness Tourism Package
+    based on demographics, travel preferences, and sales interaction data.
+    """)
+
+if __name__ == "__main__":
+    main()

tourism_package_prediction/deployment/deploy_to_hf.py

@@ -0,0 +1,52 @@
+#!/usr/bin/env python3
+"""
+Deployment Script for Tourism Package Prediction Project
+"""
+
+from huggingface_hub import HfApi, login
+import numpy as np
+import os
+
+#login(token=HF_TOKEN)
+
+def deploy_to_huggingface_space():
+    """Deploy application to HuggingFace Spaces"""
+    print("Deploying to HuggingFace Spaces...")
+
+    try:
+        api = HfApi()
+        space_id = "RavindraDubey12/Tourism-Package-Prediction"
+
+        repo_root = os.getcwd()
+        deployment_dir = os.path.join(repo_root, "tourism_package_prediction", "deployment")
+
+        files_to_upload = [
+            (os.path.join(deployment_dir, "app.py"), "app.py"),
+            (os.path.join(deployment_dir, "requirements.txt"), "requirements.txt"),
+            (os.path.join(deployment_dir, "Dockerfile"), "Dockerfile"),
+        ]
+
+        print(f"Uploading files to space: {space_id}")
+
+        for local_path, repo_path in files_to_upload:
+            if os.path.exists(local_path):
+                print(f"Uploading {local_path}...")
+                api.upload_file(
+                    path_or_fileobj=local_path,
+                    path_in_repo=repo_path,
+                    repo_id=space_id,
+                    repo_type="space",
+                    token=os.getenv('HF_TOKEN')
+                )
+                print(f"{local_path} uploaded successfully")
+
+        print(f"\nDeployment completed!")
+        print(f"App URL: https://huggingface.co/spaces/{space_id}")
+        return True
+
+    except Exception as e:
+        print(f"Deployment error: {e}")
+        return False
+
+if __name__ == "__main__":
+    deploy_to_huggingface_space()

tourism_package_prediction/deployment/requirements.txt

@@ -0,0 +1,9 @@
+streamlit==1.28.1
+pandas==2.0.3
+numpy==1.24.3
+scikit-learn==1.3.0
+xgboost==1.7.6
+joblib==1.3.2
+huggingface-hub==0.16.4
+datasets==2.14.4
+mlflow

tourism_package_prediction/model_building/data_register.py

@@ -0,0 +1,24 @@
+from huggingface_hub.utils import RepositoryNotFoundError, HfHubHTTPError
+from huggingface_hub import HfApi, create_repo
+import os
+
+repo_id = "RavindraDubey12/Tourism-Package-Prediction"
+repo_type = "dataset"
+
+# Initialize API client
+api = HfApi(token=os.getenv("HF_TOKEN"))
+
+# Step 1: Check if the space exists
+try:
+    api.repo_info(repo_id=repo_id, repo_type=repo_type)
+    print(f"Space '{repo_id}' already exists. Using it.")
+except RepositoryNotFoundError:
+    print(f"Space '{repo_id}' not found. Creating new space...")
+    create_repo(repo_id=repo_id, repo_type=repo_type, private=False)
+    print(f"Space '{repo_id}' created.")
+
+api.upload_folder(
+    folder_path="tourism_package_prediction/data",
+    repo_id=repo_id,
+    repo_type=repo_type,
+)

tourism_package_prediction/model_building/prep.py

@@ -0,0 +1,124 @@
+# for data manipulation
+import pandas as pd
+import sklearn
+# for creating a folder
+import os
+# for data preprocessing and pipeline creation
+from sklearn.model_selection import train_test_split
+# for converting text data in to numerical representation
+from sklearn.preprocessing import LabelEncoder
+# for hugging face space authentication to upload files
+from huggingface_hub import login, HfApi
+from datasets import Dataset, load_dataset # Import Dataset and load_dataset class
+
+# Load data from HuggingFace
+HF_TOKEN = os.getenv("HF_TOKEN")
+login(HF_TOKEN)
+try:
+    dataset = load_dataset("RavindraDubey12/Tourism-Package-Prediction", split="train")
+    df = dataset.to_pandas()
+    if "__index_level_0__" in df.columns:
+       df = df.drop(columns="__index_level_0__")
+    print(f"Dataset loaded from HuggingFace: {len(df)} rows")
+except:
+    df = pd.read_csv("tourism_package_prediction/data/tourism.csv")
+    print(f"Dataset loaded locally: {len(df)} rows")
+
+# Data cleaning and preprocessing
+print("Starting data cleaning...")
+
+# Remove unnecessary columns
+if 'Unnamed: 0' in df.columns:
+    df = df.drop('Unnamed: 0', axis=1)
+
+# Handle missing values
+print("Missing values before cleaning:")
+print(df.isnull().sum()[df.isnull().sum() > 0])
+
+# Fill missing values
+numerical_cols = ['Age', 'DurationOfPitch', 'NumberOfFollowups', 'PreferredPropertyStar',
+                 'NumberOfTrips', 'PitchSatisfactionScore', 'NumberOfChildrenVisiting', 'MonthlyIncome']
+
+for col in numerical_cols:
+    if col in df.columns:
+        df[col] = df[col].fillna(df[col].median())
+
+categorical_cols = ['TypeofContact', 'Occupation', 'Gender', 'MaritalStatus',
+                   'ProductPitched', 'Designation']
+
+for col in categorical_cols:
+    if col in df.columns:
+        df[col] = df[col].fillna(df[col].mode()[0] if not df[col].mode().empty else 'Unknown')
+
+# Fix data inconsistencies
+df['Gender'] = df['Gender'].replace('Fe Male', 'Female')
+
+print("Missing values after cleaning:")
+print(df.isnull().sum()[df.isnull().sum() > 0])
+
+# Feature engineering
+print("Feature engineering...")
+
+# Create income categories
+df['IncomeCategory'] = pd.cut(df['MonthlyIncome'],
+                             bins=[0, 15000, 25000, 35000, float('inf')],
+                             labels=[0, 1, 2, 3])  # Use numeric labels
+
+# Create age groups
+df['AgeGroup'] = pd.cut(df['Age'],
+                       bins=[0, 25, 35, 45, 55, float('inf')],
+                       labels=[0, 1, 2, 3, 4])  # Use numeric labels
+
+# Encode categorical variables
+label_encoders = {}
+categorical_columns = df.select_dtypes(include=['object']).columns
+
+for col in categorical_columns:
+    if col != 'CustomerID':
+        le = LabelEncoder()
+        df[col] = le.fit_transform(df[col].astype(str))
+        label_encoders[col] = le
+
+print(f"Data preprocessing completed! Final shape: {df.shape}")
+
+# Split data
+print("Splitting data...")
+X = df.drop(['CustomerID', 'ProdTaken'], axis=1)
+y = df['ProdTaken']
+
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y, test_size=0.2, random_state=42, stratify=y
+)
+
+# Create train and test dataframes
+train_df = pd.concat([X_train, y_train], axis=1)
+test_df = pd.concat([X_test, y_test], axis=1)
+
+# Add CustomerID
+train_df.insert(0, 'CustomerID', range(300000, 300000 + len(train_df)))
+test_df.insert(0, 'CustomerID', range(400000, 400000 + len(test_df)))
+
+# Save locally
+train_df.to_csv("tourism_package_prediction/data/train_data.csv", index=False)
+test_df.to_csv("tourism_package_prediction/data/test_data.csv", index=False)
+
+print(f"Data split completed!")
+print(f"Training set: {len(train_df)} samples")
+print(f"Test set: {len(test_df)} samples")
+
+# Upload train dataset
+train_dataset = Dataset.from_pandas(train_df,preserve_index=False)
+train_dataset.push_to_hub(
+    "RavindraDubey12/Tourism-Package-Prediction-train",
+    private=False,
+    token=HF_TOKEN
+)
+
+# Upload test dataset
+test_dataset = Dataset.from_pandas(test_df,preserve_index=False)
+test_dataset.push_to_hub(
+    "RavindraDubey12/Tourism-Package-Prediction-test",
+    private=False,
+    token=HF_TOKEN
+)
+print("Processed datasets uploaded to HuggingFace!")

tourism_package_prediction/model_building/train.py

@@ -0,0 +1,230 @@
+# for data manipulation
+import pandas as pd
+from datasets import load_dataset
+from sklearn.preprocessing import StandardScaler, OneHotEncoder
+from sklearn.compose import make_column_transformer
+from sklearn.pipeline import make_pipeline
+from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, classification_report
+# for model training, tuning, and evaluation
+import xgboost as xgb
+from sklearn.model_selection import GridSearchCV
+# for model serialization
+import joblib
+# for creating a folder
+import os
+# for hugging face space authentication to upload files
+from huggingface_hub import login, HfApi, create_repo
+from huggingface_hub import HfApi
+from huggingface_hub.utils import RepositoryNotFoundError, HfHubHTTPError
+import mlflow
+
+mlflow.set_tracking_uri("http://localhost:5000")
+mlflow.set_experiment("mlops-training-experiment")
+
+api = HfApi()
+
+HF_TOKEN = os.getenv("HF_TOKEN")
+
+# Setup MLflow
+mlflow.set_experiment("tourism_package_prediction")
+
+# Load train and test data
+try:
+    train_dataset = load_dataset("RavindraDubey12/Tourism-Package-Prediction-train", split="train")
+    train_df = train_dataset.to_pandas()
+    test_dataset = load_dataset("RavindraDubey12/Tourism-Package-Prediction-test", split="train")
+    test_df = test_dataset.to_pandas()
+    print("Data loaded from HuggingFace")
+except Exception as e :
+    print(f"Error loading from HF: {e}") 
+    train_df = pd.read_csv("tourism_package_prediction/data/train_data.csv")
+    test_df = pd.read_csv("tourism_package_prediction/data/test_data.csv")
+    print("Data loaded locally")
+
+for df in (train_df, test_df):
+    if "__index_level_0__" in df.columns:
+        df.drop(columns="__index_level_0__", inplace=True)
+    if "Unnamed: 0" in df.columns:
+        df.drop(columns="Unnamed: 0", inplace=True)
+
+# Prepare features
+X_train = train_df.drop(['CustomerID', 'ProdTaken'], axis=1)
+y_train = train_df['ProdTaken']
+X_test = test_df.drop(['CustomerID', 'ProdTaken'], axis=1)
+y_test = test_df['ProdTaken']
+
+print(f"Training features shape: {X_train.shape}")
+print(f"Test features shape: {X_test.shape}")
+
+# Function to evaluate models
+def evaluate_model(model, X_test, y_test, model_name):
+    y_pred = model.predict(X_test)
+    y_pred_proba = model.predict_proba(X_test)[:, 1] if hasattr(model, 'predict_proba') else y_pred
+
+    metrics = {
+        'accuracy': accuracy_score(y_test, y_pred),
+        'precision': precision_score(y_test, y_pred),
+        'recall': recall_score(y_test, y_pred),
+        'f1_score': f1_score(y_test, y_pred),
+        'roc_auc': roc_auc_score(y_test, y_pred_proba)
+    }
+
+    print(f"\n{model_name} Performance:")
+    for metric, value in metrics.items():
+        print(f"   {metric.capitalize()}: {value:.4f}")
+
+    return metrics
+
+# Train models with hyperparameter tuning
+models_results = []
+
+# 1. Decision Tree
+print("Training Decision Tree...")
+with mlflow.start_run(run_name="DecisionTree"):
+    param_grid = {
+        'max_depth': [5, 10, 15],
+        'min_samples_split': [2, 5, 10],
+        'min_samples_leaf': [1, 2, 4]
+    }
+
+    dt = DecisionTreeClassifier(random_state=42)
+    grid_search = GridSearchCV(dt, param_grid, cv=5, scoring='roc_auc', n_jobs=-1)
+    grid_search.fit(X_train, y_train)
+
+    best_dt = grid_search.best_estimator_
+    mlflow.log_params(grid_search.best_params_)
+    mlflow.log_param("model_type", "DecisionTree")
+
+    dt_metrics = evaluate_model(best_dt, X_test, y_test, "Decision Tree")
+    mlflow.log_metrics(dt_metrics)
+    mlflow.sklearn.log_model(best_dt, "model")
+
+    models_results.append(("Decision Tree", best_dt, dt_metrics['roc_auc']))
+
+# 2. Random Forest
+print("Training Random Forest...")
+with mlflow.start_run(run_name="RandomForest"):
+    param_grid = {
+        'n_estimators': [100, 200],
+        'max_depth': [10, 15, None],
+        'min_samples_split': [2, 5],
+        'min_samples_leaf': [1, 2]
+    }
+
+    rf = RandomForestClassifier(random_state=42)
+    grid_search = GridSearchCV(rf, param_grid, cv=5, scoring='roc_auc', n_jobs=-1)
+    grid_search.fit(X_train, y_train)
+
+    best_rf = grid_search.best_estimator_
+    mlflow.log_params(grid_search.best_params_)
+    mlflow.log_param("model_type", "RandomForest")
+
+    rf_metrics = evaluate_model(best_rf, X_test, y_test, "Random Forest")
+    mlflow.log_metrics(rf_metrics)
+    mlflow.sklearn.log_model(best_rf, "model")
+
+    models_results.append(("Random Forest", best_rf, rf_metrics['roc_auc']))
+
+# 3. Gradient Boosting
+print("Training Gradient Boosting...")
+with mlflow.start_run(run_name="GradientBoosting"):
+    param_grid = {
+        'n_estimators': [100, 200],
+        'learning_rate': [0.05, 0.1, 0.15],
+        'max_depth': [3, 5, 7]
+    }
+
+    gb = GradientBoostingClassifier(random_state=42)
+    grid_search = GridSearchCV(gb, param_grid, cv=5, scoring='roc_auc', n_jobs=-1)
+    grid_search.fit(X_train, y_train)
+
+    best_gb = grid_search.best_estimator_
+    mlflow.log_params(grid_search.best_params_)
+    mlflow.log_param("model_type", "GradientBoosting")
+
+    gb_metrics = evaluate_model(best_gb, X_test, y_test, "Gradient Boosting")
+    mlflow.log_metrics(gb_metrics)
+    mlflow.sklearn.log_model(best_gb, "model")
+
+    models_results.append(("Gradient Boosting", best_gb, gb_metrics['roc_auc']))
+
+# 4. XGBoost
+print("Training XGBoost...")
+with mlflow.start_run(run_name="XGBoost"):
+    param_grid = {
+        'n_estimators': [100, 200],
+        'learning_rate': [0.05, 0.1, 0.15],
+        'max_depth': [3, 5, 7],
+        'subsample': [0.8, 0.9]
+    }
+
+    xgb_model = xgb.XGBClassifier(random_state=42, eval_metric='logloss')
+    grid_search = GridSearchCV(xgb_model, param_grid, cv=5, scoring='roc_auc', n_jobs=-1)
+    grid_search.fit(X_train, y_train)
+
+    best_xgb = grid_search.best_estimator_
+    mlflow.log_params(grid_search.best_params_)
+    mlflow.log_param("model_type", "XGBoost")
+
+    xgb_metrics = evaluate_model(best_xgb, X_test, y_test, "XGBoost")
+    mlflow.log_metrics(xgb_metrics)
+    mlflow.xgboost.log_model(best_xgb, "model")
+
+    models_results.append(("XGBoost", best_xgb, xgb_metrics['roc_auc']))
+
+# 5. AdaBoost
+print("Training AdaBoost...")
+with mlflow.start_run(run_name="AdaBoost"):
+    param_grid = {
+        'n_estimators': [50, 100, 200],
+        'learning_rate': [0.5, 1.0, 1.5]
+    }
+
+    ada = AdaBoostClassifier(random_state=42)
+    grid_search = GridSearchCV(ada, param_grid, cv=5, scoring='roc_auc', n_jobs=-1)
+    grid_search.fit(X_train, y_train)
+
+    best_ada = grid_search.best_estimator_
+    mlflow.log_params(grid_search.best_params_)
+    mlflow.log_param("model_type", "AdaBoost")
+
+    ada_metrics = evaluate_model(best_ada, X_test, y_test, "AdaBoost")
+    mlflow.log_metrics(ada_metrics)
+    mlflow.sklearn.log_model(best_ada, "model")
+
+    models_results.append(("AdaBoost", best_ada, ada_metrics['roc_auc']))
+
+# Compare models and select best
+print("\n" + "="*60)
+print("MODEL COMPARISON RESULTS")
+print("="*60)
+
+results_df = pd.DataFrame([(name, score) for name, model, score in models_results],
+                         columns=['Model', 'ROC_AUC'])
+print(results_df)
+
+# Find best model
+best_model_name, best_model, best_score = max(models_results, key=lambda x: x[2])
+print(f"\nBest Model: {best_model_name} (ROC-AUC: {best_score:.4f})")
+
+# Save best model
+os.makedirs("tourism_package_prediction/model_building", exist_ok=True)
+joblib.dump(best_model, "tourism_package_prediction/model_building/best_model.joblib")
+
+# Register best model to HuggingFace
+api = HfApi()
+repo_id = "RavindraDubey12/Tourism-Package-Prediction-model"
+
+try:
+    api.create_repo(repo_id=repo_id, exist_ok=True, private=False)
+    api.upload_file(
+        path_or_fileobj="tourism_package_prediction/model_building/best_model.joblib",
+        path_in_repo="best_model.joblib",
+        repo_id=repo_id,
+        token=HF_TOKEN
+    )
+    print(f"Best model registered to HuggingFace: {repo_id}")
+except Exception as e:
+    print(f"Error registering model: {e}")

tourism_package_prediction/requirements.txt

@@ -0,0 +1,6 @@
+huggingface_hub==0.32.6
+datasets==3.6.0
+pandas==2.2.2
+scikit-learn==1.6.0
+xgboost==2.1.4
+mlflow==3.0.1