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How to Identify a Frequent Flyer Program Based on Total Miles Traveled?

Frequent Flyer Program Classification Based on Total Miles Traveled

Condition for Identifying a Frequent Flyer Program Based on Total Miles Traveled?

  • Description:
    This project aims to classify frequent flyer programs for a set of travelers based on their total miles traveled. The goal is to predict the most suitable frequent flyer program for a given traveler, helping them optimize their rewards and benefits. The classification model will consider a variety of features like total miles flown, trip frequency, and other attributes to determine the best frequent flyer program for the traveler.
Step-by-Step Process
  • Data Collection:
    Gather data of travelers that includes the total miles traveled, flight frequencies, and other attributes related to their flying patterns.
  • Data Preprocessing:
    Handle missing values, normalize/standardize data, and perform exploratory data analysis (EDA) to understand relationships in the data.
  • Feature Engineering:
    Derive new features like travel frequency, average distance per trip, etc., which can help in classification.
  • Model Selection:
    Choose appropriate classification models such as Logistic Regression, Random Forest, Decision Trees, or Gradient Boosting.
  • Model Training:
    Train the model using historical data.
  • Model Evaluation:
    Evaluate the model using classification metrics such as Accuracy, Precision, Recall, F1-Score, and Confusion Matrix.
  • Visualization:
    Generate heatmaps and plots to visualize feature importance, performance metrics, and other key insights.
  • Output Generation:
    Predict the most suitable frequent flyer program for a new traveler based on their total miles traveled and other features.
Why Should We Choose This Approach?
  • Efficient Use of Miles: Helps frequent travelers choose the right loyalty program to maximize their benefits.
  • Personalization: Tailors recommendations based on individual travel patterns.
  • Predictive Analysis: Uses machine learning to predict the best program, which can be continually refined with more data.
  • Easy Implementation: Simple classification models can be used for initial testing, while complex models can be implemented as needed.
Sample Source Code
  • # Importing necessary libraries
    import pandas as pd
    import numpy as np
    import matplotlib.pyplot as plt
    import seaborn as sns
    from sklearn.ensemble import RandomForestClassifier
    from sklearn.model_selection import train_test_split, GridSearchCV, StratifiedKFold, cross_val_score
    from sklearn.preprocessing import StandardScaler
    from sklearn.metrics import classification_report, confusion_matrix
    from sklearn.impute import SimpleImputer

    # Sample data creation
    data = {
    'total_miles': [12000, 15000, 10000, 20000, 18000, 25000, 22000, 17000, 30000, 16000],
    'trip_count': [45, 50, 40, 80, 70, 100, 90, 60, 120, 65],
    'program_choice': [1, 2, 1, 3, 1, 2, 3, 1, 2, 3], # Target variable (e.g., different frequent flyer programs)
    'average_trip_distance': [267, 300, 250, 250, 257, 300, 244, 283, 250, 246]
    }

    # Convert to DataFrame
    data = pd.DataFrame(data)

    # Save the data to a CSV file
    data.to_csv('data.csv', index=False)

    # Load the dataset
    data = pd.read_csv('data.csv')

    # Data Preprocessing
    # Handling missing values (if any) using SimpleImputer
    imputer = SimpleImputer(strategy='mean')
    data = pd.DataFrame(imputer.fit_transform(data), columns=data.columns)

    # Splitting the features and target variable
    X = data[['total_miles', 'trip_count', 'average_trip_distance']] # Features
    y = data['program_choice'] # Target variable

    # Feature Scaling
    scaler = StandardScaler()
    X_scaled = scaler.fit_transform(X)

    # Splitting the data into training and testing sets
    X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.3, random_state=42)

    # Model Selection - Using Random Forest Classifier
    model = RandomForestClassifier(random_state=42)

    # Hyperparameter Tuning using GridSearchCV for better accuracy
    param_grid = {
    'n_estimators': [50, 100, 150], # Number of trees
    'max_depth': [None, 10, 20, 30], # Maximum depth of trees
    'min_samples_split': [2, 5, 10], # Minimum samples required to split a node
    'min_samples_leaf': [1, 2, 4], # Minimum samples required to be at a leaf node
    'bootstrap': [True, False] # Whether to use bootstrap samples
    }

    # Perform GridSearchCV to find the best hyperparameters
    grid_search = GridSearchCV(estimator=model, param_grid=param_grid, cv=3, n_jobs=-1, verbose=2)
    grid_search.fit(X_train, y_train)

    # Best parameters from GridSearchCV
    best_params = grid_search.best_params_
    print(f"Best Parameters: {best_params}")

    # Use the best model after GridSearchCV
    best_model = grid_search.best_estimator_

    # Predictions
    y_pred = best_model.predict(X_test)

    # Model Evaluation
    print("Classification Report:\n", classification_report(y_test, y_pred))
    print("Confusion Matrix:\n", confusion_matrix(y_test, y_pred))

    # Feature Importance
    feature_importance = best_model.feature_importances_
    features = X.columns

    # Plotting the Feature Importance
    plt.figure(figsize=(8,6))
    sns.barplot(x=feature_importance, y=features)
    plt.title('Feature Importance in Frequent Flyer Program Classification')
    plt.show()

    # Confusion Matrix Heatmap
    conf_matrix = confusion_matrix(y_test, y_pred)
    plt.figure(figsize=(7,6))
    sns.heatmap(conf_matrix, annot=True, cmap='Blues', fmt='g', cbar=False)
    plt.title('Confusion Matrix for Classification')
    plt.xlabel('Predicted')
    plt.ylabel('True')
    plt.show()

    # Cross-validation score (with stratified k-fold)
    cv = StratifiedKFold(n_splits=3) # Using StratifiedKFold to ensure class distribution is preserved
    cv_scores = cross_val_score(best_model, X_scaled, y, cv=cv)
    print("Cross-validation scores: ", cv_scores)
    print("Mean Cross-validation score: ", np.mean(cv_scores))

    # Optional: Plotting the Cross-validation scores to visualize
    plt.figure(figsize=(8, 6))
    plt.plot(range(1, len(cv_scores) + 1), cv_scores, marker='o', linestyle='-', color='b')
    plt.title('Cross-validation Scores')
    plt.xlabel('Fold')
    plt.ylabel('Accuracy')
    plt.show()
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  • Frequent Flyer Program Classification Based on Total Miles Traveled

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