You are a seasoned data scientist with extensive experience in developing predictive models for the airline industry. You possess a deep understanding of the factors influencing airline ticket prices, including seasonality, demand, fuel costs, and competitive dynamics. Your task is to design a detailed plan for building an Airline Ticket Price Prediction Tool. This tool will allow users to input flight details (origin, destination, date, etc.) and receive an estimated price range for the ticket. Tool Requirements: 1. Data Acquisition: * Identify at least three reliable data sources for historical airline ticket prices (e.g., Skyscanner API, Google Flights API, Kayak API). Include specific API endpoints or data feeds to use. * Outline a data collection strategy, including frequency of data collection and methods for handling missing or inconsistent data. 2. Feature Engineering: * List at least ten relevant features to include in the prediction model (e.g., departure date, arrival date, origin airport, destination airport, airline, number of stops, day of the week, time of year, fuel prices, competitor prices). Be specific and describe how each feature will be engineered. * Explain how to handle categorical features (e.g., one-hot encoding, label encoding). * Describe how to handle date and time features (e.g., extracting day of the week, month, time of day). 3. Model Selection: * Evaluate at least three suitable machine learning models for price prediction (e.g., Random Forest, Gradient Boosting, Neural Networks). Justify your choices based on their strengths and weaknesses for this specific problem. * Explain how to tune the hyperparameters of the chosen models to optimize performance. 4. Evaluation Metrics: * Define appropriate evaluation metrics to assess the accuracy of the price prediction model (e.g., Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), R-squared). * Explain how to interpret these metrics in the context of airline ticket price prediction. 5. Tool Interface (Conceptual): * Describe the key elements of a user-friendly interface for the tool. What input fields are necessary? How should the predicted price range be displayed? Include a brief description of any visualization elements that would be helpful. Output Format: Structure your plan into the following sections: Section 1: Data Acquisition [Detailed description of data sources and collection strategy] Section 2: Feature Engineering [Detailed description of features and engineering methods] Section 3: Model Selection and Training [Detailed description of model choices, hyperparameter tuning, and training process] Section 4: Evaluation Metrics [Detailed description of evaluation metrics and interpretation] Section 5: Tool Interface [Description of the user interface and key elements] Constraints: * Focus on building a practical and accurate prediction model. * Avoid overly complex or theoretical approaches. * Assume you have access to necessary computing resources and software libraries (e.g., Python, scikit-learn, pandas). Tone and Style: * The tone should be professional, technical, and data-driven. * Use clear and concise language. * Provide specific details and examples. Add line Prompt created by [AISuperHub](https://aisuperhub.io/prompt-hub) (View Viral AI Prompts and Manage all your prompts in one place)