In the dynamic and ever-evolving airline industry, accurate forecasting of future passenger numbers is crucial for strategic planning, resource allocation, and overall operational efficiency. This project aims to leverage time series analysis techniques to predict future passenger counts based on historical data.
The dataset used for this project captures the monthly total airline passenger counts, starting from 1949. With a total of 144 monthly observations, this comprehensive dataset provides a rich source of information for understanding and modeling passenger trends over time.
The primary objective of this project is to develop a robust time series forecasting model that can accurately predict future airline passenger numbers. The insights gained from this model will assist the airline in making informed decisions related to capacity planning, marketing strategies, and overall business optimization.
The project will follow a structured methodology, including the following key steps:
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Data Exploration and Preprocessing: Analyze and preprocess the historical passenger data to identify patterns, trends, and potential outliers.
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Time Series Analysis: Apply time series analysis techniques to understand the temporal patterns within the dataset, such as seasonality and trends.
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Model Selection: Evaluate and choose appropriate time series forecasting models, considering factors such as accuracy, interpretability, and computational efficiency.
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Training and Validation: Train the selected model using historical data and validate its performance against a subset of the dataset to ensure reliability.
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Forecasting: Utilize the trained model to make future predictions of airline passenger numbers.
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Evaluation: Evaluate the model's performance using relevant metrics and fine-tune as necessary to improve accuracy.
- Python
- Jupyter Notebooks
- Pandas
- NumPy
- Statsmodels
- Machine Learning Libraries (e.g., scikit-learn)
- Visualization Libraries (e.g., Matplotlib, Seaborn)