woxy-sensei / lstm-based-airline-passenger-prediction

Welcome to the "Airline Passenger Prediction using LSTM" project! This project demonstrates the application of Long Short-Term Memory (LSTM) neural networks to predict future passenger counts for an airline. By leveraging historical passenger data, we aim to build a predictive model that can provide insights into future passenger trends.

Airline Passenger (Time Series) Prediction using LSTM

About the Project

Welcome to the "Airline Passenger Prediction using LSTM" project! This project demonstrates the application of Long Short-Term Memory (LSTM) neural networks to predict future passenger counts for an airline. By leveraging historical passenger data, we aim to build a predictive model that can provide insights into future passenger trends.

Dataset

For this project, we utilized a dataset containing historical airline passenger counts over time. The dataset provides a time series of passenger data, which serves as the basis for training and evaluating our LSTM model.

LSTM Neural Network

Long Short-Term Memory (LSTM) is a type of recurrent neural network that excels at capturing patterns in sequential data, making it particularly suitable for time series prediction tasks. In this project, we implemented an LSTM neural network to learn from the temporal patterns in the passenger data and make predictions about future passenger counts.

How to Use

To explore and use the "Airline Passenger Prediction using LSTM" project, follow these steps:

1. Clone the Repository: Clone this repository to your local machine.

2. Install Dependencies: Ensure that you have the required libraries and packages installed. You can usually find the necessary dependencies in a "requirements.txt" file.

3. Data Preprocessing: Preprocess the dataset, which may involve normalizing data, handling missing values, and splitting it into training and testing sets.

4. Build and Train the LSTM Model: Develop the LSTM neural network, configure hyperparameters, and train the model on the training data.

5. Evaluate the Model: Assess the model's performance on the testing data. You can use various evaluation metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE).

6. Make Predictions: Use the trained model to make predictions on future passenger counts based on new data.

Contribution

Contributions to enhance this project are always welcome! If you have ideas for improvements, new features, or come across any issues, feel free to open an issue or submit a pull request. Your contributions and feedback are highly appreciated!

License

This project is open-source and licensed under the MIT License. For more details, please see the "LICENSE" file.