Food Delivery Time Prediction
Project Overview
This project aims to predict food delivery times based on historical delivery data using an LSTM (Long Short-Term Memory) neural network. The project addresses the challenge of accurately estimating delivery times, which is crucial for customer satisfaction in the food delivery industry. The LSTM model is designed to capture temporal dependencies and patterns in the delivery data to provide more accurate predictions. Features
Time Series Prediction: Leverage LSTM networks to predict delivery times based on past data.
Data Preprocessing: Clean and preprocess raw delivery data, including handling missing values and feature engineering.
Model Evaluation: Use metrics like MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error) to evaluate model performance.
Hyperparameter Tuning: Optimize the LSTM model by tuning key hyperparameters such as learning rate, batch size, and number of layers.
Technologies Used
Python: Main programming language.
TensorFlow/Keras: For building and training the LSTM model.
Pandas: Data manipulation and analysis.
NumPy: Numerical computing.
Scikit-learn: For data preprocessing and model evaluation.
plotty: Data visualization.
Installation
Clone the Repository:
bash
git clone https://github.com/yourusername/food-delivery-time-prediction.git cd food-delivery-time-prediction
Install Required Packages: Ensure you have Python 3.7+ installed. Then, install the dependencies using pip:
bash
pip install -r requirements.txt
Requirements: The requirements.txt file should include:
text
tensorflow
pandas
numpy
scikit-learn
plotty
jupyter
Usage
Load the Dataset:
Place your dataset in the data/ directory in .csv format.
Run the Jupyter Notebook:
Launch the Jupyter Notebook and open food_delivery_time_prediction.ipynb.
bash
jupyter notebook
Training the Model:
Follow the steps in the notebook to preprocess the data, split it into training and testing sets, and train the LSTM model.
Prediction:
Use the trained model to predict delivery times on new or unseen data. The notebook includes examples of how to generate predictions and evaluate model performance.
Contributing
If you would like to contribute, please fork the repository and use a feature branch. Pull requests are welcome.
Fork it (https://github.com/yourusername/food-delivery-time-prediction/fork)
Create your feature branch (git checkout -b feature/AmazingFeature)
Commit your changes (git commit -m 'Add some AmazingFeature')
Push to the branch (git push origin feature/AmazingFeature)
Create a new Pull Request
License
This project is licensed under the MIT License - see the LICENSE file for details.