pARkitML - Machine Learning Models for Dynamic Pricing in AR-Based Parking Solution - pARkit

Welcome to the pARkitML repository, home to machine learning models designed to enhance the 'pARkit' project—an AR-based parking solution with dynamic pricing capabilities. Our repository includes a pre-trained neural network model for predicting parking prices.

Table of Contents

• Introduction
• Getting Started
• Dependencies
• Contributing

Introduction

pARkit is an innovative parking solution that leverages Augmented Reality (AR) technology to simplify and optimize the parking experience. One of the key features of pARkit is dynamic pricing, which allows us to adjust parking rates based on various factors such as demand, location, and time of day. The machine learning model in this repository is crucial for accurately predicting parking prices.

Getting Started

To make new predictions using our pre-trained neural network model, you can use the saved_model_prediction_nn.py script. Here's how to get started:

1. Clone this repository to your local machine:

   git clone https://github.com/NarcoTech/pARkitML.git

2. Navigate to the NeuralNetwork directory:

    cd pARkitML/NeuralNetwork

3. Run the 'saved_model_prediction_nn.py' script with your input data to obtain parking price predictions.

Dependencies

To run the prediction script, you will need the following dependencies:

1. Python 3.x
2. TensorFlow
3. NumPy
4. Pandas
5. Keras
6. joblib

Contributing

We welcome contributions to pARkitML. If you have improvements or additional features to suggest, please feel free to submit a pull request or open an issue.