- 📖 Table of Contents
- 📍 Overview
- 📦 Features
- 📂 repository Structure
- ⚙️ Modules
- 🚀 Getting Started
- 🛣 Roadmap
- 🤝 Contributing
- 📄 License
- 👏 Acknowledgments
The Python Vehicle Project is a data-driven, machine learning Python initiative designed to analyze and process vehicle-related data. Using a range of libraries including pandas, numpy, matplotlib, and sklearn, it performs data manipulation, numerical computations, data visualization and applies machine learning algorithms. This offering posits a compelling proposition for those seeking to delve into automotive analytics and predictive modelling.
Here this is a system where we are analyzing a AirBus Management System(ABS) Sample where the main purpose of this architecture is that here we can see how vehicles are used in a business similar to Car Rental System connecting Drivers with Airbus First all the operations corresponding to AirBus Management System is related to different Statuses mainly the document Status where the Vehicle undergoes operation to Move from the Business Premises to customer
Here we are denoting 3 business models based on which the architecture is made about .
- Vehicles - This is the business model which is the base like these are the vehicles which the customer will utilize or take a rent of these vehicles from the business client .
- Prospects - This is the next business model corresponding to the customers who have came to take up the vehicles
- Champions - This is the next ongoing business model corresponding to the customers who are potential prospects binded with the vehicles and are ready to take out the vehicle .
| Feature | Description | |
|---|---|---|
| ⚙️ | Architecture | The architecture is a single Jupyter notebook. Data preprocessing, model training, and visualization occur linearly, with less focus on system design. |
| 📄 | Documentation | The flow is corresponding to vehicles which are used by customers to send accross the systems .The Architecture is like architecturelink |
| 🔗 | Dependencies | Main dependencies are pandas, numpy, matplotlib, and sklearn, which cover a broad range of data processing, analysis, visualization and machine learning. |
| 🧩 | Modularity | Modularity is limited due to the use of a single notebook. However, logical separation of code cells partially compensates for this. |
└── Python-Project-Vehicles/
├── MachineLearning.ipynb (Click on to See **Machine Learning** )
├── dataScience.ipynb (Click on to See **Data Science** )
├── requirements.txt
Root
| File | Summary |
|---|---|
| requirements.txt | The provided code indicates a project directory for a Python-based vehicle project. The requirements.txt file lists the necessary libraries for the project: pandas for data manipulation, numpy for numerical computations, matplotlib for data visualization, and sklearn for machine learning tasks. This setup is typical for data analysis or machine learning projects. |
| index.ipynb | The provided code is a part of a Python project related to vehicles, specifically contained within an IPython notebook (index.ipynb). It notably imports NumPy, Pandas, Scikit-learn, and Matplotlib libraries, suggesting usage for numerical computation, data manipulation and analysis, machine learning, and data visualization respectively in the ensuing codebase. |
Dependencies
Please ensure you have the following dependencies installed on your system:
- ℹ️ Anaconda
- ℹ️ git
- Clone the Python-Project-Vehicles repository:
git clone https://github.com/Debanil1996/Python-Project-Vehicles.git- Change to the project directory:
cd Python-Project-Vehicles- Install the dependencies:
► pip3 install -r requirements.txt► jupter notebook
ℹ️ Task 1: Analysis Of Vehiclesℹ️ Task 2: Machine Learning Algorithms in VAMS
Contributions are welcome! Here are several ways you can contribute:
- Submit Pull Requests: Review open PRs, and submit your own PRs.
- Join the Discussions: Share your insights, provide feedback, or ask questions.
- Report Issues: Submit bugs found or log feature requests for DEBANIL1996.
Click to expand
- Fork the Repository: Start by forking the project repository to your GitHub account.
- Clone Locally: Clone the forked repository to your local machine using a Git client.
git clone <your-forked-repo-url>
- Create a New Branch: Always work on a new branch, giving it a descriptive name.
git checkout -b new-feature-x
- Make Your Changes: Develop and test your changes locally.
- Commit Your Changes: Commit with a clear and concise message describing your updates.
git commit -m 'Implemented new feature x.' - Push to GitHub: Push the changes to your forked repository.
git push origin new-feature-x
- Submit a Pull Request: Create a PR against the original project repository. Clearly describe the changes and their motivations.
Once your PR is reviewed and approved, it will be merged into the main branch.
This project is protected under the MIT-LICENSE License.
- List any resources, contributors, inspiration, etc. here.