https://github.com/blacksujit/location-based-analysis
This project aims to perform restaurant location-based analysis using machine learning techniques. It involves predicting restaurant ratings based on user inputs and displaying restaurant locations on a map.
https://github.com/blacksujit/location-based-analysis
anaylsis deployement-strategy flask flask-application forms location-based location-based-analysis location-services machine-learning machine-learning-algorithms machine-learning-models ml ml-ops mlops model-development model-development-repository
Last synced: 10 months ago
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This project aims to perform restaurant location-based analysis using machine learning techniques. It involves predicting restaurant ratings based on user inputs and displaying restaurant locations on a map.
- Host: GitHub
- URL: https://github.com/blacksujit/location-based-analysis
- Owner: Blacksujit
- Created: 2024-07-04T14:25:33.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-14T18:45:42.000Z (about 1 year ago)
- Last Synced: 2025-01-29T11:22:45.027Z (12 months ago)
- Topics: anaylsis, deployement-strategy, flask, flask-application, forms, location-based, location-based-analysis, location-services, machine-learning, machine-learning-algorithms, machine-learning-models, ml, ml-ops, mlops, model-development, model-development-repository
- Language: HTML
- Homepage:
- Size: 81 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: Readme.md
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README
# Restaurant Location-Based Analysis
This project aims to perform restaurant location-based analysis using machine learning techniques. It involves predicting restaurant ratings based on user inputs and displaying restaurant locations on a map.
## Table of Contents
- [Overview](#overview)
- [Features](#features)
- [Installation](#installation)
- [Usage](#usage)
- [File Structure](#file-structure)
- [Technologies Used](#technologies-used)
- [Contributing](#contributing)
- [License](#license)
## File Structure:
```
restaurant-location-analysis/
│
├── model/
│ ├── ML_model.ipynb
│ └── ...
│
├── static/
│ ├── css/
│ │ ├── styles.css
│ │ ├── other css if yu have to add
│ │ └── ...
│ ├── js/
│ │ ├── script.js
│ │ └── ...
│ ├── templates/
│ ├── ..all templates here
│ └── ...
│
│
│
│
├── .env.example
|-- app.py
├── .gitignore
├── package.json
├── README.md
└── ...
```
## Project Frontend:
## Video Link:
https://github.com/Blacksujit/Location_Based_Analysis/assets/148805811/a8b3d6d3-96a1-41fe-b346-acd301857e99
## Home Page (User interactiveness):
![alt text]()
## Some Restaurents recoomeded by ML model on maps:
## 1st map:
## 2nd map:
## Overview
The project integrates machine learning models to predict restaurant ratings and displays their locations on a map. Users can input various criteria, and the ML model provides predictions based on historical data.
## Features
- **ML Model Integration:** Uses machine learning to predict restaurant ratings.
- **Interactive Map:** Displays restaurant locations on a map for easy visualization.
- **User Input:** Accepts user inputs to customize the analysis and predictions.
## Installation
Follow these steps to set up the project locally:
1. **Clone the repository:**
```bash
git clone https://github.com/Blacksujit/Location_Based_Analysis.git
cd restaurant-location-analysis
2. **Install dependencies:**
```bash
pip install -r requirements.txt
```
3. **Run the application:**
```bash
python app.py
```
## Technologies Used:
1.) Frontend: js , HTML, CSS
2.) Backend: Python (for ML model):
restaurents prediction using various ML algorithms
## Usage:
1.) It can be used to achive the solutions in small segements of the ecoomerece sites where they find things difficult to track
2.) It can also be used in hilly areas if implemented propely with the proper software base to track near by locations
## Contributing:
Contributions are welcome! If you have any ideas, improvements, or issues, feel free to open a pull request or raise an issue.
## License:
This project is licensed under the MIT License - see the