https://github.com/samuellucas97/ml-e2e-flask
https://github.com/samuellucas97/ml-e2e-flask
flask machine-learning random-forest-regression scikit-learn seaborn sqlite3 yellowbrick
Last synced: 2 months ago
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- Host: GitHub
- URL: https://github.com/samuellucas97/ml-e2e-flask
- Owner: Samuellucas97
- Created: 2022-10-21T21:26:26.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-10-26T19:12:31.000Z (over 3 years ago)
- Last Synced: 2025-05-16T19:09:31.388Z (about 1 year ago)
- Topics: flask, machine-learning, random-forest-regression, scikit-learn, seaborn, sqlite3, yellowbrick
- Language: Jupyter Notebook
- Homepage:
- Size: 2.46 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
## Machine Learning project with Flask API
It contains my ML project involving rental price recommendation based on `area`, `rooms`, `bathroom`, `parking_space`, `floor`, `animal`, `furniture`, `hoa`, and `property tax`. This project was accomplished during [Machine Learning | Solução completa end-to-end (Python)](https://www.udemy.com/course/machine-learning-solucao-completa-end-to-end-api/), an Udemy course. I
The command below clone this repository.
```
$ git clone https://github.com/Samuellucas97/ML-E2E-Course.git
$ cd ML-E2E-Course
```
### Requirements
- Python ( version _3.8.10_ )
- Numpy ( version _1.23.4_ )
- Use the following command to install: `pip install numpy`
- Pandas ( version _1.5.1_ )
- Use the following command to install: `pip install pandas`
- Seaborn ( version _0.12.1_ )
- Use the following command to install: `pip install seaborn`
- Sckit-learn ( version _1.1.3_ )
- Use the following command to install: `pip install sklearn`
- Yellowbrick (version _1.5_ )
- Use the following command to install: `pip install yellowbrick`
- Joblib ( version _1.2.0_ )
- Use the following command to install: `pip install joblib`
- Flask ( version _2.2.2_ )
- Use the following command to install: `pip install flask`
You could check your Sckit-learn lib version, for example, using the following commands on Python interpreter:
```
>>> import sklearn
>>> print('The scikit-learn version is {}.'.format(sklearn.__version__))
```
### How to run
Since you have installed software requirements, you need to execute on the terminal the following command:
```
$ ./run.sh
```
A Flask server will be running on [http://127.0.0.1:5000](http://127.0.0.1:5000).
You can use the `/api/predictor/` API endpoint to predict rent. We show an example about how to use this in `/test/api.ipynb`.