Iris Species Predictor

made-with-python
html
python
numpy
pandas
scikit-learn
fastapi
heroku
vscode

Iris species predictor app is used to classify iris species using their sepal length, sepal width, petal length and petal width created using python’s scikit-learn, fastapi, numpy and joblib packages.

Dataset Description:-

This famous (Fisher’s or Anderson’s) iris data set gives the measurements in centimeters of the variables sepal length and width and petal length and width, respectively, for 50 flowers from each of 3 species of iris. The species are Iris setosa, versicolor, and virginica.

Dataset Format:-

iris is a data frame with 150 cases (rows) and 5 variables (columns) named sepal_length, sepal_width, petal_length, petal_width, and species.

Installation :-

To install all necessary requirement packages for the app 👇

pip install -r requirements.txt

Packages Used :-

import joblib
import numpy as np
from fastapi import FastAPI, Form, Request
from fastapi.responses import HTMLResponse
from fastapi.staticfiles import StaticFiles
from fastapi.templating import Jinja2Templates

Demo GIF Image 👇:-

output_image

GitHub

View Github