Building a Flower Species Predictor: A Step-by-Step Guide to Creating a Machine Learning Web App with Python Flask
Tags: machinelearning, webdev, tutorial, productivity
Published: 2023-02-15 12:01:35

Machine learning has become an increasingly popular topic in recent years. As technology continues to advance, machine learning has become a key tool for businesses and individuals alike to optimize their decision making processes. In this article, we will explore how Python and Flask Framework can be used to develop machine learning applications.

![Machine Learning Chart](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/i6l9vbn5rt9lka8vglzl.png)

One of the benefits of Python is its versatility as a programming language. It is well suited for data analysis and has a vast array of libraries that support machine learning. Flask Framework is a web application framework that allows developers to easily build and deploy web applications. Together, Python and Flask Framework can be used to create machine learning applications that can be accessed via the web.

## Steps

In order to create a Python app using Flask Framework and machine learning, the following steps are required:

1. Collect data: The first step in any machine learning project is to collect and clean the data. This can be done using Python libraries such as Pandas or Numpy.

![Numpy](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/qjishnpll69dh9me8hno.png)

2. Choose a machine learning model: There are several machine learning models to choose from, depending on the nature of the problem. Some of the popular ones include linear regression, decision trees, and neural networks.

3. Train the model: Once the data and the model have been selected, the model needs to be trained. This involves feeding the data into the model and adjusting its parameters until it can accurately predict the desired outcome.
 
4. Deploy the model: Finally, the trained model needs to be deployed so that it can be accessed via the web. Flask Framework makes this process simple by providing tools to build web applications that can interact with the machine learning model.

Using these steps, a Python app using Flask Framework and machine learning can be created. One example of this is a predictive model that can be used to predict whether a customer is likely to buy a product. The data would be collected from the customer's past purchases, and the model would be trained to predict their future behavior. The app would then be deployed via the web so that customers could access it and receive personalized product recommendations.

## Flower Species Predictor

![Flower Species Predictor Machine Learning Programe](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/br06peikuyzosqd9elim.png)

The webpage "Python App using Flask Framework and Machine Learning" provides a [detailed guide on how to develop a Python web application using the Flask framework and machine learning techniques](https://appcode.app/python-app-using-flask-framework-and-machine-learning/). The page is designed for developers with an intermediate level of programming knowledge who are interested in building web applications with machine learning capabilities.

The page starts by introducing the Flask framework and its various features, followed by an overview of machine learning and how it can be used in web applications. The article then explains how to set up a development environment and install the necessary dependencies for the project.

**Creating Python Backend for Web App**
- [Installing the Flask Framework](https://appcode.app/python-app-using-flask-framework-and-machine-learning/#one)
- [Creating the Machine Learning Model](https://appcode.app/python-app-using-flask-framework-and-machine-learning/#two)
- [Using the Iris Dataset for Training](https://appcode.app/python-app-using-flask-framework-and-machine-learning/#three)
- [Creating the app.py File](https://appcode.app/python-app-using-flask-framework-and-machine-learning/#four)

**Creating Web App Frontend With HTML, CSS and Bootstrap**
- [Creating Python HTML templates](https://appcode.app/python-app-using-flask-framework-and-machine-learning/#five)
- [Creating a Dynamic Form and Table](https://appcode.app/python-app-using-flask-framework-and-machine-learning/#six)
- [Running Flask on Localhost](https://appcode.app/python-app-using-flask-framework-and-machine-learning/#seven)
- [Runing app.py in Terminal](https://appcode.app/python-app-using-flask-framework-and-machine-learning/#eight)

By following the steps outlined in this article, developers can create their own Python app using Flask Framework and machine learning to solve a wide range of problems.