Machine learning is an extensively utilized prediction technology. A large number of methods for prediction are accessible in various libraries. In this post, we will develop a prediction model using historical data using several machine learning algorithms and classifiers, plot the results, and calculate the model’s accuracy on testing data. In this blog, we will look at the Deploy Machine Learning Model using Flask. Learn all about the content techniques and tools with the Flask Training in Chennai.
Deploy Machine Learning Model using Flask
Building/training a model on a large dataset using various algorithms is one aspect of the data. However, implementing these models into various applications is the second step in applying machine learning in the real world.
To utilize it to anticipate fresh data, we must deploy it via the internet so that the rest of the world can use it. In this article, we will discuss how we trained a machine learning model and built a web application using Flask.
Many libraries that will be utilized in this model must be installed. To install all of the libraries, use the pip command.
- Install pip install pandas
- pip install numpy
- pip install sklearn
Because it is simple to use, durable, and versatile, Decision Tree is a well-known supervised machine learning technique. I ran the algorithm on the Adult dataset from UCI’s machine learning library.
Obtaining the dataset is not the end of the process. We must preprocess the data, which entails cleaning the dataset. Cleaning the dataset entails a variety of methods such as deleting missing values, filling NA values, and so on.
Preprocessing the dataset
It consists of 14 attributes plus a class label indicating whether the individual’s annual income is less than or greater than $50,000. These characteristics range from a person’s age and working-class status to their relationship status and race. All of the qualities’ information may be found here.
We begin by locating and removing any missing values from the data. We replaced the missing values in that column with the mode value. There are many different approaches to replace missing values, but this seemed the most appropriate for this type of dataset. Want to persue a career in Flask Developers? Enroll in our Flask Online Course!
Categorical data values cannot be processed by the machine learning algorithm. It can only deal with numbers.
We must transform categorical values to numerical ones in order to fit the data into the prediction model. Before that, we’ll see if any transformations on category columns are required.
Discretization is a popular technique for making classified data more orderly and comprehensible. We used discretization on the field marital_status, which limits the values to married or not married. In the remaining data columns, we will later use a label encoder. There are also two unnecessary columns: ‘education’ and ‘educational-num’. As a result, one of them has been eliminated.
We must first collect data (new attribute values) to predict revenue from multiple attributes, and then use the decision tree model we built previously to predict whether the income is greater than or less than $50,000. As a result, in order to collect the data, we develop an HTML form with all of the numerous alternatives to choose from for each characteristic. We’ve constructed a simple form here using only HTML. You may also make the form more interactive if you wish.
Before we begin coding, we must first download flask and a few additional libraries. Here, we employ a virtual environment where all of the libraries are handled, making development and deployment easier.
In conclusion, deploying machine learning models using Flask offers an efficient and accessible solution for implementing predictive analytics in real-world applications. By harnessing the power of Flask, developers can create robust and scalable web services that can seamlessly integrate machine learning algorithms into existing systems. To know more about deploying machine learning using flask. FITA Academy provides a top-notch Advanced Training Institute in Chennai to assist you to explore the flask framework project.