Deploy House Price Prediction Using Flask - we all known that real estate market is a focused regarding pricing and keep fluctuating, this project help to find a price for property based
First of all we have to download dataset from kaggle . Then we have to apply feature engineering into dataset to clean the data , feature scaling , data pre-processing and like many more things . Then we have to divide our dataset into two part , first part says independent feature and dependent feature . In dependent feature we consider price and independent feature consider rest of the column . Then we have to divide dataset into two part, first part train dataset and second part test dataset . Then after we have to apply some regression model to train the data . After that we have test the model and check the accuracy of model .After checking the models I have conclude that the linear regression model is best for this project and the accuracy of the linear regression model is 88% .
After successfully created web application , we have to host our web application . For that I have use Heroku platform . Heroku is use for hosting our web application .
The Association of Data Scientists has announced a hands-on workshop on deep learning model deployment and management.
This article will highlight the different techniques used in Machine Learning development. After that, we will focus on the top Machine Learning models examples and algorithms that enable the execution of applications for deriving insights from data.
Machine Learning Model Deployment using Flask. The Deployment of a Machine Learning Model is a key aspect of every Machine Learning project. Model Deployment is also one of the core topics that interviewers are keen to ask. Model Deployment can be referred to as the last stage of any project. After the completion of our project, we want our model to be available for the end-users so that they can make use of it. Here Flask plays an important role.
I hope this overview on how to deploy machine learning models helped you understand the basic steps to deploying your models. Read more.