Goto

Collaborating Authors

Regression with Keras - PyImageSearch

#artificialintelligence

In this tutorial, you will learn how to perform regression using Keras and Deep Learning. You will learn how to train a Keras neural network for regression and continuous value prediction, specifically in the context of house price prediction. Today's post kicks off a 3-part series on deep learning, regression, and continuous value prediction. We'll be studying Keras regression prediction in the context of house price prediction: Unlike classification (which predicts labels), regression enables us to predict continuous values. For example, classification may be able to predict one of the following values: {cheap, affordable, expensive}.


Identifying Real Estate Opportunities using Machine Learning

arXiv.org Machine Learning

The real estate market is exposed to many fluctuations in prices, because of existing correlations with many variables, some of which cannot be controlled or might even be unknown. Housing prices can increase rapidly (or in some cases, also drop very fast), yet the numerous listings available online where houses are sold or rented are not likely to be updated that often. In some cases, individuals interested in selling a house (or apartment) might include it in some online listing, and forget about updating the price. In other cases, some individuals might be interested in deliberately setting a price below the market price in order to sell the home faster, for various reasons. In this paper we aim at developing a machine learning application that identifies opportunities in the real estate market in real time, i.e., houses that are listed with a price substantially below the market price. This program can be useful for investors interested in the housing market. The application is formally implemented as a regression problem, that tries to estimate the market price of a house given features retrieved from public online listings. For building this application, we have performed a feature engineering stage in order to discover relevant features that allows attaining a high predictive performance. Several machine learning algorithms have been tested, including regression trees, k-NN and neural networks, identifying advantages and handicaps of each of them.


Machine learning for Java developers, Part 2: Deploying your machine learning model

#artificialintelligence

My previous tutorial, "Machine Learning for Java developers," introduced setting up a machine learning algorithm and developing a prediction function in Java. I demonstrated the inner workings of a machine learning algorithm and walked through the process of developing and training a machine learning model. This tutorial picks up where that one left off. I'll show you how to set up a machine learning data pipeline, introduce a step-by-step process for taking your machine learning model from development into production, and briefly discuss technologies for deploying a trained machine learning model in a Java-based production environment. Deploying a machine learning model is a separate endeavor from developing one, often implemented by a different team.


Housing Market Prediction Problem using Different Machine Learning Algorithms: A Case Study

arXiv.org Machine Learning

Developing an accurate prediction model for housing prices is always needed for socioeconomic development and wellbeing of citizens. In this paper, a diverse set of machine learning algorithms such as XGBoost, CatBoost, Random Forest, Lasso, Voting Regressor, and others, are being employed to predict the housing prices using public available datasets. The housing datasets of 62,723 records from January 2015 to November 2019 is obtained from the Florida's Volusia County Property Appraiser website. The records are publicly available and include the real estate/economic database, maps, and other associated information. The database is usually updated weekly according to the State of Florida regulations. Then, the housing price prediction models using machine learning techniques are developed and their regression model performances are compared. Finally, an improved housing price prediction model for assisting the housing market is proposed. Particularly, a house seller/buyer or a real estate broker can get insight in making better-informed decisions considering the housing price prediction. Keywords: Housing Price Prediction, Machine Learning Algorithms, XGBoost Method, Target Binning. 1) Introduction Starting with 2005, the increasing interest rates in the U.S. housing market have slowed down the market considerably. Particularly, the investment bank Lehman Brothers Holdings was affected significantly, and forced into bankruptcy in 2008. This resulted in a sharp decline in the housing prices and, combined with the subprime mortgage crisis, increased the slowing down of the economy and weakened the asset values, which ultimately led to the depreciation of the global housing market and caused a global crisis (Park & Kwon Bae, 2015). Consequently, economists turned their attention to predicting these types of threats that could jeopardize the economic stability.


Training ML models on Melbourne Housing Prices dataset

#artificialintelligence

The Dataset is downloaded from Kaggle, it is collected for housing prices in Melbourne, and includes Address, Type of Real estate, Suburb, Method of Selling, Rooms, Price, Real Estate Agent, Date of Sale and distance from C.B.D for different houses in Melbourne. Our task is to train a machine learning model and tune it for maximum accuracy, so that it can be used to predict the likely price of an unsold house. I use two approaches to solve this problem: Regression and Classification. I explore the data by using the describe and head commands in sklearn, this tells me that some of the features in the data are categorical variables, so they might need to be encoded. The variables Car, YearBuilt, BuildingArea and YearBuilt and Council Area seem to be the only ones with missing values, so we need to either remove the samples with missing values or impute them, since the missing values comprise a relatively large part of the dataset, greater than 10%, I choose to impute the missing values instead.