A value count on the target label shows that only 3.5% of the transactions are labeled fraudulent. Typically, fraudulent transactions make up a small percentage of transactions. Correlation can help you understand the linear relationship between features and between features and the target. A correlation can range between -1 (perfect negative relationship) and 1 (perfect positive relationship), with 0 indicating no straight-line relationship. Visualizing the data helps with feature selection by revealing trends in the data.
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.
Reading through a data science book or taking a course, it can feel like you have the individual pieces, but don't quite know how to put them together. Taking the next step and solving a complete machine learning problem can be daunting, but preserving and completing a first project will give you the confidence to tackle any data science problem. This series of articles will walk through a complete machine learning solution with a real-world dataset to let you see how all the pieces come together. We'll follow the general machine learning workflow step-by-step: Along the way, we'll see how each step flows into the next and how to specifically implement each part in Python. The complete project is available on GitHub, with the first notebook here. After completing the work, I was offered the job, but then the CTO of the company quit and they weren't able to bring on any new employees. I guess that's how things go on the start-up scene!) The first step before we get coding is to understand the problem we are trying to solve and the available data. In this project, we will work with publicly available building energy data from New York City. The objective is to use the energy data to build a model that can predict the Energy Star Score of a building and interpret the results to find the factors which influence the score. We want to develop a model that is both accurate *-- it can predict the Energy Star Score close to the true value -- and *interpretable -- we can understand the model predictions. Once we know the goal, we can use it to guide our decisions as we dig into the data and build models. Contrary to what most data science courses would have you believe, not every dataset is a perfectly curated group of observations with no missing values or anomalies (looking at you mtcars and iris datasets). Real-world data is messy which means we need to clean and wrangle it into an acceptable format before we can even start the analysis.