House Price Prediction using Machine Learning Algorithms

House prices are a major financial decision for everyone involved in the housing market, including potential home buyers. A major part of the real estate industry is housing. An accurate housing price prediction is a valuable tool for buyer and seller as well as real estate agents. The study is done for the purpose of knowledge among the people to understand and estimate the pricing of their houses. The prediction will be made using four machine learning algorithms such as linear regression, polynomial regression, random forest, decision tree. Linear Regression has good interpretability. Decision tree is a graphical representation of all possible solutions. Polynomial regression can be easily fitted to a wide variety of curves. Regression and classification issues are resolved with random forests .Among the given algorithm, Random forest provides better accuracy of about 89% for given dataset

Estimating house price with spatial and land use accessibility components using a data science approach at the national scale

Extensive research had been conducted studying the spatial configuration effects on house price using the hedonic price approach. Previous research has mostly focused on using econometric approaches in estimating house price. With the growing popularity of machine learning methods, there is an opportunity to study this problem from a data science perspective. Following Law et al (2017) which studied how economic value of closeness centrality (integration) differed across cities in England, we conduct here a similar experiment examining these differences using a data science approach. We leveraged on an integrated urban model, a large-scale geographic database to compute a series of land use accessibility and space syntax accessibility measures at the country scale (~120 measures). We then use a compressed set of spatial and land use accessibility components to estimate a set of hedonic price models in England; i. first for the entire country, then ii. for all 22 cities and then iii. for 22 cities individually. We found that spatial and land use accessibility features improve house price prediction accuracy jointly and the improvements are greater when using nonlinear methods. This research serves as a basis on the application of data science approaches in space syntax research for predicting real estate outcomes at the National-Scale

Customer Churn Prediction of Telecom Company Using Machine Learning Algorithms

We can’t escape the fact that using telecommunications has become a significant part of our everyday lives. Since the Covid-19 pandemic, the telecommunication industry has become crucial.  Hence, the industry now enjoys growth opportunities. In this study, KNN, Random Forest (RF), AdaBoost, Logistic Regression (LR), XGBoost, and Support Vector Machine (SVM) are 6 supervised machine learning algorithms that will be used in this study to predict the customer churn of a telecom company in California. The goal of this study is to identify the classifier that predicts customer churn the most effectively. As evidenced by its accuracy of 79.67%, precision of 64.67%, recall of 51.87%, and F1-score of 57.57%, XGBoost is the overall most effective classifier in this study. Next, the purpose of this study is to identify the characteristics of customers who are most likely to leave the telecom company. These characteristics were discovered based on customers’ demographics and account information. Lastly, this study also provides the company with advice on how to retain customers. The study advises company to personalize the customer experience, implement a customer loyalty program, and apply AI in customer relationship management in retaining customers

Machine Learning for Predicting the Prices of Dwellings in Small and Large Cities of Finland

Dwellings are one of the most expensive purchases individuals make in their lifetime. Additionally, dwellings can be considered as investment assets. Therefore, it is important to be able to precisely appraise the value of a dwelling, a task which can be achieved through the use of machine learning techniques. The first part of the study is a literature review which covers dwelling market dynamics, pricing of dwellings and use of machine learning in the field. The second part of the thesis presents a study on the Finnish dwelling markets, with a focus on the development of machine learning models for predicting dwelling prices in both large and small Finnish cities. Cities that have over 100,000 residents are considered as large cities and less than 100,000 residents small cities. The research datasets are divided based on the size of the cities into three datasets, with one containing all observations, one containing observations only from large cities, and one containing observations from small cities. The study tests different machine learning algorithms with each dataset and compares the best performing models of each dataset. The results show that the XGBoost algorithm is the best performing algorithm for predicting dwelling prices in Finnish cities. Furthermore, the study found that the importance of residents having a master’s degree in a district decreases in small cities, while it is the most important feature in large cities

Estimating UK House Prices using Machine Learning

House price estimation is an important subject for property owners, property developers, investors and buyers. It has featured in many academic research papers and some government and commercial reports. The price of a house may vary depending on several features including geographic location, tenure, age, type, size, market, etc. Existing studies have largely focused on applying single or multiple machine learning techniques to single or groups of datasets to identify the best performing algorithms, models and/or most important predictors, but this paper proposes a cumulative layering approach to what it describes as a Multi-feature House Price Estimation (MfHPE) framework. The MfHPE is a process-oriented, data-driven and machine learning based framework that does not just identify the best performing algorithms or features that drive the accuracy of models but also exploits a cumulative multi-feature layering approach to creating machine learning models, optimising and evaluating them so as to produce tangible insights that enable the decision-making process for stakeholders within the housing ecosystem for a more realistic estimation of house prices. Fundamentally, the MfHPE framework development leverages the Design Science Research Methodology (DSRM) and HM Land Registry’s Price Paid Data is ingested as the base transactions data. 1.1 million London-based transaction records between January 2011 and December 2020 have been exploited for model design, optimisation and evaluation, while 84,051 2021 transactions have been used for model validation. With the capacity for updates to existing datasets and the introduction of new datasets and algorithms, the proposed framework has also leveraged a range of neighbourhood and macroeconomic features including the location of rail stations, supermarkets, bus stops, inflation rate, GDP, employment rate, Consumer Price Index (CPIH) and unemployment rate to explore their impact on the estimation of house prices and their influence on the behaviours of machine learning algorithms. Five machine learning algorithms have been exploited and three evaluation metrics have been used. Results show that the layered introduction of new variety of features in multiple tiers led to improved performance in 50% of models, a change in the best performing models as new variety of features are introduced, and that the choice of evaluation metrics should not just be based on technical problem types but on three components: (i) critical business objectives or project goals; (ii) variety of features; and (iii) machine learning algorithms