Abstract
Finding a house for rent in a new city within the budget is a major issue especially for new college students and employees. In this scenario, an effective house rent prediction algorithm will be extremely beneficial. The rent for a house is affected by certain aspects such as number of rooms, distance from the market, region, availability of transport, and many more. With the help of different machine learning algorithms, the authors try to analyze, predict, and visualize the rent of a house. In this chapter, the authors have implemented multiple linear regression models and other ensemble learning methods like Adaboost regressor, random forest regressor, gradient boost regressor, and XGboost regressor to tune the overall model performance. The authors self-surveyed data set contains records of a city in West Bengal, India. So far, almost no work has been done in this context for Haldia. The authors' proposed house rent prediction model predicts rent with an accuracy of 98.20%.
TopLiterature Survey
Many researchers already developed various traditional systems or models for house rent prediction; few of them are mentioned below.
Key Terms in this Chapter
AdaBoost: It is also known as Adaptive boost is a technique in Machine learning used as an ensemble method.
RMSE (Root Mean Square Error): It is a frequently used measure of the differences between values (sample or population values) predicted by a model.
XGBoost: It stands for Extreme Gradient Boosting, is scalable, distributed gradient boosted decision tree machine learning library.
Real-Time Data: It is the information that is delivered immediately after collection.
R2 Score: It is a statistical measure in a regression model that determines the proportion of variance in the dependent variable that can be explained by the independent variable.
Regression: It is a supervised machine learning technique which is used to predict continuous values. Loss function is being calculated using Gradient Descent Algorithm.
Gradient Boost: It is a machine learning algorithm which relies on the intuition that the best possible next model, when combined with the previous models, minimizes the overall prediction error.