COMPARING THE PRACTICAL DIFFERENCES BETWEEN DECISION TREE AND RANDOM FOREST ALGORITHMS IN ESTIMATING HOUSING PRICES

Çağrı Sel

Article Details

COMPARING THE PRACTICAL DIFFERENCES BETWEEN DECISION TREE AND RANDOM FOREST ALGORITHMS IN ESTIMATING HOUSING PRICES

Authors:

Çağrı Sel

Publisher:

Karabuk University

Main Topics:

Machine Learning Algorithms

Journal:

Current Trends in Computing Vol.1 Issue 2, pp.149-157

Document Type:

Article

Publication Date:

2/2024

Document Language:

English

Document ID (Org No.):

2001311379

pISSN:

2980-3152

Keywords:

Abstract:

 The process of estimating the price of houses is becoming increasingly important in light of the changing economy worldwide, as houses are considered a basic need and a source of investment. This process is aimed at preventing losses, market monitoring, minimizing problems, and arriving at accurate conclusions in the face of complex structures and issues. To achieve this, modern technology introduces the concepts of artificial intelligence and machine learning, which are integrated into all areas of life, to make progress in the process. Although machine learning and the algorithms used in this field have become widespread in recent years, there are still not enough studies on housing pricing. At the same time, people remain unaware of the field of machine learning and its applicability in every sector. Machine learning in general; It expands the data pool and enables new prediction results to be created by making future predictions based on data. Decision Tree Algorithm; In addition to facilitating understanding and interpretation in every field, it can handle multi-output problems and minimize preparation with its easy integration structure. Random Forest Algorithm can prevent the overfitting problem in classification problems and can be applied in both regression and classification problems. The aim of the study is to popularize the use of machine learning algorithms in the real estate sector. This will allow for effective housing price predictions during times of uncertainty and help in selecting the appropriate method by comparing the algorithms. Additionally, this study aims to reduce existing problems using these algorithms. A data set called California Housing Prices, containing 20.640 samples and eight features, was used in this study. The results of the Decision Tree and Random Forest Algorithms were examined on this data set. Performance evaluation and comparison were made using MSE, RMSE, R2, and MAE metrics. It was observed that the Random Forest Algorithm produced better results and was superior to the Decision Tree Method when predicting house prices.

 The process of estimating the price of houses is becoming increasingly important in light of the changing economy worldwide, as houses are considered a basic need and a source of investment. This process is aimed at preventing losses, market monitoring, minimizing problems, and arriving at accurate conclusions in the face of complex structures and issues. To achieve this, modern technology introduces the concepts of artificial intelligence and machine learning, which are integrated into all areas of life, to make progress in the process. Although machine learning and the algorithms used in this field have become widespread in recent years, there are still not enough studies on housing pricing. At the same time, people remain unaware of the field of machine learning and its applicability in every sector. Machine learning in general; It expands the data pool and enables new prediction results to be created by making future predictions based on data. Decision Tree Algorithm; In addition to facilitating understanding and interpretation in every field, it can handle multi-output problems and minimize preparation with its easy integration structure. Random Forest Algorithm can prevent the overfitting problem in classification problems and can be applied in both regression and classification problems. The aim of the study is to popularize the use of machine learning algorithms in the real estate sector. This will allow for effective housing price predictions during times of uncertainty and help in selecting the appropriate method by comparing the algorithms. Additionally, this study aims to reduce existing problems using these algorithms. A data set called California Housing Prices, containing 20.640 samples and eight features, was used in this study. The results of the Decision Tree and Random Forest Algorithms were examined on this data set. Performance evaluation and comparison were made using MSE, RMSE, R2, and MAE metrics. It was observed that the Random Forest Algorithm produced better results and was superior to the Decision Tree Method when predicting house prices.

 The process of estimating the price of houses is becoming increasingly important in light of the changing economy worldwide, as houses are considered a basic need and a source of investment. This process is aimed at preventing losses, market monitoring, minimizing problems, and arriving at accurate conclusions in the face of complex structures and issues. To achieve this, modern technology introduces the concepts of artificial intelligence and machine learning, which are integrated into all areas of life, to make progress in the process. Although machine learning and the algorithms used in this field have become widespread in recent years, there are still not enough studies on housing pricing. At the same time, people remain unaware of the field of machine learning and its applicability in every sector. Machine learning in general; It expands the data pool and enables new prediction results to be created by making future predictions based on data. Decision Tree Algorithm; In addition to facilitating understanding and interpretation in every field, it can handle multi-output problems and minimize preparation with its easy integration structure. Random Forest Algorithm can prevent the overfitting problem in classification problems and can be applied in both regression and classification problems. The aim of the study is to popularize the use of machine learning algorithms in the real estate sector. This will allow for effective housing price predictions during times of uncertainty and help in selecting the appropriate method by comparing the algorithms. Additionally, this study aims to reduce existing problems using these algorithms. A data set called California Housing Prices, containing 20.640 samples and eight features, was used in this study. The results of the Decision Tree and Random Forest Algorithms were examined on this data set. Performance evaluation and comparison were made using MSE, RMSE, R2, and MAE metrics. It was observed that the Random Forest Algorithm produced better results and was superior to the Decision Tree Method when predicting house prices.

 The process of estimating the price of houses is becoming increasingly important in light of the changing economy worldwide, as houses are considered a basic need and a source of investment. This process is aimed at preventing losses, market monitoring, minimizing problems, and arriving at accurate conclusions in the face of complex structures and issues. To achieve this, modern technology introduces the concepts of artificial intelligence and machine learning, which are integrated into all areas of life, to make progress in the process. Although machine learning and the algorithms used in this field have become widespread in recent years, there are still not enough studies on housing pricing. At the same time, people remain unaware of the field of machine learning and its applicability in every sector. Machine learning in general; It expands the data pool and enables new prediction results to be created by making future predictions based on data. Decision Tree Algorithm; In addition to facilitating understanding and interpretation in every field, it can handle multi-output problems and minimize preparation with its easy integration structure. Random Forest Algorithm can prevent the overfitting problem in classification problems and can be applied in both regression and classification problems. The aim of the study is to popularize the use of machine learning algorithms in the real estate sector. This will allow for effective housing price predictions during times of uncertainty and help in selecting the appropriate method by comparing the algorithms. Additionally, this study aims to reduce existing problems using these algorithms. A data set called California Housing Prices, containing 20.640 samples and eight features, was used in this study. The results of the Decision Tree and Random Forest Algorithms were examined on this data set. Performance evaluation and comparison were made using MSE, RMSE, R2, and MAE metrics. It was observed that the Random Forest Algorithm produced better results and was superior to the Decision Tree Method when predicting house prices.

Article Details

COMPARING THE PRACTICAL DIFFERENCES BETWEEN DECISION TREE AND RANDOM FOREST ALGORITHMS IN ESTIMATING HOUSING PRICES

Authors: Çağrı Sel

Publisher: Karabuk University

Main Topics: Machine Learning Algorithms

Journal: Current Trends in Computing Vol.1 Issue 2, pp.149-157

Document Type: Article

Publication Date: 2/2024

Document Language: English

Document ID (Org No.): 2001311379

pISSN: 2980-3152

Machine Learning

Decision Tree Algorithm

Random Forest Algorithm

Issues

Authors:

Çağrı Sel

Publisher:

Karabuk University

Main Topics:

Machine Learning Algorithms

Journal:

Current Trends in Computing Vol.1 Issue 2, pp.149-157

Document Type:

Article

Publication Date:

2/2024

Document Language:

English

Document ID (Org No.):

2001311379

pISSN:

2980-3152