House Price Prediction Using Exploratory Data Analysis and Machine Learning with Feature Selection

Basysyar, Fadhil Muhammad; Dwilestari, Gifthera

doi:10.56578/ataiml010103

Cited by 10 publications

(5 citation statements)

References 24 publications

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“…Pre-processing is one of the important stages in data analysis which often improves the quality of the results of a method [12][13][14][15][16]. In cluster analysis, pre-processing can be done with transformations to standardize data [12,13,16].…”

Section: Transformation Methods and Proximity Measurementioning

confidence: 99%

Block-Based K-Medoids Partitioning Method with Standardized Data to Improve Clustering Accuracy

Kariyam¹,

Abdurakhman²,

Subanar³

et al. 2022

MMEP

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Most of the existing k-medoid algorithms select the initial medoid randomly or use a specific formula based on the proximity matrix. This study proposes a block-based k-medoids partitioning method for clustering objects. To get the initial medoids, we search for an object representative from the block of the standard deviation and the sum of the variable values. We optimized the initial groups to update medoids, so this step can reduce the number of iterations to obtain partitioned data. The block-based k-medoids partitioning method applies to all types of data. To improve clustering accuracy, we operate pre-processing through data standardization. We conducted a series of experiments on eight real data sets and three artificial data to evaluate the proposed method's performance in terms of clustering accuracy. The experiment results show that the Block-based K-Medoids partitioning is more efficient in reducing the number of iterations. The clustering accuracy of the Block-KM for eight real datasets is also comparable to other methods. The data standardization is effective to increase clustering accuracy, especially for block k-medoids, k-means, simple and fast k-medoids, and the Ward method.

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Section: Transformation Methods and Proximity Measurementioning

confidence: 99%

Block-Based K-Medoids Partitioning Method with Standardized Data to Improve Clustering Accuracy

Kariyam¹,

Abdurakhman²,

Subanar³

et al. 2022

MMEP

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show abstract

“…In this study, we apply the suggested model to the UCF-Crime dataset [23], which contains a large amount of footage from public surveillance cameras documenting anomalous, unlawful, and violent behaviour in settings as diverse as schools, businesses, and streets. This dataset was chosen because its events are representative of those that occur often and in a variety of settings [24][25][26]. Furthermore, these aberrant behaviours can cause significant issues for both people and society.…”

Section: Datasetmentioning

confidence: 99%

Normalized Attention Neural Network with Adaptive Feature Recalibration for Detecting the Unusual Activities Using Video Surveillance Camera

Damera¹,

Vatambeti²,

Mekala³

et al. 2023

IJSSE

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Over the past few years, surveillance cameras have become common in many homes and businesses. Many businesses still employ a human monitor of their cameras, despite the fact that this individual is more probable to miss some anomalous occurrences in the video feeds owing to the inherent limitations of human perception. Numerous scholars have investigated surveillance data and offered several strategies for automatically identifying anomalous occurrences. Therefore, it is important to build a model for identifying unusual occurrences in the live stream from the security cameras. Recognizing potentially dangerous situations automatically so that appropriate action may be taken is crucial and can be of great assistance to law enforcement. In this research work, starting with an MRCNN for feature extraction and AFR for fine-tuning, this architecture has a number of key components (AFR). To increase the quality of the features extracted by the MRCNN, the AFR replicas the inter-dependencies among the features to enhance the quality of the low-and high-frequency features extracted. Then, a normalized attention network (NAN) is used to learn the relationships between channels, which used to identify the violence and speeds up the convergence process for training a perfect. Furthermore, the dataset took real-time security camera feeds from a variety of subjects and situations, as opposed to the hand-crafted datasets utilized in prior efforts. We also demonstrate the method's capability of assigning the correct category to each anomaly by classifying normal and abnormal occurrences. The method divided the information gathered into three primary groups: those in need of fire protection, those experiencing theft or violence, and everyone else. The study applied the proposed approach to the UCF-Crime dataset, where it outperformed other models on the same dataset.

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“…Although not all solvers support all penalty types, the type of penalty may be chosen through the "penalty" parameter with values of "l1", "l2", or "elasticnet" (e.g., both). The "C" option allows the penalty's coefficient weighting to be modified [23].…”

Section: Logistic Regression (Lr)mentioning

confidence: 99%

Implementation of Blockchain with Machine Learning Intrusion Detection System for Defending IoT Botnet and Cloud Networks

Siddamsetti¹,

Srivenkatesh²

2022

ISI

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Significant research has been done on combining intrusion detection and blockchain to increase data privacy and find both current and future threats. This research suggests a machine blockchain framework (MBF) in order to provide distributed intrusion detection with security and blockchain with privacy with the help of smart contracts in IoT networks. An XGBoost algorithm was implemented to work with sequential network data and the intrusion detection approach is explored using the N-BaIoT dataset. In order to protect the network against known malware threats (Mirai, Gafgyt, or Bashlite), the IoT malware attack prediction model created in this study offers a deterrent strategy based on the network traffic statistics. On the other hand, the models need to be taught to recognize new varieties of malware. In this work, we observe how different machine learning models, like Random Forest algorithm and proposed XGBoost algorithm, can accurately predict the infected malware in certain traffic instance. However, we provide a honeypot-based strategy that employs machine learning techniques for the detection of malware in this study. Using data from an IoT Botnet as a dataset helps train a machine learning model in a way that is effective and changes over time.

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House Price Prediction Using Exploratory Data Analysis and Machine Learning with Feature Selection

Cited by 10 publications

References 24 publications

Block-Based K-Medoids Partitioning Method with Standardized Data to Improve Clustering Accuracy

Block-Based K-Medoids Partitioning Method with Standardized Data to Improve Clustering Accuracy

Normalized Attention Neural Network with Adaptive Feature Recalibration for Detecting the Unusual Activities Using Video Surveillance Camera

Implementation of Blockchain with Machine Learning Intrusion Detection System for Defending IoT Botnet and Cloud Networks

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