scholarly journals A Comparative Study on House Price Prediction

2020 ◽  
Vol 6 (12) ◽  
pp. 103-107
Author(s):  
Akash Dagar and Shreya Kapoor
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Medical Diagnosis ◽  
Learning Algorithm ◽  
House Price ◽  
Machine Learning Algorithms ◽  
Random Forest Regression ◽  
Image Detection ◽  
Price Prediction ◽  
Normal Speech

Machine learning plays a major role from past years in image detection, spam reorganization, normal speech command, product recommendation and medical diagnosis. Present machine learning algorithm helps us in enhancing security alerts, ensuring public safety and improve medical enhancements. Due to increase in urbanization, there is an increase in demand for renting houses and purchasing houses. Therefore, to determine a more effective way to calculate house price accurately is the need of the hour. So, an effort has been made to determine the most accurate way of predicting house price by using machine learning algorithms: Multivariable Linear Regression, Decision Tree Regression and Random Forest Regression and it is determined that Multivariable Linear Regression has showed most accuracy and less error.

scholarly journals Automatic solution for solar cell photo-current prediction using machine learning

2021 ◽  
Vol 297 ◽  
pp. 01029
Author(s):  
Mohammed Azza ◽  
Jabran Daaif ◽  
Adnane Aouidate ◽  
El Hadi Chahid ◽  
Said Belaaouad
Keyword(s):  
Machine Learning ◽  
Solar Cell ◽  
Linear Regression ◽  
Prediction Model ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Prediction Methods ◽  
Lasso Regression ◽  
Photo Current

In this paper, we discuss the prediction of future solar cell photo-current generated by the machine learning algorithm. For the selection of prediction methods, we compared and explored different prediction methods. Precision, MSE and MAE were used as models due to its adaptable and probabilistic methodology on model selection. This study uses machine learning algorithms as a research method that develops models for predicting solar cell photo-current. We create an electric current prediction model. In view of the models of machine learning algorithms for example, linear regression, Lasso regression, K Nearest Neighbors, decision tree and random forest, watch their order precision execution. In this point, we recommend a solar cell photocurrent prediction model for better information based on resistance assessment. These reviews show that the linear regression algorithm, given the precision, reliably outperforms alternative models in performing the solar cell photo-current prediction Iph

scholarly journals Predicting Future Products Rate using Machine Learning Algorithms

2020 ◽  
Vol 12 (5) ◽  
pp. 41-51
Author(s):  
Shaimaa Mahmoud ◽  
◽  
Mahmoud Hussein ◽  
Arabi Keshk
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Linear Regression ◽  
Mean Squared Error ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Random Forest Regression ◽  
Data Set ◽  
Squared Error

Opinion mining in social networks data is considered as one of most important research areas because a large number of users interact with different topics on it. This paper discusses the problem of predicting future products rate according to users’ comments. Researchers interacted with this problem by using machine learning algorithms (e.g. Logistic Regression, Random Forest Regression, Support Vector Regression, Simple Linear Regression, Multiple Linear Regression, Polynomial Regression and Decision Tree). However, the accuracy of these techniques still needs to be improved. In this study, we introduce an approach for predicting future products rate using LR, RFR, and SVR. Our data set consists of tweets and its rate from 1:5. The main goal of our approach is improving the prediction accuracy about existing techniques. SVR can predict future product rate with a Mean Squared Error (MSE) of 0.4122, Linear Regression model predict with a Mean Squared Error of 0.4986 and Random Forest Regression can predict with a Mean Squared Error of 0.4770. This is better than the existing approaches accuracy.

scholarly journals Housing-Price Prediction in Colombia using Machine Learning

2021 ◽  
Author(s):  
MIGUEL ANGEL CORREA MANRIQUE ◽  
Omar Becerra Sierra ◽  
Daniel Otero Gomez ◽  
Henry Laniado ◽  
Rafael Mateus C ◽  
...  
Keyword(s):  
Machine Learning ◽  
Evaluation Studies ◽  
Housing Prices ◽  
House Price ◽  
Housing Price ◽  
Machine Learning Algorithms ◽  
Learning Tools ◽  
Price Prediction ◽  
Statistical Indicator ◽  
Real Estate Company

It is a common practice to price a house without proper evaluation studies being performed for assurance. That is why the purpose of this study provide an explanatory model by establishing parameters for accuracy in interpretation and projection of housing prices. In addition, it is intentioned to establish proper data preprocessing practices in order to increase the accuracy of machine learning algorithms. Indeed, according to our literature review, there are few articles and reports on the use of Machine Learning tools for the prediction of property prices in Colombia. The dataset in which the research is built upon was provided by an existing real estate company. It contains near 940,000 items (housing advertisements) posted on the platform from the year 2018 to 2020. The database was enriched using statistical imputation techniques. Housing prices prediction was performed using Decision Tree Regressors and LightGBM methods, thus deriving in better alternatives for house price prediction in Colombia. Moreover, to measure the accuracy of the proposed models, the Root Mean Squared Logarithmic Error (RMSLE) statistical indicator was used. The best cross validation results obtained were 0.25354±0.00699 for the LightGBM, 0.25296 ±0.00511 for the Bagging Regressor, and 0.25312±0.00559 for the ExtraTree Regressor with Bagging Regressor, and it was not found a statistical difference between their performances.

scholarly journals Natural Gas Price Prediction Using Machine Learning

2021 ◽  
Vol 9 (VIII) ◽  
pp. 126-134
Author(s):  
Sanjana G P
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Natural Gas ◽  
Machine Learning Algorithms ◽  
Multilinear Regression ◽  
Random Forest Regression ◽  
Price Prediction ◽  
Proposed Model ◽  
Natural Gas Prices ◽  
Gas Price

Natural gas varies with season. In addition, natural gas supply, demand, storage, and imports are important indicators related to natural gas price. There are plenty of methods for analyzing and forecasting natural gas prices and machine learning is increasingly used. Machine learning algorithms can learn from historical relationships and trends in the data and make data-driven predictions or decisions. Here a new model for predicting price for natural gas by using Machine Learning concepts. Here some algorithms have been used to build the proposed model: Random Forest Regression, Linear Regression, Decision Tree, Multilinear Regression. By using the algorithm, a Flask model has been implemented and tested. The results have been discussed and a full comparison between algorithms was conducted. Random forest Regression was selected as best algorithm based on accuracy.

scholarly journals Stock Prediction Analysis by using Linear Regression Machine Learning Algorithm

2020 ◽  
Vol 9 (4) ◽  
pp. 841-844
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Stock Market ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Government Policies ◽  
Machine Learning Algorithm ◽  
Machine Learn ◽  
Market Sentiment ◽  
Industry Performance

Stock market is varying day to day. Many factors such as government policies, industry performance, market sentiment etc are the main cause of up and downs in stock market. To invest money in stock market, study and analysis of stock market is essential. This type of analysis can be done by using Machine learning algorithms. The main objective of this paper is to predict the stock market future values by using linear regression machine learn algorithms based on past values. The methodology is developed and implemented in python on APPLE and TSLA stock.

Intelligent system of English composition scoring model based on improved machine learning algorithm

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment System ◽  
English Composition ◽  
Region Extraction ◽  
Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

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