scholarly journals Click Prediction for Advertisement in Websites using Linear Regression

2021 ◽  
Vol 12 (4) ◽  
pp. 271-276
Author(s):  
D. Ramya , Et. al.
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Learning Algorithms ◽  
Vital Role ◽  
Machine Learning Algorithms ◽  
The Future ◽  
Online Advertisement ◽  
Commercial Value

Prediction is one of the most powerful and effective method used nowadays for improvement in business. Machine Learning Algorithms plays a vital role in predicting the future of business. It is widely used in the field of Marketing and Advertising fields also. The Commercial Value for the advertisement is gained based on the user click on the website. Digital advertisement and marketing play very important role in influencing the profit of business. Many Machine Learning algorithms were used for predicting and analyzing the online advertisement. In this paper, Linear Regression is used for predicting the user click on the advertisement.

scholarly journals Comparative Analysis of Supervised Machine Learning Algorithms to Build a Predictive Model for Evaluating Students’ Performance

2021 ◽  
Vol 17 (02) ◽  
pp. 90
Author(s):  
Inssaf El Guabassi ◽  
Zakaria Bousalem ◽  
Rim Marah ◽  
Aimad Qazdar
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Predictive Model ◽  
Learning Algorithms ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
The Future ◽  
Log Linear

In recent years, the world's population is increasingly demanding to predict the future with certainty, predicting the right information in any area is becoming a necessity. One of the ways to predict the future with certainty is to determine the possible future. In this sense, machine learning is a way to analyze huge datasets to make strong predictions or decisions. The main objective of this research work is to build a predictive model for evaluating students’ performance. Hence, the contributions are threefold. The first is to apply several supervised machine learning algorithms (i.e. ANCOVA, Logistic Regression, Support Vector Regression, Log-linear Regression, Decision Tree Regression, Random Forest Regression, and Partial Least Squares Regression) on our education dataset. The second purpose is to compare and evaluate algorithms used to create a predictive model based on various evaluation metrics. The last purpose is to determine the most important factors that influence the success or failure of the students. The experimental results showed that the Log-linear Regression provides a better prediction as well as the behavioral factors that influence students’ performance.

scholarly journals Mean Received Resources Meet Machine Learning Algorithms to Improve Link Prediction Methods

Information ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 35
Author(s):  
Jibouni Ayoub ◽  
Dounia Lotfi ◽  
Ahmed Hammouch
Keyword(s):  
Machine Learning ◽  
Link Prediction ◽  
Learning Algorithms ◽  
Area Under The Curve ◽  
Machine Learning Algorithms ◽  
Actual State ◽  
The Future ◽  
Auc Value ◽  
The Mean ◽  
Analysis Of Social Networks

The analysis of social networks has attracted a lot of attention during the last two decades. These networks are dynamic: new links appear and disappear. Link prediction is the problem of inferring links that will appear in the future from the actual state of the network. We use information from nodes and edges and calculate the similarity between users. The more users are similar, the higher the probability of their connection in the future will be. The similarity metrics play an important role in the link prediction field. Due to their simplicity and flexibility, many authors have proposed several metrics such as Jaccard, AA, and Katz and evaluated them using the area under the curve (AUC). In this paper, we propose a new parameterized method to enhance the AUC value of the link prediction metrics by combining them with the mean received resources (MRRs). Experiments show that the proposed method improves the performance of the state-of-the-art metrics. Moreover, we used machine learning algorithms to classify links and confirm the efficiency of the proposed combination.

scholarly journals Stock Market Prediction using Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 3628-3632
Author(s):  
Prof. Gowrishankar B S
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Stock Price ◽  
Predictive Analytics ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Stock Market Prediction ◽  
The Future ◽  
Complicated Model

Stock market is one of the most complicated and sophisticated ways to do business. Small ownerships, brokerage corporations, banking sectors, all depend on this very body to make revenue and divide risks; a very complicated model. However, this paper proposes to use machine learning algorithms to predict the future stock price for exchange by using pre-existing algorithms to help make this unpredictable format of business a little more predictable. The use of machine learning which makes predictions based on the values of current stock market indices by training on their previous values. Machine learning itself employs different models to make prediction easier and authentic. The data has to be cleansed before it can be used for predictions. This paper focuses on categorizing various methods used for predictive analytics in different domains to date, their shortcomings.

scholarly journals A Review on Linear Regression Comprehensive in Machine Learning

2020 ◽  
Vol 1 (4) ◽  
pp. 140-147
Author(s):  
Dastan Maulud ◽  
Adnan M. Abdulazeez
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Linear Relationship ◽  
Multiple Regression ◽  
Polynomial Regression ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Explanatory Variables ◽  
Almost All ◽  
Simple Regression

Perhaps one of the most common and comprehensive statistical and machine learning algorithms are linear regression. Linear regression is used to find a linear relationship between one or more predictors. The linear regression has two types: simple regression and multiple regression (MLR). This paper discusses various works by different researchers on linear regression and polynomial regression and compares their performance using the best approach to optimize prediction and precision. Almost all of the articles analyzed in this review is focused on datasets; in order to determine a model's efficiency, it must be correlated with the actual values obtained for the explanatory variables.

scholarly journals Survey on machine learning algorithms for liver disease diagnosis and prediction

2018 ◽  
Vol 7 (1.8) ◽  
pp. 99 ◽  
Author(s):  
M Kiran Kumar ◽  
M Sreedevi ◽  
Y C. A. Padmanabha Reddy
Keyword(s):  
Machine Learning ◽  
Liver Disease ◽  
Learning Algorithms ◽  
Disease Diagnosis ◽  
Computer Aided Diagnosis ◽  
Vital Role ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Computer Aided ◽  
Aided Diagnosis

Machine learning plays a vital role in health care industry. It is very important in Computer Aided Diagnosis. Computer Aided Diagnosis is a quickly developing dynamic region of research in medicinal industry. The current specialists in machine learning guarantee the enhanced precision of discernment and analysis of diseases. The computers are empowered to think by creating knowledge by learning. This procedure enables the computers to self-learn individually without being explicitly programed by the programmer .There are numerous sorts of Machine Learning Techniques and which are utilized to classify the data sets. They are Supervised, Unsupervised and Semi-Supervised, Reinforcement, deep learning algorithms. The principle point of this paper is to give comparative analysis of supervised learning algorithms in medicinal area and few of the techniques utilized as a part of liver disease prediction.

scholarly journals Analyzing the Performance Factors of Machine Learning Algorithms for COVID'19 Data

2020 ◽  
Vol 9 (9) ◽  
pp. 149-155
Keyword(s):  
Machine Learning ◽  
Supervised Learning ◽  
Learning Process ◽  
Learning Algorithms ◽  
Vital Role ◽  
Machine Learning Algorithms ◽  
Algorithm Selection ◽  
Data Set ◽  
Performance Factors ◽  
Selection Decision

Machine learning is a branch of Artificial intelligence which provides algorithms that can learn from data and improve from experience, without human intervention. Now a day's many of the machine learning algorithms playing a vital role in data analytics. Such algorithms are possible to apply with the recent pandemic COVID situation across the globe. Machine learning algorithms are classified into 3 different groups based on the type of learning process, such as supervised learning, unsupervised learning, and reinforcement learning. By considering the medical observations on the COVID across the globe it has been discussed and concluded to analyze under the supervised learning process. The data set is acquired from the reliable source, it is processed and fed into the classification algorithms. Since learning behaviors are carried out by knowing the input data and expected output data. The data is labeled and has been classified based on labels. In the proposed work, three different algorithms are used to experiment with the COVID'19 dataset and compared for their efficiency and algorithm selection decision is made.

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 Evaluation and calibration of a low-cost particle sensor in ambient conditions using machine-learning methods

2020 ◽  
Vol 13 (4) ◽  
pp. 1693-1707 ◽  
Author(s):  
Minxing Si ◽  
Ying Xiong ◽  
Shan Du ◽  
Ke Du
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Random Search ◽  
Low Cost ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Ambient Conditions ◽  
Test Dataset ◽  
Compact Size ◽  
Significant Difference

Abstract. Particle sensing technology has shown great potential for monitoring particulate matter (PM) with very few temporal and spatial restrictions because of its low cost, compact size, and easy operation. However, the performance of low-cost sensors for PM monitoring in ambient conditions has not been thoroughly evaluated. Monitoring results by low-cost sensors are often questionable. In this study, a low-cost fine particle monitor (Plantower PMS 5003) was colocated with a reference instrument, the Synchronized Hybrid Ambient Real-time Particulate (SHARP) monitor, at the Calgary Varsity air monitoring station from December 2018 to April 2019. The study evaluated the performance of this low-cost PM sensor in ambient conditions and calibrated its readings using simple linear regression (SLR), multiple linear regression (MLR), and two more powerful machine-learning algorithms using random search techniques for the best model architectures. The two machine-learning algorithms are XGBoost and a feedforward neural network (NN). Field evaluation showed that the Pearson correlation (r) between the low-cost sensor and the SHARP instrument was 0.78. The Fligner and Killeen (F–K) test indicated a statistically significant difference between the variances of the PM2.5 values by the low-cost sensor and the SHARP instrument. Large overestimations by the low-cost sensor before calibration were observed in the field and were believed to be caused by the variation of ambient relative humidity. The root mean square error (RMSE) was 9.93 when comparing the low-cost sensor with the SHARP instrument. The calibration by the feedforward NN had the smallest RMSE of 3.91 in the test dataset compared to the calibrations by SLR (4.91), MLR (4.65), and XGBoost (4.19). After calibrations, the F–K test using the test dataset showed that the variances of the PM2.5 values by the NN, XGBoost, and the reference method were not statistically significantly different. From this study, we conclude that a feedforward NN is a promising method to address the poor performance of low-cost sensors for PM2.5 monitoring. In addition, the random search method for hyperparameters was demonstrated to be an efficient approach for selecting the best model structure.

scholarly journals Using Big Data-machine learning models for diabetes prediction and flight delays analytics

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Thérence Nibareke ◽  
Jalal Laassiri
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Linear Regression ◽  
Decision Tree ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Smart Devices ◽  
Learning Models ◽  
Flight Delays ◽  
Machine Learning Models

Abstract Introduction Nowadays large data volumes are daily generated at a high rate. Data from health system, social network, financial, government, marketing, bank transactions as well as the censors and smart devices are increasing. The tools and models have to be optimized. In this paper we applied and compared Machine Learning algorithms (Linear Regression, Naïve bayes, Decision Tree) to predict diabetes. Further more, we performed analytics on flight delays. The main contribution of this paper is to give an overview of Big Data tools and machine learning models. We highlight some metrics that allow us to choose a more accurate model. We predict diabetes disease using three machine learning models and then compared their performance. Further more we analyzed flight delay and produced a dashboard which can help managers of flight companies to have a 360° view of their flights and take strategic decisions. Case description We applied three Machine Learning algorithms for predicting diabetes and we compared the performance to see what model give the best results. We performed analytics on flights datasets to help decision making and predict flight delays. Discussion and evaluation The experiment shows that the Linear Regression, Naive Bayesian and Decision Tree give the same accuracy (0.766) but Decision Tree outperforms the two other models with the greatest score (1) and the smallest error (0). For the flight delays analytics, the model could show for example the airport that recorded the most flight delays. Conclusions Several tools and machine learning models to deal with big data analytics have been discussed in this paper. We concluded that for the same datasets, we have to carefully choose the model to use in prediction. In our future works, we will test different models in other fields (climate, banking, insurance.).

scholarly journals Crop Selection and it’s Yield Prediction

2020 ◽  
Vol 8 (6) ◽  
pp. 4126-4128
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Decision Tree ◽  
Vital Role ◽  
Machine Learning Algorithms ◽  
Yield Prediction ◽  
Crop Selection ◽  
Proposed Model ◽  
Approximate Percentage ◽  
Selection Of

The field of Agriculture plays a major role in the Indian economy. This sector helps to meet the basic needs of human and their civilization. Hence agriculture would be the enterprise in the globe. Considering the parameters of the agriculture, selection of crops plays a very vital role in farming. The proposed model for Crop selection and it’s yield prediction mainly focusses on the season and location to display the desired crop for cultivation . This requirement is implemented with Machine Learning algorithms like Decision tree for classification and Linear regression for yield prediction to maximize the crop yield. This model helps the farmers to know about the correct crop to be cultivated in a particular location . And also gives a approximate percentage of yield based on the data available in the dataset. Thus selecting crop for cultivation becomes a easier task for farmers because selection of correct crop for their location is precisely implemented using this project.

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