scholarly journals A Comparative Study of Bitcoin Price Prediction Using Machine Learning Algorithms

2020 ◽  
pp. 160-163
Author(s):  
M. Akhil Sai ◽  
K. Sarath Chandra Sai ◽  
M. Manu Koushik ◽  
K. Gowri Raghavendra Narayan
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Comparative Study ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Price Prediction ◽  
The Cost ◽  
Day By Day

ML and AI-helped exchanging have pulled in developing enthusiasm for as far back as not many years.We examine day-by-day information for different digital currencies over some stretch of time. We show that straightforward exchanging methodologies helped by innovative AI calculations outflank standard benchmarks. We have picked two Machine Learning Algorithms to play out a Comparative Study to foresee cost of a Bitcoin; we have utilized Decision tree regressor and LSTM Algorithms and watched execution of every calculation as far as anticipating the cost of Bitcoin. We saw that Decision tree regressor gives progressively effective and precise outcomes when contrasted with others.

COMPARATIVE STUDY OF VARIOUS MACHINE LEARNING ALGORITHMS FOR TWEET CLASSIFICATION

2019 ◽  
Vol 6 (4) ◽  
pp. 12
Author(s):  
ABUBAKAR UMAR ◽  
A. BASHIR SULAIMON ◽  
BASHIR ABDULLAHI MUHAMMAD ◽  
S. ADEBAYO OLAWALE ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Tweet Classification

scholarly journals Crop price prediction using supervised machine learning algorithms

2021 ◽  
Vol 1916 (1) ◽  
pp. 012042
Author(s):  
Ranjani Dhanapal ◽  
A AjanRaj ◽  
S Balavinayagapragathish ◽  
J Balaji
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Price Prediction

scholarly journals Performance Improvement of Decision Tree: A Robust Classifier Using Tabu Search Algorithm

2021 ◽  
Vol 11 (15) ◽  
pp. 6728
Author(s):  
Muhammad Asfand Hafeez ◽  
Muhammad Rashid ◽  
Hassan Tariq ◽  
Zain Ul Abideen ◽  
Saud S. Alotaibi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Tabu Search ◽  
Decision Tree ◽  
Decision Trees ◽  
Search Algorithm ◽  
Learning Algorithms ◽  
Performance Comparison ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Tabu Search Algorithm

Classification and regression are the major applications of machine learning algorithms which are widely used to solve problems in numerous domains of engineering and computer science. Different classifiers based on the optimization of the decision tree have been proposed, however, it is still evolving over time. This paper presents a novel and robust classifier based on a decision tree and tabu search algorithms, respectively. In the aim of improving performance, our proposed algorithm constructs multiple decision trees while employing a tabu search algorithm to consistently monitor the leaf and decision nodes in the corresponding decision trees. Additionally, the used tabu search algorithm is responsible to balance the entropy of the corresponding decision trees. For training the model, we used the clinical data of COVID-19 patients to predict whether a patient is suffering. The experimental results were obtained using our proposed classifier based on the built-in sci-kit learn library in Python. The extensive analysis for the performance comparison was presented using Big O and statistical analysis for conventional supervised machine learning algorithms. Moreover, the performance comparison to optimized state-of-the-art classifiers is also presented. The achieved accuracy of 98%, the required execution time of 55.6 ms and the area under receiver operating characteristic (AUROC) for proposed method of 0.95 reveals that the proposed classifier algorithm is convenient for large datasets.

scholarly journals 174 A comparison of machine learning algorithms in the classification of beef steers finished in feedlot

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 126-127
Author(s):  
Lucas S Lopes ◽  
Christine F Baes ◽  
Dan Tulpan ◽  
Luis Artur Loyola Chardulo ◽  
Otavio Machado Neto ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Final Decision ◽  
Relevant Parameter ◽  
Good Prediction ◽  
Quality Traits ◽  
C4.5 Decision Tree

Abstract The aim of this project is to compare some of the state-of-the-art machine learning algorithms on the classification of steers finished in feedlots based on performance, carcass and meat quality traits. The precise classification of animals allows for fast, real-time decision making in animal food industry, such as culling or retention of herd animals. Beef production presents high variability in its numerous carcass and beef quality traits. Machine learning algorithms and software provide an opportunity to evaluate the interactions between traits to better classify animals. Four different treatment levels of wet distiller’s grain were applied to 97 Angus-Nellore animals and used as features for the classification problem. The C4.5 decision tree, Naïve Bayes (NB), Random Forest (RF) and Multilayer Perceptron (MLP) Artificial Neural Network algorithms were used to predict and classify the animals based on recorded traits measurements, which include initial and final weights, sheer force and meat color. The top performing classifier was the C4.5 decision tree algorithm with a classification accuracy of 96.90%, while the RF, the MLP and NB classifiers had accuracies of 55.67%, 39.17% and 29.89% respectively. We observed that the final decision tree model constructed with C4.5 selected only the dry matter intake (DMI) feature as a differentiator. When DMI was removed, no other feature or combination of features was sufficiently strong to provide good prediction accuracies for any of the classifiers. We plan to investigate in a follow-up study on a significantly larger sample size, the reasons behind DMI being a more relevant parameter than the other measurements.

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