Flat Price Prediction Using Linear and Random Forest Regression Based on Machine Learning Techniques

2020 ◽  
pp. 205-217
Author(s):  
Julakha Jahan Jui ◽  
M. M. Imran Molla ◽  
Bifta Sama Bari ◽  
Mamunur Rashid ◽  
Md Jahid Hasan
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Machine Learning Techniques ◽  
Random Forest Regression ◽  
Price Prediction ◽  
Learning Techniques

scholarly journals Heart Disease Prediction using Machine Learning Techniques

2019 ◽  
Vol 8 (4) ◽  
pp. 10316-10320
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Random Forest ◽  
Descriptive Analysis ◽  
Random Forest Classifier ◽  
Machine Learning Techniques ◽  
Decision Tree Classifier ◽  
Random Forest Regression ◽  
Learning Techniques ◽  
Tree Classifier

Nowadays, heart disease has become a major disease among the people irrespective of the age. We are seeing this even in children dying due to the heart disease. If we can predict this even before they die, there may be huge chances of surviving. Everybody has various qualities of beat rate (pulse rate) and circulatory strain (blood pressure). We are living in a period of data. Due to the rise in the technology, the amount of data that is generated is increasing daily. Some terabytes of data are being produced and stored. For example, the huge amount of data about the patients is produced in the hospitals such as chest pain, heart rate, blood pressure, pulse rate etc. If we can get this data and apply some machine learning techniques, we can reduce the probability of people dying. In this paper we have done survey using different classification and grouping strategies, for example, KNN, Decision tree classifier, Gaussian Naïve Bayes, Support vector machine, Linear regression, Logistic regression, Random forest classifier, Random forest regression, linear descriptive analysis. We have taken the 14 attributes that are present in the dataset as an input and applying on the dataset which is taken from the UCI repository to develop and accurate model of predicting the heart disease contains colossal (huge) therapeutic (medical) information. In the proposed research, the exhibition of the conclusion model is acquired by using utilizing classification strategies. In this paper proposed an accuracy model to predict whether a person has coronary disease or not. This is implemented by comparing the accuracies of different machine-learning strategies such as KNN, Decision tree classifier, Gaussian Naïve Bayes, SVM, Logistic regression, Random forest classifier, Linear regression, Random forest regression, linear descriptive analysis

scholarly journals Enhancement of the Ball Balancing on the Plate using hybrid PD/Machine learning techniques

2021 ◽  
Vol 2128 (1) ◽  
pp. 012028
Author(s):  
Mohamed R. Elshamy ◽  
Essam Nabil ◽  
Amged Sayed ◽  
Belal Abozalam
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Mathematical Formulation ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Servo Motor ◽  
Random Forest Regression ◽  
Learning Techniques ◽  
Motor Parameter ◽  
Simulation Results

Abstract This paper discusses an efficient method to improve the balancing and tracking of the trajectory of the BOPS based on machine learning (ML) algorithm with the Pseudo proportional-derivative (PPD) controller. The proposed controller depends on a ML technique that detect the angle of the servo motor required to correct the ball position on the plate. This paper presents three different ML algorithms for the servo motor angle prediction and achieved higher accuracy which are 99.855%, 99.999%, and 99.998% for support vector regression, decision tree regression, and random forest regression, respectively. The simulation results demonstrate that the proposed strategy has significantly improved the settling time and overshoot of the system. The mathematical formulation can be obtained using the Lagrangian formulation and the servo motor parameter obtained by a practical identification experiment.

Crop Quality Prediction Using Ml And Neural Networks

2021 ◽  
Vol 10 (02) ◽  
pp. 07-11
Author(s):  
Kanakaveti Narasimha Dheeraj ◽  
Goutham. R. J ◽  
Arthi. L
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Random Forest ◽  
Machine Learning Techniques ◽  
Quality Prediction ◽  
Random Forest Regression ◽  
Power Efficient ◽  
Learning Techniques ◽  
Better Than

Agriculture is said to be the backbone of the economy. Farmers toil hard with different kinds of crops to make good and healthy food for the country. There are more existing systems but uses outdated machine-learning techniques based on RNN( Recurrent neural network) which makes the process slower and more time-consuming. Here We are proposing a new CNN(Convolutional neural network ) based system which is fast and gives accurate results within seconds. CNN is power-efficient and is more suitable for real-time implementation. In this project, we use CNN algorithms which is very much better than the RNN algorithms used in the existing system.More parameters will be taken for the consideration of prediction in the proposed system. And we use Random Forest Regression, Multiple Linear Regression

scholarly journals Learning from Imbalanced Educational Data Using Ensemble Machine Learning Algorithms

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 01) ◽  
pp. 183-195
Author(s):  
Thingbaijam Lenin ◽  
N. Chandrasekaran
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Missing Values ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Adaptive Boosting ◽  
Stochastic Gradient Boosting ◽  
Ensemble Machine Learning ◽  
Learning Techniques ◽  
Student’S Performance

Student’s academic performance is one of the most important parameters for evaluating the standard of any institute. It has become a paramount importance for any institute to identify the student at risk of underperforming or failing or even drop out from the course. Machine Learning techniques may be used to develop a model for predicting student’s performance as early as at the time of admission. The task however is challenging as the educational data required to explore for modelling are usually imbalanced. We explore ensemble machine learning techniques namely bagging algorithm like random forest (rf) and boosting algorithms like adaptive boosting (adaboost), stochastic gradient boosting (gbm), extreme gradient boosting (xgbTree) in an attempt to develop a model for predicting the student’s performance of a private university at Meghalaya using three categories of data namely demographic, prior academic record, personality. The collected data are found to be highly imbalanced and also consists of missing values. We employ k-nearest neighbor (knn) data imputation technique to tackle the missing values. The models are developed on the imputed data with 10 fold cross validation technique and are evaluated using precision, specificity, recall, kappa metrics. As the data are imbalanced, we avoid using accuracy as the metrics of evaluating the model and instead use balanced accuracy and F-score. We compare the ensemble technique with single classifier C4.5. The best result is provided by random forest and adaboost with F-score of 66.67%, balanced accuracy of 75%, and accuracy of 96.94%.

scholarly journals Heart Disease Prediction using Machine Learning Techniques

2021 ◽  
pp. 42-47
Author(s):  
Ramesh Ponnala ◽  
K. Sai Sowjanya
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Random Forest ◽  
Linear Model ◽  
Machine Learning Techniques ◽  
Disease Prediction ◽  
Huge Amount ◽  
Healthcare Enterprise ◽  
Learning Techniques ◽  
Accuracy Level

Prediction of Cardiovascular ailment is an important task inside the vicinity of clinical facts evaluation. Machine learning knowledge of has been proven to be effective in helping in making selections and predicting from the huge amount of facts produced by using the healthcare enterprise. on this paper, we advocate a unique technique that pursuits via finding good sized functions by means of applying ML strategies ensuing in improving the accuracy inside the prediction of heart ailment. The severity of the heart disease is classified primarily based on diverse methods like KNN, choice timber and so on. The prediction version is added with special combos of capabilities and several known classification techniques. We produce a stronger performance level with an accuracy level of a 100% through the prediction version for heart ailment with the Hybrid Random forest area with a linear model (HRFLM).

Classification study of solvation free energies of organic molecules using machine learning techniques

RSC Advances ◽  
2014 ◽  
Vol 4 (106) ◽  
pp. 61624-61630 ◽  
Author(s):  
N. S. Hari Narayana Moorthy ◽  
Silvia A. Martins ◽  
Sergio F. Sousa ◽  
Maria J. Ramos ◽  
Pedro A. Fernandes
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Organic Molecules ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Classification Models ◽  
Free Energies ◽  
Learning Techniques ◽  
Solvation Free Energies

Classification models to predict the solvation free energies of organic molecules were developed using decision tree, random forest and support vector machine approaches and with MACCS fingerprints, MOE and PaDEL descriptors.

Enhancing alpine glacial lakes detection and mapping using multi-source data and machine learning techniques

2020 ◽  
Author(s):  
Sonam Wangchuk ◽  
Tobias Bolch
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Satellite Images ◽  
Random Forest Classifier ◽  
Machine Learning Techniques ◽  
Glacial Lake ◽  
Glacial Lakes ◽  
Alpine Regions ◽  
Learning Techniques ◽  
Source Data

<p>An accurate detection and mapping of glacial lakes in the Alpine regions such as the Himalayas, the Alps and the Andes are challenged by many factors. These factors include 1) a small size of glacial lakes, 2) cloud cover in optical satellite images, 3) cast shadows from mountains and clouds, 4) seasonal snow in satellite images, 5) varying degree of turbidity amongst glacial lakes, and 6) frozen glacial lake surface. In our study, we propose a fully automated approach, that overcomes most of the above mentioned challenges, to detect and map glacial lakes accurately using multi-source data and machine learning techniques such as the random forest classifier algorithm. The multi-source data are from the Sentinel-1 Synthetic Aperture Radar data (radar backscatter), the Sentinel-2 multispectral instrument data (NDWI), and the SRTM digital elevation model (slope). We use these data as inputs for the rule-based segmentation of potential glacial lakes, where decision rules are implemented from the expert system. The potential glacial lake polygons are then classified either as glacial lakes or non-glacial lakes by the trained and tested random forest classifier algorithm. The performance of the method was assessed in eight test sites located across the Alpine regions (e.g. the Boshula mountain range and Koshi basin in the Himalayas, the Tajiks Pamirs, the Swiss Alps and the Peruvian Andes) of the word. We show that the proposed method performs efficiently irrespective of geographic, geologic, climatic, and glacial lake conditions.</p>

Sign in / Sign up

Export Citation Format

Share Document