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Predicting the identification of human activities using k closest neighbour method compared to random forest (RF) method is the major purpose of this study work. For this study, we used G-power 0.8, alpha=0.05, and a 95% credibility range to predict the likelihood of human identification using the k-nearest neighbour (KNN) method. Our numbers of samples were 25 for Group 1 and 25 for Group 2. With an accuracy of 94.739, RF outperforms the Novel KNN. Through combining the two methods' precision, Novel KNN outperforms RF by a wide margin. [ABSTRACT FROM AUTHOR]

Continuous loan forecasts are made more accurate in this study by using new logistic regression (NLR) and random forest techniques. These are estimations that are close to what is reported. By playing about with the NLRA value, we may attempt to mimic the pH-altering effects of a 10-number random forest method and a 10-number new logistic regression calculation. Twenty instances were employed for this study, with Gpower 80% for both groups used to choose the test size. The basic accuracy achieved by NLRA is greater (83.29% vs. 81.64%), when comparing the two approaches. When comparing the new logistic regression model with the random forest model, a statistically significant difference was observed (p<0.05, 2 tailed, 0.003). Compared to random forest, the novel logistic regression approach outperforms it when it comes to predicting the results of loans. [ABSTRACT FROM AUTHOR]

In this era, technology has revolutionized the health industry to a great extent. The proposed model aims to design a diagnostic system for various diseases based on their symptoms. The Disease Prediction System has implemented different ML prediction models for the prediction of the user's disease based on various symptoms inputted by the user. Machine learning classification algorithms analyse the inputs given by the user and then predict the disease and probability of occurrence of the disease as output. The proposed system predicts the diseases such as i) Diabetes, ii) Kidney, iii) Cancer, iv) Heart and v) Liver. Four prediction models, Naive Bayes, Decision Tree, Random Forest and Logistic Regression, has been implemented in the proposed system for various disease. The dataset "Disease Prediction Using Machine Learning," with a count of 132 symptoms, has been used in the proposed model. The main goal of the proposed model is to predict the disease; however, the user doesn't need a medical report to use this system as the prediction is based on the symptoms, which will save time and money. The system also has an easy-to-use user interface, and all the users can use it to predict genetic diseases. [ABSTRACT FROM AUTHOR]

Nowadays, the presence of humans significantly impacts indoor air quality, necessitating continuous monitoring of pollutant gases, especially CO2, for a healthy living environment. The proposed framework leverages machine learning, specifically the Random Forest algorithm, known for its versatility and accuracy, to predict CO2 levels in real-time. By optimizing the window size through extensive experimentation, the framework achieves the lowest Root Mean Squared Error (RMSE) of 12.0045 at 13. On the other hand, Mean Absolute Percentage Error (MAPE) analysis affirm the framework's high accuracy, consistently maintaining a percentage error below 10%. [ABSTRACT FROM AUTHOR]

Indian Premier League (IPL) is a famous Twenty-20 League conducted by the Board of Control for Cricket in India (BCCI). It was started in April 2008 and completed its fifteen seasons in 2022. The current, i.e., the fifteenth IPL season, was held in May 2022. IPL is a popular sport where it has a large set of the audience throughout the country. Therefore, every cricket fan would be eager to know and predict the IPL match results. This project is about a detailed exploratory data analysis of IPL matches conducted from the year 2008 till matches held in 2019. Here, we analyze the overall IPL match scores, best batting and bowling performances, the team with a more significant number of wins, the most successful IPL team, the most valuable players and their best performance range, and so on. The complete dataset is collected from officials of BCCI of IPL matches held from 2008-2019 through Kaggle. Here, the testing accuracy of SVM classifier is highest at 0.9159, and the next highest is the Decision Tree algorithm which gave 0.8225 accuracies. The second highest is Logistic Regression, which gave 0.8159 accuracies, and the Random Forest algorithm, with 0.7563 accuracies. As the SVM classifier has the highest accuracy of all the four models, we use that model to develop the analyzer model for the project. [ABSTRACT FROM AUTHOR]

The rate of penetration (ROP) prediction is carried out using a recorded drilling dataset from 02-Well. The prediction approaches use machine learning random forest regressor model and artificial neural network, especially the MLP regressor. The aim is to make the best machine learning model accurately predict the ROP parameter at 02-Well. The method used is designing the architecture of the machine learning model, which is divided into five stages: exploratory data analysis, data pre-processing, prediction and modeling using the selected algorithm, hyper-parameter tuning, and model evaluation. The 02-Well dataset would be divided into a 70% training set and a 30% test set as the base case. The model evaluation results show that modeling using a random forest regressor has a mean absolute percentage error (MAPE) score of 19.81%, which belongs to the "Good Forecasting" criteria. Meanwhile, modeling using MLP regressors has a MAPE score of 22.84% with the "Reasonable Forecasting" criteria. [ABSTRACT FROM AUTHOR]

Traffic collisions occur at an alarming rate in many parts of the world. More than 151,000 people were killed on India's roads in 2019, for example. Finding out what factors contribute to traffic collisions is crucial in lowering financial losses. We explore two models using the US accident cluster dataset: logistic regression and random forest. Random Forest achieves higher accuracy ratings and Auc curve results. We use them for forecasting and incorporate them into our web app to display the likelihood of each accident hotspot. [ABSTRACT FROM AUTHOR]

The study's overarching goal is to enhance the accuracy with which the healthcare dataset's Random forest classifier can predict disease from symptoms in the face of climate change. There are two groups in this research. A random forest classifier is first created, then compared to the Gradient boosting classifier. With a sample size of 25, we may achieve a significance level of 0.001 when comparing the models' accuracies to those of these algorithms. The purpose of this research was to determine whether or not the more accurate Random forest classifier (97.16 percent) or the less accurate Gradient boosting classifier (97.1 percent) could be used to predict diseases based on symptoms (75 percent). When using an independent sample test, the Random forest classifier consistently achieves a high level of statistical significance (p0.05). In a head-to-head comparison with a Gradient boosting classifier, the suggested model's results were shown to be more accurate. [ABSTRACT FROM AUTHOR]

The purpose is to use attenuation measurements of dynamic tuning parameters to classify and predict floods in advance using rainfall data patterns in India. This may be done by using rainfall data patterns in India. We use two distinct categorization approaches in order to achieve the best possible outcomes. Each of these approaches has a sample size of five, a G power of eighty percent, a threshold of five percent, a confidence interval (CI) of ninety-five percent, as well as a mean and standard deviation. Using information on previous rainfall, we conducted this investigation in which we examined the accuracy of forecasts generated by two distinct methods: Random Forest and K-Nearest Neighbor. The findings demonstrated that Random Forest had a performance that was 51.47 percentage points higher than that of the K-Nearest Neighbor algorithm (50.35 percent). It has been demonstrated that Rainfall-based Random Forests are superior to K-Nearest Neighbor algorithms when it comes to flood prediction. [ABSTRACT FROM AUTHOR]

This research evaluates the Logistic Regression (LR) and Random Forest (RF) algorithms, two popular statistical approaches for long-term pollution forecasting. The Parts and Methods: Logistic Regression may effectively predict air pollution better than other machine learning methods. Logistic Regression and Random Forest were used to create a framework for diagnosing air pollution to reduce its impacts. G power indicated that each group required 96 participants. Pretest power was 92%, and the sample size was 2 groups of 48 samples. The dataset showed that Logistic Regression predicted air pollution with 92% accuracy, outperforming Random Forest with a significance of 0.001(p=0.005). Logistic Regression trumps Random Forest in accuracy and precision. [ABSTRACT FROM AUTHOR]

Now-a-days, Health care industries are literally playing a major role in curing the diseases that are suffering the people. And this will be one kind of help to health care industries. In present days, people are facing lot of issues related to their health due to their life style and their livelihood. Due to their busy schedule in their lives, people are not at all taking care of their health. They are not having the time to consult doctors and know what they are going through and it may lead to severe risk for them. So, People should be aware of what they are going through at early stage will reduce the high risk. In our proposed system we used logistic regression, Random Forest Classifier and Decision tree classifier in prediction of the disease. Disease Prediction is a supervised model that is used for prediction of diseases from the symptoms or the information provides by the user. This proposed system will process the symptoms entered by the user and provide the predicted disease as an output. [ABSTRACT FROM AUTHOR]

This paper discusses the bullish and bearish candlestick signals using random forest (RF) and multilayer perceptron (MLP). We have two scenarios to apply. First, we used the stochastics measurements as the features of the RF and MLP. Second, we added the candlestick features into the models. In the first scenario, the accuracy rate for the random forest is 61.68%, while the MLP gets an accuracy of 64.15%. Adding the candlestick features increases the accuracy of the prediction both for the RF and the MLP. In the second scenario, the random forest improved up to 82.08%, and MLP gained 72.88% accuracy. [ABSTRACT FROM AUTHOR]

To predict enhanced employee attrition for increasing accuracy of non-numerical data using logistic regression and decision tree classifier. Materials and Methods: Accuracy is performed with dataset Employee Attrition with samples of 1470 samples. Classification of Employee Attrition is performed by Logistic Regression of sample size (N=62) and Decision Tree of sample size (N=62) obtained using G-power value 80%. Results: The accuracy rate of logistic regression is 83.26 % whereas results of random forest accuracy rate are 77.99%. The significance value is determined as 0.487 (p>0.05) for accuracy. Logistic Regression performs better in finding accuracy when compared to Decision Tree. [ABSTRACT FROM AUTHOR]

Prediction of Employee Attrition by getting the accuracy using Logistic Regression in comparison with Random Forest Algorithm. Accuracy is performed with dataset Employee Attrition with samples of 1470 samples. Classification of Employee Attrition is performed by Logistic Regression of sample size (N=62) and Random Forest Algorithm of sample size (N=62) obtained using G-power value 80%. The accuracy of logistic regression is 85.06 % whereas for random forest accuracy rate is 84.44%. The significance value is determined as 0.536 (p>0.05) for accuracy. Logistic Regression performs better in finding accuracy when compared to Random Forest Algorithm. [ABSTRACT FROM AUTHOR]

The aim of the work is to detect the problems for housing prices to estimate the relationship by Random Forest (RF) algorithm for predictions using supervised techniques. Materials and Methods: Data collection was carried out and the analysis was done in the Google Collab for the execution of results and to estimate the relationship of given algorithms. Proposed work involves two groups for detection of problems for housing prices. Group 1 was the predicted price and Group 2 was the actual price performed by the Random Forest (RF) algorithm. The sample size was calculated. It was identified that 10 samples/group and 20 samples were taken totally. The improved Random Forest (RF) machine learning technique is used for predicting the accuracy based on Coefficient of Determination (CD), Mean Square Error (MSE), Mean Absolute Error (MAE). Results and Discussion: The significant difference of Random Forest (RF) algorithm (p <0.05), in is 0.0372, MSE is 0.0332 and MAE is 0.0355. Conclusion: The data was collected from various resources for the detection of problems for housing prices to estimate the relationship. The Novel Random Forest (RF) algorithm obtains better performance when analyzed with other existing algorithms in detection of problems for housing prices. [ABSTRACT FROM AUTHOR]

The study's primary goal is to identify accuracy in auto loan eligibility utilising machine learning techniques, namely the Logistic Regression Algorithm and the Novel Random Forest Algorithm. The Logistic Regression and Random Forest algorithms were iterated 180 times with a sample size of N=90 to determine the accuracy of automobile loan eligibility. The novel Random Forest method outperforms the Random Forest algorithm in terms of accuracy (87.02%). The loan prediction independent samples T-test has a high significance (p<0.05). When the results were compared, the Random Forest algorithm outperformed the Logistic Regression approach in terms of discovering accuracy in automobile loan eligibility. [ABSTRACT FROM AUTHOR]

Software estimation is vital for the success of software engineering projects. However, predicting software effort is difficult due to software solutions' complexity, intangibility, and diversity and their involved expertise and underlying technology. This paper aims to enhance the accuracy of software estimation using a data mining approach that combines Random Forests Regression with Use-Case Points analysis, which is typically used in estimating effort in object-oriented software engineering projects. The experimental results of applying our proposed approach have demonstrated a significant improvement in the prediction accuracy of software effort estimation when compared to Use-Case Points estimation based on R-Squared (R2) and other metrics such as Mean Absolute Error (MAE) and the Mean Squared Error (MSE). [ABSTRACT FROM AUTHOR]

Due to the environmental condition people face various health hazards and it affects their living habits. So predicting the disease at earlier stage becomes important. As a specific symptom can be responsible for various diseases, it is time consuming to predict the perfect one for the doctors. Data mining plays a significant role in this most challenging task i.e. the accurate prediction of diseases. Due to increase in data growth in every year in healthcare field the precise analysis of data helps patients to take early care. Using the disease data, hidden pattern information was found by data mining in the enormous amount of medical data. Here in this study based on the symptoms general disease prediction techniques have been proposed. For the disease prediction, K-Nearest Neighbor (KNN), decision tree and random forest machine learning algorithm were used. [ABSTRACT FROM AUTHOR]

Software is becoming more complex, and lengthier in size, and needs to be updated on a time basis. But the constant change in codes has a high chance of arising faults that adversely affect the system performance. To avoid this interruption, and to improve the overall performance of software, a fault prediction system is required. The nature of the fault depends upon various parameters, but it is also important to identify the role of parameters in fault generation. In this paper, four feature selection techniques (Correlation Coefficient, Fisher Score, LASSO, Recursive Feature Selection) are applied on three machine learning methods individually (Random Forest, Logistic Regression, SVM) using Python 3.8 to compare the result for highest performance. The result clearly shows that Random Forest gives the highest accuracy of 95% when combined with the Fisher Score feature selection technique. The outcome shows that rather than using all features of any data set, a selected feature should be used for better fault-free software performance. [ABSTRACT FROM AUTHOR]

Over 17.3 million people are dying because of cardio-vascular disease. In past, predicting heart failure (HF) disease was a challenging task. In the modern era, we have relevant training data for HF prediction. Using state-of-the-art machine learning (ML) models, the HF can be predicted with high precision. In this paper, by employment of different ML algorithms, we predict whether a person has cardio-vascular disease (CVD) or not using relevant symptoms of the person. This research predicts the heart failure chances using discriminative attributes that are collected from the patients. A standard dataset from the university of California at Irvine (UCI) that contains 14 parameters related to heart disease has been examined in this study. Our machine learning models are trained using five different classification techniques. The algorithms are logistic regression, k-nearest neighbours (KNN), support vector machines (SVM), random forest, and gradient boosting. The SVM classifier has shown the highest accuracy of 86.84%. The accuracy of predictions has also been enhanced by suitable data pre-processing and cross validation techniques. [ABSTRACT FROM AUTHOR]

The prediction by the machine learning using molecular orbital energies as an explanatory variable is attempted to predict the strength of anxiolytics, anti-anxiety, and muscle relaxant of benzodiazepine anxiolytics. We also attempt to predict half-life of concentration in the body T1/2, and time to reach maximum body concentration Tmax of benzodiazepine anxiolytics with the same procedure. The molecular orbital calculations are performed at 6-31G(d, p) level and random forest is used as regression method. The number of molecular orbitals is varied from 2 to 20 and it is found that 4 or 6 is almost sufficient for the prediction of these 5 objective variables. Finally, the predictions of five properties in the present study are fairly well agreed with the experiments by machine learning employing the molecular orbital energies as the only explanatory variables. [ABSTRACT FROM AUTHOR]

There is a tremendous increment of liver sickness patients as a result of exorbitant utilization of liquor, breathe in of hurtful gases, admission of polluted food, pickles and medications. Therefore lot of burden is put on the doctors to identify a patient whether he is having any liver disease are not.This paper helps to reduce the burden on the doctor by analyzing patients conditions using machine learning techniques[1]. We also make a comparison of few machine learning algorithms like random forest,logistic regression and SVM and compare their accuracy levels in predicting the liver disease. [ABSTRACT FROM AUTHOR]