Online ISSN: 2515-8260

Keywords : Random Forest

Movie Prior Release Box Office Prediction A Machine Learning Based Approach

V. Gangadhara Reddy; K. Radheer Reddy; A. Krishnamoorthy; R. Kannadasan; P. Boominathan

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 2, Pages 5016-528

The “Movie Box Office Prediction” includes different factors that influence the movie revenue at the Box Office. Some of the factors include Budget, Genres, Spoken languages, Cast, Crew. In this paper, various plots are made in order to understand and observe the relations between the variables and the amount of effect of factors on the Revenue. Linear Regression, Random Forest and XGBoost are the models used for Training and Testing the Data.

Detection of Human Activity Performance Analysis Utilizing Machine Learning Algorithms

Ashish Sharma; Dilip Kumar Sharma

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 82-87

Human Activity detection is a talented region has the capacity to earn the human culture by creating assistive advances so that assist old, incessantly sick and for those with exceptional requirements. Precise movement acknowledgment is testing since human action is mind boggling and profoundly assorted. Writing overview acted approximately that has exposed data mining technique are utilized for grouping of exercises. Data mining methods, Naive Bayes with SVM and KNN with Neural Network are end up by proficient in ordering the accelerometers understanding data. This datasets have huge preparation of occurrence by numerous earnings by values. Building categorisers the group like data is as yet a difficult errand. Arbitrary woodland is known for accomplishing high precision in characterization. Its strength in arranging enormous informational indexes is capable. Present paper projects random forest representation for characterizing/anticipating the way of performance. Present data is pre handled to complete stability. Occurrences by organizing dataset are attracted irregular for n tests, and n choice tree are built. Thus, a random based forest is built for ordering initiates depended accelerometers information esteems. To anticipate unlabeled exercise information, total of n trees is presented. Exploratory investigations are led to consider the action acknowledgment capacity of the representation; the outcomes are contrasted and well known managed order strategies. It is seen that the projected representation hits the other grouping methods in relative examination. The planned grouping representation is constrained to perform movement acknowledgment with regards to weight lifting works out. Human Activity acknowledgment is can be applied to some reality, human-driven issues

Prediction of Admission Process for Gradational Studies using Al Algorithm

Saurabh Singhal; Ashish Sharma

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 116-120

In the present time there are plenty of scholars seeking after their instruction away from their nations of origin. The fundamental nation focused through these worldwide scholars is The United States of America. The popular of the universal scholars in the United States of America are from India and China. With the expansion in the quantity of worldwide scholars concentrating in the USA, every candidate needs to confront extreme rivalry to get admission to their fantasy college. This work is to build up a framework utilizing AI algorithms, named it as Graduate Admission Prediction(GAP). GAP will assist the scholars by predicting the chance to get seat in Fantasy College. This paper compares and recognizes which AI algorithm is going to give precise outcome. A straightforward UI will be created for clients to get to the framework

Crop Value Forecasting using Decision Tree Regressor and Model s

AkshayPrassanna S; B A Harshanand, B Srishti; Chaitanya R; KirubakaranNithiyaSoundari .; SwathiSriram .; V Manoj Kumar; VarshithaChennamsetti .; Venkateshwaran G; Dr.Pramod Kumar Maurya

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 2, Pages 3702-32722

Machine Learning is an emerging research field which can be used for the analysis of crop
price prediction and accurately provide solutions for the same. We can use this system as a backhand
while we decide what a farmer should plant while considering factors such as annual rainfall, WPI
and so on which is provided from the dataset and produce a logical conclusion on which products
would give a more reliable outcome. The performance between Random forest ensemble learning and
decision tree regressor is compared and it has been observed that the Random Forest Ensemble
learning method gives a higher accuracy. In this system there are 23 crops whose information can be
accessed upon for deciding collaborated with a simple user friendly UI