scholarly journals Movie Success Prediction Using Data Mining

2021 ◽  
Vol 4 (2) ◽  
pp. 22-30
Author(s):  
Olubukola D.A. ◽  
Stephen O.M. ◽  
Funmilayo A.K. ◽  
Ayokunle O. ◽  
Oyebola A. ◽  
...  
Keyword(s):  
Data Mining ◽  
Machine Learning Algorithms ◽  
Success Prediction ◽  
Movie Industry ◽  
Release Date ◽  
Data Mining Techniques ◽  
Box Office ◽  
Public Consumption ◽  
Using Data ◽  
Movie Success

The movie industry is arguably one of the biggest entertainment sectors. Nollywood, the Nigerian movie industry produces tons of movies for public consumption, but only a few make it to box-office or end up becoming blockbusters. The introduction of movie success prediction can play an important role in the industry not only to predict movie success but to help directors and producers make better decisions for the purpose of profit. This study proposes a movie prediction model that applies data mining techniques and machine learning algorithms to predict the success or failure of an upcoming movie (based on predefined parameters). The parameters needed for predicting the success or failure of a movie include dataset needed for the process of data mining such as the historical data of actors, actresses, writers, directors, marketing and production budget, audience, location, release date, and competing movies on same release date. This model also helps movie consumers to determine a blockbuster, hit, success rating and quality of upcoming movies before deciding on a movie ticket. The data mining techniques was applied to Internet Movie Database MetaData which was initially passed through cleaning and integration process.

scholarly journals An Integrated  Approach for Cancer Survival Prediction Using Data Mining Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Ishleen Kaur ◽  
M. N. Doja ◽  
Tanvir Ahmad ◽  
Musheer Ahmad ◽  
Amir Hussain ◽  
...  
Keyword(s):  
Data Mining ◽  
Ovarian Cancer ◽  
Cancer Patients ◽  
Missing Values ◽  
Life Quality ◽  
Advanced Ovarian Cancer ◽  
Machine Learning Algorithms ◽  
Survival Prediction ◽  
Data Mining Techniques ◽  
Using Data

Ovarian cancer is the third most common gynecologic cancers worldwide. Advanced ovarian cancer patients bear a significant mortality rate. Survival estimation is essential for clinicians and patients to understand better and tolerate future outcomes. The present study intends to investigate different survival predictors available for cancer prognosis using data mining techniques. Dataset of 140 advanced ovarian cancer patients containing data from different data profiles (clinical, treatment, and overall life quality) has been collected and used to foresee cancer patients’ survival. Attributes from each data profile have been processed accordingly. Clinical data has been prepared corresponding to missing values and outliers. Treatment data including varying time periods were created using sequence mining techniques to identify the treatments given to the patients. And lastly, different comorbidities were combined into a single factor by computing Charlson Comorbidity Index for each patient. After appropriate preprocessing, the integrated dataset is classified using appropriate machine learning algorithms. The proposed integrated model approach gave the highest accuracy of 76.4% using ensemble technique with sequential pattern mining including time intervals of 2 months between treatments. Thus, the treatment sequences and, most importantly, life quality attributes significantly contribute to the survival prediction of cancer patients.

scholarly journals A Potential Solution for Crop Yield Prediction by using Data Mining Techniques

2021 ◽  
Vol 9 (VI) ◽  
pp. 1211-1214
Author(s):  
Mrs. S.S.N.L. Priyanka
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Ph Value ◽  
Machine Learning Algorithms ◽  
Potential Solution ◽  
Factors Affecting ◽  
Data Mining Techniques ◽  
Managerial Decisions ◽  
Using Data

Agriculture is undoubtedly the largest livelihood provider in India and also contributes a significant figure to the economy of our Country. The technological factors affecting the crop production includes practices used and also managerial decisions. So, predicting the crop yield prior to its harvest would help farmers to take appropriate steps. We attempt to resolve the issue by building a user-friendly prediction system. The results of the prediction are suggested to the farmer such that suitable changes can be made in order to improve the produce. There are different techniques or algorithms which help to predict crop yield. By analyzing all the parameters like location, soil nutrients, pH value, rainfall, moisture a potential solution can be obtained to overcome the situation faced by farmers. This paper focuses on the analysis of the agriculture data and finding optimal yield to provide an insight before the actual crop production using data mining techniques and Machine Learning algorithms.

Drug Prediction System Using Data Mining Techniques - A Survey

2018 ◽  
pp. 32-43
Author(s):  
Jagadeesan V. ◽  
Dr. Palanivel K
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Learning Strategies ◽  
Clinical Decision Making ◽  
Machine Learning Algorithms ◽  
Knowledge Discovery In Databases ◽  
Treatment Quality ◽  
Mining Equipment ◽  
Data Mining Techniques ◽  
Using Data

The thriving Medical applications of Data mining in the fields of Medicine and Public health has led to the popularity of its use in Knowledge Discovery in Databases (KDD). Data mining has revealed novel Biomedical and Healthcare acquaintances for Clinical decision making that has great potential to improve the treatment quality of hospitals and increase the survival rate of patients. Drug Prediction is one of the applications where data mining tools are establishing the successful results. Data mining intends to endow with a systematic survey of current techniques of Knowledge discovery in Databases using Data mining techniques that are in use in today’s Medical research. To enable the drug retrieval and the breakthrough of hidden retrieval patterns from related databases, a study is made. Also, the use of data mining to discover such relationships as those between Supervised and Unsupervised are presented. This paper summarizes various Machine learning algorithms based on various Data mining techniques in learning strategies. It has also been targeted on contemporary research being done the usage of the Data mining strategies to beautify the retrieval manner. This research paper offers destiny developments of modern-day strategies of KDD, using data mining equipment for medicinal drug industry. It also confers huge troubles and demanding situations related to information mining and medication area. The research discovered a developing quantity of records mining packages, such as evaluation of drugs names for higher fitness policy-making, detection of accurate effects with outbreaks and preventable from misclassified drug names.

Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

2017 ◽  
Vol 7 (11) ◽  
pp. 79
Author(s):  
Sujata Mulik
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Climatic Factors ◽  
Crop Productivity ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Agriculture Sector ◽  
Using Data ◽  
Rabi Crops

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 

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