Journal of Data Mining and Management

Journal Information
EISSN : 2456-9437
Total articles ≅ 21
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, Farhat Anjum, Chetna Sahu
Journal of Data Mining and Management, Volume 7, pp 1-9; https://doi.org/10.46610/jodmm.2022.v07i03.001

Abstract:
Predicting student success in advance can help educational institutions enhance their teaching quality. This research offers insight into predicting student success not only based on academic information but also on their social structure and living area. The goal of this study is to predict students' grades using machine learning based models such as Decision Tree, Linear Regressor, and Random Forest Regressor and to select the best model among these three.
Sivakumar V, Rohit J Kashyap, Rachel Rose Oommen, R. Swathi
Journal of Data Mining and Management, Volume 7, pp 32-37; https://doi.org/10.46610/jodmm.2022.v07i02.005

Abstract:
The given system includes a vast dataset of India's states, but the previous system, only single state was selected. All the farmers will get a better knowledge of the crops to cultivate by using a pictorial depiction. Machine learning features give a detailed structure with the information and it gives the predictions. The main problems like knowing about the crop prediction, rotation techniques, utilization of water, need for fertilizer and safety will be taken care of. Due to varying climatic changes of the surrounding the need to have a proficient techniques are required for development of crops and to help the farmers in their knowledge of production and management features. The project gives the proper results for advanced farming techniques by choosing the land for farming, which can help the farmers to gain huge knowledge about this.
Saritha M, Manoj B R, Neola Sendril Dias, Ms. Nisha Joshal Pinto, Padma Prasad H M
Journal of Data Mining and Management, Volume 7, pp 38-43; https://doi.org/10.46610/jodmm.2022.v78i02.006

Abstract:
Customer Segmentation is the technique of separating customers into different clusters based on their specific characteristics. Segmenting customers is very essential in every business sector because each individual is different from one another and has distinct interests. But with the help of machine learning techniques, the data can be sorted to find the target group by applying algorithms to the dataset. Based on Recency, frequency and monetary (RFM) value customers purchasing behavior is segmented and the scope of this project is to divide customers based on different groups like loyal, new and churned customers and this is done by RFM table which is used to analyze customer value and K means algorithm is used to cluster the data and to determine the optimal clusters, elbow method is used. The obtained data is then used for further analysis by the organizations to improve the quality of the product, services offered to the customers and develop their relation which can help to improve sales and plan marketing strategy. Every person is different from one another and we don’t know what he/she buys or what their likes are but, with the help of machine learning technique one can sort out the data and can find the target group by applying several algorithms to the dataset.
Suchetha N V, Susheel C. Nagur, Varun Hebbar B S, Varun S Hiremat
Journal of Data Mining and Management, Volume 7, pp 9-14; https://doi.org/10.46610/jodmm.2022.v78i02.002

Abstract:
Stroke is one of the major causes of mortality all over the world. Stroke is caused when the blood flow to the brain is obstructed. The poor blood flow causes death of brain cells and eventually, it may result in death of the person. In this work, three different machine learning algorithms are being used for the prediction of stroke risk, Decision Tree, K Nearest Neighbors and Random Forest. Among these, Random Forest model provides better accuracy of 94.1%. As Compared to traditional methods, using machine learning for the prediction of stroke is convenient and also economical.
Saritha M, Manoj B R, Neola Sendril Dias, Ms. Nisha Joshal Pinto, Padma Prasad H M
Journal of Data Mining and Management, Volume 7, pp 38-43; https://doi.org/10.46610/jodmm.2022.v07i02.006

Abstract:
Customer Segmentation is the technique of separating customers into different clusters based on their specific characteristics. Segmenting customers is very essential in every business sector because each individual is different from one another and has distinct interests. But with the help of machine learning techniques, the data can be sorted to find the target group by applying algorithms to the dataset. Based on Recency, frequency and monetary (RFM) value customers purchasing behavior is segmented and the scope of this project is to divide customers based on different groups like loyal, new and churned customers and this is done by RFM table which is used to analyze customer value and K means algorithm is used to cluster the data and to determine the optimal clusters, elbow method is used. The obtained data is then used for further analysis by the organizations to improve the quality of the product, services offered to the customers and develop their relation which can help to improve sales and plan marketing strategy. Every person is different from one another and we don’t know what he/she buys or what their likes are but, with the help of machine learning technique one can sort out the data and can find the target group by applying several algorithms to the dataset.
Suchetha N V, Susheel C. Nagur, Varun Hebbar B S, Varun S Hiremat
Journal of Data Mining and Management, Volume 7, pp 9-14; https://doi.org/10.46610/jodmm.2022.v07i02.002

Abstract:
Stroke is one of the major causes of mortality all over the world. Stroke is caused when the blood flow to the brain is obstructed. The poor blood flow causes death of brain cells and eventually, it may result in death of the person. In this work, three different machine learning algorithms are being used for the prediction of stroke risk, Decision Tree, K Nearest Neighbors and Random Forest. Among these, Random Forest model provides better accuracy of 94.1%. As Compared to traditional methods, using machine learning for the prediction of stroke is convenient and also economical.
Kaaviasudhan V S, Saran Nithish T S, Kishore Kumar S, N. Yamuna Devi
Journal of Data Mining and Management, Volume 7, pp 17-27; https://doi.org/10.46610/jodmm.2022.v07i01.003

Abstract:
Technology creates the generation gap by how well older people can learn and use new technology. Each generation have different values and opinions. Due to innovation develop its leads to the generation gap. A difference in the attitude of people from different generations leads to lack of understanding. And also, generation gap is also referred to as difference in the point of view between young and old generations specially between parents and children.
Vijeeta Patil, Shanta Kallur, Vani Hiremani
Journal of Data Mining and Management, Volume 6; https://doi.org/10.46610/jodmm.2021.v06i03.004

Abstract:
Face recognizable proof has drawn in numerous scientists because of its novel benefit, for example, non-contact measure for include obtaining. Varieties in brightening, posture and appearance are significant difficulties of face acknowledgment particularly when pictures are taken as dim scale. To mitigate these difficulties partially many exploration works have been completed by considering shading pictures and they have yielded better face acknowledgment rate. A strategy for perceiving face utilizing shading nearby surface highlights is depicted. Test results show that Face ID approaches utilizing shading neighborhood surface highlights astonishingly yield preferred acknowledgment rates over Face acknowledgment approaches utilizing just shading or surface data. Especially, contrasted and grayscale surface highlights, the proposed shading neighborhood surface highlights can give great coordinating with rates to confront pictures taken under extreme varieties in enlightenment and furthermore for low goal face pictures. The other biometric framework utilizes palmprint as quality for the recognizable proof and validation of people. The principal point is to extract Haralick highlights and utilization of probabilistic neural organizations for confirmation utilizing palmprint biometric quality. PolyUdatabase tests are taken from around 200 clients every client's 2 examples are gained. This palm print biometric recognizes the phony (fake) palmprint made of POP (Plaster of paris) and separates among living and non-living dependent on the entropy highlight. Test results portray that the eleven Haralick feature values are acquired in execution stage and productive precision is accomplished.
Nihar M. Ranjan, Rajesh S. Prasad, Deepak T. Mane
Journal of Data Mining and Management, Volume 6, pp 13-23; https://doi.org/10.46610/jodmm.2021.v06i03.003

Abstract:
About 80% organizational data are present in the unstructured (Text) format. E-mails, Social media, notes, and wide variety of different types of documents in text formats are present, but all these data are not got importance and analyzed in meaningful ways. It has been observed that information workers spend their significant time (up to one third) to locating this information and trying to make sense of it. Text analytics (TA) is the process which analyzed all these available unstructured text information and converts it into useful information which helps the organization significantly in their business processes. In this paper we have discussed the business values, methods of text analytics, and business application of text analytics.
Nihar M. Ranjan, Rajesh S. Prasad
Journal of Data Mining and Management, Volume 6, pp 1-6; https://doi.org/10.46610/jodmm.2021.v06i03.001

Abstract:
About 80% organizational data are present in the unstructured (Text) format. E-mails, social media, notes, and wide variety of different types of documents in text formats are present, but all these data are not get importance and analyzed in meaningful ways. It has been observed that information workers spend their significant time (up to one third) to locating this information and trying to make sense of it. Text analytics is the process which analyzed all these available unstructured text information and converts it into useful information which helps the organization significantly in their business processes. In this paper, we have highlighted the business values, some of the methods, and business application of text analytics.
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