Dataset-[ https://www.kaggle.com/datasets/mohannapd/mobile-price-prediction ]

(Mobile Price Prediction)

• Usage –

1. Personalized Mobile recommendations.
2. To configure marketing strategies between companies for example- Apple vs Samsung.
3. To customize featured according to usage.
4. To figure out how many people prefer high resolution phones over basic models.
5. To predict how much cost will increase for a mobile.
6. To see how weight of the phone affects sales.
7. To know what the standard memory is for average people.
8. To see camera preferences (Rear + Front)
9. Field comparisons such as Battery life vs Storage with Sales can be compared to know how much people prefer a good battery over Storage.
10. To predict an ideal model suitable for average group that might result in highest sale.
11. For companies such as Amazon, Facebook that review a phone and suggest people on their websites.

BOOK- Knowledge-Oriented Applications in Data Mining

LINK- [https://www.intechopen.com/books/1358]

INTRODUCTION-

One of the reasons why the Data Mining techniques are widely used is that there is a need to transform a large amount of data on information and knowledge useful. Many real-world problems in different domains such as Marketing, Health, Finance, Education require processing large amount of data. For this, models with precise rule sets are to be defined.

Two main categories of segregation are –

1)Classification (Describe important data classes)

2)Prediction (To predict future data trends) These techniques are used to make intelligent decisions for data analysis and enhance ability to make automated decisions like humans. Furthermore,

Classification has two phases-

1)Learning Process (The training data will be analyzed by the classification algorithm)

2)Classify/Test Process (The test data are used to estimate the accuracy of the classification model or classifier)

TECHNIQUES FOR DATA MINING-

Decision tree, Bayesian methods, Bayesian network, rule-based algorithms, neural network, support vector machine, association rule mining, k-nearest-neighbor, case-based reasoning, genetic algorithms, rough sets, and fuzzy logic.

DECISION TREE-

A decision tree is a structure consisting of nodes (internal, leaves and arches). Its internal nodes are characterized by one or more attributes of these nodes test and emerge one or more arcs. Leaf nodes contain information that determines the object belongs to a class. Some dataset rules are-

• Precise classification of objects in the training set (TS).

• Ensure that highest results are obtained by processing the TS.

• If the dataset is dynamic, the structure of DT should be upgraded easily.

APPLICATIONS-

1)Trash collectors routes organized by profiles- In the management of solid waste have the problem relates to the household waste is the individual decision-making over waste generation and disposal. When the people decide how much to consume and what to consume, they do not take into account how much waste they produce.

2. Fraud analysis in saving houses- Methods employed to identify potential fraud trying to minimize potential losses. These methods are called fraud detection systems (FDS), and a variety of ways are used to detect the most behavior potential fraudulent.

HYBRID SYSTEMS-

Many developed algorithms do not follow entirely the concepts of a simple metaheuristic to solve this problem is looking for the best from a combination of metaheuristics (and any other kind of optimization methods) that perform together to complement each other and produce a profitable synergy, to which is called hybridization

1. Improve the performance of evolutionary algorithms.

2. Improve the quality of solutions obtained by evolutionary algorithms.

3. Incorporate evolutionary algorithms as part of a larger system.

MONITORING OF WATER QUALITY USING REMOTE SENSING DATA MINING-

The relationship between quality of water and the reflection is analyzed using stepwise multiple linear regression analysis.

CLIMATE PREDICTION-

To investigate methods of data mining suitable for developing a seasonal rainfall predictive model.

PROCESS-

1.Data Input

2.Feature Extraction

3.Feature Selection

4.Implement Discriminant Analysis

5.Model Validation

DATA MINING IN NEUROLOGY-

The reach of Data Mining in medicine are mainly in the identification of relations, patterns and models supporting prediction and the decision-making processes, e.g. for diagnosis, prognosis, and treatment planning. When validated, these predictive models could be embedded in the clinical information systems as clinical decision support modules, reducing both subjectivity and time in making decisions.

Applies to-

1.Therapy Planning and Rehabilitation.

2.Image and signal analysis- MRI (Magnetic Resonance Imaging).

3.Personalized Speech Disorder Therapy Optimization.

4.Visual Gene Ontology.

5.Nutrition Database and planning.

About the Author-

Kimito Funatsu- (Dr. Science, Physical Organic Chemistry) “The progress of data mining technology and large public popularity establish a need for a comprehensive text on the subject. The series of books entitled by "Data Mining" address the need by presenting in-depth description of novel mining algorithms and many useful applications”