Predictive Analytics Using Statistics and Big Data: Concepts and Modeling

Data Analytics on Various Domains with Categorized Machine Learning Algorithms

Author(s): R. Suguna and R. Uma Rani

Pp: 1-18 (18)

DOI: 10.2174/9789811490491120010004

* (Excluding Mailing and Handling)


Data Analytics is an emerging area for analyzing various kinds of data. Predictive analytics is one of the essential techniques under data analytics, which is used to predict the data gainfully with machine learning algorithms. There are various types of machine learning algorithms available coming under the umbrella of supervised and unsupervised methods, which give suitable and better performance on data along with various analytics methods. Regression is a useful and familiar statistical method to analyze the data fruitfully. Analysis of medical data is most helpful to both patients as well as the experts to identify and rectify the problems to overcome future problems. Autism is a brain nerve disorder that is increasing in the children by birth due to some most chemical food items and some side effects of other treatments and various causes. Logistic Regression is one of the supervised machine learning algorithms which can operate the dataset of binary data that is 0 and 1.

Agriculture is one of the primary data which should be considered and analyzed for saving the future generation. Rainfall is a more elementary requirement for the global level and also countries which are having backbone as agriculture. Due to the topography, geography, political, and other socio-economic factors, agriculture is affected. Thus, the demand for food and food products is intensifying. Especially crop production is depending upon the rainfall, so, prediction of rainfall and crop production is essential. Analysis of social crime relevant data is indispensable because analytics can produce better results, which leads to reducing the crime level. Unexpectedly child abuse is increasing day by day in India. Linear regression is the supervised machine learning algorithm to predict quantitative data efficiently.

This chapter is roofed with various datasets such as autism from medical, rainfall, and crop production from agriculture and child abuse data from the social domain. Predictive analytics is one of the analytical models which predict the data for the future era. Supervised machine learning algorithms such as linear and logistic regression will be used to perform the prediction.

Keywords: Data analytics, Exponential distribution, Inomial distribution, Linear regression, Logistic regression, Machine learning, Normal distribution, Prediction analytics.

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