Agriculture is the primary source of livelihood which forms the backbone of our country. Current challenges of water shortages, uncontrolled cost due to demand-supply, and weather uncertainty necessitate farmers to be equipped with smart farming. In particular, low yield of crops due to uncertain climatic changes, poor irrigation facilities, reduction in soil fertility and traditional farming techniques need to be addressed. Machine learning is one such technique employed to predict crop yield in agriculture. Various machine learning techniques such as prediction, classification, regression and clustering are utilized to forecast crop yield. Artificial neural networks, support vector machines, linear and logistic regression, decision trees, Naïve Bayes are some of the algorithms used to implement prediction. However, the selection of the appropriate algorithm from the pool of available algorithms imposes challenge to the researchers with respect to the chosen crop. In this paper, an investigation has been performed on how various machine learning algorithms are useful in prediction of crop yield. An approach has been proposed for prediction of crop yield using machine learning techniques in big data computing paradigm.

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.

Feature selection is an important part of machine learning. The Feature selection refers to the process of reducing the inputs for processing and analysis, or of finding the most meaningful inputs. A related term, feature engineering (or feature extraction), refers to the process of extracting useful information or features from existing data. Mining of particular information related to a concept is done on the basis of the feature of the data. The accessing of these features hence for data retrieval can be termed as the feature extraction mechanism. Different type of feature extraction methods used. In this paper, the different feature selection methodologies are examined in terms of need and method adopted for feature selection. The three types of method are mainly available, such as Shannon's Entropy, Bayesian with K2 Prior and Bayesi Dirichlet with uniform prior (default). The objectives of this survey paper is to identify the existing contribution made by using their above mentioned algorithms and the result obtained.

The yield of crops is influenced by various factors such as weather conditions, soil characteristics, irrigation facility, solar radiation, fertilizer application, tillage, etc. Accurate prediction of crop yield is an important issue in agriculture as un-presented changes in yield will significantly influence food supply and market prices. Data pre-processing and selection of relevant features is an essential step while perform prediction using machine learning algorithms. In this work, Monte Carlo simulation for random selection of data and binary cuckoo search for relevant feature selection are used with an objective of enhancing the accuracy of prediction using multiple linear regression technique. Experimental results are discussed.

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