Assessment and prediction of stress‐related using SPSS statistical analysis

Abstract

Predictions provide a reference point for the scientist. If the predictions are confirmed, the scientist hypothesizes supported. If the predictions are not supported, the hypothesis false anyway, the scientist studies increased knowledge of the process being performed. Therefore, for such data analysis in crop forecasting, there are various techniques or methods and those Crop yield can be predicted with the help of methods. A random forest algorithm is used. Weather, Temperature, Humidity, Precipitation, Humidity All these problems and problems like Analyzing the situation we face There is no perfect solution and technologies to deal with. Economic growth in agriculture sector in India There is many ways to increase. To predict crop yield Data mining is also used. Generally, data Mining data from different perspectives Analyzes and summarizes important information is the process. This paper presents a new approach for learning from asymmetric data sets, SMOTE is based on a combination of algorithm and motivational process. Unlike standard boosting where all misclassified examples are equally given weights, SMOTE Boost generates artificial examples from rare or minority groups class, thus implicitly changing and compensating update weights Skewed distributions. SMOTE Boost was used for high and moderate multiples Imbalanced data sets show an improvement in predictive performance for minority classes and an overall improvement