Big Data Prediction Framework for Weather Temperature Based on Map-Reduce Algorithm

Weather is the most critical for human in many aspects of life. The study and knowledge of how weather Temperature evolves over time in some location or country in the world can be beneficial for several purposes. Processing, Collecting and storing of huge amounts of weather data is necessary for accurate prediction of weather. Meteorological departments use different types of sensors such as temperature, humidity etc. to get the data. The sensors volume and velocity of data in each of the sensor make the data processing time consuming and complex. This project aims to build analytical Big Data prediction framework for weather temperature based on MapReduce algorithm. Information Mining Package, can perform administered grouping methodology on immense measures of information, normally alluded as large information, on a conveyed framework utilizing Hadoop MapReduce. The instrument has arrangement calculations actualized, taken from Support Vector Machines (SVM). The aftereffects of an exploratory examination utilizing a SVM classifier on informational collections of various sizes for various bunch designs like K-Means shows the capability of the apparatus, just as perspectives that influence its execution.