Data Science for Genomics (GSK- XGBoost) for Prediction Six Types of Gas Based on Intelligent Analytics

Gas is one of the most important sources in world's economy and play a prominent role in controlling the country's development in various directions. Therefore, the question of forecasting its production rate for subsequent years is a very important point for drawing plans for a country according to rules and values that are closer to reality. Therefore, this work attempts to build an optimization model to predict the values of produced crude oil as well as the associated natural gas associated with those networks using Intelligent data analysis. In this study; we will build a system consisting of five steps: (a) Collect data from Gas network through IoT Platform in real-time, (b) Pre-processing that data based on split it into different intervals, and determining the main limitation and rules on it. (c) build predictive model called (GSK-XGBoost). This predictor is based on Extreme Gradient Boosting (XGBoost) typically using Decision Trees (DTs) to produce the predictor. But it will replace the DTs with Gaining-Sharing Knowledge-based algorithm (GSK) because it would potentially give more accurate and optimum results than DTs by themselves. (d) Finally, the results of GSK-XGboost would be evaluated based on five experimental measures generated by a confusion matrix, namely: Accuracy (AC), recall or true positive rate (TP), precision (P), F-measure (considers both precision and recall), and Fb. In addition, Cross-Validation will use to validate the accuracy of GSK-XGboost.