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RAINFALL PREDICTION USING XGBOOST

Erusu Kata Raju Reddy, Thamada Saikumar

Assistant professor, MCA Final Semester, Master of Computer Applications, Sanketika Vidya Parishad Engineering College,

Vishakhapatnam, Andhra Pradesh, India.

ABSTRACT:

India is a farming nation and its economy heavily depends on crop productivity and rainfall. Rainfall prediction is necessary and required to all farmers in order to analyze the crop productivity. Rainfall Prediction is the use of science and technology for forecasting the condition of the atmosphere. It is necessary to precisely calculate the rainfall for proper utilization of water resources, crop productivity and pre planning of water structures. With various data mining methods it can forecast rainfall. Data mining methods are employed to estimate the rainfall in numerical terms. This paper emphasizes some of the trending data mining algorithms for rainfall forecasting. Random Forest, K- Nearest Neighbor algorithm, Logistic regression, SVM, Decision Tree are some of the algorithms have been employed. Based on that comparison, it can examine which method provides better accuracy for rainfall forecasting.

Index Terms: Rainfall prediction, XGBoost, Random Forest, Logistic Regression, supervised learning, data preprocessing, Exploratory Data Analysis (EDA), classification models, sklearn, weather data, feature selection.