Accurate rainfall prediction is crucial for effective water resource management, agriculture planning, and disaster mitigation in India, where the monsoon plays a vital role in the economy and livelihoods. This project focuses on developing a predictive model for rainfall in India using historical weather data and machine learning techniques. By analyzing key meteorological parameters such as temperature, humidity, wind speed, and previous rainfall patterns, the model aims to forecast rainfall intensity with high accuracy. The study employs data prepossessing, feature selection, and multiple algorithms including Linear Regression, Decision Trees, and Random Forests, comparing their performance to identify the most suitable model. The results demonstrate that machine learning can significantly enhance rainfall forecasting capabilities compared to traditional statistical methods. The project contributes to building a more reliable early warning system, supporting farmers, policy-makers, and disaster management authorities in making informed decisions.
Weather forecasting, artificial intelligence, machine learning, neural networks, deep learning, big data analytics.
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