Abstract: A Weather Forecast & Prediction System is envisioned to leverage the power of Machine Learning (ML) to provide accurate and accessible weather information, as depicted in the provided image. This system would process user input, specifically a city name, to fetch and utilize historical meteorological data encompassing parameters such as temperature, humidity, wind speed, and atmospheric pressure. At its core, the system would employ a suite of well-established ML algorithms for prediction. Linear Regression would be applied for forecasting continuous numerical values like temperature, humidity, wind speed, and pressure, learning the linear relationships between various features and these target variables. For predicting categorical outputs, such as the "Condition" (e.g., Sunny, Rainy, Cloudy), the K-Nearest Neighbours (KNN) algorithm would classify the current or future weather state based on the similarity to historical weather patterns. Furthermore, Random Forest, an ensemble learning method, would be utilized for its robustness and ability to handle both regression and classification tasks, capturing more complex, non-linear interactions within the weather data and providing highly accurate predictions for all mentioned parameters. The system's architecture would involve efficient data acquisition from historical archives and real-time APIs, robust data pre-processing and feature engineering to prepare the data for the ML models, and a user-friendly interface to display the predicted current conditions and future forecasts clearly and concisely.
Keywords: Weather Prediction, Forecasting, K-Nearest Neighbours (KNN), Linear Regression, Random Forest, User Interface (UI), Web Application , and Classification.
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DOI:
10.17148/IJARCCE.2025.14726
