TEMPERATURE PREDICTION USING MACHINE LEARNING BASED ON HISTORICAL WEATHER DATA

Abstract

Weather forecasting plays an important role in many fields such as agriculture, disaster
management, transportation, and environmental monitoring. Temperature is one of the most
important parameters in weather prediction.This project focuses on predicting temperature using
machine learning techniques based on historical weather data.Machinelearning models can
analyze large datasets and identify patterns that help predict future temperature values.The
system uses historical weather data including humidity,windspeed,pressure, and past temperature
records to algorithmssuchasLinearRegression,DecisionTree,andRandomForestareusedtobuild the
prediction model. The dataset is preprocessed and cleaned before training the model. After
training, the system can predict future temperature values with good accuracy. The project is
implemented using Python programming language and machine learning libraries such as
Pandas, NumPy, and Scikit-learn. A user interface is developed using Streamlit to allow users to
input weather parameters and get.
Temperature prediction plays a vital role in weather forecasting, agriculture, and environmental
monitoring. This project focuses on predicting temperature using machine learning techniques
based on historical weather data. The system collects past data such as temperature, humidity,
wind speed, and atmospheric pressure, and processes it to identify patterns and trends. Machine
learning algorithms like Linear Regression and Random Forest are used to build predictive
models. The proposed system improves accuracy compared to traditional methods by learning
complex relationships in data. It provides reliable and real-time temperature predictions with
minimal human intervention. The system can be extended to different regions and helps in better
decision-making for climate-related applications