• N.Sujatha
• Lokesh V S
• Latha Narayanan Valli
• Mukul Mech

: Predicting the classification of data into a suitable class is a challenging task. It depends on various factors to predict the dependent variables. Since Random Forest and K-nearest neighbor evaluation can be quantified and is simple to use, here proposed a model using Random Forest and K-nearest neighbor to predict events like expected temperature by inputting maximum temperature, minimum temperature, dew point, humidity, and pressure. Which can be used by farmers or by people from all walks of life to make intelligent decisions. This model can be used in machine learning, and further, the proposed model has scope for improvement as more and more relevant attributes can be used in predicting the dependent variable. This system will take this parameter and predict the weather after analyzing the input information. The outcome demonstrated that these algorithmic procedures can be sufficient for weather forecasting. This project is one of the first to compare the performance of K-nearest neighbor and Random forest methods in weather forecasting using machine learning in Python.

K Nearest Neighbour, Machine Learning, Random Forest, Weather Forecasting.

"Weather Prediction Using Machine Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 1, page no.e550-e555, January-2024, Available :http://www.jetir.org/papers/JETIR2401465.pdf

"Weather Prediction Using Machine Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 1, page no. ppe550-e555, January-2024, Available at : http://www.jetir.org/papers/JETIR2401465.pdf