190403040 Mustafa Melik Ayanoğlu Deadline 17-12-2024 dd/MM/yyyy Overview of the Project This project involves applying Random Forest Regression to a dataset containing three numerical features: 1. Humidity (relative humidity) 2. Pressure (hectopascal) 3. Temperature (celsius, target variable) The objective is to predict the Temperature values based on Humidity and Pressure using the Random Forest algorithm. Additionally, the correlation matrix is generated to analyze relationships between the features. Conclusion The Random Forest Regression model performed well on the given dataset, achieving an MSE of 1.3295 and an R² score of 0.6503. The correlation matrix provided valuable insights into the relationships between features, highlighting key dependencies. While the results are promising, further tuning and feature engineering could enhance model performance.