The project centered around analyzing a real-world dataset as part of a competition. Its aim was to develop skills in addressing specific questions using the provided dataset and its metadata.

This project aimed to develop and assess a predictive model using Random Forest for diagnosing Diabetes Disease and identifying its significant risk factors among individuals, while also determining key features contributing to the model's performance.

The objective was to develop and evaluate a predictive model using a Random Forest algorithm for assessing the 10-year risk of Coronary Heart Disease (CHD).

The primary objective of this project was to predict the prices of used cars in the United States, leveraging various attributes such as the year of purchase, mileage, geographical location, manufacturer, and model. A regression model was constructed using decision tree techniques to provide buyers, sellers, and manufacturers with informed insights derived from predicted prices. Data: The dataset utilized for this project could be accessed through Kaggle.com.