tdhayanithi / lgmvip--datascience--task--03

Through the Decision Tree Algorithm project, I developed a predictive model using libraries like sklearn and implemented feature selection techniques to enhance accuracy, enabling data-driven decision-making in various domains.

LGMVIP--DataScience--Task--03

Project 3 involved implementing a Decision Tree algorithm for prediction purposes. Decision Trees are powerful machine learning models that recursively split the data based on different features, ultimately leading to a decision or prediction.

In this project, the dataset was preprocessed and split into training and testing sets. The Decision Tree algorithm, often implemented using libraries such as scikit-learn, was applied to the training data. The algorithm used various attributes to create decision rules and partitions, optimizing for accuracy or other specified metrics. By implementing the Decision Tree algorithm and analyzing the evaluation metrics, we gained insights into the dataset's patterns and the decision-making process of the algorithm. This project demonstrates the potential of Decision Trees in predicting outcomes and providing valuable insights for decision-making in various domains.