Day: May 3, 2020

Machine Learning and Data Science in Python using GB with Boston House Price Dataset | Pandas

  Gradient Boosting Machine (GBM) is a powerful machine learning algorithm that is used for both classification and regression tasks. It is a type of ensemble learning method, which means it combines multiple weak models to create a strong model. GBM is a popular algorithm for data science and machine learning projects because it is …

Machine Learning and Data Science in Python using Random Forest Algorithm | Boston Housing Dataset

    Random Forest is a type of ensemble learning method, which is used to build a model by combining multiple decision trees. The main idea behind using Random Forest is that multiple decision trees will provide a more accurate and stable prediction than a single decision tree. The Boston Housing Price dataset from UCI …

Data Science and Machine Learning in Python using Decision Tree with Boston Housing Price Dataset

      Decision trees are a popular machine learning algorithm that can be used for both classification and regression tasks. They work by creating a tree-like structure where each internal node represents a feature and each leaf node represents a prediction. The algorithm starts at the root of the tree and makes a decision …

Machine Learning and Data Science in Python using Neural Networks with Ames Housing Dataset | Pandas

  Neural networks are a powerful machine learning technique that can be used for a wide range of tasks, including both classification and regression problems. They are particularly useful for tasks where there are complex relationships between the input features and the output variable. Neural networks are based on the idea of simulating the way …