Tag Archives: Python machine learning with Pandas and Scikit-Learn

Machine Learning Regression in Python using Keras and Tensorflow | Boston House Price Dataset | Data Science Tutorials

    Machine learning regression is a type of machine learning that is used to predict a continuous value. In this case, we are going to use a deep learning approach using Keras and Tensorflow to predict the median value of a house in Boston using the Boston House Price dataset from UCI. Keras is …

Machine Learning and Data Science in Python using LightGBM with Boston House Price Dataset Tutorials

    LightGBM is another powerful machine learning algorithm that is widely used in data science and machine learning projects. It is an open-source algorithm that is based on the Gradient Boosting framework and is designed to be highly efficient and scalable. Like XGBoost, LightGBM is a boosting algorithm that creates multiple decision trees to …

Machine Learning Regression in Python using XGBoost | Boston Housing Dataset | Data Science Tutorials

      Regression is a type of machine learning task that is used to predict a continuous value. It is a commonly used technique in data science and machine learning projects to make predictions about numerical values. Regression algorithms can be used for a wide range of applications such as predicting stock prices, sales, …

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 …