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

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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 known for its high accuracy and ability to handle large datasets.

The Boston Housing Price dataset from UCI is a popular dataset for machine learning and data science. It contains information about various properties in Boston and includes a variety of features such as the crime rate, the number of rooms, and the median value of owner-occupied homes. The goal of this dataset is to predict the median value of a property based on the other features.

To use GBM with the Boston Housing Price dataset, you would first need to load the dataset into Python. This can be done using a library such as pandas. Once the dataset is loaded, you would then need to split it into training and testing sets. The training set is used to train the GBM model, and the testing set is used to evaluate its performance.

Next, you would need to define the GBM model using a library such as xgboost or LightGBM. These libraries provide a wide range of tools for building GBM models in Python. You would then need to train the model using the training set and evaluate its performance using the testing set.

The GBM algorithm works by creating multiple decision trees, and then boosting them to create a strong model. Each decision tree is built using a random subset of the features and a random subset of the training data. The final prediction is made by combining the predictions of all the decision trees.

One of the key advantages of GBM is that it can handle missing values and outliers. The algorithm can handle missing values and outliers by building multiple decision trees and combining the predictions.

Another advantage of GBM is that it is less prone to overfitting than other algorithms. Overfitting occurs when a model is too complex and performs well on the training data but not on the testing data. GBM reduces overfitting by building multiple decision trees and combining the predictions.

In conclusion, GBM is a powerful machine learning algorithm that can be used for both classification and regression tasks. The Boston Housing Price dataset from UCI is a popular dataset for machine learning and data science that can be used with GBM to predict the median value of a property based on the other features. GBM can handle missing values and outliers and is less prone to overfitting than other algorithms.

In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in R programming: Machine Learning and Data Science in Python using GB with Boston House Price Dataset | Pandas.



 

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