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

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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 at each internal node based on the value of the feature. The decision leads to a path which ends at a leaf node and the prediction is made.

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 decision trees 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 decision tree model, and the testing set is used to evaluate its performance.

Next, you would need to define the decision tree model using a library such as scikit-learn. This library provides a wide range of tools for building decision tree models in Python. You would then need to train the model using the training set and evaluate its performance using the testing set.

One of the key advantages of decision trees is that they are easy to understand and interpret. The tree structure makes it easy to see how the algorithm is making decisions and what features are most important for the prediction. Additionally, decision trees are able to handle both categorical and numerical features, which makes them well-suited for a wide range of tasks.

Another advantage of decision trees is that they can be used to estimate feature importance. The algorithm calculates the importance of each feature in the dataset by measuring the decrease in accuracy when the feature is not used in the decision tree. This can be useful for understanding the factors that contribute to the median value of a property.

In conclusion, decision trees are a popular machine learning algorithm that can be used for both classification and regression tasks, they are easy to understand and interpret, handle both categorical and numerical features, and can estimate feature importance. The Boston Housing Price dataset from UCI is a popular dataset for machine learning and data science that can be used with decision trees to predict the median value of a property based on the other features.

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



 

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