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 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 Random Forest 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 Random Forest model, and the testing set is used to evaluate its performance.

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

The Random Forest algorithm works by creating multiple decision trees and then combining the results. 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 averaging the predictions of all the decision trees.

One of the key advantages of Random Forest is that it reduces overfitting. Overfitting occurs when a model is too complex and performs well on the training data but not on the testing data. Random Forest reduces overfitting by creating multiple decision trees and averaging the predictions.

Another advantage of Random Forest is that it can handle missing values and outliers. The algorithm can handle missing values and outliers by building multiple decision trees and averaging the predictions.

In conclusion, Random Forest 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 Random Forest to predict the median value of a property based on the other features. Random Forest reduces overfitting and handles missing values and outliers.

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 Random Forest Algorithm | Boston Housing Dataset.



 

Personal Career & Learning Guide for Data Analyst, Data Engineer and Data Scientist

Applied Machine Learning & Data Science Projects and Coding Recipes for Beginners

A list of FREE programming examples together with eTutorials & eBooks @ SETScholars

95% Discount on “Projects & Recipes, tutorials, ebooks”

Projects and Coding Recipes, eTutorials and eBooks: The best All-in-One resources for Data Analyst, Data Scientist, Machine Learning Engineer and Software Developer

Topics included: Classification, Clustering, Regression, Forecasting, Algorithms, Data Structures, Data Analytics & Data Science, Deep Learning, Machine Learning, Programming Languages and Software Tools & Packages.
(Discount is valid for limited time only)

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.

Learn by Coding: v-Tutorials on Applied Machine Learning and Data Science for Beginners

Please do not waste your valuable time by watching videos, rather use end-to-end (Python and R) recipes from Professional Data Scientists to practice coding, and land the most demandable jobs in the fields of Predictive analytics & AI (Machine Learning and Data Science).

The objective is to guide the developers & analysts to “Learn how to Code” for Applied AI using end-to-end coding solutions, and unlock the world of opportunities!