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Machine learning is a method of teaching computers to learn from data, without being explicitly programmed. In R, there are many libraries available for machine learning, such as caret, randomForest, and nnet. One of the most popular datasets for machine learning is the Boston house price dataset, which is available in the UCI repository. This dataset contains information about houses in the Boston area, including the number of rooms, the median value of homes, and the crime rate.

In this article, we will focus on using neural networks for machine learning in R, specifically for regression tasks using the Boston house price dataset. Neural networks are a type of machine learning algorithm that are based on the structure of the human brain. They are composed of layers of interconnected nodes, called neurons, which are responsible for processing the input data and making predictions.

To begin, we will start by loading the Boston house price dataset into R. The dataset can be easily imported using the built-in library called MASS. Once the dataset is loaded, we will split the data into training and testing sets, so that we can evaluate the performance of the model.

Next, we will use the nnet library to create a neural network model. The nnet library has a function called nnet() which allows us to specify the number of layers, the number of neurons in each layer, and the activation function. In this example, we will use one hidden layer with 10 neurons and the sigmoid activation function.

Once the model is created, we will use the caret library to perform cross-validation. This is an important step because it allows us to evaluate the performance of the model using different subsets of the data. Cross-validation is a powerful technique that is commonly used in machine learning to prevent overfitting and to estimate the generalization performance of a model.

After the cross-validation process is completed, we can evaluate the performance of the model by looking at the mean square error (MSE) and the root mean square error (RMSE). These metrics are commonly used to evaluate the performance of regression models. The lower the MSE and RMSE, the better the model is at predicting the target variable.

In conclusion, using neural networks for machine learning in R is a powerful technique that can be used to predict continuous target variables, such as the Boston house prices. Neural networks are a versatile algorithm that can be used for a variety of tasks, and are particularly useful for regression problems. By using the nnet library in R, we can create a neural network model, perform cross-validation and evaluate the performance of the model. With the use of the caret library, we can also compare the performance of different machine learning models, and choose the best one for our specific problem.

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 in R | Data Science for Beginners | Random Forest | Boston House Data | Regression.

### What should I learn from this Applied Machine Learning & Data Science tutorials?

You will learn:

- Machine Learning in R | Data Science for Beginners | Neural Networks | House Dataset | Regression.
- Practical Data Science tutorials with Python and R for Beginners and Citizen Data Scientists.
- Practical Machine Learning tutorials with Python and R for Beginners and Machine Learning Developers.

Machine Learning in R | Data Science for Beginners | Neural Networks | House Dataset | Regression:

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.

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