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Deep learning is a subset of machine learning that involves training artificial neural networks to perform tasks such as image or speech recognition, natural language processing, and predictive modeling. In this article, we will discuss how to use deep learning in R to perform regression on a housing price dataset using the Tensorflow and Keras libraries.

The first step in using deep learning for regression is to load and prepare the dataset. In this case, we will be using the Boston House Price dataset from UCI. This dataset contains information on various characteristics of houses in the Boston area, including the median value of owner-occupied homes. We will use this information to predict the median value of homes in the Boston area.

Once the dataset is loaded and prepared, we can begin building our deep learning model. In this case, we will be using Tensorflow and Keras to create a neural network. Tensorflow is an open-source library for deep learning and Keras is a high-level API that can be used to quickly and easily build neural networks.

To start, we will create a sequential model in Keras, which is a linear stack of layers. We will then add layers to the model, including an input layer, hidden layers, and an output layer. The input layer will take in the data from the dataset, the hidden layers will process the data, and the output layer will make the prediction.

Once the model is built, we will train it using the Boston House Price dataset. Training the model involves feeding the data into the model and adjusting the weights and biases of the layers to minimize the error between the predicted values and the actual values.

After the model is trained, we can use it to make predictions on new data. We can also evaluate the model’s performance using metrics such as mean squared error and mean absolute error.

In conclusion, deep learning is a powerful technique that can be used to perform regression on housing price datasets using the Tensorflow and Keras libraries in R. By loading and preparing the dataset, building and training a neural network, and evaluating the model’s performance, we can make accurate predictions on the median value of homes in the Boston area.

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: Deep Learning in R | Data Science for Beginners | Tensorflow | Keras | House Price Data | Regression.

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

You will learn:

- Deep Learning in R | Data Science for Beginners | Tensorflow | Keras | House Price Data | 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.

Deep Learning in R | Data Science for Beginners | Tensorflow | Keras | House Price Data | 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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