Machine Learning Regression in Python using Keras and Tensorflow | Boston House Price Dataset | Data Science Tutorials

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Machine learning regression is a type of machine learning that is used to predict a continuous value. In this case, we are going to use a deep learning approach using Keras and Tensorflow to predict the median value of a house in Boston using the Boston House Price dataset from UCI.

Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.

TensorFlow is a powerful open-source software library for machine learning developed by researchers and engineers working on the Google Brain Team. It provides a wide range of tools for building and deploying machine learning models, including support for deep learning.

To use Keras and Tensorflow for machine learning regression with the Boston Housing Price dataset, you would first need to load the dataset into Python 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 model, and the testing set is used to evaluate its performance.

Next, you would need to define the neural network model using Keras. Keras provides a wide range of tools for building neural networks 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 using deep learning approach with Keras and Tensorflow is the ability to handle complex data and make accurate predictions. The neural network model can learn features from the data that may not be visible to traditional machine learning algorithms.

Another advantage of using deep learning is the ability to handle large datasets. The neural network model can be trained on large datasets in a relatively short amount of time.

In conclusion, using Keras and Tensorflow for machine learning regression is a powerful approach to predicting the median value of a house in Boston. The Boston House Price dataset from UCI is a suitable dataset for this task. Deep learning approach can handle complex data, make accurate predictions and handle large datasets. Keras and Tensorflow provide a wide range of tools for building and deploying machine learning models, including support for deep learning. The neural network model can learn features from the data that may not be visible to traditional machine learning 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 Regression in Python using Keras and Tensorflow | Boston House Price Dataset | Data Science Tutorials.



 

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