https://github.com/sauravwel/boston-housing-price-prediction-using-deep-learning

This project uses deep learning techniques to predict median housing prices in the Boston area using the Boston Housing dataset. The model employs TensorFlow, Keras, and Numpy, with a mean squared error loss function and Adam optimization algorithm. The results show high accuracy.
https://github.com/sauravwel/boston-housing-price-prediction-using-deep-learning

boston-housing-price-prediction deep deep-learning housing housing-price-prediction housing-prices tensorflow tensorflow-tutorials

Last synced: 14 days ago
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This project uses deep learning techniques to predict median housing prices in the Boston area using the Boston Housing dataset. The model employs TensorFlow, Keras, and Numpy, with a mean squared error loss function and Adam optimization algorithm. The results show high accuracy.

• Host: GitHub
• URL: https://github.com/sauravwel/boston-housing-price-prediction-using-deep-learning
• Owner: sauravwel
• Created: 2023-01-30T09:27:28.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2023-01-30T09:31:56.000Z (almost 2 years ago)
• Last Synced: 2024-12-21T06:13:16.823Z (14 days ago)
• Topics: boston-housing-price-prediction, deep, deep-learning, housing, housing-price-prediction, housing-prices, tensorflow, tensorflow-tutorials
• Language: Jupyter Notebook
• Homepage:
• Size: 4.88 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Boston-Housing-Price-Prediction-using-Deep-Learning

This project aims to predict the median value of owner-occupied homes in the Boston area using deep learning techniques. The model is trained on the Boston Housing dataset, which consists of various features such as crime rate, average number of rooms, and accessibility to radial highways.

## Requirements

TensorFlow

Keras

Numpy

Pandas

Matplotlib

## Model Description

The model uses a fully connected neural network architecture with multiple hidden layers to make predictions. The mean squared error is used as the loss function and the model is optimized using the Adam optimization algorithm.

## Evaluation

The model is evaluated on the test data, and the results show that it can predict the median value of owner-occupied homes in the Boston area with a high degree of accuracy.

## Conclusion

This project demonstrates the effectiveness of deep learning in solving regression problems, specifically in the case of predicting housing prices. Further improvements can be made by exploring different neural network architectures and hyperparameter tuning.