• RavindraYadav
• Vikas Vankhede

As per the records of World Housing Society 19.5 million people buy a new house every year. By the year 2050 it will rise up to 35 million [1]. The prices of the house is the biggest hindrance factor for the middle class family at the same time and also the lake of information of the property attribute wise .As the people are also facing the time problem for searching the right house for their live hood. we proposed a method through which we can calculate all the attribute of the house using Keras and deep learning .The actual algorithm is being suggested by Adrian Rosebrock (2019) training a Keras neural network to predict house prices based on categorical and numerical attributes such as the number of bedrooms/bathrooms, square footage, zip code, etc.

Machine learning, Keras, Decision Tree, Regression, Neural Network, Deep Learning and SVM.

"House Prices Prediction Using Categorical and Numerical Attributes With Keras.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 3, page no.123-130, March-2019, Available :http://www.jetir.org/papers/JETIR1903H59.pdf

"House Prices Prediction Using Categorical and Numerical Attributes With Keras.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 3, page no. pp123-130, March-2019, Available at : http://www.jetir.org/papers/JETIR1903H59.pdf