Performance Evaluation of Machine Learning and Deep Learning Techniques: A Comparative Analysis for House Price Prediction

Prediction is the act of forecasting what will happen in the future. The field of prediction is gaining more importance in almost all the fields. Machine learning techniques have been used widely for predictions also in recent time deep learning algorithms gain more importance. In this paper, we will be performing prediction over a dataset using both machine learning and deep learning techniques, and the performance of each method will be identified and compared with each other. We have used the house price dataset, which consists of 80 features, which will help to explore data visualization methods, data splitting, data normalization techniques. We have implemented five regression-based machine learning models including Simple Linear Regression, Random Forest Regression, Ada Boosting Regression, Gradient Boosting Regression, Support Vector Regression were used. Deep learning models, including artificial neural network, multi output regression, regression using Tensorflow-Keras were also used for regression.