Comparing 170,303.89 to the results from LR, we observe that LR performs better in predicting housing prices. So, why do we need deep learning?

Now that we have built a predictive model using Keras, building a classification model sounds like a trivial task. Most tutorials consider a classification model (where the desired outcome to be predicted is binary or categorical) easier than a predictive model (where the desired outcome to be predicted is continuous). However, due to sequential nature of Keras models, building these models are equally easy (or difficult, depending on how you found previous example). For this example, we use another Kaggle dataset.

The Otto Group is one of the world’s biggest e-commerce companies, a consistent analysis of the performance of products is crucial. However, due to diverse global infrastructure, many identical products get classified differently. Kaggle provided a dataset with 93 features for more than 200,000 products. The objective is to build a predictive model which is able to distinguish between our main product categories. Each row corresponds to a single product. There are a total of 93 numerical features, which represent counts of different events. All features have been obfuscated and will not be defined any further. You can download the data directly from Kaggle website: