# <!-- TITLE --> Regression with a Dense Network (DNN)
<!-- DESC --> A Simple regression with a Dense Neural Network (DNN) - BHPD dataset
<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->
## Objectives :
- Predicts **housing prices** from a set of house features.
- Understanding the **principle** and the **architecture** of a regression with a **dense neural network**
The **[Boston Housing Dataset](https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html)** consists of price of houses in various places in Boston.
Alongside with price, the dataset also provide information such as Crime, areas of non-retail business in the town,
age of people who own the house and many other attributes...
## What we're going to do :
- Retrieve data
- Preparing the data
- Build a model
- Train the model
- Evaluate the result
%% Cell type:markdown id: tags:
## Step 1 - Import and init
%% Cell type:code id: tags:
``` python
importtensorflowastf
fromtensorflowimportkeras
importnumpyasnp
importmatplotlib.pyplotasplt
importpandasaspd
importos,sys
fromimportlibimportreload
sys.path.append('..')
importfidle.pwkasooo
ooo.init()
```
%% Output
FIDLE 2020 - Practical Work Module
Version : 0.2.9
Run time : Wednesday 19 February 2020, 09:49:10
TensorFlow version : 2.0.0
Keras version : 2.2.4-tf
%% Cell type:markdown id: tags:
## Step 2 - Retrieve data
### 2.1 - Option 1 : From Keras
Boston housing is a famous historic dataset, so we can get it directly from [Keras datasets](https://www.tensorflow.org/api_docs/python/tf/keras/datasets)
