Now, let's explore setting up a model and apply it to a practical scenario. We'll aim to predict house prices using the well-known **Boston Housing Price Regression Dataset**.

## Data Overview

First, we need to examine the data before loading it.

## Missing Values

We need to verify if there are any **missing values** in the dataset. This requires first loading the dataset from Keras and then checking for missing values.

from tensorflow import keras
import pandas as pd

# Loading the dataset
(X_train, y_train), (X_test, y_test) = keras.datasets.boston_housing.load_data()

# Converting each subset to DataFrame
X_train = pd.DataFrame(X_train)
y_train = pd.DataFrame(y_train)

# Summing up number of empty values of each set
print('Null values in X_Train:', X_train.isnull().sum().sum())
print('Null values in y_train:', y_train.isnull().sum().sum())

As it turns out, there are no empty values, so we don't need to address this issue.

## Data Preprocessing

- **Outliers**: Although Keras datasets are typically free of outliers, we will demonstrate outlier removal using `IsolationForest`, eliminating 5% of the data as outliers.
    > **Note**
    > 
    > - `IsolationForest`'s `predict` method returns a list indicating valid samples (`1`) or outliers (`-1`).
    > - To set up contamination rate you can set up `contamination` parameter of the `IsolationForest` constructor.
    > - Outliers should be removed only from the **training set**.
    
- **Rescaling**: To ensure consistency and compatibility with our model, the data needs to be rescaled.

Dive into the world of neural network development with an immersive program that blends foundational knowledge with advanced techniques. The course emphasizes hands-on learning, providing learners with the opportunity to apply these techniques. Additionally, you will face numerous data preprocessing tasks and real-world examples of neural network training, so stay prepared and motivated, as it will present a significant challenge for you.

This segment introduces the fundamentals of Keras through hands-on experiences with the Spotify dataset, covering everything from layer configuration and model creation to compilation, data preprocessing, and training.

The regularization module, utilizing the Mushrooms dataset, addresses the critical issues of overfitting and underfitting, explaining the concept of regularization and its importance in model training.

Exploring cutting-edge methodologies, this section delves into the Diamonds dataset to uncover the intricacies of optimizers, learning rate adjustments, TensorFlow Datasets, data generators, and advanced modeling techniques like non-sequential models, transfer learning, and multitask learning in a comprehensive overview.

Data Preprocessing

Data Overview

Missing Values

Data Preprocessing

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