Data Preprocessing

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.


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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.

Tâche

Swipe to start coding

Throughout this course, you'll engage extensively in data handling independently. Therefore, let's revisit how to preprocess data using the scikit-learn library, making it ready for use in a neural network developed with Keras.

Initialize an Isolation Forest with a 5% contamination rate.
Apply the Isolation Forest to the training set and discard outliers.
Rescale the data using MinMaxScaler.

Solution

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Section 1. Chapitre 5

Data Preprocessing

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.


              12345678910111213
            
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.

Tâche

Swipe to start coding

Initialize an Isolation Forest with a 5% contamination rate.
Apply the Isolation Forest to the training set and discard outliers.
Rescale the data using MinMaxScaler.

Solution

Passez à un bureau pour une pratique réelleContinuez d'où vous êtes en utilisant l'une des options ci-dessous

Tout était clair ?

Merci pour vos commentaires !

Section 1. Chapitre 5

Passez à un bureau pour une pratique réelleContinuez d'où vous êtes en utilisant l'une des options ci-dessous