Data Handling: Preparing the California Housing Dataset | CodeSignal Learn

Data Handling: Preparing the California Housing Dataset | CodeSignal Learn

Introduction

Welcome back to lesson 4 of "Building and Applying Your Neural Network Library"! You've accomplished so much on this journey. We began by modularizing our core components into clean, reusable modules for layers and activations. Then, we organized our training components by creating dedicated modules for loss functions and optimizers. Most recently, we built a powerful orchestration layer with our Model and SequentialModel classes that provide a clean, high-level API for building and training neural networks. Now it's time to put our complete neural network library to work on a real-world problem! While our XOR examples have been perfect for learning and testing, real machine learning applications involve working with actual datasets that come with their own challenges: multiple features, varying scales, missing values, and the need for proper data preprocessing. In this lesson, we'll prepare the California Housing dataset — a classic regression problem that predicts house prices based on various demographic and geographic features. We'll learn essential data handling techniques, including loading real datasets, understanding feature characteristics, splitting data properly for evaluation, and applying feature scaling to ensure our neural network can learn effectively. This foundation will set us up perfectly for our final lesson, where we'll apply our complete neural network library to solve this practical prediction problem.

Understanding Real-World Datasets

Real datasets come with complexities that require careful handling before we can apply machine learning algorithms effectively: Feature diversity is one key challenge — real datasets often contain features measured in completely different units and scales. For example, our California Housing dataset includes features like median income (measured in tens of thousands of dollars), house age (measured in years), and geographic coordinates (latitude and longitude). These dramatically different scales can cause problems for neural networks, which work best when all inputs are in similar ranges. Data splitting becomes crucial when working with real datasets. Unlike our toy examples, where we could evaluate on the same data we trained on, real applications require us to reserve some data for testing. This allows us to get an honest estimate of how our model will perform on new, unseen data — the true test of machine learning success. Preprocessing requirements also become more sophisticated. We need to standardize features so they have similar scales, handle the train-test split properly to avoid data leakage, and ensure our preprocessing steps are applied consistently between training and testing phases.

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Splitting Data for Proper Evaluation

Now we need to split our data into training and testing sets. This is crucial for getting an honest evaluation of our model's performance. We'll use the caret package's createDataPartition() function, which provides stratified sampling for better train-test splits: # Set seed for reproducibility set.seed(42) # Create train-test split (80% train, 20% test) train_indices <- createDataPartition(y, p = 0.8, list = FALSE) X_train <- X[train_indices, ] X_test <- X[-train_indices, ] y_train <- y[train_indices, , drop = FALSE] y_test <- y[-train_indices, , drop = FALSE] cat("\nData split shapes:\n") cat(" X_train:", dim(X_train), ", y_train:", dim(y_train), "\n") cat(" X_test:", dim(X_test), ", y_test:", dim(y_test), "\n") The output confirms our split: Data split shapes: X_train: 16512 8 , y_train: 16512 1 X_test: 4128 8 , y_test: 4128 1 We've allocated 80% of our data (16,512 samples) for training and 20% (4,128 samples) for testing. The set.seed(42) function ensures reproducible results — we'll get the same split every time we run the code. The createDataPartition() function from caret provides stratified sampling, which helps ensure our train and test sets have similar distributions of the target variable. The key principle here is that our model will never see the test data during training. This separation allows us to evaluate how well our neural network generalizes to new, unseen examples, which is the ultimate goal of machine learning.

Feature Scaling for Neural Networks

Neural networks are particularly sensitive to the scale of input features . When features have very different ranges, the network may have difficulty learning effectively. Let's apply standardization (also called z-score normalization) using the caret package's preprocessing functions: # Apply feature scaling (Standardization) # Initialize preprocessing parameters on training data preproc_X <- preProcess(X_train, method = c("center", "scale")) # Apply scaling to training data X_train_scaled <- predict(preproc_X, X_train) # Apply same scaling to test data X_test_scaled <- predict(preproc_X, X_test) Notice the crucial distinction here: we use preProcess() on the training data, which calculates the scaling parameters (mean and standard deviation) without applying the transformation. Then we use predict() to apply these parameters to both training and test data. This ensures we don't introduce data leakage — the test data statistics don't influence our preprocessing parameters. We also scale our target variable, which often helps with regression problems : # Scale target variable y as well (often beneficial for regression) preproc_y <- preProcess(y_train, method = c("center", "scale")) y_train_scaled <- predict(preproc_y, y_train) y_test_scaled <- predict(preproc_y, y_test) Scaling the target variable helps our neural network learn more effectively by keeping the output values in a reasonable range, typically around zero with unit variance.

Verifying Our Preprocessing

Let's verify that our scaling worked correctly by examining the statistical properties of our transformed data: cat("\n--- After Scaling ---\n") cat("X_train_scaled mean (should be close to 0):", round(colMeans(X_train_scaled), 2), "\n") cat("X_train_scaled std (should be close to 1):", round(apply(X_train_scaled, 2, sd), 2), "\n") cat("Sample X_train_scaled[1,]:", round(X_train_scaled[1,], 2), "\n") cat("\ny_train_scaled mean:", round(mean(y_train_scaled), 2), "\n") cat("y_train_scaled std:", round(sd(y_train_scaled), 2), "\n") cat("Sample y_train_scaled[1,]:", round(y_train_scaled[1,], 2), "\n") The output confirms our scaling is working correctly: --- After Scaling --- X_train_scaled mean (should be close to 0): 0 0 0 0 0 0 0 0 X_train_scaled std (should be close to 1): 1 1 1 1 1 1 1 1 Sample X_train_scaled[1,]: 2.34 0.98 0.63 -0.15 0.22 -0.04 -0.84 1.07 y_train_scaled mean: 0 y_train_scaled std: 1 Sample y_train_scaled[1,]: 2.48 Perfect! Our scaled features now have means approximately 0 and standard deviations of 1 across all dimensions. The sample that originally had values ranging from 1.02 to -122.23 now has standardized values ranging from -0.84 to 2.34. Similarly, our target variable has been scaled to have zero mean and unit variance. This transformation puts all our features on equal footing, allowing our neural network to learn effectively without being dominated by features that happen to have larger numerical values. The network can now focus on the actual patterns and relationships in the data rather than fighting against scale differences.

Let's start by loading and exploring our dataset. We'll use R's reticulate package to access Python's scikit-learn library, which provides the California Housing dataset in a convenient format:

R

# Load required libraries
library(reticulate)
library(caret)

py_require("scikit-learn")

# Import sklearn datasets module
sklearn_datasets <- import("sklearn.datasets")

# Load the California Housing dataset
housing <- sklearn_datasets$fetch_california_housing()
X <- housing$data
y <- housing$target

# Set column names for X (required for caret preprocessing)
colnames(X) <- housing$feature_names

# Convert y to a column matrix (N, 1) as our network expects
y <- matrix(y, ncol = 1)
colnames(y) <- "MedHouseVal"

cat("Raw data shapes: X=", dim(X), ", y=", dim(y), "\n")
cat("Number of features:", length(housing$feature_names), "\n")
cat("Feature names:", paste(housing$feature_names, collapse = ", "), "\n")
cat("Target name: MedHouseVal (Median House Value in $100,000s)\n")
cat("Sample X[1,]:", X[1,], "\n")
cat("Sample y[1,]:", y[1,], "\n")

This code loads the dataset and reveals important characteristics. The output shows us:

text

Raw data shapes: X= 20640 8 , y= 20640 1 
Number of features: 8 
Feature names: MedInc, HouseAge, AveRooms, AveBedrms, Population, AveOccup, Latitude, Longitude 
Target name: MedHouseVal (Median House Value in $100,000s)
Sample X[1,]: 8.3252 41 6.984127 1.023810 322 2.555556 37.88 -122.23 
Sample y[1,]: 4.526

We have 20,640 housing records with 8 features each. The features include median income (MedInc), house age (HouseAge), average rooms per household (AveRooms), and geographic coordinates (Latitude, Longitude). Looking at the sample data, notice the dramatic scale differences: median income is around 8.3, house age is 41 years, while geographic coordinates are around 37.88 for latitude and -122.23 for longitude. The target variable (MedHouseVal) represents median house values in units of $100,000, so our first sample shows a house worth approximately $452,600.

R

# Set seed for reproducibility
set.seed(42)

# Create train-test split (80% train, 20% test)
train_indices <- createDataPartition(y, p = 0.8, list = FALSE)
X_train <- X[train_indices, ]
X_test <- X[-train_indices, ]
y_train <- y[train_indices, , drop = FALSE]
y_test <- y[-train_indices, , drop = FALSE]

cat("\nData split shapes:\n")
cat("  X_train:", dim(X_train), ", y_train:", dim(y_train), "\n")
cat("  X_test:", dim(X_test), ", y_test:", dim(y_test), "\n")

R

# Apply feature scaling (Standardization)
# Initialize preprocessing parameters on training data
preproc_X <- preProcess(X_train, method = c("center", "scale"))
# Apply scaling to training data
X_train_scaled <- predict(preproc_X, X_train)
# Apply same scaling to test data
X_test_scaled <- predict(preproc_X, X_test)

R

# Scale target variable y as well (often beneficial for regression)
preproc_y <- preProcess(y_train, method = c("center", "scale"))
y_train_scaled <- predict(preproc_y, y_train)
y_test_scaled <- predict(preproc_y, y_test)

R

cat("\n--- After Scaling ---\n")
cat("X_train_scaled mean (should be close to 0):", round(colMeans(X_train_scaled), 2), "\n")
cat("X_train_scaled std (should be close to 1):", round(apply(X_train_scaled, 2, sd), 2), "\n")
cat("Sample X_train_scaled[1,]:", round(X_train_scaled[1,], 2), "\n")

cat("\ny_train_scaled mean:", round(mean(y_train_scaled), 2), "\n")
cat("y_train_scaled std:", round(sd(y_train_scaled), 2), "\n")
cat("Sample y_train_scaled[1,]:", round(y_train_scaled[1,], 2), "\n")

text

--- After Scaling ---
X_train_scaled mean (should be close to 0): 0 0 0 0 0 0 0 0 
X_train_scaled std (should be close to 1): 1 1 1 1 1 1 1 1 
Sample X_train_scaled[1,]: 2.34 0.98 0.63 -0.15 0.22 -0.04 -0.84 1.07 

y_train_scaled mean: 0 
y_train_scaled std: 1 
Sample y_train_scaled[1,]: 2.48