Airbnb EDA: Fixing Data Leakage & Scaling Issues

by Alex Johnson 49 views

This article discusses critical errors identified in an Exploratory Data Analysis (EDA) notebook focused on Airbnb data in New York City. Specifically, it addresses data leakage, feature selection on unscaled data, and saving incorrect datasets. Addressing these issues is crucial for building reliable and accurate machine learning models.

Understanding the Context

This analysis is based on a review of an EDA notebook. The original notebook, while demonstrating strengths in visualization and exploratory analysis, contained several flaws that could compromise the integrity of any subsequent modeling efforts. The issues were highlighted by @josefina-aispuro-merelles, focusing on aspects of data preprocessing and feature selection.

Key Errors and Solutions

Let's dive into the identified errors and how to correct them.

1. Data Leakage Due to Encoding Before Splitting

The Problem: The original notebook applied One-Hot Encoding to the entire dataset before splitting it into training and testing sets. This is a classic example of data leakage.

# OneHotEncoder BEFORE the split
encoded = encoder.fit_transform(df[cat_columns])  # Learns from the ENTIRE dataset
df_encoded = pd.concat([df.drop(columns = cat_columns), encoded_df], axis = 1)

# Split AFTER the encoding
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)

Why is this a problem? OneHotEncoder learns the unique categories present in all the data, including the test set. The test set is supposed to mimic unseen, real-world data. By allowing the encoder to see the test data, we are inadvertently giving the model information about the future. This leads to an overly optimistic evaluation of the model's performance on the test set, because information from the test is now influencing the training of the model. The model essentially has seen the test data, albeit indirectly, which defeats the purpose of having a holdout set.

The Solution: The correct approach is to split the data first, then apply One-Hot Encoding separately to the training and testing sets. This ensures that the encoder only learns from the training data and generalizes to the unseen test data.

# Split BEFORE encoding
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)

# OneHotEncoder AFTER the split
encoder = OneHotEncoder(handle_unknown='ignore') # handle_unknown ensures robustness
encoder.fit(X_train[cat_columns])

X_train_encoded = encoder.transform(X_train[cat_columns])
X_test_encoded = encoder.transform(X_test[cat_columns])

X_train_encoded = pd.DataFrame(X_train_encoded, index=X_train.index, columns=encoder.get_feature_names_out(cat_columns))
X_test_encoded = pd.DataFrame(X_test_encoded, index=X_test.index, columns=encoder.get_feature_names_out(cat_columns))

X_train = pd.concat([X_train.drop(columns=cat_columns), X_train_encoded], axis=1)
X_test = pd.concat([X_test.drop(columns=cat_columns), X_test_encoded], axis=1)

In this corrected code:

  1. We split X into X_train and X_test before any encoding.
  2. We initialize a OneHotEncoder with handle_unknown='ignore'. This is important because it tells the encoder how to deal with categories that might be present in the test set but not in the training set. 'ignore' tells it to simply ignore such categories, which prevents errors during the transformation of the test set.
  3. We fit the encoder only on the X_train data.
  4. We then transform both X_train and X_test using the encoder that was fit on the training data. This ensures consistency in the encoding and prevents leakage.
  5. Ensure that the encoded dataframes have the correct index to ensure proper concatenation.

By following this approach, we prevent data leakage and ensure a more realistic evaluation of our model's performance.

2. Feature Selection on Unscaled Data

The Problem: Feature selection was performed on the original, unscaled data.

# SelectKBest on X_train/X_test WITHOUT scaling
selection_model.fit(X_train, y_train)

Why is this a problem? Feature selection methods that rely on distance calculations or variance (which is very sensitive to scaling) are heavily influenced by the scale of the features. Features with larger values will appear more important, regardless of their actual predictive power. For example, if one feature is in the range of 0-1 and another is in the range of 1000-10000, the latter will likely be chosen as more important simply because of its larger magnitude. When features aren't on the same scale, feature selection algorithms may incorrectly favor features with larger values, resulting in a suboptimal feature subset. This can lead to a biased model and reduce its generalization performance.

The Solution: Always scale your data before performing feature selection when using methods sensitive to feature scaling (like those based on L1 regularization, distance metrics, or variance). Common scaling methods include StandardScaler and MinMaxScaler.

from sklearn.preprocessing import StandardScaler
from sklearn.feature_selection import SelectKBest, f_regression
from sklearn.pipeline import Pipeline

# Create a pipeline
feature_selection_pipeline = Pipeline([
    ('scaler', StandardScaler()),  # Step 1: Scale the data
    ('selector', SelectKBest(score_func=f_regression, k=10))  # Step 2: Select top k features
])

# Fit the pipeline to the training data
feature_selection_pipeline.fit(X_train, y_train)

# Transform the training and testing data
X_train_selected = feature_selection_pipeline.transform(X_train)
X_test_selected = feature_selection_pipeline.transform(X_test)


# Get the selected feature names
selected_features_mask = feature_selection_pipeline.named_steps['selector'].get_support()
selected_features = X_train.columns[selected_features_mask]

print("Selected Features:", selected_features)

This corrected code does the following:

  1. Scales the Data: Applies StandardScaler to bring all features to a similar scale (mean of 0 and standard deviation of 1). It is crucial to fit the scaler only on the training data and then transform both training and test data to avoid data leakage. Other scalers like MinMaxScaler may be suitable depending on the data's distribution.
  2. Performs Feature Selection: Uses SelectKBest with the f_regression scoring function to select the top k features based on their correlation with the target variable. The value of k should be chosen based on the specific problem and data.
  3. Transforms the Data: Transforms both the training and test data using the fitted SelectKBest transformer, keeping only the selected features.

3. Saving Incorrect Data

The Problem: The notebook saved the feature-selected data before scaling. This meant that the effort of scaling was effectively ignored.

# Lines 220-223 - Saves X_train_sel/X_test_sel (unscaled, with feature selection)
X_train_sel.to_csv("X_train_sel.csv")

Why is this a problem? Saving unscaled data after performing scaling and feature selection defeats the entire purpose of preprocessing. The model will be trained on data that is not in the expected format, leading to poor performance. The scaled and feature-selected data represents the processed data ready for model training. Not saving this data means that all preprocessing steps will have to be repeated every time the model is trained or used for predictions.

The Solution: Save the scaled and feature-selected data.

# Save the SCALED and feature-selected data
pd.DataFrame(X_train_selected, columns=selected_features).to_csv("X_train_scaled_selected.csv", index=False)
pd.DataFrame(X_test_selected, columns=selected_features).to_csv("X_test_scaled_selected.csv", index=False)

This ensures that you are saving the data that is actually used for training and evaluation.

Fortalezas (Strengths)

Despite the errors, the original notebook demonstrated several strengths:

  • Professional Visualizations: The use of regplot and heatmaps was excellent for exploring relationships and patterns in the data.
  • Good Exploratory Data Analysis: The overall EDA process was well-executed, providing valuable insights into the Airbnb dataset.
  • Correct Use of f_regression: The f_regression scoring function was appropriately used for feature selection.
  • Clear Documentation: The notebook was well-documented, making it easier to understand the steps involved.

Conclusion

By addressing the issues of data leakage, feature selection on unscaled data, and saving the correct processed data, you can significantly improve the reliability and accuracy of your Airbnb EDA and subsequent modeling efforts. Remember to always split your data before encoding, scale your data before feature selection (when appropriate), and save the final processed data for consistent and reproducible results.

For more information on data leakage, refer to this article on Preventing Data Leakage.