To choose a dataset for a regression problem, focus on three key factors: relevance to the problem, data quality, and feature suitability. Start by identifying the target variable (the value you’re predicting) and ensure the dataset includes features that logically relate to it. For example, if predicting house prices, relevant features might include square footage, location, and number of bedrooms. Avoid datasets with irrelevant or redundant columns, as they can introduce noise or complicate model training. Additionally, check for data completeness—missing values in critical features can undermine accuracy. Tools like pandas in Python can help inspect null values, and techniques like imputation (e.g., filling missing values with medians) or column removal might be necessary.
Next, evaluate the dataset’s size and balance. Regression models generally require sufficient data to capture patterns, especially if relationships between features and the target are complex. A good rule of thumb is to have at least 10 times as many rows as there are features, though this varies by use case. For instance, a dataset with 100 samples and 5 features might suffice for a simple linear regression, but a neural network would need significantly more. Also, ensure the target variable’s distribution is appropriate. If predicting rare events (e.g., extreme house prices), check for sufficient representation of those cases. For skewed targets, consider transformations like log scaling or using evaluation metrics (e.g., RMSE, MAE) that align with the problem’s goals.
Finally, validate the dataset’s usability through preprocessing and testing. Split the data into training and testing sets early to avoid leakage and assess model performance realistically. For example, use scikit-learn’s train_test_split to reserve 20-30% of data for validation. Preprocess features by normalizing or standardizing them, especially if using algorithms like SVM or gradient-boosted trees. Check for multicollinearity (high correlation between features) using variance inflation factor (VIF) or correlation matrices, as this can destabilize linear models. If the dataset lacks key features, consider augmenting it with external sources—for instance, adding weather data to a bike rental prediction model. Always test multiple regression algorithms (linear regression, decision trees, etc.) to ensure the dataset supports consistent, reliable predictions across methods.