When working with the Titanic dataset in Python, which contains information about passengers aboard the Titanic, there are common mistakes that users may make. Let’s explore some of these mistakes:
1. Not Handling Missing Values:
Neglecting to handle missing values can lead to errors or biased analysis results. It’s important to address missing data appropriately.
To address this mistake, handle missing values through techniques such as imputation or removal of missing data.
2. Incorrect Feature Encoding:
Failing to encode categorical features properly can result in incorrect analysis or modeling outcomes. Categorical variables need appropriate encoding before being used in models.
To address this mistake, encode categorical features using techniques like one-hot encoding or label encoding.
3. Ignoring Feature Engineering:
Neglecting to perform feature engineering can result in suboptimal model performance. Feature engineering involves creating new features or transforming existing ones to improve predictive power.
To address this mistake, consider feature engineering techniques such as creating interaction terms, deriving new variables, or applying mathematical transformations.
These are some common mistakes when working with the Titanic dataset in Python. By being aware of these issues and applying best practices, you can ensure more accurate and reliable analysis using the dataset.