I was currently exploring a dataset and asked my friend that should I handle missing values before EDA(Exploratory Data Analysis) or after and he told me to do it afterwards as it happens in pre processing phase, before giving and fitting data to a model. Well my understanding was that we process data before applying models etc but then after showing him my findings he asked me why I didn’t process the data before analysis and told me there is also a data pre processing stage before EDA. I am completely confuse now. Can anyone help me and :
- differentiate these two data processing phases.
- what is the difference in their steps.
- why do we need one before EDA.
- and if we also process the data for EDA and data analysis, is there any benefit of applying encoding and normalization in this step?
Let me try to help you clarify the differences between data processing before EDA and after EDA.
- and 2.
- Before EDA: This phase involves data cleaning and pre-processing. It aims to prepare raw data for further analysis. During this stage, you address issues like missing values, outliers, and data inconsistencies.
- After EDA: After exploring the data, you proceed with feature engineering and transformation. This step includes encoding categorical values, scaling features, and creating new features.
- Why pre-process before EDA.
- Understanding the data: EDA relies on clean, well-structured data. By addressing missing values and inconsistencies earlier, you ensure meaningful insights during exploration.
- Identifying patterns: EDA helps you identify patterns, correlations, and trends. If data is messy, these insights may be misleading or inaccurate.
- Benefits of encoding and normalization in EDA
- Encoding: encoding categorical variables allows you to visualize their relationship with the target variable. For ex., you can explore how different categories impact outcomes.
- Normalization: normalizing features ensures that their scales don’t skew EDA results. It helps maintain consistency when comparing different features.
Keep learning!
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Differentiating Data Processing Phases:
- Preprocessing for EDA (Exploratory Data Analysis):
- Purpose: Prepare data to make it suitable for exploration and analysis.
- Steps:
- Handle missing values (impute or drop).
- Remove or address outliers.
- Perform initial data transformation (normalization or scaling if needed).
- Encode categorical variables into numerical format.
- Why Before EDA?: Ensures data is clean and structured, leading to accurate and meaningful insights during exploration. It helps in creating reliable visualizations and summaries.
- Preprocessing for Modeling:
- Purpose: Prepare data for training and evaluating machine learning models.
- Steps:
- Feature engineering (create or modify features).
- Normalize or standardize features (important for many algorithms).
- Encode categorical variables appropriately (e.g., one-hot encoding).
- Handle missing values in a way that suits model requirements.
- Why Before Modeling?: Ensures the data is formatted and scaled correctly for optimal model performance, improving accuracy and preventing issues during training.
Why Do We Need Both?
- EDA Preprocessing: Provides a clean and manageable dataset for accurate exploration, helping to identify patterns, trends, and potential issues.
- Modeling Preprocessing: Ensures data is suitable for the model, which is crucial for training effective and accurate models.
Benefits of Preprocessing for EDA:
- Enhances the quality of exploratory results by providing a cleaner dataset.
- Helps in detecting and addressing issues early.
- Facilitates better and more insightful visualizations.
In summary, preprocessing before EDA helps in understanding and analyzing data effectively, while preprocessing before modeling ensures data is ready for high-quality machine learning. Both stages are essential for a robust data analysis pipeline.
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