EDA preprocessing using MYSQL steps

EDA Preprocessing in MySQL:

1. Connect to the MySQL database.
2. Retrieve the dataset or table to be analyzed.
3. Handle missing values:
   • Identify columns with missing values.
   • Decide on a strategy to handle missing values (e.g., removing rows, imputation).
   • Implement the chosen strategy to fill or remove missing values.
4. Handle duplicate values:
   • Identify duplicate rows or columns.
   • Decide on a strategy to handle duplicates (e.g., removing duplicates, keeping the first or last occurrence).
   • Implement the chosen strategy to remove or modify duplicate values.
5. Handle outliers:
   • Identify columns with outliers.
   • Decide on an approach to handle outliers (e.g., removing outliers, transforming values).
   • Implement the chosen approach to handle outliers.
6. Perform data type conversion and normalization:
   • Convert columns to the appropriate data types (e.g., dates to datetime, numbers to numeric types).
   • Normalize numerical columns if required (e.g., scaling to a specific range).
7. Handle categorical variables:
   • Identify categorical columns.
   • Decide on an encoding strategy (e.g., one-hot encoding, label encoding).
   • Implement the chosen encoding strategy.
8. Perform feature engineering:
   • Create new features based on existing columns or domain knowledge.
   • Transform variables or derive new features as necessary.
9. Perform data aggregation if needed:
   • Group data by relevant columns.
   • Compute summary statistics or aggregates (e.g., mean, sum, count).
10. Perform data exploration:
    • Generate descriptive statistics (e.g., mean, median, standard deviation).
    • Create visualizations (e.g., histograms, box plots, scatter plots) to understand the distribution and relationships between variables.