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Cleaning-Visualization

This repository includes:

Data Cleaning Techniques:

  • Handling Missing Data
  • Removing Duplicate Data
  • Correcting Data Types
  • Transforming Data
  • Filtering Irrelevant Data

Data Visualization:

  • Exploratory Data Analysis (EDA):

    • Histograms: For understanding the distribution of numerical data.
    • Boxplots: For detecting outliers and understanding spread.
    • Scatter Plots: To observe relationships between two numerical variables.
  • Summary Charts:

    • Bar Charts: For categorical data.
    • Pie Charts: For proportions and percentages.
    • Line Charts: For trends over time.