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.