Conclusion

Dealing with Missing Data in Python

Suraj Donthi

Deep Learning & Computer Vision Consultant

Chapter 1

• Null Value operations
• Detecting missing values
• Replacing missing values
• Analyzing amount of missingness

Chapter 2

• Types of missingness
  • MCAR
  • MAR
  • MNAR
• Correlations of missingness
  • Heatmaps
  • Dendrograms
• Visualize missingness across a variable
• Deleting missing values

Chapter 3

• Imputation techniques
• Treating time-series data
• Graphical comparison of imputed time-series data

Chapter 4

• Advanced imputation techniques
  • KNN
  • MICE
• Imputing categorical data
• Evaluating and comparing the different imputations

Congratulations!!

Dealing with Missing Data in Python