• Missing Value Detection & Handling
• Data visualization
• Outliers Detection & Handling
• Multi Colinearity Detection & Handling

• Data Cleaning
• Data transformation
  • Feature Scaling
    1. Normalization And Standardization
    2. Scaling to Minimum And Maximum values
    3. Scaling To Median And Quantiles
  • Gaussian Distribution
    1. Logarithmic Transformation
    2. Reciprocal Trnasformation
    3. Square Root Transformation
    4. Exponential Trnasformation
  • Box Cox Transformation
• Parsing Dates
• Character Encoading
• Categorical Encoading
• Feature Selection
• Handling Imbalanced Data
• Data Reduction
  • Dimentionlity Reduction

• Bias and Varience
• Overfitting and Underfitting

• Datasets usually contain large volumes of data that may be stored in formats that are not easy to use.
• That’s why data scientists need first to make sure that data is correctly formatted and conforms to the set of rules.
• Data sparseness and formatting inconsistencies are the biggest challenges – and that’s what data cleansing is all about.

Data cleaning is a task that identifies incorrect, incomplete, inaccurate, or irrelevant data, fixes the problems, and makes sure that all such issues will be fixed automatically in the future.

• According to Appen, data scientists spend 60% of the time organizing and cleansing data!