1. Why do missing values exist

2. How sparse is my data

3. Finding the missing values

4. Dealing with missing values (I)

5. List wise deletion

6. Replacing missing values with constants

7. Dealing with missing values (II)

8. Filling continuous missing values

9. Dealing with other data issues

10. Dealing with stray characters (I)

11. Dealing with stray characters (II)

12. Method chaining

PART 3. Conforming to Statistical Assumptions
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#MODULE9 #FeatureEngineering

1.Data distributions

2.What does your data look like (I)

3.What does your data look like (II)

4.Scaling and transformations

5.Normalization

6.Standardization

7.Log transformation