Data Pre-Processing and EDA

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Section 1: Introduction to Univariate Analysis

  1. Download Resources
  2. How to Get Queries Resolved
  3. Measures of Central Tendency
  4. When to Use Geometric and Harmonic Means
  5. Measures of Dispersion - Part 1
  6. Measures of Dispersion - Part 2
  7. Assignment
  8. Assignment Solution

Section 2: Significance Tests

  1. Chi Squared Test Theory and Maths
  2. Chi Square Test and Odds Ratio Demo
  3. ANOVA (Maths)
  4. ANOVA Concept (Intuition)
  5. ANOVA Demo
  6. Course Review
  7. Assignment
  8. Assignment Solution

Section 3: Data Imputation Methods

  1. Representing Missing Values
  2. Types of Missing Values
  3. Identifying Missing Values
  4. Visualize Missing Values with missingno
  5. When to Drop Rows and Columns
  6. Approaches to Filling in Missing Data
  7. Implementing Imputation
  8. Interpolation
  9. MICE Predictions - Part 1
  10. MICE Predictions - Part 2
  11. MICE Predictions - Part 3
  12. Validating Missing Value Imputations
  13. Course Review
  14. Assignment
  15. Assignment Solution

Section 4: Outlier Detection Approaches

  1. What are Outliers and Why They Matter
  2. Detecting Outliers with Box and Whiskers Plot
  3. Detecting Outliers with Z Score
  4. Ways to Treat Outliiers
  5. Mahalanobis Distance - Part 1
  6. Mahalanobis Distance - Part 2
  7. Cooks Distance
  8. Isolation Forest Algorithm - Part 1
  9. Isolation Forest Algorithm - Part 2 10.Isolation Forest Algorithm - Part 3
  10. LOF Part 1 Introduction
  11. LOF Part 2 Problem with simple density
  12. LOF Part 3 K Distance and Local Reachability Density
  13. LOF Part 4 Main Concept
  14. LOF Part 5 - Python Demo
  15. Local Outlier Factor Algorithm - Part 6
  16. Assignment
  17. Assignment Solution

Section 5: Feature Encoding Approaches

  1. Label Encoding
  2. Need for Feature Encoding
  3. Label and Ordinal
  4. One Hot Encoding
  5. Frequency Encoding
  6. Target Encoding - Part 1
  7. Target Encoding - Part 2
  8. Blending Method- Part 1
  9. Blending Method- Part 2
  10. Leave One Out Encoding
  11. Weights of Evidence and Information Value
  12. Weights of Evidence for Continuous Dependent
  13. Course Review
  14. Assignment
  15. Assignment Solution

Section 6: Feature Transformations

  1. Need For scaling
  2. Standardardization and Normalization
  3. Robust Scaling
  4. Assignment
  5. Assignment Solution

Section 7: Plotting for Data Analysis

  1. Getting started with Matplotlib
  2. Making your first plot- Part 1
  3. Color, Marker and Style- Part 2
  4. Controlling plot components- Part 3
  5. Plotting two sets of points- Part 4
  6. Axis Ticks Positions- Part 5
  7. Anatomy of a Matplotlib Plot- Part 6
  8. Annotations- Part 7
  9. Analysing relationships between numeric variables
  10. Analyzing Distributions Histogram- Part 1
  11. Bar Charts
  12. Analysing Distributions Boxplots- Part 2
  13. Numeric vs Cat Overview- Part 1
  14. Numeric vs Cat Multi Box Plots- Part 2
  15. Numeric vs Cat Bubble Plots- Part 3
  16. Numeric vs Cat Pairs and Trellis- Part 4
  17. Categorical vs Categorical
  18. How is Analyzing Time Series different
  19. Line Plot
  20. Dual Axis Time Series Plot
  21. Interpreting ACF and PACF Plots.
  22. Assignment
  23. Assignment Solution

Section 8: Project and Assignment

  1. EDA Project - Classification Part - 1
  2. EDA Project - Classification Part - 2
  3. EDA Project - Classification Part - 3
  4. EDA Project - Classification Part - 4
  5. EDA Project - Classification Part - 5
  6. Assignment
  7. Assignment Solution
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