English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 5 Hours | 1.80 GB

Learn how to use Linear & Logistic Regressions by solving 2 Business Case studies in Python & R. Code templates included

Regression analysis is the most common tool at the disposal of anyone looking to analyze data. If you are looking to derive meaning insights from your data, then this course is for you.

3 reasons this course is unique:

You learn not only techniques, but you also learn about Business. The intuition tutorials have their beginning dedicated to explaining to you the relevance of the business problem. By the end of the course, you will be able to discuss matters with your stakeholders related to Pricing or Customer Churn.

Real-life experience. Coding a Regression is a matter of just a couple of lines of code. However, life is not that simple. Almost always, you get a dirty dataset that you need to transform and manipulate to make it a usable and useful dataset. The practice tutorials mirror that experience. We will go through standard techniques to:

- Transform data
- Visualize outliers
- Assess which variables are the best to use.

We code together. In R or Python, I will guide you every step of the way, explaining all steps required to make an excellent regression analysis.

What you’ll learn

- Linear Regression
- Logistic Regression
- Pricing
- Churn drivers
- Data Manipulation
- R and Python

## Table of Contents

**Introduction**

1 Introduction

2 Installing Python and Spyder

3 Installing R and RStudio

4 Reviews and future of this course

5 Let’s connect!

**Linear Regression – Intuition**

6 Linear Regression objectives

7 What influences pricing

8 Pricing demand factors

9 Supply demand factors

10 Linear Regression

11 Linear Regression summary

12 How to read coefficients

13 Dummy variable trap

14 (Adjusted) R-squared

15 RSME vs MAE

16 Outliers

17 Linear regression step by step guide

18 Case study briefing

**Linear Regression – Python**

19 Importing libraries and dataset

20 Handpicking variables

21 Transforming objects into dummy variables

22 Transforming the floor variable into numeric

23 Transforming the dependent variable

24 Summary statistics

25 Scatterplotting

26 Removing outliers

27 Correlation Matrix

28 Dropping variables

29 Log transforming variables

30 Isolate X and Y variables

31 Linear regression

32 How much would my apartment cost

**Linear Regression – R**

33 Loading data

34 Handpicking variables

35 Dataset strategy

36 Transforming variables into factors

37 Transforming variable into numeric

38 Transforming variable into numeric advanced

39 Summary statistics

40 Scatterplotting

41 Removing outliers

42 Correlation Matrix

43 Dropping variables

44 Log transforming variables

45 Linear Regression

46 Regression summary

47 How much would my apartment cost

**Logistic Regression – Intuition**

48 Logistic Regression Objectives

49 Understanding Churn

50 Preventing Churn

51 Logistic Regression

52 Training and test set

53 Over vs underfitting & Bias Variance trade off

54 Confusion Matrix

55 Logistic Regression – step by step guide

56 Case study briefing

**Logistic Regression – Python**

57 Importing libraries and dataset

58 Data structure

59 Transforming objects into dummy variables

60 Summary statistics

61 Outlier detection

62 Removing outliers

63 Transforming variable into its logarithm

64 Correlation Matrix

65 Isolating X and Y variables

66 Logistic regression preparation

67 Training and test set

68 Logistic Regression

69 Predictions

70 Confusion Matrix

**Logistic Regression – R**

71 Loading libraries and dataset

72 Data structure

73 Transforming objects into factors

74 Summary statistics

75 Outlier detection

76 Removing outliers

77 Transforming variable into its logarithm

78 Correlation Matrix

79 Training and test set

80 Logistic Regression

81 Predictions

82 Confusion Matrix

**Bonus lecture**

83 Last Lecture

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