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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