**Logistic Regression, LDA and KNN in R for Predictive Modeling**

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 5h 01m | 2.52 GB

eLearning | Skill level: All Levels

A complete classification modeling course that teaches you everything you need to create a Classification model in R

You’re looking for a complete Classification modeling course that teaches you everything you need to create a Classification model in R, right? You’ve found the right Classification modeling course covering logistic regression, LDA and KNN in R studio!

The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course. Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.

This course teaches you all the steps of creating a Linear Regression model, which is the most popular Machine Learning model, to solve business problems. We have covered the basic theory behind each concept without getting too mathematical about it so that you understand where the concept is coming from and how it is important. But even if you don’t understand it, it will be okay if you learn how to run and interpret the result as taught in the practical lectures. We also look at how to quantify model’s performance using confusion matrix, how categorical variables in the independent variables dataset are interpreted in the results, test-train split and how do we finally interpret the result to find out the answer to a business problem. By the end of this course, your confidence in creating a classification model in R will soar. You’ll have a thorough understanding of how to use Classification modeling to create predictive models and solve business problems.

Learn

- Understand how to interpret the result of Logistic Regression model and translate them into actionable insight
- Learn the linear discriminant analysis and K-Nearest Neighbors technique in R studio
- Learn how to solve the real-life problem using the different classification techniques
- Preliminary analysis of data using Univariate analysis before running the classification model
- Predict future outcomes basis past data by implementing a Machine Learning algorithm
- Graphically representing data in R before and after analysis

**+ Table of Contents**

**Introduction**

1 Welcome to the course!

**Introduction to Machine Learning**

2 Introduction to Machine Learning

3 Building a Machine Learning model

**Basics of Statistics**

4 Types of Data

5 Types of Statistics

6 Describing data Graphically

7 Measures of Centers

8 Measures of Dispersion

**Getting started with R and R studio**

9 Installing R and R studio

10 Basics of R and R studio

11 Packages in R

12 Inputting data part 1 – Inbuilt datasets of R

13 Inputting data part 2 – Manual data entry

14 Inputting data part 3 – Importing from CSV or Text files

15 Creating Barplots in R

16 Creating Histograms in R

**Data Pre-processing**

17 Gathering Business Knowledge

18 Data Exploration

19 The Data and the Data Dictionary

20 Importing the dataset into R

21 Univariate analysis and EDD

22 EDD in R

23 Outlier Treatment

24 Outlier Treatment in R

25 Missing Value Imputation

26 Missing Value imputation in R

27 Seasonality in Data

28 Variable transformation in R

29 Dummy variable creation – Handling qualitative data

30 Dummy variable creation in R

**Classification Models**

31 Three Classifiers and the problem statement

32 Why can’t we use Linear Regression

33 Logistic Regression

34 Training a Simple Logistic model in R

35 Results of Simple Logistic Regression

36 Logistic with multiple predictors

37 Training multiple predictor Logistic model in R

38 Confusion Matrix

39 Evaluating Model performance

40 Predicting probabilities, assigning classes and making Confusion Matrix

41 Linear Discriminant Analysis

42 Linear Discriminant Analysis in R

43 Test-Train Split

44 Test-Train Split in R

45 K-Nearest Neighbors classifier

46 K-Nearest Neighbors in R

47 Understanding the results of classification models

48 Summary of the three models

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