Logistic Regression, LDA and KNN in R for Predictive Modeling

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

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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.


  • 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

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